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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 43-52     DOI: 10.6046/zrzyyg.2022398
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Exploring ecological environment quality of typical coastal cities based on an improved remote sensing ecological index: A case study of Zhanjiang City
WANG Jing1,2(), WANG Jia1,2, XU Jiangqi1,2, HUANG Shaodong1,2, LIU Dongyun3()
1. Beijing Key Laboratory of Precision Forestry, Beijing Forestry University,Beijing 100091,China
2. College of Forestry, Beijing Forestry University,Beijing 100091,China
3. School of Landscape Architecture, Beijing Forestry University,Beijing 100091,China
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Abstract  

Urbanization has decreased the area of ecological land and deteriorated ecological environment in Zhanjiang City. Therefore, it is significant to quickly, comprehensively, and accurately monitor the changes the ecological environment quality in this city. Based on the Landsat images in 2000, 2005, 2009, 2015, and 2020, this study constructed the improved remote sensing ecological index (IRSEI) using six indicators, namely greenness (NDVI), humidity (WET), dryness (NDBSI), heatiness (LST), land use (LUI), and population distribution (POP). Using IRSEI, this study quantitatively analyzed the changes in the ecological environment quality in Zhanjiang during 2000—2020. The results are as follows: ① The mean IRSEI values of 2000, 2005, 2009, 2015, and 2020 are 0.18, 0.18, 0.35, 0.42, and 0.38, respectively, showing a first increasing and then decreasing trend. ② According to the difference processing on IRSEIs during 2000—2020, the proportions of ecological environment areas with significant improvement (dominant), improvement, no change, deterioration, and significant deterioration in the study area are 78.95%, 8.70%, 8.01%, 1.35%, and 2.99%, respectively. ③ The IRSEI can effectively reflect the poor urban environment along the coastal zone during 2000—2020, specifically manifested as a low IRSEI value of building land along the coastal zone. The results of this study can provide a theoretical and scientific basis for Zhanjiang’s ecological environment protection.
Keywords ecological environment quality      coastal zone      improved remote sensing ecological index     
ZTFLH:  TP79  
Issue Date: 19 September 2023
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Jing WANG
Jia WANG
Jiangqi XU
Shaodong HUANG
Dongyun LIU
Cite this article:   
Jing WANG,Jia WANG,Jiangqi XU, et al. Exploring ecological environment quality of typical coastal cities based on an improved remote sensing ecological index: A case study of Zhanjiang City[J]. Remote Sensing for Natural Resources, 2023, 35(3): 43-52.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022398     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/43
Fig.1  Location of study area
数据类型 空间分辨率/ m 数据来源
Landsat5 SR 30 GEE
Landsat8 SR 30 GEE
土地利用类型 30 武汉大学黄昕团队研究的产品数据(earth system science data)[20]
人口分布 100 南安普大学Worldpop研究小组的产品数据(open data for spatial demography)[21]
行政区矢量边界 30 地理国情监测平台(http://www.dsac.cn)
Tab.1  Data sources
Fig.2  Flow chart of research technology
年份 主成分分量 NDVI WET NDBSI LST LUI POP 特征值 特征值贡献率
2000年 PC1 0.530 0.229 -0.204 -0.777 -0.002 -0.149 0.517 2 0.887
PC2 -0.472 -0.096 0.131 -0.224 0.007 -0.837 0.049 0 0.084
PC3 -0.544 -0.137 -0.729 -0.274 -0.006 0.281 0.010 2 0.017
2005年 PC1 0.495 0.232 -0.225 -0.795 -0.001 -0.136 0.571 6 0.895
PC2 -0.573 -0.109 0.127 -0.297 0.005 -0.745 0.049 1 0.077
PC3 -0.520 -0.118 -0.723 -0.219 -0.006 0.381 0.010 8 0.017
2009年 PC1 0.517 0.241 -0.074 -0.171 -0.580 -0.551 0.551 4 0.868
PC2 0.463 0.130 0.387 0.113 0.298 0.719 0.061 6 0.097
PC3 0.528 0.217 -0.536 -0.255 -0.540 0.173 0.011 0 0.017
2015年 PC1 0.444 0.327 -0.250 -0.790 -0.091 -0.002 0.244 6 0.744
PC2 0.327 -0.214 -0.053 -0.151 -0.906 0.027 0.052 8 0.161
PC3 0.619 -0.398 -0.217 -0.492 0.412 -0.013 0.021 4 0.065
2020年 PC1 0.444 0.310 -0.738 -0.231 -0.330 -0.012 0.241 3 0.753
PC2 0.206 0.170 -0.221 -0.018 0.939 0.001 0.052 5 0.164
PC3 0.516 -0.041 0.291 -0.953 0.046 -0.002 0.021 7 0.068
Tab.2  Results of PCA of each index in each year
年份 NDVI WET NDBSI LST LUI POP IRSEI
均值
2000年 0.72 0.75 0.73 0.75 0.53 0.20 0.18
2005年 0.71 0.75 0.73 0.74 0.53 0.21 0.18
2009年 0.74 0.75 0.75 0.74 0.57 0.21 0.35
2015年 0.55 0.57 0.55 0.16 0.42 0.17 0.42
2020年 0.59 0.56 0.54 0.15 0.43 0.17 0.38
均值 0.66 0.68 0.66 0.51 0.50 0.19 0.30
Tab.3  Mean value of IRSEI and mean correlation between IRSEI and each index in different years
Fig.3  Spatial and temporal distribution of IRSEI from 2000 to 2020
等级 2000年 2005年 2009年 2015年 2020年
面积/km2 比例/% 面积/km2 比例/% 面积/km2 比例/% 面积/km2 比例/% 面积/km2 比例/%
差[0,0.2) 6 385.38 48.28 8 659.39 65.48 0.01 0.00 1 043.53 7.89 4 072.28 30.79
较差[0.02,0.35) 6 605.60 49.95 4 326.90 32.72 4 478.66 33.87 2 529.22 19.12 0.00 0.00
中[0.35,0.55) 137.20 1.04 79.37 0.60 8 674.53 65.59 9 495.07 71.80 9 083.81 68.69
良[0.55,0.75) 27.78 0.21 0.03 0.00 37.34 0.28 142.90 1.08 63.43 0.48
优[0.75,1] 69.04 0.52 159.31 1.20 34.46 0.26 14.27 0.11 5.42 0.04
Tab.4  Area and proportion of IRSEI of each grade in each year
Fig.4  Classification of ecological environment quality from 2000 to 2020
变化程度 2000—2005年 2005—2009年 2009—2015年 2015—2020年 2000—2020年
面积/km2 比例/% 面积/km2 比例/% 面积/km2 比例/% 面积/km2 比例/% 面积/km2 比例/%
明显变差
[-1,-0.1)		 821.62 6.21 167.38 1.27 1 223.97 9.25 1 433.22 10.84 395.50 2.99
轻度变差
[-0.1,0.02)		 1 318.17 9.97 2.63 0.02 1 008.76 7.63 2 706.29 20.46 178.73 1.35
基本不变
[0.02,0.05)		 9 777.17 73.93 400.89 3.03 1 612.28 12.19 8 453.99 63.92 1 059.36 8.01
轻度变好
[0.05,0.1)		 348.94 2.64 879.65 6.65 2 656.56 20.09 70.91 0.54 1 149.94 8.70
明显变好
[0.1,1]		 959.11 7.25 11 774.46 89.03 6 723.43 50.84 560.58 4.24 10 441.48 78.95
Tab.5  Changes of ecological environment quality to different degrees
Fig.5  Space change of ecological environment in Zhanjiang City from 2000 to 2020
[1] 李加林, 汪海峰, 田鹏. 海岸带开发利用研究进展[J]. 海洋经济, 2022, 12(2):21-31.
[1] Li J L, Wang H F, Tian P. Research progress of coastal zone develo-pment[J]. Marine Economy, 2022, 12(2):21-31.
[2] 许学工, 彭慧芳, 徐勤政. 海岸带快速城市化的土地资源冲突与协调——以山东半岛为例[J]. 北京大学学报(自然科学版), 2006, 42(4):527-533.
[2] Xu X G, Peng H F, Xu Q Z. Land resource conflicts and coordination in fast urbanized coastal zone:A case study of the Shandong Peninsula[J]. Journal of Peking University (Natural Science Edition), 2006, 42(4):527-533.
[3] 陈晔倩, 李杨帆, 祁新华, 等. 基于栖息地风险评价模型的海岸带滩涂湿地风险评价——以闽三角为例[J]. 生态学报, 2018, 38(12):4214-4225.
[3] Chen Y Q, Li Y F, Qi X H, et al. Assessing ecosystem risk in coastal wetland of the South Fujian Golden Delta using a habitat risk assessment model based on the land-use effect on tidal flat wetland[J]. Ecological Journal, 2018, 38 (12):4214-4225.
[4] 环境保护部. HJ 192—2015生态环境状况评价技术规范[S]. 北京: 中国环境科学出版社, 2015.
[4] Ministry of Environmental Protection. HJ 192—2015 Technical specifications for ecological environment assessment[S]. Beijing: China Environmental Science Press, 2015.
[5] 徐涵秋. 城市遥感生态指数的创建及其应用[J]. 生态学报, 2013, 33(24):7853-7862.
[5] Xu H Q. A remote sensing urban ecological index and its application[J]. Acta Ecologica Sinica, 2013, 33(24):7853-7862.
[6] 农兰萍, 王金亮. 基于 RSEI 模型的昆明市生态环境质量动态监测[J]. 生态学杂志, 2020, 39(6):2042-2050.
[6] Nong L P, Wang J L. Dynamic monitoring of ecological environment quality in Kunming based on RSEI model[J]. Chinese Journal of Ecology, 2020, 39(6):2042-2050.
[7] 吴可人, 高祺, 王让会, 等. 基于 RSEI模型的石家庄生态环境质量评价[J]. 地球物理学进展, 2021, 36(3):968-976.
[7] Wu K R, Gao Q, Wang R H, et al. Evaluation of ecological environment quality in Shijiazhuang based on RSEI model[J]. Progress in Geophysics, 2021, 36(3):968-976.
[8] Gao P, Kasimu A, Zhao Y, et al. Evaluation of the temporal and spatial changes of ecological quality in the Hami Oasis based on RSEI[J]. Sustainability, 2020, 12(18):7716.
doi: 10.3390/su12187716 url: https://www.mdpi.com/2071-1050/12/18/7716
[9] Ji J, Wang S, Zhou Y, et al. Spatiotemporal change and landscape pattern variation of eco-environmental quality in Jing-Jin-Ji urban agglomeration from 2001 to 2015[J]. IEEE Access, 2022, 8:125534-125548.
doi: 10.1109/Access.6287639 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639
[10] 缪鑫辉, 梁勤欧. 基于遥感生态指数的甬江流域生态环境变化分析[J]. 长江流域资源与环境, 2021, 30(2):427-438.
[10] Miao X H, Liang Q O. Analysis of ecological environment changes in Yongjiang River basin based on remote sensing ecological index[J]. Resources and Environment in the Yangtze River Basin, 2021, 30(2):427-438.
[11] 宋美杰, 罗艳云, 段利民. 基于改进遥感生态指数模型的锡林郭勒草原生态环境评价[J]. 干旱区研究, 2019, 36(6):1521-1527.
[11] Song M J, Luo Y Y, Duan L M. Evaluation of ecological environment in the Xilin Gol Steppe based on modified remote sensing eco-logical index model[J]. Arid Zone Research, 2019, 36(6):1521-1527.
[12] 程琳琳, 王振威, 田素锋, 等. 基于改进的遥感生态指数的北京市门头沟区生态环境质量评价[J]. 生态学杂志, 2021, 40(4):1177-1185.
[12] Cheng L L, Wang Z W, Tian S F, et al. Evaluation of eco-environmental quality in Mentougou District of Beijing based on improved remote sensing ecological index[J]. Chinese Journal of Ecology, 2021, 40(4): 1177-1185.
[13] 王杰, 马佳丽, 解斐斐, 等. 干旱地区遥感生态指数的改进——以乌兰布和沙漠为例[J]. 应用生态学报, 2020, 31(11):3795-3804.
doi: 10.13287/j.1001-9332.202011.011
[13] Wang J, Ma J L, Xie F F, et al. Improvement of remote sensing eco-logical index in arid regions:Taking Ulan Buh Desert as an example[J]. Chinese Journal of Applied Ecology, 2020, 31(11):3795-3804.
[14] 蒋超亮, 吴玲, 刘丹, 等. 干旱荒漠区生态环境质量遥感动态监测——以古尔班通古特沙漠为例[J]. 应用生态学报, 2019, 30(3):877-883.
doi: 10.13287/j.1001-9332.201903.008
[14] Jiang C L, Wu L, Liu D, et al. Dynamic monitoring of eco-environmental quality in arid desert area by remote sensing:Taking the Gurbantunggut Desert China as an example[J]. Chinese Journal of Applied Ecology, 2019, 30(3):877-883.
[15] 张伟, 杜培军, 郭山川, 等. 改进型遥感生态指数及干旱区生态环境评价[J]. 遥感学报, 2022, 26(7):1-19.
[15] Zhang W, Du P J, Guo S C, et al. Enhanced remote sensing ecological index and ecological environment evaluation in arid area[J]. National Remote Sensing Bulletin, 2022, 26(7):1-19.
[16] 李洋. 基于多要素的海岛城市生态状况评价[D]. 福州: 福州大学, 2017.
[16] Li Y. Evaluation of island city’s ecological status based on multiple elelments:A case study of Pingtan comprehensive experimentation zone,Fujian Province[D]. Fuzhou: Fuzhou University, 2017.
[17] 付杰, 王萍, 张清, 等. 基于改进遥感生态指数的海南岛生态环境质量动态变化[J]. 农业资源与环境学报, 2021, 38(6):1102-1111.
[17] Fu J, Wang P, Zhang Q, et al. Dynamic monitoring of ecological environment quality of Hainan Island based on improved remote sensing ecological index[J]. Journal of Agricultural Resources and Environment, 2021, 38(6):1102-1111.
[18] 许妍, 曹可, 李冕, 等. 海岸带生态风险评价研究进展[J]. 地球科学进展, 2016, 31(2):137-146.
doi: 10.11867/j.issn.1001-8166.2016.02.0137.
[18] Xu Y, Cao K, Li M, et al. Coastal ecological risk assessment:Its research progress and prospect[J]. Advances in Earth Science, 2016, 31(2):137-146.
[19] 湛江红树林国家级自然保护区[J]. 环境, 2021, 44(10):51.
[19] Zhanjiang mangrove national nature reserve[J]. Environment, 2021, 44(10):51.
[20] Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021, 13(8):3907-3925.
doi: 10.5194/essd-13-3907-2021 url: https://essd.copernicus.org/articles/13/3907/2021/
[21] Xu H, Wang Y, Guan H, et al. Detecting ecological changes with a remote sensing based ecological index(RSEI)produced time series and change vector analysis[J]. Remote Sensing, 2019, 11(20):2345.
doi: 10.3390/rs11202345 url: https://www.mdpi.com/2072-4292/11/20/2345
[22] 李凤莹, 张饮江, 赵志淼, 等. 基于遥感生态指数的上海南汇东滩海岸带湿地生态格局变化评价[J]. 上海海洋大学学报, 2020, 29(5):746-756.
[22] Li F Y, Zhang Y J, Zhao Z M, et al. Evaluation of ecological pattern change of Nanhui Dongtan wetland in Shanghai based on remote sensing ecological index[J]. Journal of Shanghai Ocean University, 2020, 29(5):746-756.
[23] 农兰萍, 王金亮, 玉院和. 基于改进型遥感生态指数(MRSEI)模型的滇中地区生态环境质量研究[J]. 生态与农村环境学报, 2021, 37(8):972-982.
[23] Nong L P, Wang J L, Yu Y H. Research on ecological environment quality in central Yunnan based on MRSEI model[J]. Journal of Ecology and Rural Environment, 2021, 37(8):972-982.
[24] 王丽春, 焦黎, 来风兵, 等. 基于遥感生态指数的新疆玛纳斯湖湿地生态变化评价[J]. 生态学报, 2019, 39(8):2963-2972.
[24] Wang L C, Jiao L, Lai F B, et al. Evaluation of ecological changes based on a remote sensing ecological index in a Manas Lake wetland,Xinjiang[J]. Acta Ecologica Sinica, 2019, 39(8):2963-2972.
[25] Chander G, Markham B L, Helder D L. Summary of current radiometric calibration coefficients for Landsat MSS,TM,ETM+,and EO-1 ALI sensors[J]. Remote Sensing of Environment, 2009, 113(5):893-903.
doi: 10.1016/j.rse.2009.01.007 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425709000169
[26] Rikimaru A, Roy P S, Miyatake S. Tropical forest cover density mapping[J]. Tropical Ecology, 2002, 43(1):39-47.
[27] 吴传钧, 郭焕成. 中国土地利用[M]. 北京: 科学出版社, 1994:35-36.
[27] Wu C J, Guo H C. Land utilization in China[M]. Beijing: Science Press, 1994:35-36.
[28] 徐勇, 孙晓一, 汤青. 陆地表层人类活动强度:概念、方法及应用[J]. 地理学报, 2015, 70(7):1068-1079.
doi: 10.11821/dlxb201507004
[28] Xu Y, Sun X Y, Tang Q. Human activity intensity of land surface:Concept,method and application in China[J]. Journal of Geography, 2015, 70 (7):1068-1079.
[29] 杭鑫, 罗晓春, 曹云, 等. 基于RSEI模型的生态质量评估及城镇化影响——以南京市为例[J]. 应用生态学报, 2020, 31(1):219-229.
doi: 10.13287/j.1001-9332.202001.030
[29] Hang X, Luo X C, Cao Y, et al. Ecological quality assessment and the impact of urbanization based on RSEI model for Nanjing,Jiangsu Province,China[J]. Chinese Journal of Applied Ecology, 2020, 31(1): 219-229.
[30] 徐涵秋, 邓文慧. MRSEI指数的合理性分析及其与RSEI指数的区别[J]. 遥感技术与应用, 2022, 37(1):1-7.
[30] Xu H Q, Deng W H. Rationality analysis of MRSEI and its difference with RSEI[J]. Remote Sensing Technology and Application, 2022, 37 (1):1-7.
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