Changes in land use impact the landscape pattern and habitat quality (LPHQ) of ecosystems. Since 1999, the Grain‐for‐Green Program (GGP) has brought dramatic changes in land use in Western China. Although many studies have reported positive contributions of the GGP to sediment reduction, the effects of the GGP on LPHQ remain unclear. This study aimed to assess the spatiotemporal characteristics of LPHQ in the Xiaolangdi Reservoir Area in China (XRAC) before and after GGP implementation (in 1990, 2000, 2010 and 2020) using the InVEST habitat quality model. The results showed that: (1) From 1990 to 2020, the main land use changes in the XRAC were a 34.97% increase in forestland and a 37.56% decrease in farmland. The landscape pattern showed decreased fragmentation and tended toward aggregation, and forestland became the dominant land use type. (2) Habitat quality in the XRAC improved from 0.63 to 0.91 between 1990 and 2020. High‐quality habitat (0.80–1) was observed in 88.24% of the XRAC in 2020. The spatial distribution of habitat degradation decreased overall, with some localized increases; areas with high levels of habitat degradation shrank and aggregated with the concentration of human activities. (3) The spatiotemporal evolution of habitat quality was influenced by both natural factors and human activities; the short‐term improvement in habitat quality in the XRAC was significantly related to the increase in forestland area, decrease in farmland area, and increase in vegetation coverage. Our results provide evidence for assessing the ecological benefit of the GGP in the Yellow River basin.
The soil and water conservation ratio (SWCR), which is a quantitative index for measuring the control degree of soil and water loss, is equal to the percentage of the land areas with a slight erosion intensity in the study area. The dynamic change in the SWCR reflects the dynamic process of realizing a specific soil and water conservation goal in a certain stage. The objectives of this study were to evaluate the change in the SWCR in the Guizhou Province in this century and to analyze its causes. The temporal and spatial variations of soil erosion intensity and SWCR were measured based on GIS technology and revised universal soil loss equation (RUSLE). The results showed that the spatial pattern of soil erosion intensity in the Guizhou Province was high in the west and low in the southeast, and that the soil erosion characteristics were obviously different between karst and non-karst areas. In the karst areas, the land with a moderate and above erosion intensity (>3 t hm−2 y−1 in the karst area; >25 t hm−2 y−1 in the non-karst area) accounted for 28.20–34.78% of the total area, while only accounting for 2.39–2.72% in the non-karst areas. From 2000 to 2019, the mean intensity of soil erosion decreased from 13.97 to 10.83 t hm−2 y−1, and the SWCR increased from 32.95% to 35.31%. According to the change in erosion intensity grades, 22.30% of the whole province’s erosion grade changed from high to low, especially in the west, with a high erosion intensity. Meanwhile, about 11.99% of the land in the central, eastern and southeastern regions, was where the erosion intensity showed a slight increase and the spatial distribution showed sporadic patch distribution characteristics, which may be related to an increase in infrastructure investment in the Guizhou Province in recent years. A large number of production and construction projects caused the destruction of surface vegetation and also caused patchy soil erosion. The spatial and temporal characteristics of the soil erosion and the SWCR in the Guizhou Province between 2000 and 2019 were mastered through this study, and our results provide an important basis for further scientific and reasonable soil and water conservation planning work.
The complex topography, severe surface fragmentation and landscape heterogeneity of the karst region of southwest China make it extremely difficult to extract information on rocky desertification in the region. In order to overcome the disadvantages of the surface parameter-based feature space approach, which is difficult to construct and apply, this study uses the reflectance of Landsat 8 Operational Land Imager (OLI) in the red (Red), near-infrared (NIR) and shortwave infrared (SWIR) bands as the feature variables, and establishes a two-dimensional SWIR-NIR, Red-NIR and SWIR-Red reflectance spectral feature space. The three models of perpendicular rocky desertification index 1 (PRDI1), perpendicular rocky desertification index 2 (PRDI2) and perpendicular rocky desertification index 3 (PRDI3) were also constructed based on the variation of the degree of rocky desertification in each spectral feature space. The accuracy of the rocky desertification extracted by these three index models was verified and compared with the karst rocky desertification index (KRDI) and rocky desertification difference index (RSDDI), which are constructed based on the surface parameter feature space. The results show that: (1) The waveband reflectance-based feature space model provides a new method for large-scale rocky desertification information extraction, characterized by easy data acquisition, simple index calculation and good stability, and is conducive to the monitoring and quantitative analysis of rocky desertification in karst areas. (2) The overall accuracy and Kappa coefficient of PRDI1 are 0.829 and 0.784, respectively, both higher than other index models, showing the best applicability, accuracy and effectiveness in rocky desertification information extraction. (3) According to the results extracted from PRDI1, the total area of rocky desertification in Huaxi District of Guizhou province is 320.44 km2, with the more serious grades of rocky desertification, such as severe and moderate, mainly distributed in the southwestern, western and southeastern areas of Huaxi District. This study provides important information on the total area and spatial distribution of different degrees of rocky desertification in the study area, and these results can be used to support the local government’s ecological and environmental management decisions. The method proposed in this study is a scientific and necessary complement to the characteristic spatial methods based on different surface parameters, and can provide important methodological support for the rapid and efficient monitoring of karstic rocky desertification over large areas.
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