Canadian Forest Service Publications

A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. 2006. Labrecque, S.; Fournier, R.A.; Luther, J.E.; Piercey, D.E. Forest Ecology and Management 226: 129-144.

Year: 2006

Available from: Atlantic Forestry Centre

Catalog ID: 26213

Language: English

CFS Availability: PDF (request by e-mail)


Spatial measures of forest biomass are important to implment 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 methods 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 using biomass tables applied on the classified TM image (LCC) provided the greatest RMSE and bias, but may be suitable for applications that do not require a high level of precision. The BioCLUST and LCC methods provided practical advantages for the type of data sets available. Overall, the choice of a method rests on both the availability of data sets and the level of precision of the results required.

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