Much attention has been paid nationally and internationally to shifting cultivation in upland Myanmar since Forest Department of Myanmar indicates that it is the main underlying cause for deforestation. However, knowledge of the explicitly spatial pattern of forest lands affected by shifting cultivation, transition characteristics and the impact of shifting cultivation for Reduced emissions from deforestation and forest degradation (REDD+) is scare. In this study, the scale, intensity and duration of shifting cultivation during 2002-2016 were detected by means of MODIS time series imageries using improved vegetation change tracker (VCT) based on Integrated Forest Z-score (IFZ) derived from the spectral–temporal properties of forest lands change processes.
It was a trend that image was classified through combining multi-source remote sensing data with non-remote sensing
data by GIS technology. In this paper, technological framework of land use information extraction was established using
multi-sources remote sensing data (TM and CBERS-02B), DEM, slope data, land use map and other geographic
auxiliary data. The result showed :( 1) It was possible to combine TM and CBERS-02B as land use sources data because
of their similar spatial resolution and spectral resolution. In this research the method of multi-level supervised
classification was adopted. (2) Interpretation accuracy was improved by establishing background database through GIS
technology. First, non-remote sensing information, such as topographic map, soil map, land-use map, transportation map,
etc, was integrated as background database. Then land use classifications were overlapped with above database. The
results showed that uncertainty could reduce by 23.2%. (3) In the study area dry land spectrums in plain area, hilly area
and the Yellow River flood plain were very different and spectrums of habitation in plain and the Yellow River land
wash were the same. As for above phenomenon of "same object with different spectrums" and "different objects with
same spectrum", expert knowledge database was established based upon relationship between remote sensing image and
geographical environment. As a result average classification accuracy was improved by 12.1%.
Data fusions from SAR and TM, SPOT and TM, ASTER and TM, MODIS and ETM, etc are the common methods. But
that from TM and CBERS-02B is rare. With HR camera working in September 19th 2007, Chinese-Brazil Earth
Resources Satellite 02B (CBERS-02B) became the first civilian high-resolution satellite in China. It could provide 2.36m
panchromatic image which is better to Landsat TM. Meanwhile the spectral resolution of TM is better than CBERS-02B.
So it's a good idea to take advantage of benefits from CBERS-02B HR and TM through data fusion.
In this study, images of TM and CBERS-02B HR in 2007 were used as data sources. After image registration and noiseremoval
process, data fusion methods of IHS and PCA were adopted. Then unsupervised classification and supervised
classification were used for land use classification. Finally, classification accuracy between original image and fusion
image was compared and evaluated.
The result shows:
(1) Compared with original TM or CBERS-02B HR image, the fusion image not only retains abundance spectrum but
also enhances the object details. Residential texture, lake morphological, the relative position between roads, industrial
and mining sites, etc, was identified easily.
(2) Results from IHS and PCA are different. IHS image had higher spatial resolution but more spectral distortion.
Spectral differences between some objects became smaller and classification accuracy was lower. Supervised
classification accuracy assessment shows that overall Kappa index and overall land use classification accuracy decreased
by 0.237 and 11% respectively. Meanwhile PCA image not only had high spatial resolution, but also smaller spectral
distortion. Different land use / cover types can be better distinguished.
(3) Disadvantages of low spatial resolution in TM and single color in CBERS-02B HR image are overcome in PCA
fusion image to a certain extent. In this research under supervised classification in PCA image Kappa index of farm land,
forest land and bare land increased by 0.097, 0.176 and 0.242 respectively. Overall Kappa index and overall land use
classification accuracy were improved by 0.092 and 7.24% respectively.
In recent years, data fusion has become a very popular method in remote sensing image enhancement. In this paper, a
comparative study was conducted on data fusion methods based upon SPOT5 image. First land types of forest, paddy
field, dry land, water and building was selected through field survey. Then supervised classification and non-supervised
classification were used upon original image and four fusion images (HIS, PCA, high-pass filtering (HPF) and Brovery)
respectively. Land change area, change rate and classification accuracy were calculated. Finally suitable SPOT5 fusion
method for every land types was presented. The result showed: (1) In all four fusing methods PCA held the highest land
change rate, being average 49.0% for non-supervised classification and average 18.9% for supervised classification. So
visual interpretation was a better way for PCA fusion image. (2) HIS produced some distortion to the original spectrum
and made flaky features into pieces. This method was suitable to extract small features in complicated urban areas
because of its high spatial resolution. In the research, building change rate in HIS fusing image under supervised
classification was lowest, only 3.32%. (3) For HPF land change rate was low for no matter non-supervised classification
or supervised classification, being average 16.3% and 11.2% respectively. This fusion method held low distortion and
more high-frequency spectrum. It was suitable to be used as the basic image data for both supervised classification and
non-supervised classification. In our research image classification accuracy of urban areas in HPF fusion image was
93.1%.
KEYWORDS: 3D modeling, Virtual reality, Navigation systems, Data modeling, 3D displays, Visualization, OpenGL, Molybdenum, Databases, 3D image processing
Virtual Reality provides a new approach for geographical research. In this paper, a framework of the Virtual Huanghe
River System was first presented, including four main modules - data sources module, 3D terrain database module, 3D
model database module and 3D simulation implementation module. Then the key technologies of Virtual Huanghe River
System and their applications were discussed in detail: 1) OpenGL technology, the 3D graphics developing tool, was
employed in Virtual Huanghe River System to realize the function of dynamic real-time navigation. 2) MultiGen Creator
was used to create the 3D model with real texture. 3) OpenGL and MO were used to make the mutual response between
3D scene and 2D electronic map available. The advantages of visualization, reality and locality in 3D scene and
macroscopic view, integrality and conciseness in 2D electronic map were integrated. And at the same time the
disadvantages of the losing direction in 3D scene and abstract and ambiguity in 2D electronic map were overcome.
KEYWORDS: Error control coding, Data modeling, Global Positioning System, Geographic information systems, Finite element methods, Accuracy assessment, Image resolution, Image processing, Image fusion, Remote sensing
Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper discusses the technological procedure of remote sensing dynamic monitoring of land use including the process of remote sensing images, the extraction of annual change information of land use, field survey, indoor post processing and accuracy assessment. Especially, we emphasize on comparative research on the choice of remote sensing rectifying models, image fusion algorithms and accuracy assessment methods. Taking Anning district in Lanzhou as an example, we extract the land use change information of the district during 2002-2003, access monitoring accuracy and analyze the reason of land use change.
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