Effects of Driving Factors on Forest Aboveground Biomass (AGB) in China’s Loess Plateau by Using Spatial Regression Models

Forests are the main body of carbon sequestration in terrestrial ecosystems and forest aboveground biomass (AGB) is an important manifestation sequestration. Reasonable accurate quantification relationship between AGB its driving factors great importance for increasing function forests. Remote sensing observations field measurements can be used to estimate large areas. To explore applicability panel data models factors, we compared results (spatial error model spatial lag model) with those geographically weighted regression (GWR) ordinary least squares (OLS) quantify factors. Furthermore, estimated tree height, diameter at breast canopy cover (CC) species diversity index (Shannon–Wiener index) Robinia pseudoacacia plantations Changwu on Loess Plateau using remote images by a random soil organic (SOC) contents laboratory kriging (OK) interpolation. We already height combined allometric growth equation. In this study, SOC OK interpolation, accuracy R2 values each layer were greater than 0.81. (DBH), CC, SW (TH) forest, 0.85, 0.82, 0.76 0.68, respectively. equation found that average was 55.80 t/ha. The OLS showed residuals exhibited obvious correlations rejected applications. GWR, SEM SLM analysis, best explaining also significantly positively correlated SW, 0–60 cm content (p < 0.05) negatively slope aspect 0.01). This study provides new idea studying basis practical management, biomass, giving full play role