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Evaluation and Prediction of Ecological Benefits in Song-Liao River Basin.
- Source :
-
Remote Sensing . Nov2024, Vol. 16 Issue 21, p3993. 27p. - Publication Year :
- 2024
-
Abstract
- The evaluation and prediction of ecological benefits are significant for regional resource development planning and path designing. This study established a novel ecological benefits evaluation system by integrating macro-ecosystem structure, Ecosystem service index (ESI), and ecological quality index (EQI). Based on this system, this study evaluated the spatiotemporal characteristics and changing trend of ecological benefits in Song-Liao River Basin (SRB) from 1990 to 2020. The results show that the macro-ecosystem structure in Song-Liao River Basin remains stable, and the ecosystem service and ecological quality generally show a trend of first decline and then increase. The average growth rates of ESI and EQI were 0.6% and 0.4%, respectively, during 1990–2020. The ecological benefits of natural areas with widely distributed forest areas are higher, while those of areas with frequent human activities are lower. The prediction model based on machine learning has achieved good modeling effect, which shows that the ecological benefits of SRB will be on the rise in the future. Based on the evaluation results, we suggest that more environmental protection policies on the basis of maintaining the existing development plan should be promoted to reduce the contradiction between human and nature in the development process. For the abundant natural forests in this area, reasonable forest management should be carried out to improve the carbon-fixation capacity of vegetation, and a Methodology for managing natural forests should be constructed to make full use of the existing carbon sinks. For the new afforestation project being promoted, carbon-sink afforestation projects of CCER (Chinese Certified Emission Reduction) should be promoted to realize the synergy between economic development and environmental protection. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 16
- Issue :
- 21
- Database :
- Academic Search Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 180782503
- Full Text :
- https://doi.org/10.3390/rs16213993