Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images

Abstract

Abstract Global Vegetation Index (GVI) data from the Advanced Very High Resolution Radiometer (AVHRR) was used to identify macro-scale vegetation/ land cover regions in the former Soviet Union (FSU). These regions are a better representation of surface vegetation and land cover than can be obtained from existing thematic maps of the FSU. Image classes were identified through cluster analysis using the ISODATA clustering algorithm and a maximum likelihood classifier. Qualitative analysis of the image variants produced with different input parameters indicated that an image with 42 classes best represented significant details in vegetation and land cover patterns without producing uninterpretable levels of details that represent artefacts of the clustering algorithm. Initial identification of image classes has been made by considering the weight of evidence provided by quantitative and qualitative analysis of existing maps, analytical tools from class statistics, ancillary data from a variety of sources and...