[Inversion of aboveground biomass of Pinus tabuliformis plantations based on GF-2 data]

Abstract

Pinus tabuliformis is an important afforestation species in the Loess Plateau. Quick and accurate estimation of aboveground biomass (AGB) of P. tabuliformis plantations plays an important role in monitoring regional forest resources. Here, we used multi-spectral remote sensing data of domestic satellite GF-2 and the field data to estimate the aboveground biomass of P. tabuliformis plantations in Shibao forest farm of Huanglong Mountain in Shaanxi Province. We calculated eight texture features and five vegetation indices, and then built models based four texture windows (3×3, 5×5, 7×7, 9×9) by using five regression methods including normal regression, stepwise regression, ridge regression, Lasso regression and principal component regression. We used the leave-one-out cross validation (LOOCV) to test the estimation accuracy of each model. We found serious multi-collinearity relationships between the extracted remote sensing factors. Most of the remote sensing factors had significant correlations with aboveground biomass of P. tabuliformis plantations. GF-2 data could achieve higher accuracy in the inversion of aboveground biomass of P. tabuliformis plantations in the Shibao forest farm. The best estimation result was the principal component regression model using 9×9 texture window, and the worst one was the normal regression model using 3×3 texture window. Inversion of aboveground biomass of P. tabuliformis plantation using domestic high-resolution satellite imagery could provide a scientific basis for forestry biomass monitoring, resource management, and sustainable management in the forestry departments of northwest China.

Keywords: GF-2; Pinus tabuliformis plantation; aboveground biomass; leave-one-out cross validation; regression modeling.