This PDF file contains the front matter associated with SPIE Proceedings Volume 6752, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

June 2005, Google has released its geographic search tool "Google earth", a new application that combines local search with satellite images and maps from around the globe. It is designed to make every person owned a computer easily "fly" to aerial views of many locations on the planet. However, just as ordinary satellite images, there inevitably exist shadows in it, made some ground objects obscure, even unidentifiable. According to the basic thinking of Radiative Transfer Theory, this paper built a image shadow removal model, which using the Radiative Transfer Theory combined with preknowledge to compensate the lost shadow area information. The results shows: shadows in images were successfully removed and the target objects were returned to their original scenes.

With the development of the earth observing satellites science and technologies, environment observing satellites can be considered as a network which can satisfy various observing requests simultaneously. The data collection planning system for the satellites network investigates how to utilize the satellites network cooperatively and optimally, which provides decision support tools to satisfy the various and multilevel observing requests. The data collection planning system, as a bridge linking users and satellite managers, is designed to try to make use of the whole satellites network sufficiently. Currently, most application systems for observing satellites deal with the simple task which only involves a single satellite or a single series satellite. The compound observing task which involves various kinds of satellites is not considered, which results that the limited observing satellites can not be employed optimally.

In order to discover those significant spectral features that are of effectiveness to target identification, some Data Mining algorithms were used to the data sets from USGS spectral library and OMIS hyperspectral remote sensing image. The candidate feature sets were generated by traditional spectral feature extraction approaches at first, and then clustering, statistical analysis and decision tree were used to characterized feature recognition and target identification model design. Derivative spectrum has the superiority of enhancing the characteristic spectral features in contrast with other algorithms. The recognition decision tree based on the knowledge and rules can identify and discriminate targets using the discovered spectral features. The experiment showed that the proposed characterized spectral features recognition approach based on Data Mining algorithm was suitable to hyperspectral remote sensing information processing.

Automatic segmentation of high resolution satellite (HRS) imagery is the first step and a very important part of object-oriented approaches. As the resolution of satellite imagery increases, the spectral within-field heterogeneity and the structural or spatial details increase at the same time. Spatial features are important to HRS image analysis in addition to spectral information. This paper presents a novel feature extraction method and evaluates its performance on segmentation of HRS images and color texture images. The first two principal component (PC) images are obtained by principal component analysis (PCA) of a multispectral image. Two texture labeled images are calculated pixel-by-pixel on the PC images through a rotation invariant local binary pattern (LBP) form that we present in this paper. The two texture labeled images are used to calculate the discrete two-dimensional texture histogram of the image. The spectral distribution of a region is the joint distribution of the pixel values of its two PC images after normalization. Then the two histograms are regarded as the texture and spectral distributions of the region and used to calculate the texture and spectral similarity between two regions which is used to determine whether to split a region or merge adjacency regions in the split and merge segmentation framework.

LIDAR is a new promising technique in obtaining instantly 3D point cloud data representing the earth surface information. In order to extract valuable earth surface feature information for further application, 3D sub-randomly spatial distributed LIDAR point cloud should be filtered and classified firstly. In this article, a new LIDAR data filtering and classification algorithm is presented. First, the points' neighboring relation and height-jump situation in TIN (triangulated irregular network) model for 3D LIDAR point cloud are analyzed. After that, the filtering algorithm based on TIN neighboring relation and height-jump is presented. Third, an assistant plane is designed in TIN neighborhood filtering algorithm in order to yield more effective filtering result. Then, the LIDAR points are classified into bare ground points, building points and vegetation points using the above filtering algorithms. The experiment is performed using the airborne LIDAR data, and the result shows that this method has better effect on filtering and classification of LIDAR point cloud data.

Remote sensing SPOT5 images have been widely applied to the surveying of agriculture and forest resources and to the monitoring of ecology environment of mountain areas. However, the accuracy of land-cover classification of mountain areas is often influenced by the topographical shadow effect. Radiometric terrain correction is important for this kind of application. In this study, a radiometric terrain correction model which based on the rationale of moment matching was made in ERDAS IMAGINE by using the Spatial Modeler Language. Lanxi city in China as the study area, a SPOT5 multispectral image with the spatial resolution of 10 m of that mountain area was corrected by the model. Furthermore, in order to present the advantage of this new model in radiometric terrain correction of remote sensing SPOT5 image, the traditional C correction approach was also applied to the same area to see its difference with the result of the radiometric terrain correction model. The results show that the C correction approach keeps the overall statistical characteristics of spectral bands. The mean and the standard deviation value of the corrected image are the same as original ones. However, the standard deviation value became smaller by using the radiometric terrain correction model and the mean value changed accordingly. The reason of these changes is that before the correction, the histogram of the original image is represented as the 'plus-skewness distribution' due to the relief-caused shade effect, after the correction of the model, the histogram of the image is represented as the normal distribution and the shade effect of the relief has been removed. But as for the result of the traditional C approach, the skewness of the histogram remains the same after the correction. Besides, some portions of the mountain area have been over-corrected. So in my study area, the C correction approach can't remove the shade effect of the relief ideally. The results show that the radiometric terrain correction model based on the rationale of moment matching is an effective model to reduce the shade effect than the traditional C correction approach, especially in the complex undulation of mountain area with lots of shade effect. In other words, the traditional C correction approach will show the better result at the plain area with less shade effect. Besides, the accuracy of the DEM data and the registration accuracy between the image and the DEM data will also influence the final correction accuracy. In order to achieve the higher radiometric terrain correction, high spatial resolution DEM data is preferred.

QuickBird satellite is quickly becoming the best choice for high-resolution mapping using satellite images. Ortho Ready Standard Product as an intermediate product between Basic and Standard enables to be generated to an orthorectified product. And it becomes mainly financial because the users can purchase sub-scenes instead full scenes. In this paper, we will describe the followings: (1) how to correct QuickBird Ortho Ready Standard Imagery using different geometric correction methods, and (2) data fusion using QuickBird panchromatic and multispectral data.

High resolution image fusion is a significant focus in the field of the image processing. A new image fusion model is presented based on the characteristic level of Empirical Mode Decomposition (EMD). The IHS transform of the multi-spectral image firstly gives the intensity image. Thereafter, the 2D EMD in terms of row-column extension of the 1D EMD model was used to decompose the detail scale image and coarse scale image from the high resolution band image and the intensity image. At last, fused intensity image is obtained by reconstruction with high frequency of high-resolution image and low frequency of intensity image and IHS inverse transform result in fused image. After presenting EMD principle, multi-scale decomposition and reconstruction algorithm of 2D EMD is defined and fusion technique scheme is advanced based on EMD. Panchromatic band and multi-spectral band3,2,1 of QUICKBIRD are used to assess the quality of the fusion algorithm. After selecting appropriate Intrinsic Mode Function(IMF) for the merger on the basis of EMD analysis on specific row (colum) pixel gray value series, the fusion scheme gives fused image, which is compared with generally used fusion algorithms (Wavelet, IHS,Brovey). The objectives of image fusion include enhancing the visibility of the image and improving the spatial resolution and the spectral information of the original images. For assessing quality of an image after fusion, information entropy and standard deviation are applied to assess spatial details of the fused images and correlation coefficient, bias index and warping degree for measuring distortion between the original image and fused image in terms of spectral information. For all proposed fusion algorithms, better results are obtained when EMD algorithm is used to perform the fusion experience.

The objective in this study is to obtain the accurate tree crown model with complex structure from airborne lidar data for latter feature extraction. The segmentation of tree crown was implemented in several phases. First, the relatively high vegetation points were filtered out from the original tree dimensional point cloud by lidar data processing software. These vegetation points were interpolated in a grid, and then lowpass filter and highpass filter method were utilized to smooth the noise and sharp the crown edge respectively. In the next phase, the points were transformed to be a grayscale image, and the contrast of the image was enhanced by a contrast stretch algorithm to help the segmentation in latter step. Before the watershed segmentation was used to segment the tree crowns, the opening and closing operation in morphology were operated on the image to optimize the segmentation. Finally, a satisfying segmentation result was shown compared to the result which the contrast stretch algorithm wasn't operated on the image, even the overlapped tree crowns were segmented successfully in our test.

In this paper, accuracy analysis and error correction for MODIS reflectance product are accomplished based on ground measurement data. Some ground measurement points (mixed pixels) are selected in MODIS multi-spectral remote sensing image at the eastern Beach of Chongming Island, Shanghai, China. In order to measure surface reflectance of these mixed pixels, registration is first made between MODIS and QuickBird images, then the same sizable areas to one MODIS pixel are chose in QuickBird image (a pixel for MODIS is equal to many pixels for QuickBird), and image classification is made in each QuikBird sample area. Finally, taking the ratio of every class area to the whole area as weight, the reflectance is measured of every ground object, and the weighted mean reflectance is calculated of the whole area (a pixel for MODIS). Regarded the weighted mean reflectance as the measurement reflectance of corresponding pixel in MODIS image, the measurement reflectance of each pixel is compared with the MODIS retrieval reflectance, the accuracy of MODIS reflectance products is analyzed, the relation between MODIS retrieval reflectance and measurement reflectance is gained through linear regression by which an error correction are made to MODIS reflectance product. The comparison between errors corrected MODIS reflectance and the reflectance of ground checking point shows that the method in this paper can improve the accuracy of MODIS surface reflectance product efficaciously.

With the quantitative remote sensing developing, the promising use of multi-angle polarization information has been recognized. Since polarized reflectance always goes with bidirectional reflectance, we can attain the polarized three-dimensional spatial distribution of the target by a polarizer while detecting its bidirectional reflectance. In this paper, polarized reflectance of dry peat with different water content was collected using a polarized bi-directional reflectance photometer. The polarized measurements for peat samples with different water content were performed at various zenith viewing angles, incidence light zenith angles and azimuth angles with band A (630-690nm) and band B(760-1100nm). The polarization changes of different peat moisture were analyzed quantitatively. The results revealed that there were close and positive relationship between peat polarized reflectance characteristics and water content, peat characteristics and sensor's viewing geometry. Polarized reflectance of peat will not only help to find a new way for peat detection remotely, but also provide a theoretical basis for further research on the polarized light remote sensing.

Hyperspectral remote sensing is capable of reflecting the detailed spectrum of ground objects; thereby it can be used for anomaly identification, quantitative retrieval, state diagnosis and fine classification. Wavelet transformation, which is viewed as 'mathematical microscope' with the capability of multi-resolution analysis, can be used for dimensionality reduction and feature extraction to hyperspectral remote sensing data, especially feature extraction at different scales. The data sets used in this study include: spectral data of several ground objects in USGS spectral library, and spectral data of some pixels in an image captured by the airborne image spectrometer OMIS II. Spectral absorption features of ground objects are quite important for ground object recognition. Wave troughs of spectral curve, which represent strong spectral absorption at some specific wavelengths, are extracted and analyzed quantitatively using wavelet transformation. Spectral angle (SA) is selected as similarity measure indicator because of its effectiveness to hyperspectral remote sensing data. The experiment results demonstrate that multi-resolution analysis of wavelet transformation provides excellent performance in spectral feature extraction and spectral similarity measure, so it can be used to target identification and image classification effectively.

This paper proposes a segmentation method based on K-mean and SOM network. Firstly remote sensing image is decomposed by wavelet transform at multiple-scale. Secondly the directional eigenvector of the image is constructed based on the wavelet transform. At coarser scale, we construct 4-dimension eigenvector with feature images, and the images are roughly segmented by K-means algorithm. Then we construct 4-dimension eigenvector with other feature images at fine scale. Based on the results in K-means segmentation and the eigenvector of remote-sensing images at fine scale the images are segmented by SOM network. The experiments about the images segmentation are done in two different ways, one of which is K-means and SOM network simultaneously, and the other of which is mere K-mean. The experiments show that the former has better segmentation results and higher efficiency.

Beijing-1 small satellite was launched Oct.27 2005 and has taken part in the plan of China high-performance earth observation after finishing on-orbit test period. Two kinds of sensors were carried on the satellite. One is 3-band multi-spectral senor whose spatial resolution was 32m, the other panchromatic sensor whose spatial resolution was 4m. In order to ensure truly utility for small satellite data, preliminary deep processing system had been developed for receiving, preprocessing, and data-distribution. Meanwhile, several key questions must be deal with including radiometric calibration, geometric precise rectification, orthographic rectification, image fusion and application demonstration. The paper will focus on the works of the second part including RPC orthographic rectification model and how to optimize algorithms of orthographic rectification which consider the feature of 4m high spatial resolution. RFM is a generalized sensor model, which uses RPC parameters to perform orthographic rectification in no need of orbit parameters and sensor imaging parameters. It is independent on sensors or platforms and supports any object space coordinate system with a variable coordinate system. Compared to linear transformation and polynomial transform, RFM has the highest positioning accuracy. Because RPC is determined by applying the least squares principle to GCP data, approximate error can be evenly distributed through RFM rectification. Based on the experiment on the Beijing-1 high resolution small satellite data using RFM and improved RFM, a generalized model of orthographic rectification of high resolution small satellite data can be developed. The experiment proves: Using second-order improved RFM to rectify the Beijing-1 small satellite image has a sub-pixel positioning accuracy that is close to the accuracy of the rigorous sensor model based on the collinearity equation when the GCPs are evenly distributed.

Atmospheric transmittance is not only an important physical parameter which affects radiation of ground, but also the main study object in remote sensing, atmosphere physics and radiation transfer. During the range of the solar spectrum, gaseous absorption is mainly related to water vapor, carbon dioxide, ozone, nitrous oxide, carbon monoxide, methane, and oxygen (H2O, CO2, O3, N2O, CO, CH4 and O2). The article studies on atmospheric transmittance based on random exponential band models. Then make use of Modtran to analyze and validate the results using the same atmospheric parameters of random exponential band models. So it will lay a foundation for regressing and fitting the polynomials to calculate atmospheric transmittance, and application on Daily BRDF/ Albedo Algorithm in future.

The paper put forward the fault remote sensing information and extraction model based on the different spatial characteristics and combination of these characteristics in order to improve its efficiency in making the computer information processing system self-adapted and automatic. Guided by the mathematical morphology, data synthesis and artificial intelligence, and generalizing the artificial intelligence technology like nerve calculation and geographic information analysis model, the paper set up the obtaining, expression and analysis mechanism of the fault space information and knowledge. Simulating the intensive geosciences analysis and space decision-making process of the geosciences experts in the understanding, information extract of the remote sensing information.

To solve the problem of interposition between trees or trees and between trees and ground in forest images which would bring on error-matching and be unable to construct a full 3D-network, a new approach for segmentation of forest images was proposed. The proposed color divergence was defined over the index class map by quantized image, which was a good indicator of whether that area was in region center or near region boundaries. Using this measure, image texture was analyzed by multi-resolution. Then the initial over-segmented regions were merged according to Laws texture energy measure. Experimental demonstrated that the segmentation results of forest images on the proposed approach hold favorable consistency in terms of human perception. The classification accuracy was 80%. The recognized trees and ground can offer dependable data for image matching and 3D modeling.

In this paper a Hyperspectral Expert Classifier (HEC) based on data-fusion technique was presented. The spectral-spatial contextual image analysis approaches were applied on hyperspectral images, ETM+ images, and GIS data. First, the samples were selected according to the available information to build the reference spectral and calculate the maximum angle after data fusion. The created maps using Spectral Angle Mapping (SAM), GIS data, hyperspectral image, and ETM+ images were used as an input data in HEC. The result showed that the Land-use in the study area could be identified from Hyperion data efficiently. The hyperspectral expert classifier approach is found to have a merit of high classification precision, low computational cost, and without much interference from the users compared with other classifiers. This methodology could easily extended to a large number of classes and used in practical applications (for example mine exploration).

According to the current level of Artificial Intelligence, it is impossible to establish a fully automatic system for parallel processing of Remote Sensing (RS) imagery. An opportunity for improvement is to separate manual operations and fully automatic processor computation tasks, in order to realize the high performance of parallel RS imagery processing. Taking RS image and map registration as an example where low-level machine calculations and high-level intelligent operations with operator support are carried out in sequence in a RS image processing system, this paper illustrates the technical method of separating manual operation from fully automation operation procedure. Further, the architecture of a RS image registration system with two series of parallel processing sub-systems is constructed, where one side of parallel processing is solely for dedicated computation utilizing parallel computing power which avoids the interaction with monitor and keyboard and the other side is several normal image terminals that run in parallel solely to perform all interactive operator-machine operations.

Uncertainty is one important feature of spatial information quality and attracting much more attentions recently. The visualization is an effective way to express the magnitude, pattern and propagation of the uncertainty. In this paper, the visualization method of geospatial information uncertainty in Landsat ETM+ imagery is put forward and described. Firstly, an improved fuzzy reasoning classification method is proposed, and farmland and grassland information are extracted from the ETM+ imagery respectively based on the algorithm. Then the uncertainty of the classification is analyzed, measured and visualized supported by GIS. The uncertainty can be expressed and visualized by different spatial distribution range of cropland and grassland when adjusting their membership values setting. The uncertainty threshold supplies a visual cognition for data users to know the data quality better and make full use of the data more correctly. At the same time, aiming at the overlay areas with similar membership values, other ancillary information can help to improve the classification accuracy and conquer the difficulties in distinguishing cropland from grassland in Landsat ETM+.

The key operation in airports extraction from remote sensing images is to extract the airport edges and obtain their approximate strait lines. For example, the Canny can be used to extract image edges and the result edges can be used to obtain approximate strait lines by Hough Transform or other strait line fitting methods. However, background of airport target is so complex that large numbers of useless edge pixels will be extracted from surroundings by Canny algorithm and those disheveled edge pixels will interfere with the following analysis. For example, it is difficult to use Hough Transform to extract useful strait lines of airport edges from the binary image composed of airport edges and other useless edge pixels because the proportion of disheveled edges is far larger than the one of airport edges. One solution is to smooth the image before edges detection. Unfortunately, most of image smoothing operation cannot weaken useless edges effectively. Moreover, it will also damage the useful ones and makes it more difficult to extract useful strait lines. Though some edge-preserve smoothing algorithms have been proposed, it is still difficult to solve this problem because too many disheveled but robust edges will be preserved together. In this paper, a novel edge-preserve image smoothing algorithm based on Convexity Model is discussed with its practical application in airport extraction. This smoothing algorithm will whittle or restrain those regions whose features accord with the Convexity Model and whose sizes are smaller than the specified one. The experimental results show that the algorithm is effective in removing noises and small regions with few influences on those edges of interested targets whose scales are larger than the specified one. The practical applications show that this smoothing algorithm can increase the efficiency and precision of airports extraction.

Remote sensing provides a useful source of data from which updated land cover information can be extraction for assessing and monitoring environment changes. This paper aims at achieving improved land cover classification performance based image segmentation and support vector machines (SVMs) classification. The object-based classification approach overcame the problem of salt-and-pepper effects found in classification results from traditional pixel-based approaches. The proposed method is a three-stage process, which makes use of the object information from neighboring pixels. Firstly, a robust image segmentation algorithm is used to achieve more homogeneous regions. Secondly, feature information is extracted from each segment and training samples is interactive selected in geographical information system platform. Thirdly, support vector machines classifier is employed to classify the land covers. The experimental results indicate that improved classification accuracy and smoother (more acceptable) is achieved compare with the traditional pixel-based method. Because of the image segmentation process significantly reduces the number of training samples, make SVMs classification method can be applied to information extraction from remotely sensed data.

The research presented in this paper is aimed at the development of multisensor image fusion. The proposed approach is suitable for integration pan-sharpening of multispectral (MS) bands and SAR imagery based on intensity modulation through the a-trous wavelet transform (ATWT) and the curvelet transform(CT). The ATWT is suitable for dealing with objects where the interesting phenomena, e.g., singularities, are associated with exceptional points, and CT as a new multiscale geometric analysis algorithm is more appropriate for the analysis of the image edges and has better approximation precision and sparsity description. This proposed fusion algorithm makes full use of advantages of these multiscale analysis tools, thus it extracts SPOT-Pan high-pass details from the panchrmomatic image by means of the ATWT and SAR texture and edges by details and rationing the despeckled SAR image to its lowpass approximation derived from the CT.SPOT-Pan high-pass details and SAR texture and edges are used to modulate intensity derived from IHS transform of MS bands. SPOT-Pan, Landsat-MS and Radarsat-SAR images covering a region of sanshui in Guangdong province are used to evaluate the effect of the proposed method. The experiment result shows that the proposed algorithm has greatly improved spatial resolution while it keeps the spectral fidelity.

The technology of image fusion now has been used in the process of remote sensing data. It can integrate the information from multi-sensor data so as to complement the shortage of single sensor image, then more suitable for the purpose of human visual perception, computer-aided object detection, and target recognition. Currently, there are many methods of image fusion we can get from other researcher's study, but these methods of image fusion have some disadvantages. This paper presented the approach of image fusion in pixel level with the fuzzy theory, the source of data used in this paper come from QuickBird image, the resolutions of multi-spectral with four bands and panchromatic image are 2.44m and 0.61m respectively. The process of image fusion is implemented mainly in the fuzzy tool of Matlab. Lastly, assessing the quality of resulting images and these methods of evaluation is based on visually and statistically. The findings that the fused image has remained the attributes of multi-spectral image and high resolution image with 0.61m instead of 2.44m. It is indicated that the fused image is finer than the original multi-spectral image. So the approach of this paper has presented could be a good method to process remote sensing image fusion.

Difference interferometric Synthetic aperture radar (DInSAR) has turned out to be a very powerful technique for the measurement of land deformations, but it requires the observed area to be correlated, and coherence degradation will seriously affect the quality of interferogram. Corner reflector DInSAR (CRDInSAR) is a new technique in recently years, which can compensate for the limitation of the classical DInSAR. Due to the stable amplitude and phase performance of the reflector, the interferometric phase difference of the reflector can be used to monitor or measure the small and slowly ground deformation for the cases of large geometrical baseline and large time interval between acquisitions. Phase unwrapping is the process where the absolute phase is reconstructed from its principal value as accurately as possible. It is a key step in the analysis of DInSAR. The classical phase unwrapping methods are either of path following type or of minimum-norm type. However, if the coherence of the two images is very low, the both methods will get error result. In application of CRDInSAR, due to the scattered points, the phase unwrapping of corner reflectors is only dealt with on a sparse grid, so all the reflectors are connected with Delaunay triangulation firstly, which can be used to define neighboring points and elementary cycles. When the monitoring ground deformation is slow, that is unwrapped neighboring-CR phase gradients are supposed to equal their wrapped-phase counterparts, then path-following method and Phase unwrapping using Coefficient of Elevation-Phase-Relation can be used to phase unwrapping. However, in the cases of unwrapped gradients exceeding one-half cycle, minimum cost flow (MCF) method can be used to unwrap the interferogram.

In this paper, the problems rise in hyperspectral data mining and some key issues should pay attention to were proposed based on the analysis on the state-of-art of hyperspectral data mining. The problems are as follows: data mining precision, mining algorithm efficiency, new hyperspectral data mining algorithms, the uncertainty in hyperspectral data mining, visualization in hyperspectral data mining process, and knowledge presentation, interpretation, estimation and management etc.. Some key issues should emphasize in the future are: systematic hyperspectral data mining theory, dimensionality reduction, mining spatial and temporal knowledge from images, and mining distributed data and mining multi-agent data. Also the framework and architecture of hyperspectral data mining were put forward in this paper. Hyperspectral data mining framework includes some subparts as follows: data selection, data preprocessing, data transfer, data mining and pattern estimation. And the architecture is composed of database, data warehouse, database management system, repository, mining process, user interface etc.. At last, an algorithm which named Relational perspective map (RPM) was introduced into the field of hyperspectral data mining. By the experiment on the spectra data from USGS spectral library, it proves that this algorithm is suitable to discover those spectral features and to identify and discriminate object classes based on their spectra.

This paper presents a novel method for image fusion that integrates improved HIS and curvelet transform, and uses it to fuse the IKONOS images. Firstly, red band is added to panchromatic band with weights to obtain a new panchromatic band, and blue, green and near-infrared bands are stacked to form the RGB space, which is used for converting to HIS space later. Secondly, the new panchromatic band and intensity component carry on curvelet transform respectively. Then fuse the coefficients in the corresponding scales to generate a new intensity component. Finally, the inverse HIS transform is applied to generate the fusion image. To prove the superiority of this method, this paper uses several parameters to assess the image comparing with other fusion images. The results show that the proposed method can increase the information entropy, decrease the spectrum distortion of the fused image, and improve the structural similarity between the fused image and the original multispectral image. So all above prove that the integrated method can enhance the fusion quality efficiently.

The Algorithm of Fuzzy C-Means (FCM) clustering is used in many fields, such as data mining, image segmentation etc. But it has the problem of cluster center initialization. Good initial cluster centers will constrain the value function to the overall situation optimal solution rapidly, and inappropriate initial cluster centers, not only need more iterative times, but also may possibly cause the algorithm finally restrained to the partial optimal solution. Aim to resolve the problem of cluster center initialization, the paper proposes a new approach of FCM based on cloud model which is an efficient transformation model between quantitative number and qualitative concept, and applied it in the field of image segmentation, the experiment results prove the method can define good initial cluster centers and produce good quality of image segmentation.

It has always been an important low-level operation to extract edges from images in the fields of computer vision and image procession, in which straight line extraction is typical and representative. Because most man-made spatial objects, e.g. buildings, roads, etc. often take on near straight-line boundaries, extracting straight lines is often the first step to extract these targets. Straight lines can then be looked as the elementary units for other higher level image interpretations. In this paper, a straight line extraction method combining edge detection and depth-first searching on the vector line layer is proposed and applied to extract runways of airports. The steps include: 1) edges are found with the Canny operator and vectorirzed. The reason to use the Canny operator is because it is designed to be an optimal edge detector, which gives very good results on detecting step or slop like edges. It takes as input a grey scale image, and produces as output an image showing the positions of tracked intensity discontinuities. After this operation, we then vectorize the edge points to be a vector layer with edge tracing.2) With the vector-formatted edge lines, the straight line searching can then be carried out. In order to complete this, topology between arcs should be cleaned and rebuilt, which includes the deletion of repetitive, one-node arcs, and splitting on the intersections, etc. 3) Straight lines are detected with the depth-first searching strategy. With the rebuilt topology, we can easily obtain the begin, end nodes of every line. If the distances of its all vertices to the line connecting the begin, end nodes of an arc are less than some pre-defined threshold, it could be looked as a 'straight line' and extracted. Besides, we are certainly only interested in the straight lines with lengths larger than certain threshold, thus a minimum length threshold should be specified to delete these very short lines. In the searching of straight lines, some arcs should be grouped as a single straight line; some un-straight lines should be split to extract its straight parts. The suitable straight lines are outputted to a vector layer after being reselected and re-grouped, with distinguishing short, long isolating, long not isolating straight lines. With all these steps, we can get the initial straight vector line layer. 4) To these lines with small interspaces but locate on a single straight line, we use a simple but effective connecting step to 'fill' the gaps. Starting from the vector layer and with the operations of broken line connecting and parallel line detection, the main airport runway can be well extracted, which helps us to locate and recognize airports from high spatial remotely sensed imagery.

To effectively solve the contradictions between speckle noise reduction and image edge preservation, edge fuzzy phenomenon appears in the traditional denoising methods (such as Gaussian filtering, median filtering) and wavelet transform also has its shortcoming for image processing owing to its threshold value selection, a new fusion filtering method is presented in the paper where smoothing SAR images can be firstly obtained by median filtering and preserved image edge information secondly by AMSS equation, then the two SAR images can be fused into a fusion image by means of PCA, Multiplicative and Brovey, which can be analyzed by the quantity and the quality. Simulation results proved that not only the quantity analysis but the quality showed the fusion image primely reduced the speckle noise and effectively preserved the boundaries and detail information of the image edge, meanwhile the PCA fusion method was better than Multiplicative and Brovey to the filtering of SAR images.

The methods of segment-based image analysis are becoming more and more important for remote sensing as a result of the progresses in spatial resolution of satellite image. An approach to segmentation of IKONOS panchromatic image based on frequency domain filtering and marker-controlled watershed transform is presented in the paper. Primarily the texture and edge features are extracted from the response of log Gabor filtering. The texture features are obtained from the amplitude response, and phase congruency is introduced as a new method to detect invariant edge features. Then an approach to combining texture with edge features is presented and used to implement the marker-controlled watershed segmentation. Combination of different frequency texture features is used to mark different complicated images. Finally empirical discrepancy is calculated to evaluate the segmentation results. It shows that the precision of right segmentation is up to 80~85%. The approach presented in the paper basically satisfies the demand of feature recognition and extraction of high-resolution remotely sensed imagery.

MODIS 1B data preprocessing consists of "bowtie" effect elimination and geometric correction. The paper proposes a fast preprocessing algorithm. First, partition the input image into small sub-images without "bowtie" effect. Secondly, do geometric correction to each sub-image. Finally, mosaic each sub-image in the output coordinate system and eliminate the "bowtie" effect in the process. The proposed algorithm shows both a better geometric performance and faster preprocessing speed. For the massive MODIS 1B data preprocessing, a parallel preprocessing method based on this algorithm above is further proposed. Analysis shows that real-time preprocessing for massive MODIS 1B data can be realized by the parallel algorithm.

With the development of remote sensing and aero photography, we can quickly get all kinds of inexpensive image with high resolution. To efficiently manage these increasing high-volume data, the spatial database management system is the best solution. In this paper many problems and key techniques are analyzed and discussed on establishing remotely sensed image database, including image dividing, image encoding, image indexing, establishment of image pyramid, etc.

Electromagnetic radiance acquired by sensors is distorted mainly by atmospheric absorbing and scattering. Atmospheric correction is required for quantitatively analysis of remote sensing information. Radiation transfer model based atmospheric correction usually needs some atmospheric parameters to be chosen and estimated reasonably in advance when atmospheric observation data is lacked. In our work, a radiometric calibration was applied on the satellite data using revised coefficients at first. Then several parameters were determined for the correction process, taking into account the earth's surface and atmospheric properties of the study area. Moreover, the atmospheric correction was implemented using 6S code and the surface reflectance was retrieved. Lastly, the influence of atmospheric correction on spectral response characteristics of different land covers was discussed in respects of the spectral response curve, NDVI and the classification process, respectively. The results showed that the reflectance of all land covers decreases evidently in three visible bands, but increases in the near-infrared and shortwave infrared bands after atmospheric correction. NDVI of land covers also increases obviously after atmospheric influence was removed, and NDVI derived from the surface reflectance is the highest comparing to that from the original digital number and the top of atmosphere reflectance. The accuracy of the supervised classification is improved greatly, which is up to 87.23%, after the atmospheric effect is corrected. Methods of the parameter determination can be used for reference in similar studies.

LIDAR is now a widely used technique in the fields of mapping and survey. Various algorithms on LIDAR data analysis and process are present as demanded in the past years. There are plenty of thresholds in these algorithms, which have great relationship with the point spacing. However, there is few researches particular on this issue. In this article, a peak value statistics-based approach aimed at the problem of uneven distribution of points cloud density is described. A grid index is established to manage the points. Then total number of points in each grid index unit is obtained. The statistic produces distribution of resolution and the peak value of the distribution. In this approach, the distribution of points data spatial resolution in the region combined with different kind of terrain feature, is estimated automatically. Compared with resolution obtained by manual method, this algorithm is accurate and effective.

Speckle noise can be introduced to a remote sensing image in many ways, starting with the lens of the imaging hardware and ending at the digitization of the captured image. The reduction of noise without degradation of the remote sensing image has attracted much attention in the past. However, the traditional noise reduction methods can usually cause the degradation of the underlying image and cannot preserve the feature of structure in remote sensing image, especially two dimensional image brightness structures. With regard to the traditional speckle noise reduction methods, their results aren't very well even though the traditional methods are improved. In this paper, a method for speckle noise reduction of remote sensing image based on SUSAN is designed. This paper tests this method in a SPOT image of 128*128 suffering from speckle noise using 3 by 3 and 5 by 5 mask and gives results of quantitative and qualitative comparisons of the SUSAN noise filter with other traditional noise reduction methods. The results of the test prove that the SUSAN filter can effectively remove speckle noise and preserve edge and texture information. The processing speed of this algorithm is faster than that of the traditional noise reduction methods.

This paper first discusses the background and details of Rational Function Model (RFM) which describes the coordinates in image space and object space, and then by incorporating additional parameters into the basic RFM, the bias-compensated RFM is introduced. With different additional parameters, four kinds of bias-compensated RFM are put forward and discussed. Finally an experimental test in Shanghai, China, is carried out with the Bias-compensated Rational Function Model using QuickBird across-track stereo imagery and highly accuracy Ground Control Points (GCP) as data source. Conclusion is drawn that with more well distributed GCPs, high 3D geo-positioning accuracy can be obtained up to about 0.6 meter in plane direction and 0.7 meter in height direction.

The sensitivity of hyperspectral indices to LAI, chlorophyll contents and leaf internal structure at canopy level are investigated using simulated canopy reflectance dataset, this method can avoid expensive and impractical surface reflectance measurement, providing a theoretical basis for satellite-borne remote sensing. The model employed is PROSAIL that couples leaf reflectance model PROSPECT and canopy radiative transfer model SAIL. Hyperspectral indices used are NDVI, EVI, GI, RI, TVI, SIPI, PRI, TCARI, OSAVI, TCARI/ OSAVI, mNDVI705 and NDWI. Using PROSAIL model, leaf and canopy reflectance under different chlorophyll contents, leaf internal structures, LAI and water contents are first simulated and compared. Then using PROSAIL simulated canopy reflectance data, different hyperspectral indices are calculated, the sensitivity of vegetation indices to LAI and chlorophyll contents is analyzed in detail. And the sensitivity of vegetation indices to leaf internal structure is also analyzed. Results show that relationships between hyperspectral indices and LAI are approximately logarithmic while the relationship between hyperspectral indices and leaf internal structure is linear. EVI and TVI are good indicators to estimate LAI. GI, RI, TCARI, MNDVI₇₀₅ can be used to estimate chlorophyll content. N has great influence on hyperspectral indices.

Inversion is an important process in remote sensing. In order to improve the stability and accuracy of inversion, in this article, we applied kernel forms of AMBRALS (Algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface) and PLS (Partial Least Square) regression technique to simplify a canopy reflectance model SAILH (Scattering by Arbitrarily Inclined Leaves, with Hotspot effect). PLS is a statistical method used for regression highly collinear variable data. Kernel-driven model is a semi-empirical model with linearity form of "kernels", and these kernels can be explained in physics. We generated 24 typical canopy cover scenes by combining the canopy parameters of SAILH model. For each scene, we used PLS regression to estimate the coefficients of our new model. The results suggest the new model is acceptable in stability and accuracy. Base on the new model, we defined sensitivity matrix to assess the correlations of directional observations data, which can help to choose appropriate directions when inversion.

Image transform, which transforms one image to another image by co-ordinate transformation, is often conducted in remote sensing applications. Image transform methods include forward mapping and reverse mapping. In this paper we propose a new forward image mapping method. Since a grid pixel in the source image may be no longer mapped to a grid pixel in the target image, gap phenomenon, i.e. some grids in the target may not receive information from any source pixel, occurs via forward mapping. Our method fills the gaps with information surrounding them using the image inpainting technique. Imagine that the image is a temperature field, i.e. the pixel value is the temperature value. The heat will transfer to the gaps, thus the gaps are filled after some time. This can be achieved by solving the classical diffusion equation. Furthermore, if the features in the image are rather complicated, we can use anisotropic heat diffusion (total variation diffusion) to fill the gaps in order to well preserve the edge features. The experimental result demonstrates the effectiveness of the proposed inpainting method.

TERCOM, ICP and TIEM algorithms, which mathematically all apply correlation matching mode, have been developed for positioning in underwater Terrain-aided Navigation System (TANS), but how to virtually improve their performance is still research puzzle now. Analyzing the characters of terrain reference data's distribution and vehicles prowling underwater, we find that grid spacing and accumulation sequence are two decisional elements of underwater TANS. Then the modified Maximum a Posteriori (MAP) estimation algorithm (M-MAP) from super-resolution images reconstruction is creatively explored for implementing interpolation to enhance the accuracy of non-surveyed points' deep-determination, and basic error mechanism model (EMM) based on Mean Absolute Difference (MAD) algorithm is deduced which can reflect the relationship of underwater TANS's inner factors. Simulation experiments indicate that adopting appropriate fundamental factors can effectively boost up underwater TANS's navigation competence based on the algorithms listed above.

There exist two kinds of imaging modes for line scanner remote sensing satellite sensors: the first is synchronous imaging mode, and the second is called asynchronous imaging mode. For asynchronous imaging mode, the sampling speed of the sensor is lower than the satellite ground velocity. A twin central line projection model is developed for geometrically corrected asynchronous sampling pushbroom line scanner satellite imagery. A collinear equation is derived to model the relation between image space coordinate and the object space coordinate based on the principle of twin central line projection model. The collinear equation and its adjustment model for spatial resection are derived from the twin central line projection model to process EROS A1 1B imagery. The applicability of twin central line to asynchronous sampling pushbroom line scanner satellite imagery is proved through test computation based on simulated data.

Exchanging water with the lower branch of Yangtze River, Poyang Lake is a seasonal lake. During the spring and summer flooding season it inundates a large area while in the winter it shrinks considerably creating a large tract of marshland for wild migratory birds. A better knowledge on the water coverage duration and the beginning and ending dates for the vast range of marshlands surrounding the lake is important for the measurement, modeling and management of marshland ecosystems. In addition, the abundance of a special type of snail (Oncomelania hupensis) (the intermediate host of parasite schistosome (Schistosoma japonicum) in this region) is also heavily dependent on the water coverage information. However, there is no accurate DEM for the lake bottom and the inundated marshland, nor is there sufficient water level information over this area. In this study, we assess the feasibility on the use of multitemporal Landsat images in mapping the spatial-temporal change of Poyang Lake water body and the temporal process of water inundating of marshlands. All eight Landsat Thematic Mapper images that are cloud free during a period of one year were used in this study. We used NDWI and MNDWI methods to map water bodies. We then examine the annual spatial-temporal change of the Poyang Lake water body. Finally we attempt to obtain the duration of water inundation of marshlands based on the temporal sequence of water extent determined from the Landsat images. The results showed although the images can be used to capture the snapshots of water coverage in this area, they are insufficient to provide accurate estimation on the spatial-temporal process of water inundating over the marshlands through linear interpolation.

The theoretical basis of water depth retrieval by optical remote sensing was analyzed based on radiation transfer process of light wave in water. After a summary of water depth retrieval methods of the predecessors, derivative spectra method for water depth retrieval was introduced. Water reflective spectra were collected using ASD field spectroradiometer, and water depths were measured by a digital echo sounding system simultaneously at radial sandy ridges in Southern Huanghai Sea of China. The turbidity was inhomogeneous in the test area and scattered signal from material in the water was also different in spatial distribution. The reflectance of near infrared band (760~900nm) was most sensitive to water depth(R = -0.73). As simulated in TM band settings, the correlation between water depth and reflectance ratio between TM4 and TM1 is better than others(R=-0.81). The correlation between water depth and the first derivative of reflectance at 711nm is significant(R=-0.87). The accuracy evaluation of Single band algorithm, Band ratio algorithm and derivative algorithm showed that the accuracy of single band and band ratio algorithms were low for the samples near the shore, which average relative error was more than 30%. The accuracy of derivative algorithm was improved as to the same samples, which average relative error was 17%. The results indicated that derivative spectra method was an effective tool for reducing the error bring by variety of water quality.

This paper presents a novel road-extraction method focusing on a road network in an urban central area. The method introduces the knowledge of road features into the extraction process and makes full use of spectral and spatial context relationships and geometric information, thus successfully discriminates roads and spectrally similar buildings and solves the problem of urban roads inconsistent morphology in the imagery. We adopt a Decision Tree model to extract the raw roads information based on the spectral knowledge of pure pixel signatures. Then an "Eliminate & Growing" algorithm is developed based on the context spatial relationships to make the roads independent and filled and reduce the "salt and pepper" effects. Next, we retrieve more accurate road information in vector format in terms of the road's geometric characteristics. Moreover, we manage to retrieve the hidden roads blocked by the trees via utilizing the information of wayside trees. And finally we use mathematical morphology to form the road network. This method has successfully extracted all the main and sub-main roads in the study area; the result has demonstrated the method's high accuracy and usefulness in practice.

When multispectral images are used to extract the area of aquatic vegetation in Taihu Lake, because of the influence of suspended matter and algae, different objects may have the same spectrum and make it difficult to mapping the distribution of aquatic vegetation exactly. Many different methods, including unsupervised classification and supervised classification, are used, but the classification accuracy didn't improve obviously. The growth of aquatic vegetation is closely to the water depth. So we try to use water depth data to increase the extraction accuracy. The whole Taihu Lake is classified into three types: open water, emerged vegetation and submersed aquatic vegetation. Suppose the DN (Digital number) of each type satisfies normal distribution. Numbers of sample points of each type in single band or combined bands are selected and put down there DNs, and then statistical method is adopted to acquire the maximum and minimum which are used to build decision tree to fulfill the classification. The single band or combined bands in which maximum and minimum interval of each type have small intersect set are considered as the suitable bands for classification. Two methods, classification based on spectral characteristics and classification based on spectral characteristics and water depth data, are used. The classification accuracies of the two methods are compared. The results show the water depth data can improve the classification accuracy and resolve the different objects with same spectrum problem partially.

To study the relationship between suspended sediment concentrations (SSC) prepared in laboratory and synchronously measured spectral reflectance, the optimal wavelengths to estimate SSC in water were selected. The single factor, band ratio and sediment parameter quantitative retrieval models of SSC were constructed using these optimal wavelength means, and the suspended sediment parameter model was used to retrieve the SSC in the Yangtze River estuary. Results show that the models built by single factor B4 (780-835nm) and band radio B4 (780-835nm)/B1(430-500nm) respectively can estimate the SSC accurately. The retrieval model of SSC constructed by suspended sediment parameter can get a relatively higher accuracy level than the single factor and band ratio models. The suspended sediment parameter model applied to the Yangtze River estuary exhibits a strong capability to map the SSC distribution. In this paper, experimental measuring of SSC in water and reflectance can not only keep the data obtained synchronously but also weaken the influence of atmosphere on reflectance. The inversion models of SSC developed by these data are of good representations.

One of the bases of remote-sensing on water quality is to analyze reflectance just beneath water surface (0^- deepness). Because reflectance just beneath water surface can not be obtained directly, and remote sensing reflectance which is ratio between water-leaving radiance and total radiance on water surface from sky can be obtained by spectroradiometer, remote sensing reflectance is used commonly for building remote sensing model instead of reflectance just beneath water surface. However, the water-leaving radiance obtained from the water surface includes some mirror reflections of the water surface which reflect little information of the waterbodies. What's more, since the mirror reflections of the same water surface are fluctuating when repeating the measurements in the same area of the water surface, the water-leaving radiances obtained from this water surface are not identical in these measurements. Obviously, there is significant difference between remote sensing reflectance and reflectance just beneath water surface. The remote-sensing model of the waterbodies based on remote sensing reflectance exists some errors in retrieving the water quality. Therefore, it is necessary to extracting reflectance just beneath water surface from measured remote sensing reflectance when building the remote-sensing model of the waterbodies and retrieving the parameters of the water quality. This paper builds a novel remote-sensing model and proposes an approach to extracting reflectance just beneath water surface from measured remote sensing reflectance based on the model. In the proposed model, there are two assumptions: the radiance from the underwater is steady and identical in all directions after the radiance entering into the under water is scattered entirely by the water molecule and the particle of other material in the waterbodies, and the measurements of the radiance are completed in a short period of time. Based on the above assumption, the differences among several remote sensing reflectance which were measured repeatedly in same area of the waterbodies in a short time are related to fluctuation of the water surface. In this model, the mirror reflectance model of water surface about wavelength is firstly obtained from a group of remote sensing reflectance spectra measured repeatedly over same area of waterbodies, and then reflectance just beneath water surface is extracted from the remote sensing reflectance using the least squared method and the Levenberg -Marquardt algorithm. After the proposed model being built, three groups of experiments for remote sensing reflectance measured above clean waterbodies, waterbodies with suspended sand and alga in Lake Taihu are conducted and reflectance just beneath water surface of these three waterbodies are extracted successfully. Both the theoretical deduction and the experimental result demonstrate that the proposed model is effective and efficient.

The major difficulty in monitoring lake areas based on low or moderate resolution Remote Sensing (RS) data is how to improve the accuracy and applicability of unmixing of mixed pixels, considering both the complexity of objects and limitations of time and space. To solve the problem, this paper proposes an efficient model uniting double-edge extraction with unmixing of mixed pixels, the accuracy and applicability of which is attested by computing lake areas of northwestern China using AVHRR images.

In this article, we present some experiments on coral reef benthic cover mapping with fused IKONOS image. The objective of our study is to establish an efficient approach for the classification task on hand. Four scenarios are designed and in each scenario two classification methods (Maximum Likelihood and Decision Tree) are implemented. Ground truth data is obtained through visual interpretation and manual digitization, against which accuracy of classification map is calculated. Results indicate that mining spectral information deeply (scenario III and IV) can increase classification accuracy dramatically. Compared with conventional utilization of spectral data (scenarioI), classification accuracy of ML and DT respectively increases by 3.94% and 5.15% under scenario IV. However, when spectral and spatial information is combined together (scenario II), accuracy of ML and DT is respectively reduced by 8.02% and 2.31%. It can be concluded from our study that when classify benthic cover with high-resolution remote sensing data in pixel-based pattern, utilization of spatial information should not be excessively emphasized. Fully exploiting spectral information may bring more benefits. Moreover, DT is more robust and can produce more accurate classification results than ML. Our results help scientists and managers in applying IKONOS-class data for coral reef mapping applications.

As an important sub-system in intelligent transportation system (ITS), the detection and recognition of traffic signs from mobile images is becoming one of the hot spots in the international research field of ITS. Considering the problem of traffic sign automatic detection in motion images, a new self-adaptive algorithm for traffic sign detection based on color and shape features is proposed in this paper. Firstly, global statistical color features of different images are computed based on statistics theory. Secondly, some self-adaptive thresholds and special segmentation rules for image segmentation are designed according to these global color features. Then, for red, yellow and blue traffic signs, the color image is segmented to three binary images by these thresholds and rules. Thirdly, if the number of white pixels in the segmented binary image exceeds the filtering threshold, the binary image should be further filtered. Fourthly, the method of gray-value projection is used to confirm top, bottom, left and right boundaries for candidate regions of traffic signs in the segmented binary image. Lastly, if the shape feature of candidate region satisfies the need of real traffic sign, this candidate region is confirmed as the detected traffic sign region. The new algorithm is applied to actual motion images of natural scenes taken by a CCD camera of the mobile photogrammetry system in Nanjing at different time. The experimental results show that the algorithm is not only simple, robust and more adaptive to natural scene images, but also reliable and high-speed on real traffic sign detection.

Crown projected area has been an important part for green cover ratio in urban green investigation. Current field-based assessment methodology provides the needed information, but is costly, subjective, time-consuming and therefore update frequency is low. Remote sensing techniques provide a potentially low-cost alternative to field-based assessment, but required the development of methods to easily and accurately extract the required information. In this paper, A semi-automated individual tree crown detection algorithm was developed which had two main steps: firstly, several spectral characteristic transect samples were fitted by high-order curve approximation method; secondly, each curve inflection point was found to connect to tree contour. Two different types of tree areas were selected to detect crown area, one was street tree area where few of tree crowns were overlain, its detection precision was 87.91%; the other was public green area where more tree crowns were overlain, its detection precision was a bit lower than the former, which was 82.24%.

Vegetation fraction is an input parameter to some vegetation modes for analysis. Based on the analysis on the methods of measuring forest fraction, an improved method was developed to estimate vegetation fraction from a complex vegetation index PI, which multiply Advanced Vegetation Index (AVI) by shadow index (SI), derived from Landsat TM image. In the mountainous areas, the hill can make the sunlit and shadow of the hill different though they are same value in the plane. Through compare the PI before topographic correction to the PI after topographic correction, we find that this complex vegetation index can make up the topographic affect, so it can be better for estimating vegetation fraction in mountainous areas. In the paper, the vegetation fraction data have been estimate using the improved method in the study area, which most terrain features are mountains in Liping county of Guizhou province, and been validated by survey data. The result shows that the new method can reduce the affect of the shadow of mountain and can better estimate the vegetation fraction, especially in the mountainous areas.

In recent years, the bio-optical model has been paid more and more attention. In order to validate its applicability in the near-infrared wavelengths to Case II waters, two simply parameterized equations employing reflectance at 808nm and 873nm were established to estimate total suspended matter (TSM) concentrations in the Shitoukoumen Reservoir that represented a turbid inland water condition. It was showed that both equations gave out comparative good performance with coefficient determination (R²) larger than 0.85 and root mean squared error (RMSE) much lower than data span for both training and test data. Based on the transfer of radiation in waters, the bio-optical model could integrate well apparent optical properties (AOPs) with inherent optical properties (IOPs). However, further investigation is needed to upgrade the bio-optical dataset and to refine the model for the universal applications.

Information on the spatial distribution of grass communities in wetland is increasingly recognized as important for effective wetland management and biological conservation. Remote sensing techniques has been proved to be an effective alternative to intensive and costly ground surveys for mapping grass community. However, the mapping accuracy of grass communities in wetland is still not preferable. The aim of this paper is to develop an effective method to map grass communities in Poyang Lake Natural Reserve. Through statistic analysis, elevation is selected as an environmental variable for its high relationship with the distribution of grass communities; NDVI stacked from images of different months was used to generate Carex community map; the image in October was used to discriminate Miscanthus and Cynodon communities. Classifications were firstly performed with maximum likelihood classifier using single date satellite image with and without elevation; then layered classifications were performed using multi-temporal satellite imagery and elevation with maximum likelihood classifier, decision tree and artificial neural network separately. The results show that environmental variables can improve the mapping accuracy; and the classification with multitemporal imagery and elevation is significantly better than that with single date image and elevation (p=0.001). Besides, maximum likelihood (a=92.71%, k=0.90) and artificial neural network (a=94.79%, k=0.93) perform significantly better than decision tree (a=86.46%, k=0.83).

The interpretation of remotely sensed images of turbid coastal waters or inland lake waters is more difficult than case 1 water, because their optical properties are complex, and their optical constituents are independent of phytoplankton concentrations. In recent years significant efforts have been made to develop ocean color satellite missions with improved spectral and radiometric performance, and in the same time, techniques for constituent retrieval have evolved from empirical towards analytical algorithms. Analytical models can be developed and inverted to yield concentrations (Carder et al. 1999) of substances in the water from reflectance measurements, which require a suitable parameterization of the optically active constituents and their optical properties. This paper focused on absorption by chromophoric dissolved organic matter (CDOM; also Gelbstoff or yellow substances), which was the pool of absorbing substances in water and one of the main optically active constituents in Case 2 waters. The absorption of CDOM is generally considered as the exponential form model, which have three important main parameters, S, a(λ₀), λ₀. The S results got from the exponential form model fit using CDOM normalization absorptions by 350nm, or 400nm, or 440nm absorption were the same, and the final value of S for CDOM in Meiliang Bay, Taihu Lake was 0.0106, namely the mean of S for all samples; Normally, a(λ₀) is simply taken to be the mean of the absorption coefficient of CDOM of field samples in the reference wavelength, however, this study found that a(λ₀) a varied greatly between samples ( λ₀ = 400, 1.93 <a(_λ0)<4.09; λ₀=440, 1.12<a(λ₀)<2.56; λ₀ =350, 4.07 <a(λ₀)<7.85), and the correlation coefficient between a(_λ) and TN(total nitrogen) concentration increased with the decrease of wavelength, in 350nm, up to 0.83, so a(_λ0); a submodel was constructed by lineal regression of a(λ₀) and TN(total nitrogen) concentration; The errors of reversion were compared and analyzed in yellow matter absorption model using different parameter, and three reference wavelengths _λ0, namely 350nm, 400nm and 440nm, were considered in the paper; and the results showed that parameter a(_λ0) was most important parameter in absorption model of CDOM than other parameters, and sub-model of a(λ₀) was more reasonable parameter to CDOM absorption model than average a(λ₀). Because absolute relative error using a(_λ0) submodel in 350-700nm was greatly reduced, its average absolute relative error was 15.1 percent, and correlation coefficients between measured absorption coefficient of CDOM and estimated absorption coefficient of CDOM in 350- 700nm were remarkably improved, its average value in 350-700nm was 0.73, whereas average absolute relative error using average a(λ₀) a was approximately 24 percent and its correlation coefficients between measured absorption coefficient of CDOM and estimated absorption coefficient of CDOM were 0. Furthermore, choice of reference wavelength had little effect on CDOM absorption coefficient reversion.

Based on in situ water sampling and field spectral measurement from June to September 2004 in Lake Chagan, this paper partly addressed to develop a new approach named inverse continuum removal to isolate fluorescence peak for the comparison of water reflectance spectra with different Chl-a concentration during the summer. Next, an attempt was made to link the reflectance changes including band depth and band area with Chl-a concentration and evaluate the potential of remote sensing data for inversion. Results show that the Chl-a determined from band depth and band area of fluorescence peak with the determination coefficient (R2) higher than 0.74. The study also proves that inverse continuum removal analysis can be used to effectively determine the Chl-a concentration of Lake Chagan in Northeast China.

This paper, based on VLL (Vertical Line Locus), proposes a cluster analysis approach for DEM change detection. The approach takes advantage of the character of VLL and combines the previous DEM and new images to find the DEM grid points where the elevation values are changed. And the strategy of multi-level image matching is adopted to improve the accuracy of aerial image matching, which also ensures the accuracy of change detection. Moreover, in order to get rid of the false change points and achieve stable result, a cluster analysis approach based on the density is used to analyze the candidate change points. Finally, experiments are given. The results show that the approach proposed by this paper is feasible. It can wipe off the false change areas calculated by VLL, and detect the areas where the elevation values are questionable in DEM exactly.

Leaf Area Index (LAI) is an important parameter describing the growth status of vegetation canopy and is also critical to various ecological, biogeochemical and meteorological models. LAI can be conventionally estimated from instantaneous remotely sensed data mainly through Vegetation Indices (VI) and inversion of canopy reflectance models. Data assimilation is a new developed and a promising technique, which can take advantages of time series observations. In this study, the variation algorithm was used to retrieve LAI, by assimilating time series remotely sensed reflectance data into a simple crop growth model, which was obtained by statistical analysis of more than 600 field samples from wheat paddock. To overcome the improper assumption that the other inputs except for LAI in the radiative transfer models are known in data assimilation, we proposed a strategy to allow the spectral parameters to be free. This strategy was evaluated by simulation. With this method, we also analyzed the influence of background on the retrieved results by simulation. It was further validated using ground measurements. The results were promising compared with field measured LAI data, with the Root-mean-square-error (RMSE) being 0.51.

We develop a multi-angular imaging power line inspection system. Its main objective is to monitor the relative distance between high voltage power line and around objects, and alert if the warning threshold is exceeded. Our multi-angular imaging power line inspection system generates DSM of the power line passage, which comprises ground surface and ground objects, for example trees and houses, etc. For the purpose of revealing the dangerous regions, where ground objects are too close to the power line, 3D power line information should be extracted at the same time. In order to improve the automation level of extraction, reduce labour costs and human errors, an automatic 3D power line reconstruction method is proposed and implemented. It can be achieved by using epipolar constraint and prior knowledge of pole tower's height. After that, the proper 3D power line information can be obtained by space intersection using found homologous projections. The flight experiment result shows that the proposed method can successfully reconstruct 3D power line, and the measurement accuracy of the relative distance satisfies the user requirement of 0.5m.

Former studies on mountain system are focused on the department or subject characters, i.e. different department and branches of learning carry out researches only for their individual purposes and with individual characters of the subject of interests. As a whole, their investigation is lacking of comprehensive study in combination with global environment. Ecological environment in mountain regions is vulnerable to the disturbance of human activities. Therefore, it is a key issue to coordinate economic development and environment protection in mountain regions. On the other hand, a lot of work is ongoing on mountain sciences, especially depending on the application of RS and GIS. Moreover, the development of the Digital Earth (DE) provides a clue to re-understand mountains. These are the background of the emergence of the Digital Mountains (DM). One of the purposes of the DM is integrating spatial related data and information about mountains. Moreover, the DM is a viewpoint and methodology of understanding and quantifying mountains holistically. The concept of the DM is that, the spatial and temporal data related to mountain regions are stored and managed in computers; moreover, manipulating, analyzing, modeling, simulating and sharing of the mountain information are implemented by utilizing technologies of RS, GIS, GPS, Geo-informatic Tupu, computer, virtual reality (VR), 3D simulation, massive storage, mutual operation and network communication. The DM aims at advancing mountain sciences and sustainable mountain development. The DM is used to providing information and method for coordinating the mountain regions development and environment protection. The fundamental work of the DM is the design of the scientific architecture. Furthermore, construct and develop massive databases of mountains are the important steps these days.

Suspended sediment is one of the most important parameters for water quality. Numerous experiential or deductive models have been advanced for detecting suspended sediment using remote sensing technology. However, due to the lack of atmospheric parameters and sufficient statistics, the precision or accuracy of these models cannot be guaranteed. In this paper, we take Lake Chaohu as an example area and process its TM/ETM+ data by applying the method of internal average relative reflectance for atmospheric correction and by extracting sediment information according to the value of SI (SI=(TM2+TM3)/(TM2/TM3)). The results show that: (1) an accurate extraction of water information of Lake Chaohu can be obtained by considering the relationship between the spectrums, (2) the data of relative suspended sediment revealed are in accordance with the instrumental data in situ, (3) the high-density suspended sediment area has expanded 1.5 times during the past 13 years, indicating changes of the lake's estuary, shoreline, and its suspended sediment content, and (4) the main sources of suspended sediment of Lake Chaohu are river transportation and erosion of the lakeshore.

This study focuses on using remote sensing for assessment of surface urban heat island and associated surface physical characteristics and selects Changsha city as study area. The TERRA/MODIS images acquired in 2005 for three different seasons were selected to generate land surface temperature maps and surface characteristics. The result showed that UHI effects were significant both in summer and spring. Land surface temperatures were 8°C and 10°C warmer than surrounding rural areas in spring and summer seasons, respectively. Although it is still existed in winter, the UHI effects are not so much significant. Land surface temperature was 4°C warmer than surrounding rural areas in winter. This study uses normalized difference building index (INBI) as indicator of impervious surfaces and investigates the relationship between NDBI and LST. Our analysis indicates there is strong positive linear relationship between NDBI and LST for all seasons. The amount of slop and intercept of linear relationship can indicate the magnitude of UHI for different seasons. Therefore, NDBI provides alternative physical indicator for analyzing LST quantitatively over the seasons for UHI study using remote sensing in an urbanized environment.

Based on MODIS data of Shanghai City from 2003 to 2005, land surface temperature (LST) was retrieved and used indirectly to get the quarterly intensity and higher temperature area of urban heat island. Results show that (1) the mean highest LST is 13.9°C in winter and 32.5°C, 45.7°C and 29.0°C in spring, summer, and autumn, respectively; (2) the relatively higher LST area distribution has obvious seasonal variation, which moves southeast in winter, northwest in spring and summer, and draws back southeast again in autumn; (3) weak urban cold island exists in urban areas during winter mornings; urban heat island exists in spring, summer and autumn; the summer's heat island is the most notable; and (4) LST of urban cold island centre in winter morning is 2.6°C lower than that of countryside and the mean urban heat island intensity is 5.7°C, 10.4°C and 4.2°C in spring, summer and autumn, respectively.

Radiosity method is based on the computer simulation of 3D real structures of vegetations, such as leaves, branches and stems, which are composed by many facets. Using this method we can simulate the canopy reflectance and its bidirectional distribution of the vegetation canopy in visible and NIR regions. But with vegetations are more complex, more facets to compose them, so large memory and lots of time to calculate view factors are required, which are the choke points of using Radiosity method to calculate canopy BRF of lager scale vegetation scenes. We derived a new method to solve the problem, and the main idea is to abstract vegetation crown shapes and to simplify their structures, which can lessen the number of facets. The facets are given optical properties according to the reflectance, transmission and absorption of the real structure canopy. Based on the above work, we can simulate the canopy BRF of the mix scenes with different species vegetation in the large scale. In this study, taking broadleaf trees as an example, based on their structure characteristics, we abstracted their crowns as ellipsoid shells, and simulated the canopy BRF in visible and NIR regions of the large scale scene with different crown shape and different height ellipsoids. Form this study, we can conclude: LAI, LAD the probability gap, the sunlit and shaded surfaces are more important parameter to simulate the simplified vegetation canopy BRF. And the Radiosity method can apply us canopy BRF data in any conditions for our research.

The automatic extraction of Digital Terrain Model (DTM) from point clouds acquired by airborne laser scanning (ALS) equipment remains a problem in ALS data filtering nowadays. Many filter algorithms have been developed to remove object points and outliers, and to extract DTM automatically. However, it is difficult to filter in areas where few points have identical morphological or geological features that can present the bare earth. Especially in sloped terrain covered by dense vegetation, points representing bare earth are often identified as noisy data below ground. To extract terrain surface in these areas, a new algorithm is proposed. First, the point clouds are cut into profiles based on a scan line segmentation algorithm. In each profile, a 1D filtering procedure is determined from the wavelet theory, which is superior in detecting high frequency discontinuities. After combining profiles from different directions, an interpolated grid data representing DTM is generated. In order to evaluate the performance of this new approach, we applied it to the data set used in the ISPRS filter test in 2003. 2 samples containing mostly vegetation on slopes have been processed by the proposed algorithm. It can be seen that it filtered most of the objects like vegetation and buildings in sloped area, and smoothed the hilly mountain to be more close to its real terrain surface.

Vegetation is a fundamental component of urban environment and its abundance is determinant of urban climate and urban ground energy fluxes. Based on the radiometric normalization of multitemporal ASTER imageries, the objectives of this study are: firstly, to estimate the vegetation abundance based on linear spectral mixture model (LSMM), and to compare it with NDVI and SDVI; secondly, to analyze the spatial distribution patterns of urban vegetation abundance in different seasons combined with some landscape metrics. The result indicates that both the vegetation abundance estimation based on LSMM and SDVI can reach high accuracy; however, NDVI is not a robust parameter for vegetation abundance estimation because there is significant non-linear effect between NDVI and vegetation abundance. This study reveals that the landscape characteristics of vegetation abundance is most complicated in summer, with spring and autumn less complicated and simplest in winter. This provides valuable information for urban vegetation abundance estimation and its seasonal change monitoring using remote sensing data.

With the globe climate warming and the extension of urban area, urban heat island has become a serious problem of urban environment. How to effectively monitor the structure and the change of urban heart island is becoming the focus of research on urban environment. Taking the urban core area of Chongqing as the research object, this paper uses Landsat TM/ETM+ imageries in 1988, 2001 and 2006, as well as the observing data of climate station, to study the urban ground temperature. The retrieve method of ground temperature is discussed, including mono-window algorithm, and the process how to get the four parameters of the algorithm. The retrieved ground temperature is standardized with extremum and classified into five temperature zones. The buffer analysis is carried out to detect the spatial structure and its change of urban heart island. The study shows that the character of heart island in Chongqing is great different from that in plain cities. Owing to the effect of special terrain of Chongqing, the heart island has clear hierarchy. The study also shows that the intension and extension of hear island is greatly enlarged from 1988 to 2006 due to the outspread of urban area.

The quantitative evaluation of desertification extent with remotely sensed imagery has been a hot spot of remote sensing application research. The evaluation process should consider the principles of dominance, integration and so on. Traditional evaluation methods to desertification extent are usually carried out at the scale of discrete pixels, which fails to taken into account of the influence of adjacent pixels and results in noises on the evaluation result images, inducing the unilateralism result. If we try to use filters to reduce the noises, then the evaluation results will be wrong contrasting with its real result. Based on former researches and the geographic science principle, this paper discusses the method of assessing desertification extent at the scale of geographic unit, in which the geographic unit is determined by vegetation coverage index and spatial information. The test results show that this method provides more accurate assessment of the ground situation avoiding the limitations of traditional methods.

The city of Jinan is the capital of Shandong province. The southern mountain of Jinan is a sensitive region of soil erosion. Severe soil erosion not only destroys the ecosystem environment roughly, affects the economic and social sustainable development, but also endangers its spring. Therefore, the assessment of soil erosion, as the basis of the comprehensive control and use planning on Jinan, must be done actively. But till now, there has been no unanimous conclusion on the quantitative assessment indexes and their thresholds for the small watershed on Jinan. The study area is the city of Jinan. With the principles of combination of qualitative and quantitative analysis, macroscopic and microscopic analysis, the assessment indexes of soil erosion are selected through a series processes such as analysis of relations between the influencing indexes and soil erosion. The soil erosion intensity of the whole watershed is assessed with the qualitative method of RS and GIS.

The earth surface information is very important in study of Cenozoic geomorphological evolution and paleo-environment evolution. In this essay, we adopt remote sensing data, DEM data and finity geological survey data to study the valley geomorphology. It indicates there are seven river terrace developed in Huangshui river area and saved integrity. we can recognize high flood plain, T1 terrace, T2 terrace and T3 terrace in remote sensing image. From the study on geomorphology using Digital elevation model(DEM), We made the diagram of geomophological geological map based on three-dimension DEM data in Minhe area, identify three planation surfaces and 11 Denudation surfaces and discuss the distribution, altitude, uplift speed of different planation surface. We also discuss the method in distinguish Cenozoic strata with other geological body.

The main aim of this paper was to identify inland fresh water wetland by using RADARSAT SAR data in combination with optical remote sensing data ETM+. The test area is a part of Hongze Lake, the fourth biggest fresh water lake in China, one of important wetlands for migratory birds in China. In this paper, two scenes of RADARSAT SAR data were acquired, one was obtained (incidence angle 39.1°) on July 9, 2003, another scene of SAR acquired on July 13, 2003(incidence angle 29.8 °). Optical remotely sensed data was Landsat ETM+ acquired on August 21, 2002. In order to explore the potential of Radarsat SAR data in the differentiation of different wetland types and wetland and upland types, two schemes were designed: one scheme was that Landsat ETM+ data and its derived data such as textural metrics were used to the classification of the study area; the other is that the Landsat ETM+ data, derived ancillary data and SAR data were used. CART algorithm was selected for the generation of decision rules, and the rules were applied to the classification of landuse/cover in the whole study area. The results showed that the combination of the SAR data and the optical remotely sensed data have achieved the highest classification accuracy (92.3% of total classification accuracy). The results also confirmed the value of classification tree in the identification of fresh water wetland. It was illustrated that radar data was a good data source for the identification of wetland.

In this paper, the goal is to found indices best for Cab estimation with leaves and heperion pixels. There are several indices chosen, which showed best results for Cab estimation at both leaf and canopy levels in other studies. Forty-eight typical leaves were sampled in middle and lower reach of the Tarim River, Xinjiang, China. Leaf reflectance and Chlorophyll of leaves collected. Result demonstrated that Indices such as red edge and derivative indices R750/R710, R740/R720, (R734-R747)/(R715+R720), Blog(1/R737), D715/D705,(R734-R747)/(R715+R726), (R694-R680)/(R732-R760) were shown to be the good indicators for Cab estimation at leaf. Hyperion data were acquired for Aqike section in the middle reaches of the Tarim River in Nine 28, 2006. Field data were collected at same day to coincide with the Hyperion, including Chlorophyll of each tree, LAI, green vegetation cover. LAI derived from scanopy 2006. Inventory field plots were 120m×120m quadrants, and Chlorophyll of pixel is deduced from field data of 360 trees. Generally good results are found for Cab estimation at pixel level with indices such as, (R734-R747)/(R715+R726), Blog(1/R737), (R694-R680)/(R732-R760), TCARI, TCARI/OSAVI, MCARI/OSAVI and so on. It was found that (R734-R747)/(R715+R720), Blog(1/R737), D715/D705, (R734-R747)/(R715+R726), (R694-R680)/(R732-R760),R740/R720 were successfully test on leaves and piexls. On the other hand, the "modified" indices (TCARI, MCAVI, TCARI/OSAVI, MCARI/OSAVI) already give good results at the piexl level.

Hulun Buir represents the best grassland in Inner Mongolia. Due to intensive anthropogenic activities especially unreasonable grazing, desertification has been an important environmental problem in the grassland. In the paper we intend to develop an applicable approach for desertification monitoring in the grassland. Since vegetation is the most essential factor of grassland and desertification actually implies the declination of vegetation in the grassland, an index indication desertification severity has been constructed from vegetation cover fraction. Using MODIS satellite data, we firstly computed NDVI and then computed vegetation cover rate in the grassland. The rate is consequently used to construct the desertification index (DI) for evaluation of desertification severity. Using precipitation and temperature data from 45 points, we validate the capability of DI in representing the severity of actual desertification in the grassland. The general accordance of precipitation and temperature with DI demonstrates the applicability of the proposed approach for desertification in the grassland. Using the approach, we analyzed the changes of desertification in the grassland in recent years. Results showed that desertification process in the grassland are accelerating in recent years, with rate of 1% annually. The acceleration of desertification implies that grassland ecosystem is under evolution of degradation in spite of rapid economic development in the region. Our study suggests that necessary measures should be urgently employed to protect the grassland from further desertification.

Corresponding to the Hyperion hyperspectral remote sensing image obtained in the Three Lakes region in the eastern part of Qaidam basin in which gas reservoirs located, 25 samples of soil were collected throughout the area covered by the image and the spectra of all samples were measured. A geochemical analysis was conducted in the lab for the content of acidolysis hydrocarbon in soil samples. Univariate correlative analysis was carried through between spectral variables in two types and total acidolysis hydrocarbon (TAH) content, and the linear and non-linear correlations between 7 characteristic parameters with higher correlation coefficient and TAH content were investigated using 6 univariate regressive models. Further, stepwise regressive analysis techniques were used to study the relationship between original and first-order derivative reflectance data and TAH content, the results show that estimation accuracy was significantly improved with first-order derivative spectra but with larger relative error, the regressive equation of reflectance spectra is the best estimating model for TAH content. Finally, the derived optimal estimation equation was applied to the Hyperion hyperspectral image for a distribution map of surface TAH content which was tested using measuring values.

Grassland degradation in grassland ecosystems of China has been highly concerned in recent decades. Grassland growing is an important element for identification of grassland degradation. In this paper we intend to develop an applicable method for grassland growing monitoring in China using the EOS/MODIS data. Firstly the normalized difference of vegetation index (NDVI) can be calculated from April to October within grassland growing period in 2005 and 2006. In order to evaluate the grassland growing, vegetation index R was proposed, which was calculated from the NDVI value difference of the two years 2006 and 2005. According to the R value, five grades (from grade1 to grade5) were obtained: worse, slightly worse, balance, slightly better and better. Grassland region in China can be divided into a number of small sub-regions for determination of different regions and grassland types. Our results indicate that grassland growing was better in 2006 than in 2005. The grassland with balance, slight better and better growing accounted for 71.43% of the total grassland area, the area is 251.42 thousand KM². The overall growing of 2006 is: Grade3>Grade4>Grade2>Grade5>Grade1.Valuation of the grassland growing is thus urgently required for better administration of the grassland ecosystem for sustainable development.

Acid rain has been a worldwide environmental problem for decades. China is one of the most serious acid deposition polluted regions in the world. How to effectively monitor acid deposition's severity and spatial distribution has constituted a great challenge to the traditionally chemistry methodology used to monitor acid rain. Long-term acid stress will change foliar internal structure and the content of pigments (such as chlorophyll a and b). Generally, such changes of foliar attributes will result increased reflectance in the visible and near-infrared wavelength regions. In this study, field and greenhouse experiments were performed separately to illustrate the influence of both natural and simulated acid rain to the spectra reflectance and chlorophyll content of masson pine (Pinus Massoniana). As measured with a portable spectroradiometer and a portable chlorophyll meter, spectra reflectance was a more sensitive indicator than chlorophyll content to indicate the severity of acid stress. In most of our cases, the reflectance of masson pine (both natural and greenhouse) was increasing with the severity of acid stress in part or in the whole wavelength regions ranged from 400 to 800nm. Vegetation indices computed using simulated Landsat Thematic Mapper (TM) bands showed that light acid stress often caused higher indices' values, and it was suggested that multispectral image data might be used to monitor acid stress from a canopy level.

Satellite images in the thermal infrared can be used for assessing the thermal urban environment as well as for defining heat islands in urban areas. In this study, characteristics and changes of surface urban heat island (SUHI) of Nanjing City Zone, China are examined using satellite images provided by the Landsat TM/ETM+ sensor respectively in 1988 and 2001. The individual characteristics are analyzed from clustering and grading images transformed from brightness temperature maps. SUHI changes of the 13 years are demonstrated by comparing a series of changing images. And both the characteristics and changes are quantified by SUHI index (SUHII) calculated from SUHI index system, which is originally built in this paper, totally basing on the remote sensing data. Additionally, a flow chart explaining the overall SUHI analysis processes is made up. The study reveals that from 1988 to 2001, both intension and extension of the SUHI effect in Nanjing City Zone have enhanced greatly and the SUHII increasing from 0.1568 to 0.8432 incorporates with the qualitative analysis, which proves that the SUHI index system is doable in practice.

The study on landscape pattern is an important field of urban land use and ecological change. Since 1990s, the widely accepted Patch-Corridor-Matrix model is generally used in qualitative description of landscape pattern. In recent years, quantitative evaluation on urban landscape dynamics is becoming hot in research. By making a critical review on existing research methods of landscape pattern, a new approach based on RS/GIS is put forward in this paper, comprising three steps, "General pattern characteristics - Gradient differentiation feature- Directional signature of the landscape", and we call it GGD. This method is applied to the case study of Xi'an metropolitan area in China. The result shows that the method is effective on quantitative study of urban landscape. The preparation of the method GGD is setting up research platform based on RS and GIS. By using the software of Geographical Information System (Arcgis9.0 & Erdas), the authors got the interpretation of remote sensing images of different years, and carried on the division of the landscape type of the research region. By calculating various index of landscape level with software Fragstats3.3 as an assistant tool and adopting three steps of GGD combined with landscape index, this paper can assesses the landscape spatial pattern of urban area: 1) General pattern characteristics analysis is to get transition probability of various landscape through Markov chain and to predict the landscape transformation by introducing CA model. The analysis emphasizes on total landscape structure and its change over time; 2) Gradient characteristic analysis, which makes gradient zone by taking city as a center outwardly with certain distance and contrastively analyzes the landscape index of each subarea, stresses the spatial character of landscape pattern, verifies urban morphology theories and provides the quantitative warranty for establishment of urban modality. Therefore, the analysis is useful for supervising urban expanding speed; 3)Direction characteristic analysis, which is setting up radiate strip on west-east, south-north, southwest-northeast and northwest-southeast and form certain width on each direction, can precisely and quantitatively indicate different characteristic of urban landscape at each development direction, and by combined with gradient analysis it is highly advantageous to the examination and planning of urban expanding direction. In the case study on Xi'an metropolitan area, remote sensing images of 1988 and 2005 Landsat-TM were handled, and the division of the landscape type of the region was also carried on. According to the above approach, the result was got and some valuable information was showed as follows: 1) The diversity of overall landscape of Xi'an metropolitan area tends to increase and the degree of fragmentation tends to deepen. With the increase of urbanization level, the visual component of landscape is more and more diversified, the shapes of landscape is more and more complicated and ecologically more and more fragmented. In the region where urbanization level is low, natural landscape is the main component of the landscape, the diversity of the landscape is low. And because landscape is seriously disturbed by human activities with urbanization, fragmentation of the landscape emerges periodically. 2) In the process of transect gradient analysis, the landscape pattern index can explore the urbanization gradient, and its trend to reduce gradually towards the suburban. The landscape of area with a high urbanization level is mainly man-created, and its patches show large number, small area, simple shape and higher landscape heterogeneity. The transect gradient analysis on different time series indicates the relationship between urbanization level and landscape pattern. The landscape of urban area suffers intensely from human being, and its pattern component and spatial collocation depends on the interference intensity to a large degree. In the area with a high urbanization level, its pattern component is more man-created and less natural landscape. The landscape collocation characteristic of its patches takes on a large number, little average area, simple shape and low polymerization degree. 3) Analysis of direction and gradient of Xi'an metropolitan area can quantitatively reflect influence of urbanization and characteristics of urban landscape in the main development axes of north-south and east. Result shows that the degree of internal integration between Xi'an city and Xianyang city is gradually enhanced with the quickly urbanization course in China.

Drought is very severe in North China Plain, where winter wheat is one of the most important cropping systems. In this paper, we present an approach to map drought status of winter wheat in the plain for better farming management. The approach is based on the temperature-vegetation dryness index (TVDI) computed from the wet and dry edges of Ts-NDVI space. Using the MODIS data, we applied the approach to map drought status in North China Plain for the winter wheat growing period from March to May in 2006. Our results show that spatial variation of agricultural drought is very obvious in the region. Severe drought was observed in eastern Hebei, western Shandong, and northwestern Henan province respectively. The weather reports from China Meteorological Administration were used to validate our mapping results of the drought status. The highly accordance of our drought mapping results with the reported drought distribution from CMA confirms the applicability of TVDI approach in drought mapping in North China Plain.

Landsat TM/ETM+ data have been proved a useful tool to measure urban heat island (UHI) effect. In this paper, five methods - DN value or at-sensor brightness temperature, land surface temperature (LST) retrieved by spectral emissivity correction, mono-window algorithm, generalized single-channel method with and without near-surface temperature, to denote the spatial distribution of the UHI effect from the thermal band of ETM+ data are compared. The land surface emissivity (LSE) values required to retrieve LST was estimated from the NDVI Thresholds Methods, combing with the other six bands provided by the ETM+ data. Finally, we presented a comparison between the five temperatures retrieved by the five algorithms over Nanjing City. The results showed that the three methods of mono-window algorithm and the generalized single-channel method with or without near-surface temperature obtained the similar UHI effect results; DN value also owned high correlation coefficient with LSTs by the three mono-channel methods, so it could be used to denote the spatial variation of UHI effect in some degree; and the value of spectral emissivity corrected LST was much different with that from other four methods.

Ground survey is restricted by the difficulty of access to wide-range and dynamic salt marsh. Waterline method and hydrodynamic model were investigated to construct Digital Elevation Model (DEM) at Jiudunasha Shoals. A series of waterlines were extracted from multi-temporal remotely sensing images collected over the period of 2000-2004. The assignment of an elevation to each waterline at the satellite overpass was performed according to hydrodynamic model. The corrected waterlines labeled elevations were used to construct Triangulated Irregular Networks (TINs). Then an interpolation for each grid elevation was performed in accordance with the associated triangle. This initial DEM, produced using the corrected waterline set, was then used to refine the topography in the intertidal zone, and the model was re-run to produce improved water levels and a new DEM. This procedure was iterated by comparing modeled and actual waterlines until no further improvement occurred. Three DEMs of different intervals were built by this approach and were compared to evaluate the effect of Deep Water Channel Project (DWCP) at the north of Jiuduansha Island. Waterline method combined with numerical model, is an effective tool for constructing digital elevation model of mudflats. The result can provide invaluable information for coastal land use and engineer construction.

There is important significance for hydrophytes extraction. It is the basis of water pollution control decision. For the purpose of hydrophytes extraction, the vegetation is classified into two species: submersed vegetation and emerged vegetation. And to obtain a better categorization map, three different classification methods as ISODATA, MLC and Decision tree are put forward in the paper. The analysis is performed on the Landsat TM image of Taihu lake acquired in 7, 2002. The result shows that the decision tree classification acquires the best extraction effect.

The east Taihu lake region is characterized by high-density and large areas of enclosure culture area which tend to cause eutrophication of the lake and worsen the quality of its water. This paper takes an area (380×380) of the east Taihu Lake from image as an example and discusses the extraction method of combing texture feature of high resolution image with spectrum information. Firstly, we choose the best combination bands of 1, 3, 4 according to the principles of the maximal entropy combination and OIF index. After applying algorithm of different bands and principal component analysis (PCA) transformation, we realize dimensional reduction and data compression. Subsequently, textures of the first principal component image are analyzed using Gray Level Co-occurrence Matrices (GLCM) getting statistic Eigen values of contrast, entropy and mean. The mean Eigen value is fixed as an optimal index and a appropriate conditional thresholds of extraction are determined. Finally, decision trees are established realizing the extraction of enclosure culture area. Combining the spectrum information with the spatial texture feature, we obtain a satisfied extracted result and provide a technical reference for a wide-spread survey of the enclosure culture area.

Methods and techniques for mapping the trophic state of water bodies in Taihu Lake based on synchronous Landsat TM images were studied. The rapid deterioration of water quality of Taihu Lake in recent years demanded effective monitoring methods. The remote sensing technology had provided effective and low-cost means of monitoring synoptic water quality over inland waters. The Landsat TM images acquired on July 13th, 2002, together with in situ measurements of chl-a, were used to retrieve chl-a concentration in Taihu Lake. The visible bands of TM images were carefully corrected for atmospheric effects using clear-water approach, and the remotely sensed reflectance of water at these bands were estimated. Then, chl-a concentration in Taihu Lake was estimated by the statistical relationship between the atmospherically corrected water reflectance at these bands and in situ measurements. In accordance with the definition of Carlson's trophic state and his formula from his previous studies, TSI (chl) was expressed as TSI(chl) = 9.81* ln(chl-a) + 30.6. The Taihu Lake map of trophic state was generated. The spatial distribution of trophic state in Taihu Lake was analyzed, as well as the errors in estimation of chl-a content and trophic state of Taihu Lake from remotely sensed data.

Guangzhou city with a large population and urban size located in the Pearl River delta, Guang Dong province, China, was chosen to study the effect of urban heat island and its spatial expansion. The land surface temperature was analyzed and divided into 7 ranges by a temperature separation method based on statistical results. In the case of urban area, it was composed by three parts: downtown and old built-up areas with high-density buildings and dwellings, new built-up areas and developing site. Along with the city development, the developing site will become to be new built-up areas and the formerly new built-up areas will become to be the downtown and old built-up areas. These three parts stand for different stages of a city. By temperature partition using the standard deviation, we can commendably find the corresponding ground covers, and three parts of the city are properly within three surface temperature ranges. Based on this, the thermal remote sensing image can be used to detect the developing stage of the built-up areas in a city. Temperature comparison of different land covers was also analyzed and showed a great difference between them. The white soil is almost 3 degrees higher than the urban area, while the water is more than 5 degree cooler than it.

Wetland water quality is a critical issue in ecology and environment science, however to identify the quality and to detect the change of wetland are still knowledge gaps. In this paper, high spatial resolution IKONOS imagery was used to assess the water quality in a small urban wetland. A water-only image was created by masking out the terrestrial features from the image with unsupervised classification method. A three-class water quality map was created by a second unsupervised classification on the water-only image, and the relationship between water quality and spectral reflectance was examined. Based on the two classified water quality maps, the water quality changes from 2003 to 2006 were detected by map comparison and cross-tabulation analysis. It concluded that high spatial resolution satellite as IKONOS is essential for water research in a local scale. The unsupervised classification based on spectral reflectance of water surface is an effective and rapid way on mapping and assessing the general water quality.

The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying 11 land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.

The database of recording the snapshots of land parcels history is the foundation for the most of the models on simulating land use/cover change (LUCC) process. But the sequences of temporal snapshots are not sufficient to deduce and describe the mechanism of LUCC process. The temporal relationship between scenarios of LUCC we recorded could not be transfer into causal relationship categorically, which was regarded as a key factor in spatial-temporal reasoning. The proprietor of land parcels adapted themselves to the policies from governments and the change of production market, and then made decisions in this or that way. The occurrence of each change of a land parcel in an urban area was often related with one or more decision texts when it was investigated on the local scale with high resolution of the background scene. These decision texts may come from different sections of a hierarchical government system on different levels, such as villages or communities, towns or counties, cities, provinces or even the paramount. All these texts were balance results between advantages and disadvantages of different interest groups. They are the essential forces of LUCC in human dimension. Up to now, a methodology is still wanted for on how to express these forces in a simulation system using GIS as a language. The presented paper was part of our initial research on this topic. The term "Event" is a very important concept in the frame of "Object-Oriented" theory in computer science. While in the domain of temporal GIS, the concept of event was developed in another category. The definitions of the event and their transformation relationship were discussed in this paper on three modeling levels as real world level, conceptual level and programming level. In this context, with a case study of LUCC in recent 30 years in Xiamen city of Fujian province, P. R. China, the paper focused on how to extract information of events and rules from the policy files collected and integrate the information into the LUCC temporal database. The paper concluded by listing the main steps of how to extract events and rules from files and build an event database, and indicating directions for future work about how to develop a spatial-temporal reasoning system on the event-oriented LUCC database.

During the last decades, researchers have mainly focused on improving of the pixel-based classification methods and their applications. As the image resolution improved, it can't get good classification result. In order to overcome this problem, the object-oriented approaches are introduced. In this paper, two methods were compared on urban area. A part of Nanjing city in china was selected as study area; TM and IKONOS imagery were employed. Three pixel-based classification methods, the maximum likelihood, ISODATA (Iterative Self-Organizing Data Analysis Technique), minimum distance method, and an object-oriented technique, the nearest neighbor method, were used to classify image, and evaluate the result. For TM imagery, the accuracy assessment of the results showed that the object-oriented classification approach couldn't get better classification result comparing to the pixel-based classification method, the salt-pepper phenomena of the pixel-based classification result images were not obvious. For IKONOS imagery, classification results provided by the object-oriented classification method were better than the pixel-based classification approaches. So, for urban classification using TM imagery, the traditional classification method could be used to get classification information and an acceptable result could be acquired. But when the IKONOS imagery was used to investigate the urban class, the object-oriented method could find the expected result.

On the remote sensing imagery classification, the traditional methods based on statistical principle has the difficulties in distinguishing the objects with similar spectral characteristics, while the back propagation neural network method has the difficulties in sufficiency and convergence. Therefore, a new method based on neural network classification with optimization by genetic algorithms for remote sensing imagery is proposed in this paper. On the basis of the back propagation( BP )neural network classification, the optimization method by genetic algorithms is presented, including the numbers, the thresholds and the connection weights of nerve nodes of the hide layer in BP neural network. An approach on float coding with alterative length for genetic algorithms is proposed, and the evolution method is improved to obtain an optimal BP neural network. In the end, an experimental test on the remote sensing classification using TM image of Dianshan Lake is carried out, and higher classification accuracy has obtained compared to other methods, which is proved the feasibility and validity of the proposed approach.

The main purpose of this paper was to explore the potential of decision tree classifier in the identification and change monitoring of coastal wetland. A part of the coastal wetland in the Northern Jiangsu Province was taken as test area. Decision tree classifiers derived using different ways were applied to the classification of coastal wetland and the results were compared by independent sampling points. It was shown that the post-classification improvements by using knowledge rules could achieve higher accuracy than automatic machine learning method. In the post-classification improvements, the selection of feature vectors was crucial to the improvement of accuracy of classification.

Scientists at home and abroad have been paying attention to researches on land use,but,less on "space and process" of lucc, especially in Karst areas. Guizhou province belongs to the model Karst areas, a most main environment question is the rock desertification, while the rock desertification phenomenon is precisely a centralism of the land use change which develops to the bad direction manifests. According to the theories of Geo-informatic Tupu and the traditional approach to land use, this paper presents the concept model of Land-use-change Tupu System based on space, temporal and attribute, attempting to improve the expression of land use change. Using traditional method,two stages of land use database in 1996 and 2005 are acquired, which are the informatic foundation for research. Moreover, the model integrates visual expression with tupu analysis composing of graphic description as well as driving influence factors. In addition, the converted appraisal scale is joined in the tupu analysis. All of these are supported by ERDAS and ARCGIS software and based on 3S. The tupu methodology would be a powerful and efficient tool on land use change in Karst areas and other areas. It is integration of space, temporal and attribute. It is helpful to mathematical model constructors for understanding the spatial information and its processes.

The goal of image fusion is to combine a high quality image from multi-images of the same object. The paper presents an image fusion scheme based on wavelet transform. Firstly, the large-scale land-use image is decomposed by orthogonal wavelet; secondly, the approximate low-frequency information just decomposed with the small-scale land-use image is replaced; finally the fused image is obtained by taking inverse wavelet transform. The fused land-use image obtains the merits of the images which have different scales. This scheme realizes that the information of spatial objects increases or decreases automatically with the differences of the scales.

The degradation and loss of the regional wetland of urbanization is a more and more concerned question in the whole world. This paper takes Nanjing as an example, adopting five Nanjing area Landsat MSS and TM data: 1979, 1986, 1996, 2000, 2004, combining historical data, land use and field observation, adopting decision tree method to extract Nanjing wetland. We analyze time and space changed characteristic of Nanjing wetland. The result shows: in 1979-2004, the wetlands of Nanjing changed obviously on the time and space. The total amount drops to 363.66 km² from 397.91 km², reduce 34.25km² together. The delta wetland has little change, but it is the largest in proportion of all kinds of Nanjing wetland by 2004. The river wetland in the rural is the greatest one that has changed in every wetland type. In suburban areas, lake and river wetland change frequently because of urban spawl. The wetland area of lake has little change in the urban, but more suburban wetland transferred to urban wetland so the amount of increase in 2000-2004 are greater than 1979-2000. Every stage, there are some Nanjing wetland (except the delta) to transfer to urban construction land and so loss. In the space change characteristic, the rural wetland is continuously substituted by the suburban and urban wetland. It is usually the main area where wetlands are interfered with around new urban area and communication lines. The wetlands are usually interfered by noise, rubbish, and pollution. We should include the development of population and traffic system in the wetland change predicting in future planning. So can we meet wetland management's demands more effectively.

Taking Xuzhou city as an example, the urban green space categories system are established using multi-temporal/-source remotely sensed images. After classification adopted decision tree and object-oriented methods, the urban green space pattern changes are captured and evolution rules are analyzed based on the landscape pattern indices on the patch/class and landscape metrics. In addition, the economic/social statistics are listed for quantitative analyzing dynamic evolution. Finally, the all driving factors impacting urban green space pattern are analyzed using the principal component analysis.

The relationship between human beings and the earth is one of the important aspects in geographic research, so the study on land use should be focused on the interactions caused by the relationship between human beings and the earth. The author begins with the current land use in the study area, and zonal farm-land use and rural residential population data can be spatialized on the map and reclassified into the grid, according to the results of geostatistcal analysis. Based on the spatialized results, using bivariate Moran indices, a correlation analysis between these two factors and the statistical test should be performed, and the results can be visualized by GIS methods. What has been showing in this research is that, comparing with classical correlation analysis in normal statistics, spatial correlation analysis can be more exact in theory and more available in application.

The identification of urban area is a basic study on urban economic, social development and urbanization process. Population density is a widely used indicator for urbanized area analysis in most countries such as the United States and Japan, while some countries such like the UK, employ the land use types as a significant difference to distinguish urban and rural areas. China has taken urban population as a basic statistic of urbanization study, but a universal way of identification urbanized area is still in search. After a brief summarization and compare of various methods about urban area identification both in China and abroad and taking both administrative and academic aspects into consideration, this paper puts forward a new method called "three-factor analysis" which is appropriate for China's land use actuality. Taking village as the basic analysis unit, Three-factor here refers to the density of construction, the ownership of land and the performance of infrastructure. Then we design three indicators to quantify the factors above. They are proportion of construction land, proportion of state-owned construction land and the performance of infrastructure. Some kinds of construction land are defined as Basic Construction Land (BCL) and the calculation of two indicators is based on it. All the three indicators will be calculated in the village unit and the urbanized area will be determined according to the calculated result of these indicators. The standards of the indicators according to which a unit will be determined as urban or rural area are formed by three steps . First, a determinate urbanized area is units that are defined as blocks by the local administration. The rest units are called the pending area that is waiting to be identified. Second, calculate the indicator value of the determinate urbanized area and sort them in a ascending sequence. Third, to avoid influence of possible abnormal unit, we choose the value of 5% position in the sequence from the beginning as a standard. The paper then practises the method in Chaoyang district in the east of Beijing which is a quickly developing area for decades, using GIS tools to calculate and perform the urbanized area that is identified in this "three-factor analysis" way. In the end a brief description of the ongoing land use condition and urban construction land proportion in this urbanized area.

In this paper, we first review the study of urban agriculture, including concept, connotation, characteristic, function and the region of urban agriculture. Many researches found that urban agriculture is located in urban and peri-urban, but it is difficult to distinguish urban and peri-urban. Some of these researches use criteria influencing the size and shape of the peri-urban area, such as the urban influences, official city boundaries, travel time or distance to the centre, it is no clear. So in our research we extract agricultural land and urban land from Beijing SPOT data by remote sensing technology, calculate the model window the ratio of urban land use, according to the ratio of urban land to determine urban and agricultural areas. We evaluated agricultural development level by using Statistical data, we compare the villages or towns range of higher level of development with remote sensing technology, extract urban agriculture area, and draw the conclusions.

With the rapid development of earth observation missions and internet technology, there is increasing recognition that internet is an effective channel for disseminating digital remotely sensed data. To cope with the security problems accompanying with internet-based remote sensing, a multipurpose watermarking technique of remote sensing images is proposed in this paper for copyright protection and authentication in the spatial domain and the wavelet domain. The proposed scheme can utilize the human visual system (HVS) to embed the copyright watermark in discrete wavelet transform (DWT) medium frequency coefficients of the host image by permuting the watermark image, and embed the authentication watermark in spatial domain to detect and indicate the counterfeit area clearly. It determines the location where the watermark can be hidden and modifies the strength of the embedded watermark adaptively based on images content. Furthermore, there is little influence on such application of remote sensing images as edge detection after embedding watermarks.

This article analyzes Kunming land use planning and management information system from the system building objectives and system building requirements aspects, nails down the system's users, functional requirements and construction requirements. On these bases, the three-tier system architecture based on C/S and B/S is defined: the user interface layer, the business logic layer and the data services layer. According to requirements for the construction of land use planning and management information database derived from standards of the Ministry of Land and Resources and the construction program of the Golden Land Project, this paper divides system databases into planning document database, planning implementation database, working map database and system maintenance database. In the design of the system interface, this paper uses various methods and data formats for data transmission and sharing between upper and lower levels. According to the system analysis results, main modules of the system are designed as follows: planning data management, the planning and annual plan preparation and control function, day-to-day planning management, planning revision management, decision-making support, thematic inquiry statistics, planning public participation and so on; besides that, the system realization technologies are discussed from the system operation mode, development platform and other aspects.

Within the last decades urban sprawl being understood here as the conversion of non-urban to urban land has become an increasingly prominent theme. There are some urban models which are used to analyze and simulate urban sprawl, but it deserves study for urban planners to find a simple, effective and vivid method to analyze urban sprawl. In this study, built-up area is subdivided into sixteen parts which correspond to urban sprawl in sixteen directions based on overlay analysis of geographic information systems (GIS). The study provides a novel approach that expresses the direction and intensity of urban sprawl as "rose map of urban sprawl". And thematic map and animated map are made to show urban sprawl based on GIS. In presenting a case study on the Chinese city of Wuhan, this paper analyzes the sprawl in more than forty years. Moreover, according to practical development of Wuhan city, the reasons of urban sprawl are analyzed and the strategies of urban development are brought forward. The study thus shows that it is helpful for macro-planning and decision-making to have an analytical method available through rose map and thematic map of urban sprawl based on GIS.

Different land-use behavior differently on its neighborhoods, and finally can influence them on the values. The research attempts to find out how the neighborhood circumstance including 10 main neighboring land-uses influences the urban residential land value variations. Firstly the influence of the general/city-scale circumstance is measured out. Then both the influential range and the influential force of neighboring individual land-use are investigated and compared in different hedonic price models, the influence of nearby main road is also included in the analysis. At last explicit case comparisons are taken for testing the result. Spatial comparing and statistic analysis are used as one combined-tool in the study. The finding of the research is not only useful for understanding the spatial patterns of land values, but also beneficial for the policy-makers concerning land administration and urban planning.

Suzhou, Wuxi and Changzhou are located in Yangtze River delta which is a hot spot for socio-economic development in China. With the rapid economy development, the urbanization of those three cities keeps a high level. Growth of urban land use is an important feature of the procession of urbanization. The urban land use growth of Suzhou, Wuxi and Changzhou during the year of 1979-2001 was studied on the base of RS and GIS methods. The growth area of urban land use and the features of spatial pattern of urban land use growth were analyzed. The study shows that since the opening and reforming of china, the urban land use of these three cities has a significant growth; the total area of urban land use in 2001 is 2.7 times bigger than that in 1979, while the rate of land use change is 7.7% and the growth intensity index is 1.08%. Local nature, traffic, economy condition and policy decision making dominate the direction of land use growth. During 1979-1991, extending directions are accordant with main rivers and major roads. During 1991-2001, extending direction has a significant tend towards the development zone.

Building harmonious society is one of the most important aims for China nowadays and the problem of severe imbalance between human and nature must be solved firstly. Geographic information system (GIS) can play an all-important role to help to this issue. Along with the rapid urbanization, it is very prominent that the agricultural lands converted to building lands, especially, in some metropolitan areas. This study took Lanzhou metropolitan area in West China as an example. Firstly, based on interpretation on the TM 2, 3, 4 integrated images 1986 and ETM + 2, 3, 4 integrated 2000 in Lanzhou metropolitan area, the basic pattern of land-use conversion was analyzed under the support of GIS. Secondly, according to the data supplied by government from 1986 to 2000, we found out the driving factors of land-use conversion by using principal component analysis and Granger causality test. Influence of the driving forces on land-use conversion was estimated by using regression analysis method. Besides, from the views of market economy and government intervention, the regulative mechanism of land-use conversion was analyzed theoretically. In the end, the paper presented some feasible suggestions.

A simple approach for incorporating tolerant rough sets (TRS) into a multi-class support vector machine (SVM) classifier for land-cover classification was presented. TRS was used to perform the sample preprocessing of the original samples set to reduce the uncertainty of sample set and make the influence of the uncertainty from sample set on the final classification accuracy least. SVM was employed after the TRS preprocessing. An application of the integrated classifiers using an ETM+ remote sensing image has been presented. The classification results were compared with those of only-SVM classifier. According to the overall accuracy and the k coefficient, the result of integrated classifier with TRS and SVM is better than that of only-SVM classifier in the experiment.

In the course of studying on regional land use potential evaluation a certain spatial basic unit is often used as the analysis groundwork. The analysis result relies on unit division methods and research scales. In the same area space unit characters correlate with the space scales. In geography, the conclusions made on a scale can't be applied to other scales. Modifiable Area Unit Problem, MAUP, is the classical theory to solve the effects of spatial scale, that is, there are many different ways to divide the surface into non-overlapping million units for spatial analysis. This paper studies on the spatial unit scale transformation process of land use potential evaluation in the same area on different scales, such as at local, regional and global levels .etc. Characteristic Scales is defined in the transformation process from large scale to Small Scale. Then the fractal characteristic of spatial unit is raised on different scales to form land use potential geospatial hierarchy. Using geo-statistical procedures, we mainly study on the transformation progress of SUs of land use potential evaluation on different scales in the same area, define the eigenvalue of SUs on each scale. We find the definition of spatial units (SUs) makes a point of the spatial analyses results on regional scales. Finally, aiding the fractal dimension, the fractal characteristic of spatial unit is raised in a broader area. Land use potential geospatial hierarchical structure is explored to aid the regional development policymaking.

Information Tupu provides a research method in the combination with spectrum, quantification and orientation for the regional land use spatial pattern and change. Taking Yuanyang County in Yunnan Province as an example, based on remote sensing, GIS spatial analysis and statistical analysis model, this paper establishes the information Tupu of land use spatial pattern and its change from the following three aspects: land use spatio-temporal change mode, spatial expansion process of land-use, and landscape characteristics. Then it analyzes the characteristics of these Tupu. The results show that: (1) The established land use spatio-temporal change premonition Tupu is more visually revealing the basic pattern of land use change in the study regions, and provides a spatio-temporal expression way. (2) The land use patch shapes and spatial expansion Tupu can provide the macroscopic and microscopic information of land use dynamic change. Through establishing mathematical models to analyze the expansion intensity and expansion patterns of various land use types, the land use spatial expansion process can be visualized, abstracted, and modeled. (3) Geographic information theory and landscape ecology theory are applied, using the VCM curve, to describe the spatial distribution features of different land-use type patches and the change of its spatial distribution features in different study periods.

This paper presents an overview of agent-based models in land-use/cover change studies. This special class of LUCC models combines a cellular landscape model with agent-based representations of decision making, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors first explain the related concepts of agent based models. Then we briefly discuss recent studies that apply agent- based modeling to study land use/cover change for practical cases. Finally, we discuss limitations of agent-based methods in LUCC modeling. We find that agent-based models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers with the goal of creating fine-scale models of LUCC phenomena that focus on human-environment interactions.

Optimizing the allocation of urban land is that layout and fix position the various types of land-use in space, maximize the overall benefits of urban space (including economic, social, environment) using a certain method and technique. There is two problems need to deal with in optimizing the allocation of urban land in the technique: one is the quantitative structure, the other is the space structure. In allusion to these problems, according to the principle of spatial coordination, a kind of new optimum collocation model about urban land was put forward in this text. In the model, we give a target function and a set of "soft" constraint conditions, and the area proportions of various types of land-use are restricted to the corresponding allowed scope. Spatial genetic algorithm is used to manipulate and calculate the space of urban land, the optimum spatial collocation scheme can be gradually approached, in which the three basic operations of reproduction, crossover and mutation are all operated on the space. Taking the built-up areas of Jinan as an example, we did the spatial optimum collocation experiment of urban land, the spatial aggregation of various types is better, and an approving result was got.

Global change study is an interdisciplinary and comprehensive research activity with international cooperation, arising in 1980s, with the largest scopes. The interaction between land use and cover change, as a research field with the crossing of natural science and social science, has become one of core subjects of global change study as well as the front edge and hot point of it. It is necessary to develop research on land use and cover change in urbanization process and build an analog model of urbanization to carry out description, simulation and analysis on dynamic behaviors in urban development change as well as to understand basic characteristics and rules of urbanization process. This has positive practical and theoretical significance for formulating urban and regional sustainable development strategy. The effect of urbanization on land use and cover change is mainly embodied in the change of quantity structure and space structure of urban space, and LUCC model in urbanization process has been an important research subject of urban geography and urban planning. In this paper, based upon previous research achievements, the writer systematically analyzes the research on land use/cover change in urbanization process with the theories of complexity science research and intelligent computation; builds a model for simulating and forecasting dynamic evolution of urban land use and cover change, on the basis of cellular automation model of complexity science research method and multi-agent theory; expands Markov model, traditional CA model and Agent model, introduces complexity science research theory and intelligent computation theory into LUCC research model to build intelligent computation-based LUCC model for analog research on land use and cover change in urbanization research, and performs case research. The concrete contents are as follows: 1. Complexity of LUCC research in urbanization process. Analyze urbanization process in combination with the contents of complexity science research and the conception of complexity feature to reveal the complexity features of LUCC research in urbanization process. Urban space system is a complex economic and cultural phenomenon as well as a social process, is the comprehensive characterization of urban society, economy and culture, and is a complex space system formed by society, economy and nature. It has dissipative structure characteristics, such as opening, dynamics, self-organization, non-balance etc. Traditional model cannot simulate these social, economic and natural driving forces of LUCC including main feedback relation from LUCC to driving force. 2. Establishment of Markov extended model of LUCC analog research in urbanization process. Firstly, use traditional LUCC research model to compute change speed of regional land use through calculating dynamic degree, exploitation degree and consumption degree of land use; use the theory of fuzzy set to rewrite the traditional Markov model, establish structure transfer matrix of land use, forecast and analyze dynamic change and development trend of land use, and present noticeable problems and corresponding measures in urbanization process according to research results. 3. Application of intelligent computation research and complexity science research method in LUCC analog model in urbanization process. On the basis of detailed elaboration of the theory and the model of LUCC research in urbanization process, analyze the problems of existing model used in LUCC research (namely, difficult to resolve many complexity phenomena in complex urban space system), discuss possible structure realization forms of LUCC analog research in combination with the theories of intelligent computation and complexity science research. Perform application analysis on BP artificial neural network and genetic algorithms of intelligent computation and CA model and MAS technology of complexity science research, discuss their theoretical origins and their own characteristics in detail, elaborate the feasibility of them in LUCC analog research, and bring forward improvement methods and measures on existing problems of this kind of model. 4. Establishment of LUCC analog model in urbanization process based on theories of intelligent computation and complexity science. Based on the research on abovementioned BP artificial neural network, genetic algorithms, CA model and multi-agent technology, put forward improvement methods and application assumption towards their expansion on geography, build LUCC analog model in urbanization process based on CA model and Agent model, realize the combination of learning mechanism of BP artificial neural network and fuzzy logic reasoning, express the regulation with explicit formula, and amend the initial regulation through self study; optimize network structure of LUCC analog model and methods and procedures of model parameters with genetic algorithms. In this paper, I introduce research theory and methods of complexity science into LUCC analog research and presents LUCC analog model based upon CA model and MAS theory. Meanwhile, I carry out corresponding expansion on traditional Markov model and introduce the theory of fuzzy set into data screening and parameter amendment of improved model to improve the accuracy and feasibility of Markov model in the research on land use/cover change.

This study put forward a standardize classification model which can meet the requirement of rural land use survey and be suitable for large-scale promotion at the same time, but not merely the research limited in the experiment lab.. In this processing, the land cover classification may adopt the mutli-feature image classification method as followed. Firstly, a class scheme was defined. Then, some suitable features were selected, and every subclass was characterized with a unique combination of these features, in which landscape pattern parameter is one of essential features in consideration. Landscape pattern parameters can treat surface features and its distribution as integration and synthesis, which would help much to the classification. Using these features, image could be recognized and classified easily. Lastly, verify the precision and accuracy of the classification model. By using this classification method, classification accuracy can be improved, and the classification results were easier interpreted when compared with the conventional classification method.

This paper analyzes and simulates the land use changes in the Pearl River Delta, China, using Longgang City as a case study. The region has pioneered the nation in economic development and urbanization process. Tremendous land use changes have been witnessed since the economic reform in 1978. Land use changes are analyzed and simulated by using stochastic cellular automata model, land use trajectories analysis, spatial indices and multi-temporal TM images of Longgang City (TM1987, TM1991, TM1995, TM1999, TM2003, TM2005) in order to understand how urbanization has transformed the non-urban land to urban land and estimate the consequent environment and ecological impacts in this region. The analysis and simulation results show that urban land continues to sprawl along road and fringe of towns, and concomitant to this development is the loss of agricultural land, orchards and fish ponds. This study provides new evidence with spatial details about the uneven land development in the Pearl River Delta.

This paper presented an analysis of land use changes of 1995-2005 in Lanzhou, China, and predictions into 2020 with GIS and DUEM model. Urban land use changes of 1995-2005 were analyzed with GIS and three indexes, including Land Use Dynamic Index (LUDI), Land Use Development (LUD) and Land Use Consumption (LUC). DUEM was an extended cellular automata (CA) model developed firstly by Xie in 1994, and refined in 1999. Two scenarios were designed to explore future urban land use changes in Lanzhou: the first was considered as the continuation of the historical trend with the parameters derived from the calibration of DUEM; the second was revised according to urban planning map of 2001-2010 and the fifth five-year plan based on the first. Future urban land use changes were both centralized at the edge of the city under the two different scenarios, mainly in Yantan of Chengguan Distric, Matan and Cuijiadatan of Qilihe District, Anning Country, Shajingyi Country, Cuijizhuan and Yingmentan of Anning District. Future urban land use change under the second scenario was faster than the first, and may be closer to the real future trend. This paper also tested the suitability of DUEM in China.

Wuyuer River watershed is one of concentrative and extensive distribution area of inland wetlands in China. Wetland ecosystem plays an important role in maintain the ecological functions in the region. Integrating topographic maps in 1954, Landsat MSS, TM/ETM imagery in 2000 and GIS, spatial-temporal pattern in land-use and ecosystem services in middle and lower Wuyuer River were analyzed in this paper. Results showed that area of marsh decreased from 56.04 ×10⁴ ha to 32.04×10⁴ ha, while the area of cropland increase 24.94×10⁴ ha from 1954 to 2000. The annual loss rate of marsh was -1.48% (from 1954 to 1976) and -0.76% (from 1976 to 2000) respectively. Marsh land were turned into dry grassland and degraded to saline-alkalined land. The grassland decreased 40.26×104 ha dramatically for having been opened up to cropland and degraded into hardly-used land. Due to the negative effect of the decline in wetlands and grassland, total values of Middle and Lower Wuyur River's ecosystem services lost 66.10×10⁸ RMB ¥ with an extent of 14.67% between 1954 and 2000. The highest ecosystem service value centralized in the middle area, and decreased gradually to surrounding regions.

Focusing on the two key aspects of the traditional sales comparison approach, case selection and case quantification of the factors, this article tries to introduce the cloud model and Case-Based Reasoning into sales comparison approach, and brings about the land appraisal approach based on the cloud model and the sales comparison approach. This approach is based on the cloud model, completely considers the fuzziness and the randomness in objects and human knowledge, describes case features with the cloud model, uses cloud uncertainty reasoning, realizes the conversion between the qualitative description and the quantification value of transaction case features ; Also based on case-based reasoning, it analyzes the correlation among different cases, searches for the required comparable one; After the modification of the different land prices of different cases, it completely considers the case features and the weight of the cases, sets up land appraisal model, and finds the price of the case. At last, it gives an example analysis whose result proves the validity and feasibility of the method.

Urbanization is the complex process of converting rural land uses to urban land uses, which has caused significantly land cover changes and associated surface characteristics. Therefore, researches on land cover and its landscape pattern change under urbanization are essential for analyzing the impacts of human activities on environment. This study firstly detected land cover (i.e., forestland, non-forest vegetation, built-up area, and water) changes in Changsha City from 1973 to 2005 by using the multi-temporal Landsat images (TM1973, TM1993, TM1998, ETM+2001) and land use map (2005); and then analyzed the spatiotemporal changes of landscape pattern at landscape-level and class-level by using FRAGSTATS, respectively. At last, the class-level metrics of each land cover class were further regressed to the degree of urbanization. The results indicated that: (1) in the context of urbanization, the built-up area and the non-forest vegetation experienced a significant changes, while the forestland and water remained relatively unchanged, and the non-forest vegetation cover bore the major burden of urbanization; (2) with the advance of urbanization, the change of overall landscape pattern of Changsha represented a complex dynamic process; (3) obvious differences of impacts of urbanization on landscape patterns of various land cover classes existed, i.e., along with the decrease of MPS of non-forest vegetation, the AI of built-up area increased dramatically; (4) some class-level metrics of various land cover classes were strongly correlated to the degree of urbanization, but the correlated extend varied along with the various land cover classes. To sum up, this study demonstrated the differences of impacts of urbanization on various land cover patterns. The results have the potential to assist land-use planning and management.

Wetland, as an ecosystem with special functions, is the transitional areas between water and land in the earth, which has the richest biology diversity in nature, and is one of the most important surviving environments of human beings. Based on analysis of the terrain maps, remote sensing images, statistic data of wetland of Hubei province from 1985 to 2005, and with the technology of Remote Sensing (abbr. RS) and Geographic Information System (abbr. GIS), the wetland landscape spatial database and attribute database of Hubei province are set up using ARCGIS software of the year of 1985, 1995 and 2005.At the same time, according to fractal geometry and landscape ecological methodology and the theories, we can quantitatively analyze the form characters, evolution rules, and change factors of the wetland landscape pattern of Hubei provinces by calculating its diversity index, dominance index, equality index, fragmentation index, isolation index and fractal dimension, and so on. As a result, the wetland's form characters and evolution process of Hubei province are compared and analyzed; its time-spatial evolution character and mechanism during the past 20 years are demonstrated. And various natural and social factors, human activities, and driving forces which exert a significant impact on the evolution of Hubei province wetland are anatomized here. In the end, some advice will be given that human beings should adjust land-use structure in lake districts, reasonably develop, recover and reconstruct positive ecoenvironment, and promote its sustainable development in Hubei province according to its ecological environment characteristics.

In remotely sensed imagery with high spatial resolution, more detail spatial information of mangrove forest can be shown. It is important to find a method to effectively use the spatial information so as to improve the accuracy of mangrove forest classification. In the study, different classification schemes (including pixel-based classification and object-based classification), different classifiers, and different texture features have been conducted. The classification results of SPOT-5 image of Matang Mangrove Forest Reserve in Malaysia show that the performances of object-based classifications are better than that of pixel-based classifications. However, the classifier type is important for object-based classification. The accuracies of nearest neighborhood classifiers, which are widely used in object-based classifications, were obviously lower that that of maximum likelihood classifiers and support vector Machines. It is also shown that the involvement of second-order texture features can't effectively improve the classification accuracy of neither object-based classifications nor pixel-based classifications.

The regularization parameter and the kernel parameters greatly affect the performance of support vector machines (SVM) models. This paper proposes an evolutionary algorithm (EA) to automatically determine the optimal parameters of SVM with the better classification accuracy and generalization ability simultaneously. The proposed ESVM model, called evolutionary SVM or ESVM, was applied to a Land-cover classification experiment in a 840×840 pixels Landsat-7 Enhanced Thematic Mapper plus (ETM+) high-resolution image of Wuhan in Hubei province of China compared with the conventional SVM model. Experimental results show that the use of EA for finding the optimal parameters results mainly in improvements in overall accuracy and generalization ability in comparison with conventional SVM. It is observed that classification accuracy of up to 91% is achievable for Landsat data produced by ESVM.

In this paper Landsat images, including MSS in 1978, TM in 1988 and ETM in 2001, and ancillary data were selected to investigate the landscape pattern change in Niyanghe watershed. Firstly, the images were classified respectively to get the three landscape maps; then some landscape pattern analytical indices such as number of patch, shape index, contagion, fractal dimension, Shannon index and evenness index were introduced to describe the temporal-spatial change of landscape pattern in the study area assisted by spatial analytical techniques of GIS. The results showed that bare-land was the main landscape type. During the periods of 1978-1988 and 1988-2001, the increased arable land area was the largest, and the increase speed was the fastest compared with all other landscape types, the significant newly-increased arable land derived from bare-land and grassland. The area of grassland increased rapidly. The area of bare-land decreased gradually every year, whereas bare-land was mainly converted into arable land and grassland. Forest land area has decreased recently because of being reclaimed into arable land. From 1978 to 2001, the extent of fragmentation, evenness and diversity increased, contagion index decreased conversely, shape of landscape types became more and more complicated. The main driving factors for landscape pattern change were human factors such as population growth, consumption demands, economy development, transportation, policy, and so on.

Texture analysis has received great attention in the interpretation of high-resolution satellite images. This paper aims to find optimal filters for discriminating between residential areas and other land cover types in high spatial resolution satellite imagery. Moreover, in order to reduce the blurring border effect, inherent in texture analysis and which introduces important errors in the transition areas between different texture units, a classification procedure is designed for such high spatial resolution satellite images as follows. Firstly, residential areas are detected using Gabor texture features, and two clusters, one a residential area and the other not, are detected using the fuzzy C-Means algorithm, in the frequency space based on Gabor filters. Sequentially, a mask is generated to eliminate residential areas so that other land-cover types would be classified accurately, and not interfered with the spectrally heterogeneous residential areas. Afterwards, other objects are classified using spectral features by the MAP (maximum a posterior) - ICM (iterated conditional mode) classification algorithm designed to enforce the spatial constraints into classification. Experimental results on high spatial resolution remote sensing data confirm that the proposed algorithm provide remarkably better detection accuracy than conventional approaches in terms of both objective measurements and visual evaluation.

An approach of the multi-scale texture classification for urban land cover /use using high-spatial resolution satellite imagery was proposed in this paper, in which the decision tree classifier was employed. The comparison with the band to be extraction was performed for three images. The grey-level co-occurrence matrix was adopted to calculate texture values of twenty windows. The J-M distance was used to optimize the texture scales for the eight classes of land cover /use. It was founded that maximum J-M distance appears in the window 15×15 for broadleaf-evergreen, conifer, 27×27 for grass land, 47×47 for bare soil, 67×67 for building and water, respectively. The experimental results showed that overall accuracy with multi-scale texture was 81.7% for eight urban types. The comparison with both the single scale texture and original spectrum showed that the overall accuracy of multi-scale texture was higher than ~6% of the single scale texture and ~11% of the original spectrum respectively. The results also indicate that multi-scale texture method is more accurate and reasonable with real world, and can reduce the "salt-and-pepper" effect. This is achieved by the proposed method, in which the classification with optimization the texture scales is of the most critical value for mapping urban land cover/use using high spatial resolution satellite image.

In this study, we proposed a sampling strategy for a single step land cover change detection method. The sampling strategy facilitates the derivation of samples of detailed "from-to" land cover change and no-change classes from images of multiple dates. It consists of two steps. Firstly, classes of interest will be defined and their training samples will be derived separately from the two date data sets. Secondly, the two sets of class data or signatures will be combined in pair artificially as one single set for both change and no-change land cover classes. As a result, a full list of possible land cover changes and no-changes classes are effectively trained. It is simple and able to eliminate those impossible land cover change directions considered by expert knowledge. Our case study on Drayton Coal Mine and surrounding area demonstrated that the sampling strategy when used together with the single-step classification method yielded a much meaningful and cleaner land cover change map than that of the traditional two-step post-classification method. In addition, the one-step classification also provided higher overall testing accuracy than that of the two-step post-classification (e.g., 82.3% vs 78.8%). On the other hand, the resultant map of the traditional two-step post-classification is more fragment, and the area of land cover changes is clearly over-estimated (e.g., close to 50%). One disturbing fact is that the two-step post-classification generated a large proportion of land cover change classes that are not existent in the study area. This problem can be overcome by the developed training strategy.

We present a new object-oriented land cover classification method integrating raster analysis and vector analysis, which adopted improved Color Structure Code (CSC) for segmentation and Support Vector Machine (SVM) for classification using Very High Resolution (VHR) QuickBird data. It synthesized the advantage of digital image processing, Geographical Information System (GIS) (vector-based feature selection) and Data Mining (intelligent SVM classification) to interpret image from pixels to segments and then to thematic information. Compared with the pixelbased SVM classification in ENVI 4.3, both of the accuracy of land cover classification by the proposed method and the computational performance for classification were improved. Moreover, the land cover classification map can update GIS database in a quick and convenient way.

Qixia District is located in the north east of Nanjing City in China. The most beautiful mountain in Nanjing is located in this district. The District serves communication of Nanjing city. There are a total of 70 ports and harbors of different types, Highways extend in all directions and radiate to all parts of Jiangsu Province (whereas Nanjing is the capital of Jiangsu). The Shanghai-Nanjing highway, Yangtze River Bridge of Nanjing passes through the District. The District is a major area with concentrated modern industry in Nanjing and has formed four pillar industries, such as medicine and electronics, machine manufacture, new building materials and petrochemistry. The District has more than 30 universities, colleges and research institutes within. Recently the IKONOS satellite is to collect images with one-meter resolution. There was a need to acquire high spatial resolution images to classify the land use and land cover in the urban sites. This district has agriculture and an important base of farming. There are historic sites of cultural. Four methods has been used to extract the information from IKONOS image, the texture algorithm for this test are represent the vegetation are depend on normalized differences and normalized difference vegetations indices NDVI the unsupervised classification has been adopted to calcified the land cover in Qixia district while a new equation used to eliminated the non -vegetation pixel depending on the reflectance spectrum of Items in the image. This investigation shows that the urban vegetation cover in this district is contributing to mitigate the climate and decrease the pollution. This study probably help Qixia District to rebuilt into a modern riverside new district with beautiful landscape, to give more favorable social environment and a more wealthy life for its people.

Remote sensing technology integrated with GIS (Geographical Information System) is an effective tool for urban expansion and pattern analysis. This paper presents a GIS/Remote sensing-aided procedure for urban space pattern evolving process at a regional scale in Wuhan city, Hubei Province of China, where grow rapidly in the past several decades. Firstly, a series of geospatial dataset was constructed, such as remote sensing images, regionalism maps, transport system maps and land use types maps. By using data of the different temporal TM and ETM+ images, three indices, i. e. Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI) and Soil Adjusted Vegetation Index (SAVI) were calculated. Then, the three index bands were used to compose a new image and a maximum likehood-based supervised classification was carried out. The extraction result from the composed image show that a 90.3% overall accuracy validated by SPOT5 images in the same period. The methods mentioned above correctly identified different land use types in the study area. Secondly, the changing information of land use was then merged into the ETM+ images through layers stacked or image data fusion. The new data fusion image was formed for evolving process analysis of urban pattern. Other data sources, such as regionalism maps, transport system maps, annotation maps, had been also overlay in GIS environment. Six urban expansion areas were distinguished by the analysis of multi-temporal remote sensing images. The results coincide well with the field census data. Finally, the features and characteristics of urban expansion in Wuhan in 1990s were also described.

Accurate estimation of impervious surface and vegetation is a key issue in monitoring urban area and assessing urban environments. It has been proved that the nonlinear models for spectral mixture analysis outperform the linear models in the literature. However, the mapping functions of nonlinear models require to be predefined which are difficult to be determined. Support vector regression (SVR) has shown success in dealing with nonlinear problem, such as estimation and prediction. In this paper, genetic algorithm (GA) was employed to determine the optimal parameters of SVR automatically, which were applied to SVR model. Further, a GA-SVR model with multi sets of parameters (Multi-GA-SVR) was applied to estimate the distributions of impervious surface and vegetation. The results showed that Multi-GA-SVR achieved a higher accuracy than GA-SVR with single set of parameters (Single-GA-SVR) and the traditional linear mixture model (LMM), with an overall root mean square error measure (RMSE) of 0.15 for three distributions. It is demonstrated that the proposed approach is a promising approach for estimation of impervious surface and vegetation.

Texture feature is becoming more and more important for classification of remote sensing image, especially in remote sensing image in mountainous area. An approach to classification of western mountainous area of Zhejiang land cover using ETM+ imagery is described in this paper. Firstly the gradient images of research area were obtained using different edge detection methods with Roberts, Sobel, Prewitt and Canny operator using ETM+ pan image. The results of four different edge detection methods were evaluated qualitatively and quantitatively. The qualitative evaluations mainly considered the visual effect so that the results of combining edge images with original image for qualitative evaluation, and the edge points,4-connected component quantities and 8-connected component quantities were adapted to quantitatively evaluate different edges. Then Canny operator was selected as the gradient operator according to the qualitative and quantitative evaluation results of different research area's edge images and fifteen texture features were obtained based on the Gray-Gradient Co-concurrency Matrix consequently through MATLAB programmer with the Canny operator. Finally, the classification results based on the spectral respond feature only and the texture feature with the spectral respond feature were evaluated separately. It shows that the texture features highlight the residents, rivers etc. which the geometric structure of space themselves are more obvious than others; enhancing the undulating the distinction between water and the shadow.

In the paper, 3S (GIS, GPS, RS) technique is the mainly tools, especial Remote Sensing. In order to get the land use status, Jian city land use is monitored in the year of 1996, 1999 and 2002. Land use type and attribute data in the year of 1996, 1999 and 2002 are compared to analyze the land use change in two different city zones. Combined by Jian city socio-economic data, such as population, finance revenue and payout, the classified land use data is constructed a correlation model on the base of village administration boundary. Land use in 2005 is forecasted by the model. All of these will support a scientific taking point for proper land use planning and paddy protection. It also offers some worthy techniques for developing dynamic land use monitoring in more domains.

With the recent availability of commercial high resolution remote sensing multispectral imagery from sensors such as IKONOS and QuickBird, we can't get the accuracy of land-cover classification expected using pixel-based approach. In this paper, we bring about object-based approach combined with the nearest neighbor to classify the QuickBird image of LianYungang city. Firstly, the image is segmented into object feature, we make the shape feature and contextual relation feature join the new feature space which is used to classify. And then we compare the classification of object-based approach accuracy with the nearest neighbor method of classification result, we can draw a conclusion that the method of classification in this paper can recognize geo-types much better. And the overall accuracy is 92.19%; the coefficient of Kappa is 0.8835. Salt and pepper effect is decreased effectively. The result indicates that the approach of land-cover classification combined object features with the nearest neighbor approach supplies another new technique for interpreting high resolution remote sensed imagery.

Land use/cover change (LUCC) has significant impacts on regional environment. Land surface temperature (LST) is an important indicator for assessment of regional environment especially in big cities where urban heat island is very obvious. In this study, remote sensing and geographic information systems (GIS) were used to detect LUCC for assessment of its impacts on spatial variation of LST in Urumqi, a big city in northwestern China. Two Landsat TM/ETM+ images respectively in 1987 and 2002 were examined for LUCC detection. LST and NDVI were computed from the images for different land use/cover types. Impacts of LUCC on regional environment can be assessment through LST difference during the period. Our results showed that land use/cover changes were very obvious in Urumqi between 1987 and 2002 due to rapid expansion of the city. Urban/built-up land increased by almost twice in the period, while the barren land, the forestland and water area declined. The increase of urban/built-up land was mainly from the barren land. Spatial distribution of LST in the city has been highly altered as a result of urban expansion. The urban/built-up area had LST increase by 4.48% during the period. The LST difference between built-up land and other land use/cover types also significantly increased between 1978 and 2002, with high LST increase area corresponding to the urban expansion regions. Moreover, changes of vegetation also had shaped many impacts on spatial variation of LST in the city. We found that NDVI has a negative correlation with LST among the land use/cover types. This probably is due to the ecological function of vegetation in cooling down the surface from high evapotranspiration. The study demonstrated that combination of remote sensing and GIS provided an efficient way to examine LUCC for assessment of its impacts on regional environment in big cities.

The article discusses the design and realization of a high quality prime farmland planning and management information system based on SDSS. Models in concept integration, management planning are used in High Quality Prime Farmland Planning in order to refine the current model system and the management information system is deigned with a triangular structure. Finally an example of Tonglu county high quality prime farmland planning and management information system is introduced.

Two dynamic characteristics, on-line map update and real time traffic information, are currently considered as the most important feature of vehicle navigation system. Therefore, the need of incorporating dynamic information into vehicle navigation system should be emphasized because the lack of data representation to integrate navigation data with models for spatio-temporal processes appears to be a major shortcoming in traditional vehicle navigation system. Consequently, in this paper we briefly summarize spatial and temporal characteristics of the DVNS (Dynamic Vehicle Navigation System). And then, based on these summaries, we propose a data model with spatio-temporal characteristic instead of static data model. In this data model, spatial feature, temporal feature and traffic feature are all integrated into a unified data model in order to supporting dynamic route plan and on-line map update in the DVNS. Finally, a prototype system based on the proposed data model is tested and implemented.

The modeling and simulation method of Galileo E1-C intermediate frequency signal is proposed in this article. The simulation results can be used for the further research of the Galileo receiver development and calibration of the core skill of signal acquisition and tracking. And we program the software based on the model and give out the simulation results. The results match very well with the theoretical ones.

Firstly, the PANDA (Position And Navigation Data Analysis) software, developed by Wuhan University, is introduced in this paper. And then we present a new method for the precise orbit determination (POD) and near real-time orbit prediction using the regional tracking network by the PANDA software. The orbit determination results are compared with final precise orbit provided by IGS and the accuracies are given detailedly. The results should encourage the realization of regional high precision orbit determination services.

In order to find the precise position of the underwater vehicles, suitable matching areas are needed to be decided in the Gravity-matching Aided Navigation System. This paper presents the segmentation suitable-matching areas method of navigation reference map. Firstly, the paper put forward the stability of matching index based on every gridding points in the reference map. The discussion indexes are roughness, main peak curvature of correlation function, trackability, and characteristic density. According to these measures, the suitable matching area was divided by curve evolution using level set method. Several simulations and experiments were performed in this paper.

Software GPS receiver can provide full access to signal processing inside the receiver channels. Thus, it has become the key component when investigating and developing advanced GPS signal processing technique. Based on the theory of GPS software receiver, we first illustrate a summary of a GPS receiver's mechanism in this paper. Then we focus on the algorithm and its implementation for signal acquisition and tracking. Finally the results and their analysis from acquisition and tracking from simulated GPS L1 data are given. The results show that the scheme in this software receiver is efficient to acquire the GPS signal and can be used to fulfill the demodulation of the input signal.

This paper applies the wavelet to dispose the GPS observation data. If the GPS phasic observation data can be regard as time list to analysis, it shows a very lubricous curve. When the cycle-slip takes place, the velvet of the curve will be damaged. From the beginning epoch of the cycle-slip, the subsequent phasic observation data list take place equal cycle-slip. According to the principle which wavelet transforms detect signal, the GPS gross error or cycle slip can be regard as the break point of the signal to be identified. Because all kinds of the yawp and multipath have certain scope of frequency, the frequency characteristics of the useful signal and the yawp are different. The technology of Wavelet Transforms is used to detect the cycle-slip and the gross error in GPS phasic observation data. The filtering method based on wavelet transforms is very effective to improve the proportion of the signal and yawp which is the GPS double differential carrier observation data. The carrier observation data is filtered to eliminate the multipath and some yawp by wavelet and the observation data can be refined. This method has obvious function to decrease the scope of searching ambiguity and to improve the validity of the ambiguity, which can improve the precision of the baseline solution.

PDA (Personal Digital Assistant) is a useful tool for navigation which has many advantages such as its smallness and portability. In the meantime, digital charts have been found a wide application in past ten years, and many users are hoping for giving up the paper chart entirely and using ENC by the law. However, traditional paper chart is a nonreplaced tool for people in hydrographical survey and other application fields, and would coexist with ENC for a long time. How to manage and display integrated chart for traditional paper chart and ENC together in PDA for navigating is still an unsolved problem. Aiming at this, a new integrated spatial data model and display techniques for ENC and paper chart are presented. The core idea of the new algorithm is to build an integrated spatial data model, structure and display environment for both paper chart and ENC. Based on the above algorithms and strategies, an Integrated Electronic Chart Pocket Navigator System named PNS based on PDA was developed. It has been applied in Tianjin Marine Safety Administration Bureau and obtained a good evaluation.

The management of network constrained moving objects is more and more practical, especially in intelligent transportation system. In the past, the location information of moving objects on network is collected by GPS, which cost high and has the problem of frequent update and privacy. The RFID (Radio Frequency IDentification) devices are used more and more widely to collect the location information. They are cheaper and have less update. And they interfere in the privacy less. They detect the id of the object and the time when moving object passed by the node of the network. They don't detect the objects' exact movement in side the edge, which lead to a problem of uncertainty. How to modeling and query the uncertainty of the network constrained moving objects based on RFID data becomes a research issue. In this paper, a model is proposed to describe the uncertainty of network constrained moving objects. A two level index is presented to provide efficient access to the network and the data of movement. The processing of imprecise time-slice query and spatio-temporal range query are studied in this paper. The processing includes four steps: spatial filter, spatial refinement, temporal filter and probability calculation. Finally, some experiments are done based on the simulated data. In the experiments the performance of the index is studied. The precision and recall of the result set are defined. And how the query arguments affect the precision and recall of the result set is also discussed.

Interferometric synthetic aperture radar (InSAR) has been demonstrated useful for topographic mapping and surface deformation measurement. However, the atmospheric disturbance, especially the tropospheric heterogeneity, represents a major limitation to accuracy. It is usually difficult to accurately model and correct the atmospheric effects. Consequently, significant errors are often resulted in misinterpretation of InSAR results. The purpose of this paper is to seek to reduce the atmospheric effects on repeat-pass InSAR using independent datasets, viz. Global Positioning System (GPS). A between-site and between-epoch double-differencing algorithm for the generation of tropospheric corrections to InSAR results based on GPS observations is applied. In order to correct the radar results on a pixel-by-pixel basis, the Support Vector Machine (SVM) with adaptive parameters is introduced to regressively estimate tropospheric corrections over unknown points using the sparse GPS-derived corrections. The feasibility of applying SVM in troposphetic corrections estimation is examined by using data from the Southern California Integrated GPS Network (SCIGN). Cross-validation tests show that SVM method is more suitable than the conventional inverse distance weighted (IDW) method; it accounts for not only topography-dependent but also topography-independent atmospheric effects, so it seems optimal to estimate the tropospheric delay corrections of unknown pixels from GPS data.

Aimed at lower-precision and time-consuming shortcomings in registration for SAR and optical remote sensing images, a region feature based registration method was proposed. A modified Lee filter was employed to suppress speckles in SAR image firstly. Than, image segmentation based on region growing restricted by the edge was used to extract closed regions in SAR and optical images as invariable features. After that precise feature matching was guided by a cost function, which considering region area, perimeter, coordinate of center point and so on. After cross correlation matching, the center points of the matched region were taken as registration control points (RCPs). Experimentation was performed by registering Envisat ASAR and Landsat TM images. The results show that the whole workflow could be accomplished automatically and the root mean square error (RMSE) of the RCPs got by this method is lower than 1 pixel.