RSEIFE: A new remote sensing ecological index for simulating the land surface eco-environment

遥感 计算机科学 适应性 过程(计算) 索引(排版) 理论(学习稳定性) 变更检测 环境科学 环境资源管理 生态学 人工智能 机器学习 地理 生物 操作系统 万维网
作者
Ziwei Wang,Tao Chen,Dongyu Zhu,Kun Jia,Antonio Plaza
出处
期刊:Journal of Environmental Management [Elsevier]
卷期号:326: 116851-116851 被引量:41
标识
DOI:10.1016/j.jenvman.2022.116851
摘要

With the development of remote sensing technology, significant progress has been made in the evaluation of the eco-environment. The remote sensing ecological index (RSEI) is one of the most widely used indices for the comprehensive evaluation of eco-environmental quality. This index is entirely based on remote sensing data and can monitor eco-environmental aspects quickly for a large area. However, the use of RSEI has some limitations. For example, its application is generally not uniform, the obtained results are stochastic in nature, and its calculation process cannot consider all ecological elements (especially the water element). In spite of the widespread application of the RSEI, improvements to its limitations are scarce. In this paper, we propose a new index named the remote sensing ecological index considering full elements (RSEIFE). The proposed RSEIFE is compared with commonly used evaluation models such as RSEI and RSEILA (Remote Sensing Ecological Index with Local Adaptability) in several types of study areas to assess the stability and accuracy of our model. The results show that the calculation process of RSEIFE is more stable than those of RSEI and RSEILA, and the results of RSEIFE are consistent with the real eco-environment surface and reveal more details about its features. Meanwhile, compared with RSEI and RSEILA, the results of RSEIFE effectively reveal the ecological benefits of both water bodies themselves and their surrounding environments, which lead to more accurate and comprehensive basis for the implementation of environmental protection policies.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
blessing应助科研通管家采纳,获得10
1秒前
yar应助科研通管家采纳,获得10
1秒前
可乐应助科研通管家采纳,获得10
1秒前
1秒前
酷波er应助科研通管家采纳,获得10
1秒前
无花果应助科研通管家采纳,获得10
1秒前
CipherSage应助科研通管家采纳,获得10
1秒前
ataybabdallah给ataybabdallah的求助进行了留言
2秒前
活泼的岂愈完成签到,获得积分10
5秒前
5秒前
6秒前
泽佳完成签到,获得积分20
6秒前
7秒前
7秒前
今后应助坤儿采纳,获得10
9秒前
Jasper应助泽佳采纳,获得10
9秒前
SciGPT应助调皮初蓝采纳,获得10
11秒前
111完成签到,获得积分10
12秒前
AABBC发布了新的文献求助10
12秒前
13秒前
Migue应助重要文龙采纳,获得10
13秒前
14秒前
17秒前
爆米花应助活泼的岂愈采纳,获得10
17秒前
apdfew发布了新的文献求助10
18秒前
英姑应助迷路伊采纳,获得10
18秒前
mhl11应助wd采纳,获得10
22秒前
readingbent完成签到,获得积分10
25秒前
25秒前
26秒前
un完成签到,获得积分10
27秒前
田様应助黎小静采纳,获得10
28秒前
29秒前
29秒前
彭凯发布了新的文献求助10
31秒前
lily完成签到,获得积分10
31秒前
专注大门发布了新的文献求助10
31秒前
杨晓锐发布了新的文献求助10
32秒前
Akim应助yu采纳,获得10
33秒前
坤儿发布了新的文献求助10
34秒前
高分求助中
Licensing Deals in Pharmaceuticals 2019-2024 3000
Cognitive Paradigms in Knowledge Organisation 2000
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger Heßler, Claudia, Rud 1000
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 1000
Natural History of Mantodea 螳螂的自然史 1000
A Photographic Guide to Mantis of China 常见螳螂野外识别手册 800
How Maoism Was Made: Reconstructing China, 1949-1965 800
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 量子力学 冶金 电极
热门帖子
关注 科研通微信公众号,转发送积分 3318402
求助须知:如何正确求助?哪些是违规求助? 2949819
关于积分的说明 8548183
捐赠科研通 2626527
什么是DOI,文献DOI怎么找? 1437251
科研通“疑难数据库(出版商)”最低求助积分说明 666193
邀请新用户注册赠送积分活动 652133