Abstract
Otindag Sandy Land in China is an important ecological barrier to Beijing; the changes in its ecological quality are major concerns for sustainable development and planning of this area. Based on principal component analysis and path analysis, we first generated a modified remote sensing ecological index (MRSEI) coupled with satellite and ground observational data during 2001–2020 that integrated four local indicators (greenness, wetness, and heatness that reflect vegetation status, water, and heat conditions, respectively, as well as soil erosion). Then, we assessed the ecological quality in Otindag Sandy Land during 2001–2020 based on the MRSEI at different time scales (i.e., the whole year, growing season, and non-growing season). MRSEI generally increased with an upward rate of 0.006/a during 2001–2020, with clear seasonal and spatial variations. Ecological quality was significantly improved in most regions of Otindag Sandy Land but degraded in the southern part. Regions with ecological degradation expanded to 18.64％ of the total area in the non-growing season. The area with the worst grade of MRSEI shrunk by 15.83％ of the total area from 2001 to 2020, while the area with the best grade of MRSEI increased by 9.77％ of the total area. The temporal heterogeneity of ecological conditions indicated that the improvement process of ecological quality in the growing season may be interrupted or deteriorated in the following non-growing season. The implementation of ecological restoration measures in Otindag Sandy Land should not ignore the seasonal characteristics and spatial heterogeneity of local ecological quality. The results can explore the effectiveness of ecological restoration and provide scientific guides on sustainable development measures for drylands.
基金项目
Special Funds for Science and Technology Innovation on Carbon Peak Carbon Neutral of Jiangsu Province,China(BK20220017)
Innovation Development Project of China Meteorological Administration(CXFZ2023J073)
国家重点研发计划(2018YFC1506606)
国家科技期刊平台
NSTL
维普
万方数据