CREATION OF LARGE AREA FOREST BIOMASS MAPS FOR NE CHINA USING ERS- 1/2 TANDEM COHERENCE
Oliver Cartus(1), Maurizio Santoro(2), Christiane Schmullius(1) , Pang Yong(3) , Li Zengyuan(4)
(1) Department of Earth Observation, Friedrich-Schiller University Jena, Grietgasse 6, 07745 Jena (Germany), Email: [email protected], [email protected] (2)Gamma Remote Sensing, Worbstrasse 225, 3073 Gümligen (Switzerland), Email: [email protected] (4) Center for Ecological Applications of Lidar, Colorado State University, Department of Forest Sciences, 131 Forestry Building, Fort Collins, CO 80523 (USA), Email: [email protected] (5) Forest Remote Sensing Lab, Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Wanshoushan Hou, Haidian, Beijing, 1000091(China), Email: [email protected]
ABSTRACT Space Agency (ESA) and the Ministry of Science and Technology (MOST), P.R. China. After data selection ERS-1/2 tandem coherence is known to allow forest and processing the ERS tandem dataset available for stem volume mapping with reasonable accuracy. Large- forest mapping investigations consisted of 223 pairs. scale forest mapping, however, is hindered by the The dataset comprised data from all seasons and was variability of coherence with meteorological, acquired with a range of baselines between 0 and 400m. environmental and orbital acquisition conditions. The traditional way of stem volume retrieval is based on the In the SIBERIA project a simple empirical model was training of models, relating coherence to stem volume, used, which described the relationship between forest using forest inventory which is generally available for a stem volume and ERS tandem coherence primarily in few small test sites but not for large areas. In this paper terms of temporal decorrelation. While this can be a new approach is presented that allows the training of a considered fine for cases when a single data take is used semi-empirical model on a frame-by-frame basis using for the forest mapping, it is instead not sufficient for a the MODIS Vegetation Continuous Field product multi-seasonal and multi-baseline dataset, as the one without further need of ground data. A comparison of acquired over Northeast China. This suggested the use the new approach with the traditional regression-based of a more general and robust model, which also and ground-data dependent model training procedure considers volume decorrelation effects, i.e. the and the application of the new approach to a multi- Interferometric Water Cloud Model [6]. Because of the seasonal and multi-baseline ERS-1/2 tandem coherence large area and the multi-temporal characteristic of the dataset covering Northeast China are presented. ERS dataset, coherence strongly varies with
meteorological and environmental conditions both in 1. Introduction space and in time. The model therefore needs to be During the ERS tandem mission a large dataset has been trained on a frame by frame basis, assuming that inner- generated for interferometric applications, in many frame variations of conditions are negligible. Typically ways being yet unexploited concerning forest model training exploits ground reference data to applications. Typically ERS-1/2 tandem data has been determine the model parameter unknown a priori. Since considered for methodology development over small ground-truth data was generally not available for most test sites, mostly in the boreal zone [1,2]. The SIBERIA of the coherence images, a training method needed to be project (SAR Imaging for Boreal Ecology and Radar found that does not depend on ground data. The Interferometry Applications) demonstrated for a 1 Mio SIBERIA model contained only one unknown that km2 large area in Central Siberia that large-scale forest could be determined by using the image histogram mapping is possible using ERS tandem coherence [3-5]. solely. This approach could however not be transferred Still the data used within this project was restricted to 1- to the multi-seasonal and multi-baseline dataset 2 data takes of images acquired during one season (fall covering Northeast China. We therefore analysed the 1997). To further advance knowledge in the field of use of the freely available MODIS Vegetation forest mapping with SAR interferometric data and Continuous Field product, providing global estimates of provide a further demonstration of the retrieval percent tree canopy cover at 500 m pixel size. In this capability of the ERS-1/2 tandem coherence, not only in paper a novel method will be presented, that aims at the boreal forests, the full archive of ERS-1/2 tandem data determination of the unknown parameters of the for Northeast China has been considered in the Forest Interferometric Water Cloud Model. Dragon Project. This is part of the DRAGON Cooperation Programme initiated by the European
______Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)