Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data

Abstract

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI_0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R²). A forest AGB map of the study area was generated using the optimal model.

Keywords:

• Aboveground biomass
• high accuracy surface modelling
• mapping

Additional information

Funding

This research was funded by the National Key Research and Development Program of China (Grant No. 2016YFA0600204), National Natural Science Foundation of China (Grant No. 41501428, 41421001, 41590844，41371400), and Natural Science Foundation of Shandong Province, China (Grant No. ZR2017BD010).