Presentation on theme: "Global Land Cover 2000 China Window. Data preparation climatic stratification of China VGT data’s preparation remove the cloud contamination synthesizing."— Presentation transcript:
Data preparation climatic stratification of China VGT data’s preparation remove the cloud contamination synthesizing the geo-code image reference data Classification system the land cover classification system-LCCS stratum’s land cover classes from LCCS Classification processand result Classification process and result Accuracy assessment
Phase 1 ： climatic stratification and data preparation. Phase 2 ： unsupervised classification and labeling. Phase 3 ： accuracy assessment and result integrity. Phase 1Phase 2Phase 3 Aridity index Accumulated temperature Climatic stratification integrity Accuracy assessment Synthesized imaged Unsupervised classification Forest land Non-forest land GIS information database GIS information Vegetation data Geo-image Temperature Elevation Precipitation Working process
Vegetation growth and distribution are most associate with climatic factors such as temperature and moisture. The study area was divided into sub-area based on temperature and moisture condition, this is called climatic stratification. This will contribute to ⑴ simplifying the study area into small and environmentally homogeneous sub-region, which will help to reduce the confusion in different sub-regions. ⑵ increasing the mapping accuracy. Why should we performed the climatic stratification ? Climatic stratification
According to the situation of the aridity and the above ten accumulated temperature in centigrade, at the same time considering the region integrity, the study area was divided into nine stratum. the climatic stratification in China Note: T is the above ten accumulated temperature in centigrade. Ar is the aridity: Ar=0.16 × T/r, where r is the precipitation in millimeter.
Original images36 ten-days NDVI images Environment data Which data is used for classification ?
Data preparation: Removal cloud contamination With the Harmonic Analysis of Time Series (HANTS) technique, the cloud contamination were removed for 36 NDVI images. before removing cloud contamination the first ten days in August after removing cloud contamination
Synthesizing of the geo-code image Geo-code data was used as a single band for the classification. Annual average temperature and precipitation were interpolated into 1km resolution GS+’cokriging method 313 meteorology stations DEM data was generated from 1:1 million topographical map.
Firstly, three climatic images were normalized to eliminate the dimension. Secondly, to construct the judging matrix. For each sub-region, the matrix is given by the experts who have abundance geography knowledge. Because of the subjectivity of different experts, we use a coherence index to test the accuracy. For the three factors, three dimension matrix is constituted, and the eigenvector and the maximum eigenvalue ( max ) can be gained. The eigenvector of the P matrix’s maximum eigenvalue that passed the consistency test is the fraction of the climatic factors. The consistency test is calculated with the formula : For every different dimension matrix, three have a different RI, if CI/RI≤0.1, the value given by the expert passed the test. AHP METHORD
This time, we only selected three experts to assign the matrix values. Following is the max-eigenvalue and CR value of nine stratums. The synthesizing image G(x,y) is calculated by the following formula, G(x,y)= ƒ1×T(x,y)+ ƒ2×R(x,y)+ ƒ3×E(x,y) The G(x,y) will be regarded as a band to take part in the classification. The three factors’ coefficient is showed in the following table.
the fraction of each sub-region the fraction of each sub-region
Synthesized geo-code image nwc sj im nec tb ec sc cc nc
Reference data 1:1,000,000 Vegetation map in China 1:1,000,000 land-use map of China 1:1,000,000 administration map Beijing, China remote sensing index map 1km land-use data
Classification system According to GVMU’s requirement, the classification is performed by using FAO’s classification system—LCCS, For the nine sub-region, there are 21 classes.
Classification process and result We regarded the geo-code image as a single band and added to the NDVI image(which already had 36 bands) the sub-region was masked into forest land and non-forest land. Unsupervised classification is performed in each sub-region. For the NWC, IM and SJ region, because of the poor vegetation, the classification result with NDVI is not satisfied. The original image of the last ten days in August is used. For the city, the unsupervised classification is impossible because of the different density and chroma, we used supervised classification to gain the main cities. overlaying forest and non-forest into one image. And then mosaic nine sub-region into whole one.
Accuracy assessment three sample sites: Dingxi county in GANSU province Yanchi county in NINGXIA province Daqing city in HEILONGJIANG province The reference data were land cover maps at a scale of 1:100 000, interpreted from TM data in 2000 with intensive field works. These existing land cover maps were rasterized into grid data with 100 meters. And the land cover data from VGT S10 data was converted into 100 meters resolution. There are 10 classes in the sample sites: broadleaved deciduous forest, slope grassland, plain grassland, meadow, lake, swamp, farmland, bush, desert grassland, desert.
unit:% Confusion Matrix of Classification in Yanchi
Confusion Matrix of Classification in Dingxi unit:%
Confusion Matrix of Classification in Daqing unit:%
Meta data of Land cover data File title :CHINA Data type :Unsigned 8-bit File type :Thematic Columns :8169 Rows :4836 ref. System :Geographic Lat/Lon Spheroid :Krasovsky Datum :Krasovsky Unit dist :1 ULX :68.10076166 LRX :141.02932113 ULY :57.39784891 LRY :14.22821312 Resolution :0.00892857
The land cover mapping was a complex works. For the area with poor vegetation, as north western China, instead of using NDVI dataset, the original image was used. This time, only one dataset of last decade of August image, we believe that multi-temporal images can be better. The geographic factors is useful for the classification of the vegetation distribution. And the AHP method is tested to be reliable, it will be used widely in the future. For the low and medium scale land cover mapping, the stratification was necessary. For all the land-cover classes, water bodies is easy to identify, and for the urban area, it is impossible to identify with NDVI data. The supervised classification of original image was used for the urban area. Hants program greatly improved the classification which removed the cloud and generated consistent dataset for same class. Conclusion
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