Affiliation:
1. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
2. School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
3. The Fourth Surveying and Mapping Institute of Anhui Province, Hefei 230031, China
4. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
5. Surveying and Mapping Institute, Lands and Resource Department of Guangdong Province, Guangzhou 510670, China
Abstract
The main challenge in protecting ecosystems and improving the supply of ecosystem services is to quantify the ecological services value (ESV). However, the detailed spatiotemporal changes, sensitivity, spatial autocorrelation, and driving mechanisms of ESV are not clear in rapidly developing regions, particularly subsidence, floods, landslides, and the rapid urban development of Anhui province, China. In this paper, the ecological service value of Anhui Province in the past 30 years was calculated using the improved equivalent factor assessment method from satellite remote sensing such as Landsat. The spatiotemporal evolution characteristics of ESV were analyzed and the driving mechanism of ESV changes was studied using Geodetector. Finally, The GeoSOS-FLUS model was selected to predict the ecosystem service value until 2030 with three scenarios: business as usual (BAU), ecological protection (EP), and cultivated land protection (CLP). The main results were obtained: (1) the ESV in Anhui Province continued to decrease by 2.045 billion yuan (−6.03%) from 1990 to 2020. The top two contributors were the forest land, followed by water area. (2) The global Moran’s I of ESV at the landform subdivision, county, town, and grid scales in Anhui Province were −0.157, 0.321, 0.357 and 0.759, respectively. (3) The order of influence degree of driving factors was: precipitation (F4), distance to intercity road (F9), net primary productivity, NPP (F6), distance to urban road (F8), population (F13), temperature (F5), aspect (F3), distance to settlement (F11), slope (F2), elevation (F1), GDP (F14), distance to water (F12), distance to railway (F10), and soil erosion (F7). (4) In 2030, the simulated ESV under the three scenarios will decrease to varying degrees. Compared with 2020, the ESV of the three scenarios will decrease successively as follows: BAU (−1.358 billion yuan), EP (−0.248 billion yuan), and CLP (−1.139 billion yuan).
Funder
Jiangsu Marine Science and Technology Innovation Project
Subject
General Earth and Planetary Sciences