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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (1) : 250-266     DOI: 10.6046/zrzyyg.2022389
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Post-flood recovery assessment based on multi-source remote sensing data:A case study of the “7·20” rainstorm in Henan
LI Mengqi(), LI Gongquan(), XIE Zhihui
School of Geoscience, Changjiang University,Wuhan 430100, China
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Abstract  

Quantitative post-flood recovery assessment based on vegetation and lighting indices is critical for assessing economic reconstruction and ecological restoration in afflicted areas. This study investigated the “7.20” rainstorm disaster area in Henan. Based on the daily and monthly NPP-VIIRS data, Sentinel-NDVI and MODIS-EVI data, and statistical yearbook data, this study characterized the spatial intricacies within urban areas by constructing a normalized difference urban index (NDUI). Then, it simulated the population and GDP distributions by employing a regression model. Finally, this study assessed the post-flood recovery from two distinct aspects: nighttime light data and vegetation cover data. The results are as follows: ① High- and medium-risk zones covered an area of 1 429.04 km2, accounting for 6.06% of the total study area. High-risk zones were primarily distributed in western Zhengzhou, eastern Xinxiang, eastern Anyang, and northern Hebi, with Zhengzhou suffering the most severe impact; ② In terms of the vegetation cover recovery rate (VCRR), low overall vegetation recovery was observed in Weihui and Linzhou cities and Qixian and Huaxian counties, with VCRRs mostly below 0. This indicates a deteriorating vegetation cover trend; ③ The fitting between NDUI and socio-economic statistical data yielded accuracy exceeding 0.8, suggesting that the NDUI can be applied to precise location-based rescue and targeted post-disaster reconstruction in the aftermath of floods. Additionally, the assessment results based on NPP-VIIRS and MODIS-EVI data were highly complementary, implying that the flood research based on the integration of the two types of data enjoys high application value for post-disaster rescue and recovery assessment.
Keywords “7·20” rainstorm in Henan      NDUI      NPP-VIIRS      lighting index      fractional vegetation cover      vegetation cover recovery rate     
ZTFLH:  TP315  
Issue Date: 13 March 2024
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Mengqi LI
Gongquan LI
Zhihui XIE
Cite this article:   
Mengqi LI,Gongquan LI,Zhihui XIE. Post-flood recovery assessment based on multi-source remote sensing data:A case study of the “7·20” rainstorm in Henan[J]. Remote Sensing for Natural Resources, 2024, 36(1): 250-266.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022389     OR     https://www.gtzyyg.com/EN/Y2024/V36/I1/250
Fig.1  Density map of river network in the study area
Fig.2  Main technical process
Fig.3  Changes in the total amount of EVI by cities from 2019 to 2021
Fig.4  NDVI, TNL, NDUI and GlobeLand30 in Jinshui District in 2021
Fig.5  Close-up of the urban area of Jinshui District
Fig.6  Fitting diagram of NDUI and I
模型 TNL I CNLI LC-NDUI
GDP GDPD GDP GDPD GDP GDPD GDP GDPD
线性 0.766 36 0.274 23 0.518 53 0.856 71 0.484 62 0.863 24 0.379 81 0.846 29
非线性 0.785 79 0.360 36 0.591 03 0.857 87 0.588 71 0.863 77 0.582 02 0.878 43
指数 0.684 27 0.314 87 0.577 43 0.735 23 0.571 82 0.804 52 0.596 28 0.814 53
对数 0.315 47 0.226 76 0.304 05 0.592 51 0.482 48 0.780 23 0.340 15 0.842 35
Tab.1  Correlation coefficients R2 of the light index and LC-NDUI with GDP
模型 TNL I CNLI LC-NDUI
TP PD TP PD TP PD TP PD
线性 0.815 72 0.241 83 0.311 64 0.892 19 0.311 84 0.900 62 0.179 99 0.871 39
非线性 0.815 79 0.367 55 0.558 36 0.892 67 0.494 14 0.900 97 0.486 25 0.881 12
指数 0.714 58 0.302 14 0.544 39 0.814 63 0.474 88 0.836 71 0.535 61 0.795 42
对数 0.352 41 0.183 55 0.164 63 0.712 65 0.269 89 0.843 25 0.181 97 0.764 82
Tab.2  Correlation coefficient R2 of the light index and LC with population
Fig.7-1  Statistical and simulated graphs of GDP and population in the study area
Fig.7-2  Statistical and simulated graphs of GDP and population in the study area
Fig.8  10-meter spatial resolution population and GDP density map of the study area in 2021
类型 要素 人口 GDP
地表覆盖 耕地像元数 -0.021 -0.027
林地像元数 -0.087 -0.103
草地像元数 -0.142 -0.102
人造地表像元数 0.604**① 0.581
LC-NDUI 耕地LC-NDUI总量 0.434** 0.436**
林地LC-NDUI总量 0.342** 0.231**
草地LC-NDUI总量 0.579** 0.760**
人造地表LC-NDUI
总量		 0.824** 0.813**
Tab.3  Correlation coefficients between the statistical values of GDP and population with each factor in each district and county
Fig.9  Population spatialization error statistics
Fig.10  Errors in GDP and population in different regions
模型 CNLI LC-NDUI
TRE MRE MAE RRMSE TRE MRE MAE RRMSE
线性 23.18 0.61 5 960.2 6.32 20.43 0.64 6 032.1 6.24
非线性 18.46 0.57 5 234.2 6.14 17.58 0.51 5 021.6 6.08
指数 31.52 0.73 6 243.5 7.59 34.29 0.68 6 148.9 7.95
对数 48.71 0.84 7 012.6 7.96 43.84 0.89 7 156.3 8.62
Tab.4  Error analysis of different indices and models
Fig.11  False color composite image of NPP-VIIRS DNB before and after the flood disaster
Fig.12  Distribution map of flood disaster crisis areas of the study area
Fig.13  Proportion of disaster-affected areas in each district and county
区名 区域面积/
km2		 受灾面积/
km2		 受灾面积
比例/%		 区域人口/
万人		 受灾人口/
万人		 受灾人口
比例/%		 区域GDP/
亿元		 受灾GDP/
亿元		 受灾经济
比例/%
中原区 192.34 29.46 10.32 150.88 22.88 15.16 1 294.42 248.46 19.19
二七区 147.76 22.95 11.54 106.13 17.71 16.69 784.23 192.40 24.53
管城回族区 196.10 37.88 12.32 114.82 23.35 20.34 2 429.88 253.67 10.44
金水区 225.55 55.83 16.75 230.27 27.61 21.99 2 300.76 300.11 23.04
上街区 50.36 8.38 16.73 19.74 5.59 28.34 176.80 60.63 24.29
惠济区 201.41 29.36 14.58 55.50 24.12 23.45 306.85 261.81 18.32
中牟县 1 337.81 34.55 2.58 96.24 15.75 16.36 830.56 165.27 19.90
巩义市 973.15 70.53 10.77 78.25 11.98 15.30 901.88 123.05 13.64
荥阳市 875.73 131.94 15.04 73.01 16.90 23.15 554.23 108.10 19.51
新密市 957.73 100.77 10.12 82.60 10.23 12.38 713.25 116.61 16.35
新郑市 853.87 110.88 10.39 179.36 9.24 5.15 1 966.14 103.59 5.27
登封市 1 173.37 40.43 3.38 72.93 12.54 17.19 548.20 140.60 25.65
文峰区 171.25 20.98 12.25 45.10 18.84 31.77 259.25 204.03 78.70
北关区 51.08 5.50 12.98 31.03 5.56 17.91 171.76 60.14 35.02
殷都区 58.31 2.44 4.19 28.70 6.30 21.96 327.87 68.20 20.80
龙安区 231.81 8.49 3.66 29.26 14.29 18.84 182.12 77.70 42.66
安阳县 1 065.92 49.02 4.60 84.56 12.62 14.93 107.47 137.20 27.67
汤阴县 619.19 19.74 3.19 44.31 6.53 14.74 175.36 131.29 14.87
滑县 1 758.17 20.39 1.16 116.91 11.45 9.80 409.77 207.91 12.74
内黄县 1 112.56 35.20 3.16 66.89 12.85 19.21 186.63 133.29 10.42
林州市 1 762.11 47.03 2.67 81.57 17.84 21.87 615.34 239.82 24.97
鹤山区 114.82 7.58 6.61 6.27 14.26 27.35 92.61 154.42 16.74
山城区 124.59 9.52 7.64 15.54 13.79 28.73 138.35 149.30 20.92
淇滨区 288.61 21.01 7.28 46.08 7.65 16.60 283.27 107.79 18.05
浚县 940.97 26.47 2.81 62.47 10.38 16.62 293.82 110.57 17.63
淇县 478.57 32.63 6.82 26.23 6.57 25.06 262.65 166.99 13.58
红旗区 150.76 26.87 17.82 45.74 10.82 23.64 395.74 193.49 18.89
卫滨区 70.36 6.68 9.50 22.57 8.26 36.58 322.03 89.64 17.83
凤泉区 115.35 7.49 6.49 15.86 13.02 32.09 83.14 141.15 16.78
牧野区 90.68 16.21 17.87 34.09 11.03 32.36 231.70 119.93 21.76
新乡县 372.93 24.29 6.51 34.86 7.08 20.30 239.40 145.03 11.58
获嘉县 457.85 15.23 3.33 41.48 8.78 21.18 192.11 127.86 20.56
原阳县 1 221.67 43.43 3.56 65.12 13.12 20.14 186.50 147.15 19.90
延津县 885.08 52.86 5.97 45.79 9.78 21.36 166.15 104.30 16.77
封丘县 1 152.53 88.59 7.69 71.86 12.64 17.59 264.05 138.14 19.31
卫辉市 713.36 89.27 12.51 49.14 9.21 38.74 186.25 103.45 32.54
辉县市 1420.82 94.35 6.64 75.99 11.99 15.78 359.04 193.46 16.88
长垣市 980.74 61.78 6.30 90.54 10.25 11.33 529.59 116.82 13.06
Tab.5  Simulation of the affected population, economy, area and the proportion of each affected
等级 FVC范围 植被情况
1 [0,0.2) 低植被覆盖
2 [0.2,0.4) 较低植被覆盖
3 [0.4,0.6) 中植被覆盖
4 [0.6,0.8) 较高植被覆盖
5 [0.8,1.0] 高植被覆盖
Tab.6  Classification of fractional vegetation cover
Fig.14  Spatial distribution of fractional vegetation cover in different years
Fig.15  Classification statistics of fractional vegetation cover in different years
等级 VCRR范围 植被恢复情况
1 <0 植被恢复差
2 [0,0.25) 植被恢复较差
3 [0.25,0.5) 植被恢复一般
4 [0.5,0.75) 植被恢复较好
5 [0.75,1.0] 植被恢复好
6 >1.0 植被完全恢复
Tab.7  Classification of vegetation cover recovery rate grades
Fig.16  Spatial distribution of vegetation cover recovery rate
VCRR分级 研究区 郑州市 新乡市 安阳市 鹤壁市
差 6.54 6.53 3.93 8.18 7.58
较差 14.74 11.43 11.30 20.65 15.27
一般 17.25 13.98 13.67 18.51 22.94
较好 9.95 9.15 8.68 11.34 10.63
好 16.19 17.36 18.91 14.41 14.09
完全恢复 35.36 41.55 43.51 26.91 29.50
Tab.8  Classification statistics of vegetation cover recovery rate (%)
Fig.17  Histogram of interannual variation of vegetation cover recovery rate at each grade
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