Search Results (2)

Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 10⁸ m³, which closely aligns with contemporaneous statistical records. The model yielded an R² of 0.727, an RMSE of 537.566 m³, and a MAE of 239.767 m³, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R² from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area. Full article

This study focuses on the Kunming municipal area, located in the low-latitude plateau of southwestern China, utilizing remote sensing image data from four distinct periods between 2005 and 2020 to analyze changes in its green landscape patterns. Net primary productivity (NPP) was employed as a metric for carbon sequestration analysis to assess variations in NPP within the Kunming municipal area. Based on Pearson correlation analysis and the XGBoost-SHAP model, the correlations, important indicators, and responses of changes in the green space patterns of the Kunming municipal area to changes in carbon sequestration were analyzed and combined with policy and human factors. The findings indicate the following: (1) From 2005 to 2020, the area proportions of various green space types within the Kunming municipal area were ranked as follows: forest land > grassland > cultivated land > water bodies. (2) Between 2005 and 2015, the patch shapes of green spaces became increasingly complex, with heightened fragmentation among patches. After 2015, this complexity was reduced while connectivity continued to decline alongside an increase in the landscape heterogeneity and richness. (3) Over the period from 2005 to 2020, NPP values for cultivated land, forest land, and grassland exhibited a trend of decreasing and then increasing, reaching their lowest point in 2010. High NPP areas were predominantly found in regions characterized by a hilly topography, elevated altitudes, and substantial natural vegetation cover. (4) There was a significant correlation between green space pattern indices and NPP (p < 0.01), with SHDI, CONTAG, and DIVISION identified as three critical indices influencing NPP. The relationship between landscape patterns and carbon sequestration was most pronounced during the period from 2015 to 2020, followed by that from 2005 to 2010. Full article