SIBERIA-results from the IGBP boreal forest transect

Schmullius, C.; Holz, Andrés; Vietmeier, J.

doi:10.1109/igarss.1999.775049

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…Results of this exploratory analysis were, e.g. reported in Gaveau, Balzter, and Plummer (2000), Quegan, Yu, Balzter, and LeToan (2000), Schmullius, Holz, and Vietmeier (1999), Tansey, Luckman, and Schmullius (1999), . An important finding was that the emphasis should be put on growing stock volume because (a) it is the most valuable parameter in national forest inventories and for planning forest enterprise operations; and (b) compared to other parameters collected by the Russian forest inventory, growing stock volume appears to be the one most directly related to the radar parameters.…”

Section: Growing Stock Volumementioning

confidence: 86%

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

Wagner

Luckman

Vietmeier

et al. 2003

Remote Sensing of Environment

127

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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

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Section: Growing Stock Volumementioning

confidence: 86%

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

Wagner

Luckman

Vietmeier

et al. 2003

Remote Sensing of Environment

127

View full text Add to dashboard Cite

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“…Synthetic aperture radar (SAR), instead, can ensure data acquisition owing to the independence on weather conditions and sun angle (Henderson & Lewis, 1998). Satellite SAR data have been used for forest mapping at regional scale in boreal forest (Schmullius, et al,1999;Baker, et al, 2001;Wagner, et al, 2003;Rosenqvist, et al, 2004;Rauste, 2005), tropical forest (De Grandi, et al, 2000), and globally regions (Shimada, et al, 2011). For the monitoring of forest of Northeast China, , proposed a forest growing stock volume estimation method based on European Remote Sensing ERS-1/2 tandem coherence and produced a forest growing stock volume map for Northeast China, using data acquired during 1995 and 1996.…”

Section: Introductionmentioning

confidence: 99%

Regional forest and non-forest mapping using Envisat ASAR data

2012

National Remote Sensing Bulletin

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Envisat Advanced Synthetic Aperture Radar (ASAR) dual-polarization data are shown to be effective for regional forest monitoring. To this scope, an automatic SAR image preprocessing procedure was developed using SRTM DEM and Landsat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-forest classif ication method was then proposed based on the HH (horizontal transmit and horizontal receive) to HV (horizontal transmit and vertical receive) polarization intensity ratio and HV images of ASAR data at single acquisition time in winter. The developed method was applied to forest and non-forest mapping in Northeast China. The overall accuracy, the user's accuracy and the producer's accuracy of forest were 83.7%, 85.6% and 75.7%, respectively. These results indicate that the proposed method is promising for operational forest mapping at regional scale.

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