An Integrative Classification of Vegetation in China Based on NOAA AVHRR and Vegetation-Climate Indices of the Holdridge Life Zone

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We developed a method for integrated analysis of multi-source data for vegetation classification at the continental scale, and applied it to China. Multi-temporal 1 km NOAA Advanced Very High Resolution Radiometer (AVHRR) Holdridge's life zone system and its vegetation-climate classification indices such as bio-temperature (BT), potential evapotranspiration rate (PER) and precipitation ( P ) correspond better with undisturbed vegetation types all over the world. We generated 1 km images of BT, PER and P using the quantitative model of Holdridge's life zone system with climate data of China. They were processed with principal component analysis (PCA) to produce an ancillary image. This image and 12 monthly images of maximum Normalized Difference Vegetation Index (NDVI) values at 1 km resolution were input into an ISODATA clustering algorithm to carry out a vegetation classification. As a result, 47 information classes were obtained. Seasonal NDVI parameters derived through time series analysis (TSA) of the NDVI temporal profile and a set of quantitative vegetation-climate parameters of Holdrige's life zone model were synthetically utilized to label information classes. In this method, climate, terrain and spectral data were integrated; separability between vegetation types and classification accuracy were improved. A total of 47 land cover classes were obtained. Validation data collected in the field using GPS indicated that an overall classification accuracy of 71.4% was reached, an 8.1% improvement to the map derived only from multi-temporal NDVI images. To compare our results with the International Geosphere-Biosphere Programme (IGBP) DISCover land cover dataset, we aggregated our land cover classes according to the IGBP classification system. The overall classification accuracy for the aggregated vegetation map from our classification results improved IGBP land cover map from 75.5% to 86.3%.

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International Journal of Remote Sensing, v. 24, issue 5, p. 1009-1027