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

Abstract

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 km 2 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 m 3 /ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m. D

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

“…Vegetation observed in remote-sensing data is usually described in terms of derived variables such as vegetation indices (normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI), etc.) (Tucker 1979;Tarpley 1991;Jackson and Huete 1991;Baret and Guyot 1991;Gupta 1993;Huete et al 1997), leaf area index (LAI) (Gupta, Prasad, and Vijayan 2000;Fensholt, Sandholt, and Rasmussen 2004;Casa and Jones 2005), tree cover density (Bai et al 2005;Yang, Weisberg, and Bristow 2012;Leinenkugel et al 2014, forthcoming), net primary productivity (NPP) and biomass (gC m −2 ) (Wagner et al 2003;Hese et al 2005;Lu 2006;Eisfelder, Kuenzer, and Dech 2011), canopy moisture (Brakke et al 1981), canopy height, expected crop yield (Birnie, Robertson, and Stove 1982;Hatfield 1983;Horie, Yajima, and Nakagawa 1992), measures of fragmentation and connectivity (Stenhouse 2004;Pueyo and Alados 2007;Briant, Gond, and Laurance 2010), or as detailed classification-derived map products breaking down vegetation distribution to the species level (Foody and Cutler 2006;Kutser and Jupp 2006;Pu and Landry 2012;Engler et al 2013). Vegetation height can also be derived from digital elevation model (DEM) data (Walker et al 2007).…”

Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Québec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data

“…Although such datasets can only be considered for comparison purposes as they do not qualify as reference sets for validation, they were of interest to benchmark the ASAR GSV and assess its overall reliability towards quantifying forest resources and carbon stocks in the boreal zone and to pinpoint areas of discrepancy. Table 2 provides an overview of the three different types of GSV datasets for the three study regions ( [6,7,10,29,39,[55][56][57]). If necessary, datasets were re-projected and resampled into the geographic projection used for the ASAR data.…”

“…A polygon corresponds to an area with similar forest properties in terms of tree species, age, productivity, GSV and local homogeneity. Such data were available from ten forest enterprises located in Irkutsk Oblast [29] (Tables 2 and 3) and originated from forest surveys carried out according to the Russian Forest Inventory Manual [58]. Forest stand boundaries were based on manual interpretation of aerial photographs.…”

“…LiDAR (Light Detection And Ranging) point-wise measurements have been implemented in combination with either in situ or two-dimensional data from optical imagery to provide spatially explicit estimates of forest canopy height [16,17], above-ground biomass [18][19][20][21][22], and above-ground carbon [23] at regional and global scales. Mapping efforts based on spaceborne synthetic aperture radar (SAR) are instead limited to investigations based on images acquired between 1994 and 2000 to obtain estimates of above-ground biomass in the boreal zone of Canada [24] and the Amazon basin [25], canopy height for the conterminous US [26], growing stock volume in Central Siberia [27][28][29] and Northeast China [30], and forest age for UK [31].…”

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

“…In general, non-forest areas (e.g., urban areas, bare soil), typically stable over time, have high coherence value. Since coherence is typically lower over forests (through an increase of volume and corresponding temporal decorrelation), interferometric coherence can be used for the mapping of forest/non-forest areas [11] as well as for AGB assessment [12,13]. Limitations of radar data for AGB estimation are saturation as well as strong dependence on environmental conditions (e.g., rain fall, and soil moisture conditions).…”