Improving the accuracy of forest aboveground biomass using Landsat 8 OLI images by quantile regression neural network for Pinus densata forests in southwestern China

Abstract

It is a challenge to reduce the uncertainties of the underestimation and overestimation of forest aboveground biomass (AGB) which is common in optical remote sensing imagery. In this study, four models, namely, the linear stepwise regression (LSR), artificial neural network (ANN), quantile regression (QR), and quantile regression neural network (QRNN) were used to estimate Pinus densata forest AGB data by collecting 146 sample plots combined with Landsat 8-Operational Land Imager (OLI) images in Shangri-La City, Yunnan Province, southwestern China. The results showed that compared with the LSR, the R2 and mean square error (RMSE) of the ANN, QR, and QRNN had improved significantly. In particular, the QRNN was able to significantly improve the situation of overestimation and underestimation when we estimated forest biomass, which had the highest R2 (0.971) and lowest RMSE (9.791 Mg/ha) for the whole biomass segment. Meanwhile, through model validation, we found that the QRNN had the highest R2 (0.761) and lowest RMSE (6.486 Mg/ha) on the biomass segment of <40 Mg/ha. Furthermore, it had the highest R2 (0.904) and lowest RMSE (9.059 Mg/ha) on the biomass segment of >160 Mg/ha, which offered great potential for improving the estimation accuracy of the Pinus densata forest AGB. In conclusion, the QRNN, combining the advantages of QR and ANN, provides great potential for reducing the precision influence caused by the overestimation and underestimation in forest AGB estimation using optical remote sensing data.