22 October 2004 A classification of forest and grassland in the Gansu Province of China using integrated SPOT VEGETATION, topographical, and meteorological data
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Abstract
A method is developed to integrate topographical (elevation and slope) and climatic (precipitation and temperature) information with multi-temporal VGT images into a coarse scale land cover classification. The Normalized Difference Vegetation Index (NDVI), cumulated NDVI (SNDVI), Normalized Difference Water Index (NDWI) and the cumulated NDWI (SNDWI) were used in a two-step classification approach. The two steps encompass an unsupervised classification based on the ISODATA (Iterative Self-Organizing Data Analysis Technique) method, and a supervised classification based on a dichotomous hierarchical tree classification at the landscape patch scale. Results demonstrate the potential of the integrated method to estimate forest and grassland areas with VGT imagery. The method reduces confusion between different land cover classes with same spectral characteristics and slightly improves classification accuracy. 58% forest and 57% grassland were obtained for the Gansu Province. We suggest two main reasons for the high percentage of land cover misclassification: Confusion of the different land cover classes with same spectral characteristics and the spatial scale of observation unsuited for classifications of a highly fragmented land cover. The integrated data source approach is therefore limited to applications in regional land cover classification. The classification method could be improved in the critical value initialization of the classification tree.
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Mingguo Ma, Mingguo Ma, Frank Veroustraete, Frank Veroustraete, Pinglu Wang, Pinglu Wang, Xuemei Wang, Xuemei Wang, } "A classification of forest and grassland in the Gansu Province of China using integrated SPOT VEGETATION, topographical, and meteorological data", Proc. SPIE 5574, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV, (22 October 2004); doi: 10.1117/12.564421; https://doi.org/10.1117/12.564421
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