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Abstract: ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data 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 model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~1.5millionkm2) to map four stem volume classes (0–20, 20–50, 50–80, and >80m3/ha). [ABSTRACT FROM AUTHOR]

Fire activity in boreal forests will substantially increase with prolonged growing seasons under a warming climate. This trend poses challenges to managing fires in boreal forest landscapes. A fire environment zone map offers a basis for evaluating these fire-related problems and designing more effective fire management plans to improve the allocation of management resources across a landscape. Toward that goal, we identified three fire environment zones across boreal forest landscapes in northeastern China using analytical methods to identify spatial clustering of the environmental variables of climate, vegetation, topography, and human activity. The three fire environment zones were found to be in strong agreement with the spatial distributions of the historical fire data (occurrence, size, and frequency) for 1966–2005. This paper discusses how the resulting fire environment zone map can be used to guide forest fire management and fire regime prediction. [ABSTRACT FROM AUTHOR]