No 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.

Fragmentation profoundly alters the character and pattern of forests, influencing biodiversity in the context of global climate change. Forest area density (FAD) is currently considered the most effective method for characterizing forest fragmentation. Therefore, reasonable and accurate quantification of FAD and its relationship with forest cover changes are essential for evaluating forest fragmentation in the Loess Plateau under the implementation of the Grain‐for‐Green Program (GGP). Based on land use/cover (LULC) in 1998–2022, we integrated the Random forest (RF) algorithm and the conversion of land use and its effects at small regional extent (CLUE‐S) model to derive LULC in 2030. Subsequently, we proposed a multi‐scale window threshold to evaluate forest fragmentation by constructing nonlinear models. Furthermore, we explored the relationship between FAD and forest cover changes by spatial regression models. The results are as follows: (1) in 1998–2030, the main conversion of LULC was of the cropland to broad‐leaf forests and grassland. (2) The 43 × 43 window served as the optimal multi‐scale threshold to assess forest fragmentation. (3) The GGP slowed down forest fragmentation. (4) The degree of fragmentation in broad‐leaf forests was higher than that in coniferous forests. (5) Spatial error model (SEM) was the most appropriate for establishing the spatial relationship between forest fragmentation and forest cover changes. In conclusion, since the implementation of the GGP, forest cover changes had a significant impact on forest fragmentation and its multi‐scale threshold. This research provides a basis for the reasonable configuration of land resources in the Loess Plateau and novel methods for studying forest cover changes and fragmentation.