Creation of Large Area Forest Biomass Maps for Northeast China Using ERS-1/2 Tandem Coherence

Abstract

ERS-1/2 tandem coherence is known to allow forest stem volume mapping with reasonable accuracy. Large- scale forest mapping, however, is hindered by the variability of coherence with meteorological, environmental and orbital acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, using forest inventory which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows the training of a semi-empirical model on a frame-by-frame basis using the MODIS Vegetation Continuous Field product without further need of ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training procedure and the application of the new approach to a multi- seasonal and multi-baseline ERS-1/2 tandem coherence dataset covering Northeast China are presented.