Spatiotemporal Changes in Vegetation Cover during the Growing Season and Its Implications for Chinese Grain for Green Program in the Luo River Basin

• Published in: Forests Vol. 15; no. 9; p. 1649
• Main Authors: Qiao, Xuning, Zhang, Jing, Liu, Liang, Zhang, Jinyuan, Zhao, Tongqian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Agricultural production; Aquatic resources; Biodiversity; China; Climate change; constraint lines; Constraints; Ecosystems; Environmental conditions; Environmental management; Environmental restoration; Evapotranspiration; Fluctuations; Geography; Geomorphology; Geospatial data; GFGP; Growing season; High temperature; Human beings; Influence; Influence on nature; International organizations; Investigations; kNDVI; Land degradation; Low altitude; Luo River Basin; Normalized difference vegetative index; OPGD; Optimization; Population decline; Population density; Reforestation; Regions; Regression analysis; River basins; Rivers; Soil erosion; Spatiotemporal data; Sustainable development; Thresholds; Topography; Trends; Vegetation; Vegetation cover; Watershed management; China; Yellow River
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f15091649

The Grain for Green Program (GFGP) plays a critical role in enhancing watershed vegetation cover. Analyzing changes in vegetation cover provides significant practical value in guiding ecological conservation and restoration in vulnerable regions. This study utilizes MOD13Q1 NDVI data to construct the Kernel Normalized Difference Vegetation Index (kNDVI) and analyzes the spatiotemporal evolution and future trends of vegetation cover from 2000 to 2020, covering key periods of the GFGP. The study innovatively combines the optimal parameter geographic detector with constraint lines to comprehensively reveal the nonlinear constraints, intensities, and critical thresholds imposed by various driving factors on the kNDVI. The results indicate that the following: (1) The vegetation cover of the Luo River Basin increased significantly between 2000 and 2020, with a noticeable increase in the percentage of high-quality vegetation. Spatially, the vegetation cover followed a pattern of being “high in the southwest and low in the northeast”, with 73.69% of the region displaying improved vegetation conditions. Future vegetation degradation is predicted to threaten 59.40% of the region, showing a continuous or future declining trend. (2) The primary driving factors for changes in the vegetation cover are evapotranspiration, elevation, population density, and geomorphology type, with temperatures and GDP being secondary factors. Dual-factor enhancement or nonlinear enhancement was observed in interactions among the factors, with evapotranspiration and population density having the largest interaction (q = 0.76). (3) The effects of driving factors on vegetation exhibited various patterns, with thresholds existing for the hump-shaped and concave-waved types. The stability of the kNDVI in 40.23% of the areas showed moderate to high fluctuations, with the most significant fluctuations observed in low-altitude and high-temperature areas, as well as those impacted by dense human activities. (4) By overlaying the kNDVI classifications on the GFGP areas, priority reforestation areas totaling 68.27 km2 were identified. The findings can help decisionmakers optimize the next phase of the GFGP and in effective regional ecological management.