Please wait a minute...
 
Remote Sensing for Natural Resources    2024, Vol. 36 Issue (2) : 198-206     DOI: 10.6046/zrzyyg.2023061
|
Monitoring of the area of Poyang Lake based on Landsat images and its relationship with the water level
ZHAO Hui1(), CHEN Zhen2, FENG Chaofan1, ZHANG Tong1, ZHAO Xuejing1, ZHANG Zhaoxu3,4()
1. 248 Geological Brigade of Shandong Nuclear Industry,Qingdao 266041, China
2. Qingdao Survey and Mapping Research Institute, Qingdao, 266033, China
3. School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China
4. Marine Ecological Restoration and Smart Ocean Engineering Research Center of Hebei Province, Qinhuangdao 066000,China
Download: PDF(9602 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Lakes constitute a crucial part of terrestrial ecosystems. Changes in the water areas of lakes significantly influence environments and human production activities. Poyang Lake, the largest freshwater lake in China, has experienced many floods and droughts in recent years, thus necessitating its dynamic monitoring. With 175-phase Landsat images of Poyang Lake from 2000 to 2021 as the data source, this study comparatively analyzed four water body extraction methods: the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI), the automated water extraction index (AWEI), and the spectrum photometric method (SPM), determining the optimal water body extraction index for Poyang Lake. Moreover, based on the 175-phase area data, this study delved into the inter-annual area variation trend from 2000 to 2021 as well as the intra-annual seasonal variations. Furthermore, it established the area - water level model by combining 50 sets of water level data from 2009 to 2013 and 2017 to 2018. The results show that: ① The AWEI model, outperforming the other three models in the extraction accuracy, was employed for the water body extraction of Poyang Lake; ② The area of Poyang Lake exhibited significant seasonal variations, large inter-annual fluctuations in the wet season, and relatively gentle inter-annual fluctuations in the dry season; ③ The area - water level piecewise linear model of the Tangyin gauging station proved optimal, which can predict the water coverage area based on real-time water level observations in Poyang Lake, compensating for the limitation of visible spectral remote sensing methods in monitoring the lake water coverage during cloudy and rainy weather.
Keywords Poyang Lake      Landsat      water body extraction      area variation      area - water level model     
ZTFLH:  TP79  
Issue Date: 14 June 2024
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Hui ZHAO
Zhen CHEN
Chaofan FENG
Tong ZHANG
Xuejing ZHAO
Zhaoxu ZHANG
Cite this article:   
Hui ZHAO,Zhen CHEN,Chaofan FENG, et al. Monitoring of the area of Poyang Lake based on Landsat images and its relationship with the water level[J]. Remote Sensing for Natural Resources, 2024, 36(2): 198-206.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023061     OR     https://www.gtzyyg.com/EN/Y2024/V36/I2/198
Fig.1  Location of Poyang Lake and distribution of water level stations
卫星参数 Landsat5 Landsat7 Landsat8 GF-1
传感器 TM ETM+ OLI WFV
发射时间 1984年3月 1999年4月 2013年2月 2013年4月
覆盖周期/d 16 16 16 4
空间分辨率/m 30 30 30 16
Tab.1  Remote sensing image information
模型 公式① 备注
NDWI NDWI= ( ρ B 2 - ρ B 4 ) / ( ρ B 2 + ρ B 4 ) NDWI>a为水体
MNDWI MNDWI= ( ρ B 2 - ρ B 5 ) / ( ρ B 2 + ρ B 5 ) MNDWI>b为水体
AWEI A W E I = 4 ( ρ B 2 - ρ B 5 ) - ( 0.25 ρ B 4 + 2.75 ρ B 7 ) AWEI>c为水体
SPM S P M = ( ρ B 2 + ρ B 3 ) - ( ρ B 4 + ρ B 5 ) SPM>0为水体
Tab.2  Formula of various models for extacting water body
Fig.2  Comparision of different methods for extracting water body
参数 影像时相 NDWI MNDWI SPM AWEI 参考值
面积 20141024 1 389.16 1 684.71 1 471.57 1 590.15 1 570.80
20040215 662.39 829.06 767.18 764.15 748.80
差值 20141024 -181.64 113.91 -99.23 19.35 —
20040215 -86.41 80.26 18.38 15.35 —
Tab.3  Comparison of the different models for extracting area(km2)
Fig.3  Average area of each month for many years
Fig.4  The interannual variability curve of Poyang Lake area
Fig.5  Scatter diagram of relationship between area and water level at each water level stations
模型 湖口站 星子站 都昌站 吴城站 棠荫站 康山站
线性 0.898 4 0.918 6 0.940 0 0.947 1 0.930 0 0.838 3
对数 0.874 1 0.893 2 0.921 9 0.944 7 0.952 8 0.860 8
指数 0.867 4 0.891 3 0.914 6 0.912 3 0.865 4 0.764 7
二次多项式 0.898 9 0.919 1 0.940 1 0.949 9 0.966 1 0.884 7
Tab.4  Coefficients of determination(R2) of the four models between lake area and water level at each water level station
水位站 二次多项式模型 决定系数R2
棠荫站 y = - 23.834 x 2 + 1008.8 x - 7401.6 0.956 7
吴城站 y = - 3.021 x 2 + 329.68 x - 2030.6 0.942 3
都昌站 y = 1.7096 x 2 + 176.01 - 694.32 0.931 6
Tab.5  Quadratic polynomial model and coefficient of determination of representative water level stations
影像时相 棠荫站 都昌站 吴城站
水位值/m 模拟面积
值/km2		 误差/% 水位值/m 模拟面积
值/km2		 误差/% 水位值/m 模拟面积
值/km2		 误差/%
20090316 13.99 2 046.72 0.11 12.953 1 872.37 -8.42 13.87 1 960.89 -4.09
20090604 15.40 2 481.45 0.86 15.313 2 401.80 -2.38 16.12 2 498.82 1.57
20091103 11.27 940.35 -6.39 8.983 1 024.73 2.01 9.96 953.32 -5.10
20100319 14.55 2 230.72 -2.34 13.193 1 925.34 -15.71 13.85 1 955.97 -14.36
20101208 11.34 973.24 -6.14 8.493 923.85 -10.90 9.84 920.94 -11.18
20110728 14.41 2 186.12 -2.53 14.243 2 159.41 -3.72 14.74 2 172.52 -3.14
20120620 17.71 2 988.85 -0.57 17.713 2 959.73 -1.54 18.15 2 957.91 -1.60
20120807 19.01 3 162.55 3.31 18.983 3 262.94 6.59 19.45 3 238.82 5.80
20170210 11.24 926.18 -3.75 8.623 950.53 -1.22 9.83 918.24 -4.58
20180205 11.88 1 219.15 1.15 10.093 1 256.30 4.23 11.07 1 248.75 3.61
Tab.6  Comparison between the area fitted by quadratic polynomial model and that extracted by remote sensing
模型 都昌站 棠荫站 吴城站
R2 标准差 R2 标准差 R2 标准差
二次多项式模型 0.940 1 178.899 2 0.966 1 134.626 1 0.949 9 163.564 3
分段模型 0.947 4 167.615 2 0.967 9 130.745 5 0.959 8 146.403 1
Tab.7  Comparison between the quadratic polynomial model and piecewise linear model of water area and water level
序号 影像时相 棠荫站
水位值/m		 提取面积
/km2		 分段模型
拟合值/km2		 误差/% 二次多项式
拟合值/km2		 误差/%
1 20090316 13.99 2 044.54 1996.90 -2.33 2 046.72 0.11
2 20090604 15.40 2 460.28 2 519.02 2.39 2 481.45 0.86
3 20091103 11.27 1 004.53 989.68 -1.48 940.35 -6.39
4 20100319 14.55 2 284.06 2 204.27 -3.49 2 230.72 -2.34
5 20101208 11.34 1 036.86 1 015.60 -2.05 973.24 -6.14
6 20110728 14.41 2 242.89 2 152.42 -4.03 2 186.12 -2.53
7 20120620 17.71 3 005.89 3 011.61 0.19 2 988.85 -0.57
8 20120807 19.01 3 061.28 3 085.00 0.77 3 162.55 3.31
9 20170210 11.24 962.28 978.57 1.69 926.18 -3.75
10 20180205 11.88 1 205.28 1 215.56 0.85 1 219.15 1.15
Tab.8  Comparison betwen the area axtracted by remote sensing、the area fitted by piecewise models and the area fitted by quadratic polynomial
Fig.6  Fit plot of the piecewise linear model between the lake area and water level of Tangyin station
[1] Smith L C, Sheng Y, MacDonald G M, et al. Disappearing Arctic lakes. Science[J]. Science, 2005, 308(5727):1429.
pmid: 15933192
[2] 孟恺, 石许华, 王二七, 等. 青藏高原中部色林错湖近10年来湖面急剧上涨与冰川消融[J]. 科学通报, 2012, 57(7):571-579.
[2] Meng K, Shi X H, Wang E Q, et al. High-altitude salt lake elevation changes and glacial ablation in Central Tibet,2000—2010[J]. Chinese Science Bulletin, 2012, 57(7):571-579.
[3] Benson L V, Paillet F L. The use of total lake-surface area as an indicator of climatic change:Examples from the Lahontan basin[J]. Quaternary Research, 1989, 32(3):262-275.
[4] 张歆牧. 鄱阳湖湿地生态环境评价[D]. 武汉: 华中师范大学, 2008.
[4] Zhang X M. Evaluation of the ecological environment of Poyang Lake wetland[D]. Wuhan: Central China Normal University, 2008.
[5] 闵骞. 近50年鄱阳湖形态和水情的变化及其与围垦的关系[J]. 水科学进展, 2000, 11(1):76-81.
[5] Min Q. Study on the relationship between shape,water regime and innings of Poyang Lake[J]. Advances in Water Science, 2000, 11(1):76-81.
[6] 姜鲁光. 鄱阳湖退田还湖地区洪水风险与土地利用变化研究[D]. 北京: 中国科学院地理科学与资源研究所, 2006.
[6] Jiang L G. Flood risk and land use change in the wetland restoration area around Poyang Lake,China[D]. Beijing: Institute of Geographic Sciences and Natural Resources Research, CAS, 2006.
[7] Nie Y, Zhang Y, Ding M, et al. Lake change and its implication in the vicinity of Mt.Qomolangma (Everest),central high Himalayas,1970—2009[J]. Environmental Earth Sciences, 2013, 68(1):251-265.
[8] Feng L, Hu C M, Chen X L, et al. Satellites capture the drought severity around China’s largest freshwater lake[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(4):1266-1271.
[9] Carroll M L, Townshend J R G, DiMiceli C M, et al. Shrinking lakes of the Arctic:Spatial relationships and trajectory of change[J]. Geophysical Research Letters, 2011, 38(20):L20406.
[10] 李景刚, 李纪人, 黄诗峰, 等. 近10年来洞庭湖区水面面积变化遥感监测分析[J]. 中国水利水电科学研究院学报, 2010, 8(3):201-207.
[10] Li J G, Li J R, Huang S F, et al. The remote sensing monitoring analysis of Chinese Dongting Lake water area variations in last 10 years using Terra/MODIS data time series[J]. Journal of China Institute of Water Resources and Hydropower Research, 2010, 8(3):201-207.
[11] 于欢, 张树清, 李晓峰, 等. 基于TM影像的典型内陆淡水湿地水体提取研究[J]. 遥感技术与应用, 2008, 23(3):310-315.
[11] Yu H, Zhang S Q, Li X F, et al. Inland limnetic wetlands water body extraction using TM imagery[J]. Remote Sensing Technology and Application, 2008, 23(3):310-315.
[12] 车向红. 2000—2014年青藏高原湖泊动态遥感监测与分析[D]. 太原: 太原理工大学, 2015.
[12] Che X H. Monitoring and analyzing of lake dynamics in Qinghai-Tibet Plateau using remote sensing from 2000 to 2014[D]. Taiyuan: Taiyuan University of Technology, 2015.
[13] Sokolov M, Storie J L, Henry C J, et al. Semantically-consistent Landsat8 image to Sentinel-2 image translation for alpine areas[J/OL]. arXiv, 2022 (2022-12-22)[2023-02-20]. https://arxiv.org/abs/2212.12056.
url: https://arxiv.org/abs/2212.12056
[14] Zhang G Q, Li J L, Zheng G X, et al. Lake-area mapping in the Tibetan Plateau:An evaluation of data and methods[J]. International Journal of Remote Sensing, 2017, 38(3):742-772.
[15] 刘东, 李艳. 基于遥感技术的鄱阳湖面积库容估算[J]. 遥感信息, 2012, 27(2):57-61.
[15] Liu D, Li Y. The calculation of area and storage of Poyang Lake based on remote sensing technology[J]. Remote Sensing Information, 2012, 27(2):57-61.
[16] McFeeters S K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7):1425-1432.
[17] Frazier P S, Page K J. Water body detection and delineation with Landsat TM data[J]. Photogrammetric Engineering and Remote Sensing, 2000, 66(12):1461-1467.
[18] Olmanson L G, Bauer M E, Brezonik P L. A 20-year Landsat water clarity census of Minnesota’s 10,000 lakes[J]. Remote Sensing of Environment, 2008, 112(11):4086-4097.
[19] Fisher A, Flood N, Danaher T. Comparing Landsat water index methods for automated water classification in eastern Australia[J]. Remote Sensing of Environment, 2016, 175:167-182.
[20] Li A M, Fan M, Qin G D. Comparative analysis of machine learning algorithms in water extraction[J]. Journal of Physics:Conference Series, 2021, 2076(1):012045.
[21] Qin P, Cai Y L, Wang X L. Small waterbody extraction with improved U-net using Zhuhai-1 hyperspectral remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19:5502705.
[22] 李辉, 李长安, 张利华, 等. 基于MODIS影像的鄱阳湖湖面积与水位关系研究[J]. 第四纪研究, 2008, 28(2):332-337.
[22] Li H, Li C A, Zhang L H, et al. Relationship between water level and water area in Poyang Lake based on MODIS image[J]. Quaternary Sciences, 2008, 28(2):332-337.
[23] 刘可群, 梁益同, 黄靖, 等. 基于卫星遥感的洞庭湖水体面积变化及影响因子分析[J]. 中国农业气象, 2009, 30(s2):281-284,336.
[23] Liu K Q, Liang Y T, Huang J, et al. Analysis of the variations and influencing factors of area in Dongting Lake based on remote sensing satellite[J]. Chinese Journal of Agrometeorology, 2009, 30(s2):281-284,336.
[24] 张楠楠, 王文, 王胤. 鄱阳湖面积的卫星遥感估计及其与水位关系分析[J]. 遥感技术与应用, 2012, 27(6):947-953.
[24] Zhang N N, Wang W, Wang Y. Estimate the area of the Poyang Lake using satellite remote sensing data and analyze its relationship with water level[J]. Remote Sensing Technology and Application, 2012, 27(6):947-953.
[25] 李鹏, 封志明, 姜鲁光, 等. 鄱阳湖天然湖面遥感监测及其与水位关系研究[J]. 自然资源学报, 2013, 28(9):1556-1568.
doi: 10.11849/zrzyxb.2013.09.011
[25] Li P, Feng Z M, Jiang L G, et al. Natural water surface of Poyang Lake monitoring based on remote sensing and the relationship with water level[J]. Journal of Natural Resources, 2013, 28(9):1556-1568.
[26] 周洋, 张增信, 王言鑫. 鄱阳湖湖泊水位与面积的关系评估[J]. 科技通报, 2015, 31(11):42-47.
[26] Zhou Y, Zhang Z X, Wang Y X. Discussion of relationship between water level and area of Poyang Lake[J]. Bulletin of Science and Technology, 2015, 31(11):42-47.
[27] Feyisa G L, Meilby H, Fensholt R, et al. Automated water extraction index:A new technique for surface water mapping using Landsat imagery[J]. Remote Sensing of Environment, 2014, 140:23-35.
[28] 朱宏富, 金锋, 李荣昉. 鄱阳湖调蓄功能与防灾综合治理研究[M]. 北京: 气象出版社, 2002.
[28] Zhu H F, Jin F, Li R F. Study on regulation and storage function and comprehensive disaster prevention and control of Poyang Lake[M]. Beijing: China Meteorological Press, 2002.
[29] 王大钊, 王思梦, 黄昌. Sentinel-2和Landsat8影像的四种常用水体指数地表水体提取对比[J]. 国土资源遥感, 2019, 31(3):157-165.doi:10.6046/gtzyyg.2019.03.20.
[29] Wang D Z, Wang S M, Huang C. Comparison of Sentinel-2 imagery with Landsat8 imagery for surface water extraction using four common water indexes[J]. Remote Sensing for Land and Resources, 2019, 31(3):157-165.doi:10.6046/gtzyyg.2019.03.20.
[30] 寿敬文. 陆地卫星7号ETM+图像数据缺行的修复与应用研究[D]. 哈尔滨: 哈尔滨工程大学, 2006.
[30] Shou J W. Research on recovering and application of the Landsat-7 ETM+ SLC-off image[D]. Harbin: Harbin Engineering University, 2006.
[31] 李国文, 喻中文, 陈家霖. 鄱阳湖动态水位-面积、水位-容积关系研究[J]. 江西水利科技, 2015, 41(1):21-26,34.
[31] Li G W, Yu Z W, Chen J L. Study on the relationship between dynamic level-area and level-volume of Poyang Lake[J]. Jiangxi Hydraulic Science & Technology, 2015, 41(1):21-26,34.
[32] 曾少龙. 基于EFDC模型的鄱阳湖水位与湖面面积关系分析[D]. 南昌: 江西师范大学, 2018.
[32] Zeng S L. Analysis of the relationship between water level and lake surface area of Poyang Lake based on EFDC model[D]. Nanchang: Jiangxi Normal University, 2018.
[33] 蔡晓斌. 主被动遥感辅助下的鄱阳湖水位时空动态及洲滩变化研究[D]. 武汉: 武汉大学, 2010.
[33] Cai X B. The change analysis of water stage and beach wetland in Poyang Lake with the aid of multi-temporal active and passive remote sensing data[D]. Wuhan: Wuhan University, 2010.
[34] 徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005, 9(5):589-595.
[34] Xu H Q. A Study on information extraction of water body with the modified normalized difference water index(MNDWI)[J]. Journal of Remote Sensing, 2005, 9(5):589-595.
[35] 杜云艳, 周成虎. 水体的遥感信息自动提取方法[J]. 遥感学报, 1998, 2(4):264-269.
[35] Du Y Y, Zhou C H. Automatically extraction remote sensing information for water bodies[J]. Journal of Remote Sensing, 1998, 2(4):264-269.
[1] FENG Qian, ZHANG Jiahua, DENG Fan, WU Zhenjiang, ZHAO Enling, ZHENG Peixin, HAN Yang. A mapping methodology for wetland categories of the Yellow River Delta based on optimal feature selection and spatio-temporal fusion algorithm[J]. Remote Sensing for Natural Resources, 2024, 36(2): 39-49.
[2] LUO Jiahuan, YAN Yi, XIAO Fei, LIU Huan, HU Zhengzheng, WANG Zhou. Comparing the applicability of five typical spatio-temporal information fusion algorithms based on remote sensing data in vegetation index reconstruction of wetland areas[J]. Remote Sensing for Natural Resources, 2024, 36(2): 60-69.
[3] ZHANG Haojie, YANG Lijuan, SHI Tingting, WANG Shuai. Derivation of tasseled cap transformation coefficients for GF-6 WFV sensor data[J]. Remote Sensing for Natural Resources, 2024, 36(2): 105-115.
[4] LIU Yuan, LI Ting. Analyzing spatio-temporal changes in the shoreline morphology of Xiamen City in the context of urbanization[J]. Remote Sensing for Natural Resources, 2024, 36(1): 267-274.
[5] LI Xintong, SHI Lan, CHEN Duoyan. A deep learning-based study on downscaling of GPM products in Fujian-Zhejiang-Jiangxi area[J]. Remote Sensing for Natural Resources, 2023, 35(4): 105-113.
[6] LI Na, GAN Fuping, DONG Xinfeng, LI Juan, ZHANG Shifan, LI Tongtong. Investigation and applications of rocky desertification based on GF-5 hyperspectral data[J]. Remote Sensing for Natural Resources, 2023, 35(2): 230-235.
[7] YU Sen, JIA Mingming, CHEN Gao, LU Yingying, LI Yi, ZHANG Bochun, LU Chunyan, LI Huiying. A study of the disturbance to mangrove forests in Dongzhaigang, Hainan based on LandTrendr[J]. Remote Sensing for Natural Resources, 2023, 35(2): 42-49.
[8] CHEN Huixin, CHEN Chao, ZHANG Zili, WANG Liyan, LIANG Jintao. A remote sensing information extraction method for intertidal zones based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(4): 60-67.
[9] LI Yi, CHENG Lina, LU Yingying, ZHANG Bochun, YU Sen, JIA Mingming. A study on the changes in coastal tidal flats in the Laizhou Bay based on MSIC and OTSU[J]. Remote Sensing for Natural Resources, 2022, 34(4): 68-75.
[10] QIN Le, HE Peng, MA Yuzhong, LIU Jianqiang, YANG Bin. Change detection of satellite time series images based on spatial-temporal-spectral features[J]. Remote Sensing for Natural Resources, 2022, 34(4): 105-112.
[11] DONG Shuangfa, FAN Xiao, SHI Haigang, XU Liping, ZHANG Xinyi. Study on distribution of thermal discharge in Fuqing nuclear power plant based on Landsat8 and UAV[J]. Remote Sensing for Natural Resources, 2022, 34(3): 112-120.
[12] WANG Siyao, ZHAO Chunlei, CHEN Xia, LIU Dan. Remote sensing-based green space evolution in Tangshan and its influence on heat island effect[J]. Remote Sensing for Natural Resources, 2022, 34(2): 168-175.
[13] BO Yingjie, ZENG Yelong, LI Guoqing, CAO Xingwen, YAO Qingxiu. Impacts of floating solar parks on spatial pattern of land surface temperature[J]. Remote Sensing for Natural Resources, 2022, 34(1): 158-168.
[14] SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[15] ZHANG Aizhu, WANG Wei, ZHENG Xiongwei, YAO Yanjuan, SUN Genyun, XIN Lei, WANG Ning, HU Guang. A hierarchical spatial-temporal fusion model[J]. Remote Sensing for Natural Resources, 2021, 33(3): 18-26.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech