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Research article - Peer-reviewed, 2009

Comparison of Resourcesat-1 AWiFS and SPOT-5 data over managed boreal forest stands

Reese, Heather; Nilsson, Mats; Olsson, Håkan

Abstract

In this study, the utility of Advanced Wide Field Sensor (AWiFS) data in relation to stem volume estimation for managed boreal forest stands was investigated. Multiple linear regressions were used to predict stem volume (m(3) ha(-1)) with standwise mean spectral values as the independent variables. For comparison, two SPOT-5 images were used, one with nearly simultaneous acquisition. The adjusted coefficient of determination (R(adj)(2)) using AWiFS data to predict stem volume was 0.573 (SE = 56.9%) while SPOT had an R(adj)(2) of 0.598 (SE = 55.2%). All bands were negatively correlated, with the shortwave infrared (SWIR) band having the single strongest correlation with stem volume. When stem volume was predicted based on stand size, AWiFS and SPOT produced R(adj)(2) values of 0.310 and 0.293, respectively, for stands less than 2 ha in size. Predictive ability increased with stand size, with the highest R(adj)(2) at 20 ha (R(adj)(2) = 0.677 AWiFS, R(adj)(2) = 0.692 SPOT). For stands of 20 ha and larger, the correlation between stem volume and near-infrared (NIR) reflectance increased while decreasing for the visible bands. The explanation behind the trends observed may be due to the management practices in the area. The best two-band predictor of stem volume was the NIR and red band combination for AWiFS, and the NIR and SWIR bands for SPOT. Discriminant analysis of basic forest types showed similar results for AWiFS (65.6% correct) and SPOT (66.4% correct).

Published in

International Journal of Remote Sensing
2009, volume: 30, number: 19, pages: 4957-4978
Publisher: TAYLOR & FRANCIS LTD

Authors' information

Reese, Heather
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management

UKÄ Subject classification

Forest Science
Environmental Sciences related to Agriculture and Land-use

Publication Identifiers

DOI: https://doi.org/10.1080/01431160903022985

URI (permanent link to this page)

https://res.slu.se/id/publ/49152