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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 230-237     DOI: 10.6046/zrzyyg.2021058
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Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index
ZHANG Qinrui1(), ZHAO Liangjun2(), LIN Guojun1, WAN Honglin3
1. Artificial Intelligence Key Laboratory of Sichuan Province, School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, China
2. Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
3. Department of Water Conservancy, Hebei University of Water Resources and Electric Engineering, Cangzhou Technology Innovation Center of Remote Sensing and Smart Water, Cangzhou 061001, China
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Abstract  

Urban expansion is the main characteristic of Yibin City in recent years, and the study of its impacts on ecology is significant for urban development and ecological protection. To assess the impacts of urban expansion on the ecology more accurately, this study established an improved remote sensing ecological index (IRSEI) by using the impervious surface area index as the dryness index to replace the original building index. The IRSEI coupled the improved dryness index and the indices greatly influencing the ecology, such as greenness, humidity, and temperature. This study analyzed the IRSEI using principal component analysis and correlation and established an IRSEI-based ecological assessment model of the three-river (i.e., the Jinsha River, Minjiang River, and Yangtze River) confluence in Yibin City. Then, this study analyzed and assessed the ecological environment of the confluence in 2013—2020. The results are as follows. The IRSEI can more accurately reflect the negative impacts of the dryness index on the ecology of the confluence. It can concentrate more useful information in the first principal component than the RSEI and can better apply to the quality assessment of urban ecological environment. In 2013, the IRSEI of the confluence was 0.54, indicating the moderate ecological status overall. The reason is that the original vegetation was destroyed by serious urban expansion. In 2017, the IRSEI was 0.67. The greenness was significantly improved by the continuous advancement of returning farmland to forests and the restoration of urban ecology, which is the reason that the ecology has greatly improved in 2017 compared to 2013. In 2020, the IRSEI was 0.63. The greenness, humidity, and dryness in 2020 were roughly the same as those in 2013, while the temperature rose in 2020 compared to 2017 due to the heat island effect induced by urban expansion. This is the reason for the slight decline in the ecological level in 2020.
Keywords improved remote sensing ecological index (IRSEI)      principal component analysis      correlation      three-river confluence in Yibin City      ecological assessment     
ZTFLH:  TP79  
Corresponding Authors: ZHAO Liangjun     E-mail: 1029765315@qq.com;149189602@qq.com
Issue Date: 14 March 2022
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Qinrui ZHANG
Liangjun ZHAO
Guojun LIN
Honglin WAN
Cite this article:   
Qinrui ZHANG,Liangjun ZHAO,Guojun LIN, et al. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021058     OR     https://www.gtzyyg.com/EN/Y2022/V34/I1/230

指标		 2013年 2017年 2020年
PCA1 PCA2 PCA3 PCA4 PCA1 PCA2 PCA3 PCA4 PCA1 PCA2 PCA3 PCA4
NDVI 0.79 0.37 -0.38 0.31 0.74 0.31 -0.48 0.36 0.76 0.32 -0.44 0.36
WET 0.04 -0.16 0.59 0.79 0.06 -0.13 0.60 0.78 0.07 -0.13 0.64 0.76
LST -0.32 0.92 0.25 0.02 -0.29 0.94 0.17 0.04 -0.22 0.94 0.26 -0.04
NDISSI -0.52 -0.01 -0.67 0.52 -0.60 -0.10 -0.61 0.50 -0.60 0.04 -0.58 0.55
特征值 0.04 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.05 0.01 0.00 0.00
特征值贡献率/% 88.18 9.39 2.24 0.19 89.25 8.28 2.02 0.45 88.75 8.60 2.27 0.39
Tab.1  Results of principal component analysis

指标		 2013年 2017年 2020年
NDVI WET LST NDISSI IRSEI NDVI WET LST NDISSI IRSEI NDVI WET LST NDISSI IRSEI
NDVI 1.00 0.24 -0.62 -0.95 0.99 1.00 0.37 -0.63 -0.96 0.99 1.00 0.39 -0.51 -0.96 0.98
WET 0.24 1.00 -0.49 -0.49 0.37 0.37 1.00 -0.49 -0.53 0.47 0.39 1.00 -0.45 -0.58 0.50
LST -0.62 -0.49 1.00 0.69 -0.72 -0.63 -0.49 1.00 0.66 -0.72 -0.51 -0.45 1.00 0.60 -0.62
NDISSI -0.95 -0.49 0.69 1.00 -0.98 -0.96 -0.53 0.66 1.00 -0.99 -0.96 -0.58 0.60 1.00 -0.99
IRSEI 0.99 0.37 -0.72 -0.98 1.00 0.99 0.47 -0.72 -0.99 1.00 0.98 0.50 -0.62 -0.99 1.00
平均相关度 0.70 0.40 0.63 0.78 0.76 0.74 0.47 0.62 0.79 0.79 0.71 0.48 0.54 0.78 0.77
Tab.2  Statistic of correlation between each index and IRSEI index
Fig.1  Three dimensional scatter plots of IRSEI and each factor
Fig.2  Urban expansion of the study area from 2013 to 2020
年份 NDVI WET LST/℃ NDISSI IRSEI
2013年 0.41 -0.03 23.64 -0.44 0.54
2017年 0.55 -0.04 28.77 -0.47 0.67
2020年 0.55 -0.03 24.07 -0.47 0.63
Tab.3  Statistical mean value of index and IRSEI in each year
Fig.3  Ecological area of three rivers confluence area in different periods
Fig.4  Change detection of IRSEI in study area
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