遥感生态指数(RSEI)模型及应用综述

doi:10.6046/zrzyyg.2023128

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摘要

在“双碳”背景下,开展遥感生态评价与监测分析对于及时掌握生态状况、制定科学合理的生态保护政策具有重要作用。早期遥感生态评价指标单一、过程复杂。遥感生态指数(remote sensing ecological index, RSEI)实现了生态评价效率的提高,得到广泛应用。为深入理解RSEI,文章阐述了该指数的产生背景,介绍了RSEI的计算方法及研究现状,归纳了指数存在的问题及区域适应性调整措施,并对RSEI主要应用方向,即区域生态评价和区域生态变化监测进一步分析,最后指出,未来RSEI虽有广阔发展空间,却仍需围绕影像时空尺度、存储和批处理能力、模型自适应、智能化等方面进行研究。

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	陈宜欣
	宁晓刚
	张翰超
	兰小强
	常中兵

关键词 ： RSEI, 模型改进, 应用分析, 区域生态评价, 区域生态变化监测

Abstract：

In the context of achieving peak carbon dioxide emissions and carbon neutrality, conducting a remote sensing-based ecological assessment and monitoring analysis is greatly significant for ascertaining the ecological condition in time and formulating scientific and reasonable ecological protection policies. The early remote sensing-based ecological assessment indices, simple and involving complex processes, are difficult to find wide applications. In contrast, the remote sensing ecological index (RSEI), contributing to elevated assessment efficiency, has been extensively used. To gain a deeper understanding of RSEI, this study describes its background, calculation method, and research status and provides a summary of the current issues and regional adjustments. Furthermore, it analyzes the main application directions of RSEI, namely the in-depth analyses of regional ecological assessment and change monitoring. Finally, the study proposes that despite a broad space for RSEI development, it is necessary to conduct research into the spatiotemporal scales of images, storage and batch processing capabilities, model adaption, and intelligentization.

Key words： RSEI model improvement application analysis regional ecological assessment regional ecological change monitoring

收稿日期: 2023-05-09 出版日期: 2024-09-03

ZTFLH:

TP79

基金资助:自然资源部华南热带亚热带自然资源监测重点实验室开放基金课题“基于先验知识的耕地至园林草地的变化检测”(2022NRMZ01)

通讯作者: 宁晓刚(1979-),男,博士,研究员,主要从事自然资源遥感监测、生态遥感、城市地理国情监测方向的研究。Email: ningxg@casm.ac.cn。

作者简介: 陈宜欣(2000-),女,研究生,主要研究方向为自然资源遥感监测。Email: chenyx0326@163.com。

引用本文:

陈宜欣, 宁晓刚, 张翰超, 兰小强, 常中兵. 遥感生态指数(RSEI)模型及应用综述[J]. 自然资源遥感, 2024, 36(3): 28-40.
CHEN Yixin, NING Xiaogang, ZHANG Hanchao, LAN Xiaoqiang, CHANG Zhongbing. Remote sensing ecological index (RSEI) model and its applications: A review. Remote Sensing for Natural Resources, 2024, 36(3): 28-40.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023128 或 https://www.gtzyyg.com/CN/Y2024/V36/I3/28

Tab.1 RSEI提出之前的几种综合评价指数

Fig.1 RSEI发文量统计

Fig.2 RSEI关键词图谱

问题	观点/讨论	修改方式	局限性
是否仅用第一主成分(PC₁)构建RSEI₀	仅用PC₁信息利用不充分^[32]	以PC₁,PC₂,PC₃的贡献率为权重 $ω i$ 构建MRSEI,即: $M R S E I = ∑ I - 1 3 ω i P C i$	PC₂,PC₃无生态学意义^[33]
是否仅用第一主成分(PC₁)构建RSEI₀	批驳: PC₂和PC₃会干扰原有生态信息,不能忽略主分量包含的遥感信息而仅考虑增加信息量^[34]
PCA高值的生态意义不定	PC₁不能保证较高的贡献率,PCA变换后高值部分代表的生态优劣性不定^[35]	权重计算采用熵权法,之后采用指数和法计算RSEI	比RSEI更复杂
PCA评价、权重确定方法的合理性	除利用PCA构建RSEI外,结合其他方式综合评价的可靠性更高^[36]	以层次分析法、熵值法结合博弈论思想确定组合权重	模型构建复杂
PCA评价、权重确定方法的合理性	大面积建立RSEI指标权重的方法(基于数值的权重评估法)不利于计算和决策认知^[37]	建立基于知识粒化熵的RSEI确定方法	指标变异性大小计算较为复杂
特征向量方向存在不确定性	①特征向量及其方向决定RSEI结果及空间分布,空间分布的随机性导致无法批量计算RSEI; ②特征向量方向不影响特征值贡献率,也不影响同一分量中各指标的绝对值; ③NDVI和Wet的方向、LST与NDSI的方向在PC₁中相同^[38]	人工修改PC₁的特征向量方向,取NDVI和Wet的特征向量的绝对值和NDSI和LST的相反绝对值,使用PC₁代替1-PC₁计算生态环境质量指数	需人工修改特征向量方向
是否适用PCA模型	构成RSEI的4个指标间可能存在弱线性或非线性关系^[39-40]	利用核主成分分析构建非线性遥感生态指数 nRSEI	算法论证不充分、精度验证不合理^[31]
是否适用PCA模型	批驳: 指标间呈强相关关系且改进后的nRSEI还会出现过拟合、数据爆炸等问题,核主成分分析的应用不合理^[31]

Tab.2 PCA是否适用于单一指标集成的讨论

Tab.3 RSEI对应生态等级及意义

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