Classification of forest volume resources using ERS tandem coherence and JERS backscatter data

Abstract

Considering recent progress in the development of techniques and methods to achieve biomass estimates and full carbon accounting, remote sensing research of forested ecosystems needs to be aimed towards the retrieval of information at global scales. In this paper, an algorithm for the estimation of growing stock volume, an important parameter for the commercial forest community and a proxy for woody biomass density, from ERS and JERS synthetic aperture radar (SAR) data is described. The algorithm is based on the information content of both ERS tandem coherence and JERS backscatter images and was developed using ground data, made available by the Russian Forestry Services. It is tested on SAR datasets of boreal forests in Siberia, a managed, temperate forest plantation in the United Kingdom and a semi-natural boreal forest at Siggefora in Sweden. Comparisons of the classified products, comprising three growing stock interval classes and one non-forest class are made with ground data. The results of this accuracy assessment exercise show that the algorithm yields, in all cases, overall classification accuracies of greater than 70%. A visual comparison is made of the algorithm performance over a tropical forest region of Brazil. The results indicate that the algorithm has the potential to retrieve growing stock volume estimates in forest ecosystems throughout the globe.

Acknowledgments

This study was funded primarily by the Environment and Climate Program of the European Union (ENV4-CT98-0743). Data was generously supplied by ESA (through the third ERS announcement of opportunity—AO3-120) and NASDA (through the global boreal forest monitoring initiative—GBFM, with particular thanks to Dr Shimada). ERS SAR and interferometric processing was carried out by DLR-DFD, Germany and JERS SAR processing and interferometry by GAMMA, Switzerland (SIBERIA dataset). The satellite data were received by a mobile receiving station of the German Remote Sensing Data Centre of DLR (DFD), which was deployed for this purpose in Ulaanbaatar, Mongolia. ERS data for Rondonia were supplied under ESA project AO3-178 and for Thetford under project AOT-UK316. JERS data for Rondonia were supplied through NASDA GRFM and pilot programmes. The processing of Rondonia data set was undertaken at GAMMA, Switzerland.

We are indebted to all colleagues who worked with us on the SIBERIA project including those from the following institutions: Friedrich Schiller Universität, Jena, Germany [Christiane Schmullius]; Deutsches Zentrum fuer Luft- und Raumfahrt, Institut fur Hockfrequenztechnik (DLR-HF), Germany [Jan Vietmeier, Andrea Holz]; Deutsches Zentrum fuer Luft- und Raumfahrt, Deutschhes Fernerkundungsdatenzentrum (DLR-DFD); Germany [Achim Roth, Ursula Marschalk]; International Institue for Applied Systems Analysis (IIASA), Austria [Michael Gluck, Anatoly Shvidenko, Sten Nilsson, Alf Oeskog]; Centre d'Etudes Spatiales de la Biosphere (CESBIO), France [Thuy le Toan, Malcolm Davidson]; Sheffield Centre for Earth Observation Science (SCEOS), UK [Shaun Quegan, Joing Jiong Yu]; Centre for Ecology and Hydrology, Monk's Wood (CEH), Natural Environment Research Council (NERC), UK [David Gaveau, Steve Plummer]; VTT Technical Research Centre, Finland [Yrjo Rauste]; Satellus AB, Kiruna, Sweden [Marianne Orrmalm, Roland Utsi, Hans Jonsson, Torbjorn Westin]; GAMMA, Switzerland [Urs Wegmüller, Andreas Wiesmann]; East Siberian State Forest Inventory and Planning Institute, Russia [Victor Skudon]; V.N. Sukachev Institute of Forest, Russian Academy of Sciences, Russia [Vladimir Sokolov]; Irkutsk Forestry Board, Russia [Leonid Vaschuk]; V.V. Dokuchjaev Soil Institute of Moscow, Russia [Vjacheslav Rozhkov]. Adrian Luckman is supported through the Natural Environment Research Council (NERC) Earth Observation Science Initiative (EOSI) program.