rse_siberia1_ww_revised2.pdf (1.08 MB)
Download file

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Download (1.08 MB)
journal contribution
posted on 15.09.2009, 13:40 by Wolfgang Wagner, Adrian Luckman, Jan Vietmeier, Kevin Tansey, Heiko Balzter, Christiane Schmullius, Malcolm Davidson, David Gaveau, Michael Gluck, Thuy Le Toan, Shaun Quegan, Anatoly Shvidenko, Andreas Wiesmann, Jiong Jiong Yu
Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km2 in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m3/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.

History

Citation

Remote Sensing of Environment, 2003, 85 (2), pp. 125-144

Published in

Remote Sensing of Environment

Publisher

Elsevier

issn

0034-4257

Copyright date

2003

Available date

15/09/2009

Publisher version

http://www.sciencedirect.com/science/article/pii/S0034425702001980

Language

en

Usage metrics

Categories

Keywords

Exports