Characterization of global vegetation using AVHRR data

Abstract

Increase in the levels of carbon dioxide and other greenhouse gases over the next half-century may result in an increase in global mean temperature. The recent discoveries of possible advance of arctic tree line into the tundra and earlier greening of northern vegetation provide additional warnings that global warming may indeed be occurring. On the Earth surface, land cover and its changes affect the coupling between the biosphere and the atmosphere, and control many important Earth system processes. Satellite remote sensing provides long-term, repeated coverage over extended area and is the essential data source for monitoring climate changes. An Advanced Very-High Resolution Radiometer (AVHRR) Pathfinder dataset from 1987, in 1 degree latitude-longitude resolution, is used in this study. Two reflective channels, two thermal channels, and Normalized Difference Vegetation Index are the input parameters. In conjunction with a global vegetation ground truth, a multi-layer neural network is trained and used for global vegetation characterization. As the same type of vegetation may appear very differently over different parts of the Earth at any given time, global classification is more difficult than local classification. It is shown that a multitemporal approach, in which data from multiple dates are used, may improve the accuracy.