上海国土资源2024，Vol.45Issue(3)：130-136,7.DOI:10.3969/j.issn.2095-1329.2024.03.021
基于CA-Markov-PLUS模型的佛山市土地利用及生态系统服务分析与预测
Analysis and prediction of land use and ecosystem services in Foshan City based on CA-Markov-PLUS model
摘要
Abstract
Analyzing and predicting ecosystem services not only helps to understand the health of regional ecosystems,but also has important implications for formulating effective ecological protection and restoration strategies.Based on the CA-Markov-PLUS model,this paper analyzes the changes of land use and ecosystem services from 2010 to 2020,and discusses the temporal and spatial changes of ecosystem services in 2030.The results show that:(1)From 2010 to 2020,the area of cultivated land and forest land in Foshan City decreased significantly,and the net inflow area reached 15457.95hm2,especially in Chancheng District and Nanhai District.(2)Comparing the ecosystem service index of Foshan in 2010 and 2020,it was found that habitat quality,carbon storage,soil conservation and water production services all decreased to varying degrees,with soil conservation being the most serious.In terms of space,Chancheng District and Nanhai District had the largest decline,followed by the northern part of Sanshui District and Shunde District.(3)Based on the CA-Markov-PLUS model,the predicted ecosystem services in 2030 showed that compared with 2020,the area of cultivated land and water area increased,and the growth rate of construction land area decreased,and the predicted decline rate of habitat quality and carbon storage decreased,while the soil remained basically unchanged,and the average water yield increased slightly.Based on the prediction results of land use and ecosystem services,this study can provide a reference for Foshan to enhance the future land resource management capacity and improve the contribution of ecosystem services.关键词
土地利用变化/综合生态系统服务/CA-Markov-PLUS模型Key words
spatial pattern of land use/ecosystem services/CA-Markov-PLUS mode分类
管理科学引用本文复制引用
蒋成庆,张明,多玲花,赵昱茜..基于CA-Markov-PLUS模型的佛山市土地利用及生态系统服务分析与预测[J].上海国土资源,2024,45(3):130-136,7.基金项目
江西省社会科学基金项目(23GL34) （23GL34）
