The characteristics of ecosystem functions are of great significance for biodiversity conservation and ecosystem services. Ecosystem functional types (EFTs) are land surface areas similar in carbon dynamics that are not defined by the structure and composition of vegetation and represent the spatial heterogeneity of ecosystem functions. However, identification of EFTs based on low-resolution remote sensing data cannot satisfy the needs of fine-scale characterization of regional ecosystem functionality patterns, and a more accurate and optimized method of identifying EFTs also deserves attention. Here, we characterize EFTs at a county scale based on subtractive fuzzy cluster means (SUBFCM) and Sentinel-2 time-series data. The normalized difference vegetation index, the fraction of absorbed photosynthetically active radiation, and canopy water content and their derived variables in the growing season were selected as ecosystem functional indicators to characterize regional EFT diversity patterns. The correspondence analysis method was used to reveal relationships between the EFTs and land cover structure information, and further analysis of EFTs was performed with soil type data. Our results showed that the selected variables indicating carbon and water flux of the regional ecosystems could be adopted in ecosystem functional classification. The SUBFCM algorithm can automatically divide EFTs with faster convergence speed and reduced subjectivity. The obtained EFTs based on Sentinel-2 images reflected the internal structure of carbon balance well and the distribution pattern of ecosystem functional diversity at a fine scale. A reference for further optimization of the EFT identification algorithm and development of the understanding of spatial heterogeneity of temperate terrestrial ecosystem functions was provided.
The linear polarization of light reflected from soil surfaces was measured by an instrument composed of a semi-automatic goniometer and an ASD spectroradiometer under a direct lamp to determine its potential to detect differences in different particle size. In this paper we tested and analyzed the polarization spectra of soils to determine the spectral response and changes in soil particle size, and to establish models of the relationship between spectral data and soil particle size. An orthogonal test was also designed for the various factors that affect soil spectral polarization characteristics and their interactions. All above measurements were carried out in the laboratory where the atmospheric contribution was ignored. The results show that particle size is one of the most important parameter affecting soil spectra, and is critical to soil remote sensing band selection and image interpretation. It also provides information required for soil investigation and analysis of physical and chemical properties.
Monitoring heavy metal stress on rice is of great significance for food security. In this paper, we used NDVI time series during the whole growing period of rice to identifying the rice growing differences under varied heavy metal stress. Here the NDVI time series were with high spatial-temporal resolution and obtained by blending MODIS and Landsat NDVI data. We extracted two kinds of features: Max NDVI value and time-integrated NDVI and use Fisher discrimination to explore the rice phonological differences under mild and severe stress levels. Results indicates that under severe stress the values of the metrics for presenting rice phonological differences in the experimental areas of heavy metal stress were smaller than the ones under mild stress. This means using the phenology differences can help to monitoring the heavy metal contamination.
Rain-use efficiency (RUE) acts as a typical indicator of ecosystem function. Land surface phenology (LSP) assesses the vegetation activity during the growing season at the ecosystem level. The Songnen Plain (SNP) is located in semi-humid to semi-arid transition ecological fragile zone in Northeast China. RUE in growing season (May-September) was calculated using time series GIMMS NDVI3g images and precipitation data for the period of 1983-2012. The phenology metrics including the start (SOS) and end (EOS) dates of growing season for each year was extracted. Spatial trends of RUE and LSP were examined by applying a linear regression model with time. The correlation analysis was used to analyze the effects of RUE on LSP. The results showed that RUE increased slightly with an undulating trend. Spatially, the highest positive slopes indicating increased trend of RUE were observed in northern and eastern forest. The advanced in SOS was mainly distributed in northern forest areas. 12.2% of the landscape experienced highly increase trend in EOS with a rate of 0.38 days per year. The length of growing season (LOS) was prolonged in 14.2% of the total land. EOS dates in the southern salinized grassland and cropland were mainly negatively correlated with RUE. The results of the significance test show that 2.95% of the pixels were significantly and positively correlated with RUE, indicating that an increase in the RUE would delay the EOS. Increasing RUE promoted the extension of the LOS, particularly in the forest areas.
Effectively assessing cadmium (Cd) contamination in crops is crucial for the sustainable development of an agricultural ecosystem and for environmental security. We developed an integrated stress index (SI) based on two phenological metrics to effectively evaluate Cd stress in rice crops. The selected four experimental areas are located in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. The change rate of CIre (CRCIre) and the time-integrated CIre (TICIre) were obtained from daily red-edge chlorophyll index (CIre) time-series using Sentinel-2 data. The CRCIre and TICIre were used to characterize the photosynthetic rate and biomass, respectively. SI was calculated by Fisher discriminant analysis based on CRCIre and TICIre from two experimental areas, and it was verified using another two experimental areas. The results were the following: (i) when SI ≥ 0, rice was under mild stress and when SI < 0, rice was under severe stress. (ii) The SI effectively evaluated Cd stress levels with an overall discriminatory accuracy of 86.02%. This research provides a potential new method to evaluate Cd stress in rice by remote sensing through phenology.
The assessment of ecological security is to identify the stability of the ecosystem, and to distinguish the capacity of
sustainable health and integrity under different kinds of risks. Using MODIS time series images from 2000 to 2008 as the
main data source, the derived parameters including NDVI, the ratio of NPP and GPP, forest coverage, landscape
diversity and ecological flexibility etc. are integrated to depict the properties of the ecological system. The pressure and
response indicators such as population density, industrial production intensity, arable land per capita, fertilizer
consumption, highway density, agricultural mechanization level and GDP per capita are also collected and managed by
ArcGIS. The ‘pressure–state–response’ (PSR) conceptual model and a hierarchical weighted model are applied to
construct an evaluation framework and determine the state of ecological security in Changbai Mountain area. The results
show that the ecological security index (ESI) values in 2000 and 2008 were 5.75 and 5.59 respectively, indicating the
ecological security state in Changbai Mountain area degraded. In 2000, the area of in good state of ecological security
was 21901km2, occupying 28.96% of the study region. 48201 km2 of the land were with moderate level. The grades of
ESI in Dunhua, Longjing and Antu decreased from moderate to poor. Though the ESI value of Meihekou increased by
0.12 during 2000-2008, it was still in a very poor state of ecological security induced by intensive human activities. The
ecological security situation of Changbai Mountain region was not optimistic on the whole.
The dynamics of vegetation cover changes may provide vital information for ecological environmental protection and early warning of ecosystem degradation in arid and semiarid regions. The West Liaohe River Basin is the east fringe of agro-pasture transitional zone in northern China and highly sensitive to global change. With the SPOT VEGETATION (SPOT-VGT) NDVI dataset during 1999–2010, temporal and spatial change trends of vegetation cover was investigated using yearly and seasonal average NDVI, Vegetation Anomaly Index (VAI) and correlation analysis. The relationship between vegetation change, climatic and anthropogenic factors were explored. The results indicated that yearly NDVI slightly increased with an undulating trend. 30.24% of the study area had experienced a significant vegetation increase at the 0.05 level from 1999 to 2010. The VAI negative values exhibited vegetation cover degradation impacted by the drought in 2000-2002 and 2009.The average NDVI values in autumn increased by 5.92%, whereas the spring NDVI decreased by -5.82%. 16.46% and 15.49% of the study area showed a significant vegetation increase in summer and autumn respectively. Changes in vegetation growth in the West Liaohe River Basin may be affected by spring precipitation, summer temperature and precipitation and autumn temperature. The NDVI increase trends in the study area were related to the increased crop yield.
Because vegetation affect several processes including water balance, absorption and reemission of solar radiation, latent and sensible heat fluxes, and carbon cycle, the variations in the composition and distribution of vegetation represents one of the most main source of systematic change on local, regional, or global scale. To monitor and better assess natural or man-made change in vegetation of the earth is desirable for modeling and predicting interactions between land surface and atmosphere. The temporal evolution of decadal NDVI composition is regarded as an effective time window able to show the natural seasonal variations. This paper investigates vegetation change between 1998 and 2006 in the west Liao River watershed, North China, which is the east fringe of agro-pasture transitional zone in northern China and highly sensitive to global change. Time series of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) data are used to detect the vegetation cover change during last 9 years. Results show that the yearly maximum value composite mean NDVI over the study area increased slightly from 0.277 in 1998 to 0.287 in 2006, which indicated the increasing trend of vegetation activity. The annual average NDVI value in whole area was steady. Very slight improved and slight improved area reached 113442.32 km2 and 27987.34 km2, taking up 67.81% and 16.73% of the whole study area respectively. The degraded regions occupied about 15.16%. During 1998-2006, the landscape evolution in the western Liaohe River Basin was characterized by two opposite processes, namely vegetation restoration (returning cropland for farming to grassland and close grazing) and desertification (especially land salinization). The increasing amplitude is larger than the decreasing amplitude on the whole. There was obvious decrease of monthly MNDVI in spring months, while increasing tendency of monthly MNDVI in summer and autumn was found. Results will help to provide valuable information for environmental management policies involving biodiversity preservation and rational exploitation of natural and agricultural resources in this vulnerable ecotone.
The issue of environmental pollution due to toxic heavy metals in agricultural land has caused worldwide growing
concern in recent years. Being one of toxic heavy metals, the accumulation of Plumbum (Pb) may have negative effects
on natural and agricultural vegetation growth, yield and quality. It can also constitute short-term and long-term health
risks by entering the food chain. In this study, we analyze the relationships between physical and chemical
characteristics, biological parameters of soil-vegetation system and hyperspectral spectrum responses systematically.
The relation between hyperspectral data and the biological parameters of Pb polluted wheat canopy such as leaf
pigments, leaf moisture, cell structure and leaf area index (LAI) are discussed. We detect the changes in the wheat
biological parameters and spectral response associated with Pb concentration in soil. To reveal the impact mechanisms
of Pb concentration on agricultural soil, six models including chlorophyll-leaf moisture model, chlorophyll-cell structure
model, chlorophyll-LAI model, leaf moisture-cell structure model, leaf moisture-LAI model, cell structure- LAI model
are explored. We find that changes in Pb concentration present various features in different models. Pb contamination in
agricultural soil can be identified and assessed effectively while integrating the characteristics of those developed
models.
In the past several decades, land cover in the region underwent dramatic changes and the progressive loss and conversion
of wetlands has become a key conservation issue. Based on the theory and methods of landscape and GIS, topographic
map in 1954, Landsat MSS in 1976, Landsat TM/ETM imagery in 2000 were used to detect the wetland pattern and its
change in middle and lower Wuyu'er River, west Songnen Plain since the mid 1950s. The 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 during 1954-
2000. Calculated from change dynamic model, the annual loss rate of marsh was -1.48% (from 1954 to 1976) and -
0.76% (from 1976 to 2000) respectively. Due to shortage of water supply, marsh land were turned into dry grassland and
degraded to saline-alkaline land. The number and size of marsh patch decreased significantly which indicated that
the wetland landscape became more fragmental. The grassland decreased by 40.26×104 ha dramatically due to having
been opened up to cropland and degraded into hardly-used land. The study indicates that the loss and degradation of
wetlands was closely related to warmer and drier regional climate during the past 50 years. Intensive human activities
including irrational reclamation, overgrazing, and ditches drainage and road construction accelerated the process.
A time series of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) data with 1×1 km2 spatial
resolution are used to detect the vegetation cover change in west Songnen plain, Northeast China during the period of
1998 to 2006. The MVC (Maximum Value Composites) method and difference value method were used to analyze the
inter-annual changes. Principal component analysis (PCA) of the time-series NDVI imageries was performed effectively
for discriminating land covers. During the last 9 years, the vegetation degradation is popular in most regions of the study
area. Though there are some regions where vegetation cover is increasing, the increasing amplitude is smaller than the
decreasing amplitude on the whole.
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.
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+.
KEYWORDS: Data modeling, Mathematical modeling, Statistical analysis, Systems modeling, Data conversion, Geographic information systems, Statistical modeling, Environmental sensing, Organisms, Process modeling
Grid-city management currently attracts a wider audience globally. Socio-economic data is an essential part of grid-city
management system. Social-economic data of an urban is characterized by discrete, time-varying, statistical, distributed
and complicated. Most of data are with no exactly spatial location or from various statistical units. There is obvious gap
while matching social-economic data with existing grid map of natural geographical elements emerges. It may cause
many difficulties in data input, organization, processing and analysis while the grid system constructing and executing.
The issue of how to allocate and integrate the huge social-economic data into each grid effectively is crucial for grid-city
construction. In this paper, we discussed the characteristics of social-economic data in a grid-city systematically,
thereafter a cell-based model for social-economic data representing and analyzing is presented in this paper. The kernel
issues of the cell-based model establishment include cell size determining, cell capabilities developing for multi-dimension
representation and evaluation, and cell dynamic simulation functions designing. The cell-based model
supplements the methods system of spatial data mining, and is also promising in application to the spatialization of
statistical data obtained from other researches including environmental monitoring, hydrological and meteorological
observation.
With the development of the high-resolution remote sensing image, it is important to identify and extract the image information automatically. Texture analysis has been recognized as a useful method of improving the target identification and its accuracy. In this paper we mainly discussed texture patterns analysis of main geographical objects in QuickBird image. We analyze the texture's characteristic using Gray Level Co-occurrence Matrix (GLCM) and the Texture Measure of Gray Level Co-occurrence Matrix (TMGLCM). The texture pattern analysis takes TMGLCM as the main method. We establish a unify texture pattern use the TMGLCM parameter. The method is available after experiment.
Integrated analysis of Heterogeneous spatial data sets is becoming an increasing focused and important issue in geographical information sciences at present. More and more interdisciplinary projects need the integrated analysis of heterogeneous spatial data sets from multi-sources with different quality. Especially with the development of remote sensing technology, how to make full use of the huge spatial data and how to integrate them with available history data and statistical data attract more and more attention. In this paper, the heterogeneous data integration of vector data and imagery is discussed, and a new method using fuzzy reasoning in GIS is put forwards to integrate the former vector land use data and present remote sensing imagery. One example experiment for Changling County in Northeast China with the above-mentioned method is described in detail. In the example, the real change information of earth surface is extracted exactly because the uncertainty imported by other factors is removed.
KEYWORDS: Geographic information systems, Spatial analysis, Knowledge discovery, Remote sensing, Visualization, Data mining, Data processing, Data storage, Computing systems, Databases
Supported by spatial information grid(SIG), the spatial knowledge grid (SKG) for integrated spatial analysis utilizes the
middleware technology in constructing the spatial information grid computation environment and spatial information
service system, develops spatial entity oriented spatial data organization technology, carries out the profound
computation of the spatial structure and spatial process pattern on the basis of Grid GIS infrastructure, spatial data grid
and spatial information grid (specialized definition). At the same time, it realizes the complex spatial pattern expression
and the spatial function process simulation by taking the spatial intelligent agent as the core to establish space initiative
computation. Moreover through the establishment of virtual geographical environment with man-machine interactivity
and blending, complex spatial modeling, network cooperation work and spatial community decision knowledge driven
are achieved. The framework of SKG is discussed systematically in this paper. Its implement flow and the key
technology with examples of overlay analysis are proposed as well.
Based on the introduction to the characters and constructing flow of space semantic model, the feature space and context
of house information in high resolution remote sensing image are analyzed, and the house semantic network model of
Quick Bird image is also constructed. Furthermore, the accuracy and practicability of space semantic model are checked
up through extracting house information automatically from Quick Bird image after extracting candidate semantic nodes
to the image by taking advantage of grey division method, window threshold value method and Hough transformation.
Sample result indicates that its type coherence, shape coherence and area coherence are 96.75%, 89.5 % and 88 %
respectively. Thereinto the effect of the extraction of the houses with rectangular roof is the best and that with
herringbone and the polygonal roofs is just ideal. However, the effect of the extraction of the houses with round roof is
not satisfied and thus they need the further perfection to the semantic model to make them own higher applied value.
The semi-arid and semi-humid agricultural region in Northeast China occupies parts of the Songnen Grassland and the Horqin Grassland. The region has been an important base in food production and stockbreeding. During the past 50 years land cover in the region underwent dramatic changes. Based on thematic maps and statistical data, the change trajectories of cultivated land and grassland were traced for the period from 1950 to 1985. Visual interpretation of the false composite images followed by screen digitizing was applied to obtain digital data of land cover. With the spatial analysis techniques of GIS, the spatial patterns and changing characteristics of land cover for the period of 1986-2000 were analyzed. The percentage of cultivated land expanded from 38.6% to 48.4% from 1986 to 2000. Salinized land also increased rapidly. The area of grassland decreased severely with the reduction 6.8% at annual loss rate of 1.98%. Forestland, grassland and water area are more fragmented. The dominant land cover changes were the conversion of grassland and forestland into cultivated land, dryland into paddy field, and grassland into salinized land. Land cover changes were closely related to climate fluctuation, rapid growth of population and underdeveloped economic position.
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