Estimate tropical forest stand volume using SPOT 5 Satellite Image

Abstract

Abstract This study tested solutions aiming to improve the estimates of tropical forest stand volume (SV) for the large area using SPOT 5 data and field data. Firstly, forests were stratified into four forest strata using a hybrid classification of ISODATA (Iterative Self - Organization Data Analysis) unsupervised, and Maximum likelihood supervised (MLC) based on SPOT 5 image and field data. A set of 111 sample plots was distributed in these four forest strata, which represented disturbed forests under different levels by human-induced. Within the sample plots, diameter at breast height (DBH) and tree height (H) were measured to calculate standing volume using the SV equation from the previous study for the area. The method of k-NN (k-Nearest Neighbour) was applied to estimate the stand volume for different datasets of SPOT 5 bands, normalized difference vegetation index (NDVI), and combination of SPOT 5 bands and NDVI. The estimates were separately tested for the whole area and each forest stratum. Leave one out cross-validation method was employed to validate the quality of the predictions. The results indicated the accuracy of the estimate was significantly improved when applying for each stratum comparing to for the whole area with both SPOT 5 and NDVI data. The lower errors were found in the forest strata that are fewer disturbances than the heavily degraded stratum. Among the image data, the estimates were based on the NDVI give a lower accuracy compared to others.