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A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundla.pdf

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A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundla

A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland S. Labrecque a, R.A. Fournier a,*, J.E. Luther b, D. Piercey b a Centre d’Applications et de Recherches en Te?le?de?tection (CARTEL), Universite? de Sherbrooke, 2500 boul. de l’Universite?, Sherbrooke, Que?., Canada J1K 2R1 b Natural Resources Canada, Canadian Forest Service, Atlantic Forestry Centre, P.O. Box 960, Corner Brook, Newfoundland and Labrador, Canada A2H 6J3 Received 6 July 2005; received in revised form 20 January 2006; accepted 21 January 2006 Abstract Spatial measures of forest biomass are important to implement sustainable forest management, monitor global change, and model forest productivity. Several methods for estimating forest biomass by remote sensing have been developed, but their comparative advantages have not been evaluated for large areas in Canada. This study compares four methods to map forest biomass on an extended pilot region (20,000 km2) located in western Newfoundland. The methods include: (i) Direct Radiometric Relationships (DRR), (ii) k-Nearest Neighbors (k-NN), (iii) Land Cover Classification (LCC), and (iv) Biomass from Cluster Labeling Using Structure and Type (BioCLUST). The results of each method were evaluated using an independent set of ground survey plots and compared with a baseline biomass map generated from biomass tables applied to forest inventory stand maps. Considering the root mean square error (RMSE) assessed with the inventory plots, the DRR, k-NN, and BioCLUST methods provided similar results, with average RMSE values of 59, 59, and 58 t/ha, respectively. Bias values were lowest for the k-NN method followed by DRR, BioCLUST, and LCC (6, 8, 17, and 42 t/ha, respectively). Assessed with the baseline map, the BioCLUST method produced the lowest RMSE (41 t/ha) and bias (4 t/ha) followed by the DRR and k-NN methods, with RMSE values of 47 and 54 t/ha and bias values of 9 and 23 t/ha, respectively. The method usin