Abstract:
This study aims to investigate the impacts of climate change, terrain, and human activities on ecological quality in Hebei Province. Based on the long-time-series MODIS data from 2003 to 2023, this study monitored the ecological quality in Hebei Province using the remote sensing ecological index (RSEI). Employing an optimal parameters-based geographical detector (OPGD), this study analyzed the driving effects of terrain, climate, and land use factors on ecological quality. The results indicate that from 2003 to 2023, the ecological quality in Hebei Province was moderate (39.21%) and poor (32.51%) primarily, presenting a spatial pattern characterized by higher quality in the northeastern and central parts and lower quality in the northwest and southeast. The ecological quality in the province was improved overall during the period, with the areas with good and fair ecological quality exhibiting average annual increases of 279 km
2 and 1 030 km
2, respectively. Areas with significantly improved ecological quality accounted for 42.9%, consisting primarily of forests and grasslands in the Taihang and Yanshan mountains, as well as cultivated land in southeastern Hebei. Areas with significantly degraded ecological quality accounted for merely 4.9%, concentrated in the Zhangbei Grassland and the urban expansion zones of Shijiazhuang and Baoding. Primary factors influencing the ecological quality included land-use type, slope, and elevation. The influence of climatic factors on ecological quality decreased in the order of sunshine, precipitation, and temperature. Interaction detection revealed that the interactions between various climatic factors and those between climatic factors and terrain all exhibited nonlinear enhancement effects. Compared to cultivated land and urban areas, grassland ecosystems demonstrated significantly higher sensitivity to climate change. The results of this study can provide a scientific basis for formulating regional ecosystem management measures against the backdrop of climate change.