Global patterns of allometric model parameters prediction

Abstract

AbstractVariations in biomass-carbon of forest can substantially impact the prediction of global carbon dynamics. The allometric models currently used to estimate forest biomass face limitations, as model parameters can only be used for the specific species of confirmed sites. Here, we collected allometric models LnW = a + b*Ln(D) (n = 817) and LnW = a + b*Ln(D2H) (n = 612) worldwide and selected eight variables (e.g., mean annual temperature (MAT), mean annual precipitation (MAP), altitude, aspect, slope, soil organic carbon (SOC), clay, and soil type) to predict parameters a and b using Random Forest. LnW = a + b*Ln(D), drove mainly by climate factors, showed the parameter a range from − 5.16 to − 0.90 [VaR explained (model evaluation index): 66.21%], whereas parameter b ranges from 1.84 to 2.68 (VaR explained: 49.96%). Another model LnW = a + b*Ln(D2H), drove mainly by terrain factors, showed the parameter a range from − 5.45 to − 1.89 (VaR explained: 69.04%) and parameter b ranges from 0.43 to 1.93 (VaR explained: 69.53%). Furthermore, we captured actual biomass data of 249 sample trees at six sites for predicted parameters validation, showing the R2 (0.87) for LnW = a + b*Ln(D); R2 (0.93) for LnW = a + b*Ln(D2H), indicating a better result from LnW = a + b*Ln(D2H). Consequently, our results present four global maps of allometric model parameters distribution at 0.5° resolution and provides a framework for the assessment of forest biomass by validation.