Assessment of ecosystem services in new perspective: A comprehensive ecosystem service index (CESI) as a proxy to integrate multiple ecosystem services

生态系统服务 聚类分析 计算机科学 空间分析 服务(商务) 索引(排版) 生态系统 环境资源管理 环境科学 数据挖掘 统计 生态学 数学 业务 人工智能 生物 营销 万维网
作者
Linlin Wu,Fenglei Fan
出处
期刊:Ecological Indicators [Elsevier BV]
卷期号:138: 108800-108800 被引量:22
标识
DOI:10.1016/j.ecolind.2022.108800
摘要

A comprehensive understanding of multiple ecosystem services (ES) across the landscape is a key highlight of ecosystem management. There still remain a weakness to integrate multiple ESs for mirroring the capability of the ecosystems to deliver services in a broad perspective. Here, we proposed a comprehensive ecosystem services index (CESI) for integrating multiple ESs based on multiplicative method. Water supply service, carbon storage service and soil conservation service were assessed using multi-sources remote sensing datasets and InVEST models to build CESI. To examine the suitability and performance of CESI, we took the Guangdong-Hongkong-Macao Greater Bay Areas (GBA) as a test region. Meanwhile, we applied two previous methods used commonly for assessing multiple ESs: i) cumulative method and ii) maximum value composite method to construct comparative indexes for evaluating the performance of CESI with the help of spatial autocorrelation method and regression analysis method from the spatial pattern and numerical distribution perspective. The results showed that the CESI was well applied in GBA and got rid of the limitation of single ES. The spatial pattern of CESI was observed a high-value clustering in central areas and a low-value clustering in outer regions. A comparison of CESI and comparative indexes were illustrated on the results of linear regression and spatial autocorrelation, which showed a good linear correlation between each method and a similar spatial pattern in a high-value clustering, but had subtle different in low-value clustering. Specifically, the performance of CESI in expressing the low-value clustering was better than other indexes which could indicate the multiplicative method benefits to manifest the lagging in the provision of ESs better than cumulative method and maximum value composite method. This study could pave a way for a new approach to evaluate the provision of multiple ESs from the socio-ecological systems with a comprehensive perspective.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
fjkssadjk发布了新的文献求助10
1秒前
tiptip应助yuzi采纳,获得10
1秒前
wanci应助欢呼秋珊采纳,获得10
2秒前
2秒前
CodeCraft应助niania采纳,获得10
3秒前
YUANBIAO发布了新的文献求助10
4秒前
蒋俊杰发布了新的文献求助20
5秒前
Clarence完成签到,获得积分10
5秒前
咔咔咔发布了新的文献求助10
7秒前
7秒前
weijinfen发布了新的文献求助10
10秒前
fjkssadjk完成签到,获得积分10
10秒前
11秒前
12秒前
藏11完成签到 ,获得积分10
13秒前
不想上班完成签到,获得积分10
13秒前
摸鱼大王完成签到,获得积分10
15秒前
天天完成签到,获得积分10
16秒前
赵桓宁完成签到 ,获得积分10
19秒前
张旭强发布了新的文献求助10
19秒前
张欢馨应助咔咔咔采纳,获得10
20秒前
大个应助weijinfen采纳,获得10
20秒前
21秒前
22秒前
25秒前
飞鸿踏雪泥完成签到 ,获得积分0
27秒前
星鑫完成签到,获得积分10
29秒前
memedaaaah完成签到,获得积分10
34秒前
顾矜应助陈陈采纳,获得10
35秒前
海棠之秋完成签到,获得积分10
36秒前
39秒前
40秒前
42秒前
Akim应助咚咚采纳,获得10
43秒前
复成完成签到 ,获得积分10
44秒前
宁祚完成签到,获得积分10
45秒前
Amo发布了新的文献求助10
45秒前
45秒前
46秒前
陈陈发布了新的文献求助10
51秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
信任代码:AI 时代的传播重构 450
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6357689
求助须知:如何正确求助?哪些是违规求助? 8172194
关于积分的说明 17207436
捐赠科研通 5413217
什么是DOI,文献DOI怎么找? 2864954
邀请新用户注册赠送积分活动 1842489
关于科研通互助平台的介绍 1690566