Estimation of tropical forest structure from SPOT-5 satellite images

Abstract

Predictions of tropical forest structure at the landscape level still present relatively high levels of uncertainty. In this study we explore the capabilities of high-resolution Satellite Pour l'Observation de la Terre (SPOT)-5 XS images to estimate basal area, tree volume and tree biomass of a tropical rainforest region in Chiapas, Mexico. SPOT-5 satellite images and forest inventory data from 87 sites were used to establish a multiple linear regression model. The 87 0.1-ha plots covered a wide range of forest structures, including mature forest, with values from 74.7 to 607.1 t ha−1. Spectral bands, image transformations and texture variables were explored as independent variables of a multiple linear regression model. The R2s of the final models were 0.58 for basal area, 0.70 for canopy height, 0.73 for bole volume, and 0.71 for biomass. A leave-one-out cross-validation produced a root mean square. error (RMSE) of 5.02 m2 ha−1 (relative RMSE of 22.8%) for basal area; 3.22 m (16.1%) for canopy height; 69.08 m3 ha−1 (30.7%) for timber volume, and 59.3 t ha−1 (21.2%) for biomass. In particular, the texture variable ‘variance of near-infrared’ turned out to be an excellent predictor for forest structure variables.