Retrieval of forest above-ground biomass using multi-source data in Genhe, Inner Mongolia

Forest is an important component of terrestrial ecosystems; therefore, it is necessary to estimate the forest above-ground biomass (AGB) accurately in order to reduce the uncertainty of the carbon stock in forest ecosystem. We estimated forest AGB of the Genhe forest reserve which is located in Inner Mongolia using Landsat 8 OLI image, P-band PolSAR image and ASTER GDEM product based on the multiple linear stepwise regression model and k-nearest neighbors (k-NN) model. In particular, the Random Forest (RF) was applied to select the features for constructing the optimized k-NN. The results estimated by single-sensor and multi-sensor data were compared by the accuracy indicators of R2 and RMSE, aiming to understand the effects of data source on the estimation of forest AGB. Then regional forest AGB over the Genhe forest reserve was estimated by the optimal method. Validated against the field forest measurement, the estimation of forest AGB obtained from multi-sensor outperformed those obtained from single-sensor based on the multiple linear stepwise regression model and k-nearest neighbors (k-NN) model; the estimation of forest AGB obtained from k-NN (R2=0.65, RMSE=17.49 t/ha) agreed better with the field forest measurement than that obtained from the multiple linear stepwise regression model (R2=0.36, RMSE=22.08 t/ha) using multi-sensor data. The ability of estimating forest AGB using remote-sensing-based method was improved attributed to the integration of the advantages of multi sensor; The k-NN model is a more appropriate method to estimate the forest AGB over regional area than the multiple linear stepwise regression model, because the k-NN model focuses on the nonlinear dependence between forest parameters and spectral values, and it can avoid the problem of over learning and sample imbalances.