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

Abstract

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

Effect of the Grain‐for‐Green Program on forest fragmentation

“…Spatial regression models have been widely used in economic, social, and environmental sciences (Lončar et al, 2019; Resende et al, 2016) but are rarely used in forestry‐related fields. A few scholars have attempted to reveal the relationship between different research objects using a spatial regression model in forestry research (Sannigrahi et al, 2020; Shirvani et al, 2020; Yu et al, 2022). For example, Shirvani et al (2020) explored the relationship between forest changes and road expansion and Yu et al (2022) analyzed the effects of driving factors on forest aboveground biomass in China's Loess Plateau using spatial regression models.…”

“…A few scholars have attempted to reveal the relationship between different research objects using a spatial regression model in forestry research (Sannigrahi et al, 2020; Shirvani et al, 2020; Yu et al, 2022). For example, Shirvani et al (2020) explored the relationship between forest changes and road expansion and Yu et al (2022) analyzed the effects of driving factors on forest aboveground biomass in China's Loess Plateau using spatial regression models. However, a panel data model had not been previously used to evaluate the relationship between forest fragmentation and forest cover changes.…”

“…Traditional regression methods cannot take into account the spatial autocorrelation caused by the spatial independence of the dependent variable, thus invalidating the model and producing misleading results (Yin et al, 2018). Spatial regression is a promising method for solving spatial dependence problems (Gu & You, 2022; Yu et al, 2022; Yuan et al, 2018). In this study, we chose different regression models: OLS, GWR, spatial lag model (SLM), and Spatial error model (SEM), and then the best performance regression model results to explain the spatial relationship between FAD and forest cover changes. Ordinary least squares (OLS) …”

“…Spatial regression models have been widely used in economic, social, and environmental sciences (Lončar et al, 2019;Resende et al, 2016) but are rarely used in forestry-related fields. A few scholars have attempted to reveal the relationship between different research objects using a spatial regression model in forestry research (Sannigrahi et al, 2020;Shirvani et al, 2020;Yu et al, 2022).…”

“…For example, Shirvani et al (2020) explored the relationship between forest changes and road expansion and Yu et al (2022) analyzed the effects of driving factors on forest aboveground biomass in China's Loess Plateau using spatial regression models. However, a panel data model had not been previously used to evaluate the relationship between forest fragmentation and forest cover changes.…”