Estimation of aboveground biomass in a temperate forest using texture information from WorldView-2

Abstract

The effect of forest biomass on carbon cycles has long been recognized. Therefore, an accurate assessment of forest biomassis required to understand ecosystem changes. This research uses vegetation indices and textural indices based on high spatial resolution Worldview-2 multispectral imagery to establish their relationship with forest AGB (Above Ground Biomass) and assess the accuracy of the estimation model. This research also explores the capability of spectral and textural information for AGB assessment at the Liang Shui National Nature Reserve, Northeast China. Remote sensing vegetation and texture indices were derived from high spatial resolution Worldview-2 multispectral data. We applied three different algorithms to extract the texture indices from the Worldview-2 data, including Gray Level Cooccurrence Matrix, Gray Level Difference Vector, and Sum and Difference Histograms. Six vegetation indices, namely, RVI, DVI, NDVI, EVI, SAVI, and MSAVI, were computed. The relationship among the above mentioned indices and 74 field measurements was established. However, the over fitting problems for the training regression model could occur due to the many input independent variables (i.e., vegetation indices and texture indices), which could decrease the robustness of the regression model. The random forest algorithm could avoid overfitting through the training process, so it was utilized to perform feature selection. Several optimal variables were selected to conduct the regression analysis. The support vector regression method was implemented to train and validate the AGB models. Results show that variables selection could better interpret forest AGB and obtain accurate predicted results. Comparisons between the two estimation models were made. The first model only applied vegetation indices, whereas the other model integrated vegetation and texture indices. The results also show that the accuracy of the vegetation indices model was lower than the vegetation + textural indices model (integrated vegetation indices with texture indices) at R2=0.69, RMSE=61.13 t/ha and R2=0.85, RMSE=42.30 t/ha, respectively. This research confirms that textural information could improve the accuracy of forest AGB estimation to a certain extent.