Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter

Abstract

JERS‐1 L‐band SAR backscatter from test sites in Sweden, Finland and Siberia has been investigated to determine the accuracy level achievable in the boreal zone for stand‐wise forest stem volume retrieval using a model‐based approach. The extensive ground‐data and SAR imagery datasets available allowed analysis of the backscatter temporal dynamics. In dense forests the backscatter primarily depended on the frozen/unfrozen state of the canopy, showing a ∼4 dB difference. In sparse forests, the backscatter depended primarily on the dielectric properties of the forest floor, showing smaller differences throughout the year. Backscatter modelling as a function of stem volume was carried out by means of a simple L‐band Water Cloud related scattering model. At each test site, the model fitted the measurements used for training irrespective of the weather conditions. Of the three a priori unknown model parameters, the forest transmissivity coefficient was most affected by seasonal conditions and test site specific features (stand structure, forest management, etc.). Several factors determined the coefficient's estimate, namely weather conditions at acquisition, structural heterogeneities of the forest stands within a test site, forest management practice and ground data accuracy. Stem volume retrieval was strongly influenced by these factors. It performed best under unfrozen conditions and results were temporally consistent. Multi‐temporal combination of single‐image estimates eliminated outliers and slightly decreased the estimation error. Retrieved and measured stem volumes were in good agreement up to maximum levels in Sweden and Finland. For the intensively managed test site in Sweden a 25% relative rms error was obtained. Higher errors were achieved in the larger and more heterogeneous forest test sites in Siberia. Hence, L‐band backscatter can be considered a good candidate for stand‐wise stem volume retrieval in boreal forest, although the forest site conditions play a fundamental role for the final accuracy.

When the article was submitted L. Eriksson was at the Department of Geoinformatics and Remote Sensing, Friedrich‐Schiller University, D‐07743 Jena, Germany.

Acknowledgments

Professor A. Shvidenko, IIASA, is greatly acknowledged for providing information on Siberian forests. Dr J. Fransson and Professor H. Olsson, SLU, and Dr G. Smith, CUT, are acknowledged for the ground data and the area‐fill factor measurements from Kättböle. Forest inventory data and JERS images for Tuusula were collected during the European Union (EU) project EUFORA (ENVA‐CT96‐0303). Preparation of Bolshe‐Murtinsky ground database and processing of the images acquired in 1998 was done within the EU project SIBERIA (Contract number ENV4‐CT98‐0743). All other images from Bolshe‐Murtinsky and the images from Kättböle were acquired and provided by JAXA EORC within the framework of the Global Boreal Forest Mapping (GBFM) project. The support of Dr H. Jonsson, North Europe Science Node/National Land Survey of Sweden, and Dr Å. Rosenqvist, JAXA, is acknowledged, as well as the work of Dr M. Shimada, JAXA, for processing the Kättböle dataset. Digital elevation models for Kättböle and Tuusula were provided by the Swedish and Finnish Land Survey, respectively. For Bolshe‐Murtinsky, the interferometric DEM was generated during the SIBERIA project. Special thanks go to Dr A. Wiesmann and Dr U. Wegmüller, Gamma Remote Sensing, for processing the raw data from Bolshe‐Murtinsky and Tuusula. Geocoding was carried out using the DIAPASON software provided by CNES and the DIFF&GEO software package by Gamma Remote Sensing. Meteorological measurements were provided by the Swedish Meteorological and Hydrological Institute (SMHI), Finnish Meteorological Institute (FMI) and Deutscher Wetter Dienst – German Meteorological Institute (DWD).

Notes

When the article was submitted L. Eriksson was at the Department of Geoinformatics and Remote Sensing, Friedrich‐Schiller University, D‐07743 Jena, Germany.