Search Results (3)

Ecosystem services (ESs) are irreplaceable natural resources, and their value is closely related to global change and to human well-being. Research on ecosystem services value (ESV) and its influencing factors can help rationalize ecological regulatory policies, and is especially relevant in such an ecologically significant region as the Yellow River Basin (YRB). In this study, the ecological contribution model was used to measure the contribution of intrinsic land use change to ESV, the bivariate spatial autocorrelation model was applied to investigate the relationship between land use degree and ESV, and the geographical detector model (GDM) and geographically weighted regression (GWR) were applied to reveal the impact of natural and socio-economic factors on ESV. Results showed that: (1) The total ESV increased slightly, but there were notable changes in spatial patterns of ESV in the YRB. (2) Land use changes can directly lead to ESV restoration or degradation, among which, conversion from grassland to forest land and conversion from unused land to grassland are vital for ESV restoration in the YRB, while degradation of grassland is the key factor for ESV deterioration. (3) According to GDM, NDVI is the most influential factor affecting ESV spatial heterogeneity, and the combined effect of multiple factors can exacerbate ESV spatial heterogeneity. (4) GWR reveals that NDVI is always positively correlated with ESV, GDP is mainly positively correlated with ESV, and population density is mainly negatively correlated with ESV, while positive and negative correlation areas for other factors are roughly equal. The findings can provide theoretical support and scientific guidance for ecological regulation in the YRB. Full article

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP. Full article

Grassland coverage, aboveground net primary production (ANPP), and species composition are used as indicators of grassland degradation. However, soil salinization deficiency, which is also a factor of grassland degradation, is rarely used in grassland degradation assessment in semiarid regions. We assessed grassland degradation by its quality, quantity, and spatial pattern over semiarid west Jilin, China. Considering soil salinization in west Jilin, electrical conductivity (EC) is used as an index with ANPP to assess grassland degradation. First, the spatial distribution of the grassland was measured with information mined from multi-temporal remote sensing images using an object-based image analysis combined with classification and decision tree methods. Second, with 166 field samples, we utilized the random forest (RF) algorithm as the variable selection and regression method for predicting EC and ANPP. Finally, we created a new grassland degradation model (GDM) based on ANPP and EC. The results showed the R² (0.91) and RMSE (0.057 mS/cm) of the EC model were generally highest and lowest when the ntree was 400; the ANPP model was optimal (R² = 0.85 and RMSE = 15.81 gC/m²) when the ntree was 600. Grassland area of west Jilin was 609.67 × 10³ ha in 2017, there were 373.79 × 10³ ha of degraded grassland, with 210.47 × 10³ ha being intensively degraded. This paper surpasses past limitations of excessive reliance on vegetation index to construct a grassland degradation model which considers the characteristics of the study area and soil salinity. The results confirm the positive influence of the ecological conservation projects sponsored by the government. The research outcome could offer supporting data for decision making to help alleviate grassland degradation and promote the rehabilitation of grassland vegetation. Full article