Estimating Forest Aboveground Biomass Combining Pléiades Satellite Imagery and Field Inventory Data in the Peak–Cluster Karst Region of Southwestern China

Abstract

The mountainous region of southwest China has the largest karst geomorphology in China and in the world. Quantifying the forest aboveground biomass in this karst region is of great significance for the investigation of carbon storage and carbon cycling in terrestrial ecosystems. In this study, the actual measured aboveground biomass was calculated based on the allometric functions of 106 quadrats from 2012 to 2015. A backpropagation artificial neural network (BPANN) inversion model was constructed by combining very high-resolution satellite imagery, field inventory data, and land use/land cover data to estimate the forest aboveground biomass in the Banzhai watershed, a typical peak–cluster karst basin in southern Guizhou Province. We used 70% of the actual measured aboveground biomass for training the BPANN model, 20% for accuracy verification, and 10% to prevent overtraining. The results show that the absolute root mean square error of the BPANN model was 11.80 t/ha, which accounted for 9.92% of the mean value of aboveground biomass. Based on the BPANN inversion model, the average value of the forests’ aboveground biomass was 135.63 t/ha. The results showed that our study presented a quick, easy, and relatively high-precision method for estimating forest aboveground biomass in the Banzhai watershed. This indicates that the Pléiades image-based BPANN model displayed satisfactory results for estimating the forests’ aboveground biomass in a typical peak–cluster karst basin. This method can be applied to the estimation of forest AGB in the karst mountainous areas of southwest China.