Remote sensing ecological index (RSEI) model and its applications: A review

doi:10.6046/zrzyyg.2023128

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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.

Keywords RSEI model improvement application analysis regional ecological assessment regional ecological change monitoring

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Issue Date: 03 September 2024

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	Yixin CHEN
	Xiaogang NING
	Hanchao ZHANG
	Xiaoqiang LAN
	Zhongbing CHANG

Cite this article:

Yixin CHEN,Xiaogang NING,Hanchao ZHANG, et al. Remote sensing ecological index (RSEI) model and its applications: A review[J]. Remote Sensing for Natural Resources, 2024, 36(3): 28-40.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023128 OR https://www.gtzyyg.com/EN/Y2024/V36/I3/28

Tab.1 Several comprehensive evaluation indices before RSEI proposed

Fig.1 Statistics of RSEI document quantity

Fig.2 RSEI keyword graph

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 Discussion on whether PCA is suitable for single index integration

Tab.3 RSEI corresponding ecological level and significance

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