Monitoring of spatial-temporal dynamic changes in water surface in marshes based on multi-temporal Sentinel-1A data

doi:10.6046/zrzyyg.2021205

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Abstract

Water is an important factor in the formation and maintenance of wetland ecosystems. Monitoring the changes in the water area of wetlands is of great significance for wetland conservation. Taking Sentinel-1A data from 2018 to 2019 as the data source, this study calculated the intra- and inter-annual synthetic aperture radar (SAR) backscattering coefficient (σ⁰) and coherence coefficient (μ⁰) images of the Zhalong Wetland. Then, this study assigned weights according to the proximity to water bodies of color optical images and extracted the weighted images of σ⁰ and μ⁰. Finally, this study extracted the wetland water bodies using the threshold segmentation method and random forest algorithm. The purpose is to monitor the dynamic variations in the wetland water area and explore the intra- and inter-annual variation rules of the wetland water body. The results are as follows. The random forest algorithm yielded the highest extraction accuracy of water bodies, with an absolute value of the mean difference of representative months was 6.69 km². The threshold segmentation method based on μ⁰ images yielded the lowest classification accuracy of water bodies, with an absolute value of the mean difference of 13.07 km². Overall, the intra-annual water area of the Zhalong Wetland showed significant seasonal variations during 2018—2019. The water area fluctuated in the ranges of 1 300~1 600 km² during late spring and early summer and 700~900 km² during late summer and early autumn. The inter-annual water area varied with conditions such as climate and temperature. In particular, the wetland water area in October and November 2019 was approximately 1 050 km² greater than that in 2018 due to large amounts of rainfall. As shown by the calculation based on effective data, the water area in 2019 was about 550 km² greater than that in 2018 in the Zhalong Wetland.

Keywords intra- and inter-annual variations of wetland water surface Sentinel-1A backscattering coefficient coherence coefficient threshold segmentation

ZTFLH:

TP79

Corresponding Authors: FU Bolin E-mail: 1056930549@qq.com;fbl2012@126.com

Issue Date: 20 June 2022

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	Chang WEI
	Bolin FU
	Jiaoling QIN
	Yanan WANG
	Zhihan CHEN
	Bing LIU

Cite this article:

Chang WEI,Bolin FU,Jiaoling QIN, et al. Monitoring of spatial-temporal dynamic changes in water surface in marshes based on multi-temporal Sentinel-1A data[J]. Remote Sensing for Natural Resources, 2022, 34(2): 251-260.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021205 OR https://www.gtzyyg.com/EN/Y2022/V34/I2/251

Fig.1 Geographical location of the study area

Tab.1 Image data specific information

Fig.2 Example graph of data preprocessing in August 2019

Fig.3 Technology roadmap

代表月份	加权影像	水体提取阈值分割范围
2018年3月	$0.8 σ 0 + 0.2 μ 0$	[0.001,0.180]
2018年8月	$0.5 σ 0 + 0.5 μ 0$	[0.010,0.085]
2018年10月	$0.7 σ 0 + 0.3 μ 0$	[0.005,0.155]
2018年12月	$0.6 σ 0 + 0.4 μ 0$	[0.006,0.250]

Tab.2 Calculation formulas and threshold parameters of weighted σ⁰ and μ⁰ data bands in 2018

Tab.3 Multi-source data set parameters and selected ROI sample information in October 2019

Fig.4 Example of color image of Zhalong Wetland

Fig.5 Comparison of water distribution in Zhalong Wetland obtained by threshold segmentation method and RF classification method based on each data in December 2019

Tab.4 Comparison of water body extraction results based on data obtained by different processing methods in 2019(km²)

Fig.6 Variation trend map of Zhalong Wetland water area based on σ⁰ data extraction (threshold segmentation method)

Tab.5 Water area of Zhalong Wetland extracted from σ⁰ data from 2018 to 2019 (threshold segmentation method)(km²)

Fig.7 An example map of the comparison of water changes in the Neizhalong Wetland in 2019

Fig.8 An example of the comparison of interannual water changes in Zhalong Wetland

Tab.6 Zhalong wetland water body area extracted based on μ⁰ data from 2018 to 2019 (threshold segmentation method)(km²)

Fig.9 Change trend map of Zhalong wetland water area based on μ⁰ data extraction (threshold segmentation method)

Tab.7 Water body area extracted based on weighted σ⁰and μ⁰ data (threshold segmentation method) (km²)

Fig.10 The inter-annual water body area ectracted based on the weighted σ⁰ and μ⁰ data in Zhalong Wetland (threshold segmentation method)

Tab.8 Extraction of water area in Zhalong wetland based on random forest algorithm(km²)

[1]	马玥. 基于多源遥感信息综合的湿地土地覆被分类研究[D]. 长春: 吉林大学, 2018.
[1]	Ma Y. Land cover classification of wetland based on multi-source remote sensing[D]. Changchun: Jilin University, 2018.
[2]	张鹏. 大庆市湿地动态变化分析[D]. 哈尔滨: 东北农业大学, 2018.
[2]	Zhang P. Dynamic change analysis of Daqing wetland[D]. Harbin: Northeast Agricultural University, 2018.
[3]	刘言. 湿地水文连通机理与模式分析——以莫莫格国家级自然保护区为例[D]. 长春: 吉林大学, 2020.
[3]	Liu Y. Mechanism and pattern analysis of wetland hydrological connectivity:Taking Momoge National Natural as an example[D]. Changchun: Jilin University, 2020.
[4]	王前进, 王希群, 陆诗雷, 等. 生态补偿的经济学理论基础及中国的实践[J]. 林业经济, 2019, 41(1):3-23.
[4]	Wang Q J, Wang X Q, Lu S L, et al. The basis of economic theory on the ecological compensation and its practice in China[J]. Forestry Economy, 2019, 41(1): 3-23.
[5]	李丹, 吴保生, 陈博伟, 等. 基于卫星遥感的水体信息提取研究进展与展望[J]. 清华大学学报(自然科学版), 2020, 60(2):147-161.
[5]	Li D, Wu B S, Chen B W, et al. Review of water body information extraction based on satellite remote sensing[J]. Journal of Tsinghua University (Natural Science Edition), 2020, 60(2):147-161.
[6]	甄佳宁. 基于多时相遥感的长春湿地动态变化研究[D]. 长春: 吉林大学, 2016.
[6]	Zhen J N. The research of wetland dynamic change in Changchun based on multi-temporal remote sensing[D]. Changchun: Jilin University, 2016.
[7]	Jin H R, Huang C Q, Megan W L, et al. Monitoring of wetland inundation dynamics in the Delmarva Peninsula using Landsat time-series imagery from 1985 to 2011[J]. Remote Sensing of Environment, 2017, 190:26-41. doi: 10.1016/j.rse.2016.12.001 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425716304758
[8]	常文涛, 陈欢, 常伟纲. 结合时间序列Sentinel-1数据和面向对象的湿地信息提取方法[J]. 北京测绘, 2020, 34(3):365-370.
[8]	Chang W T, Chen H, Chang W G. Time series Sentinel-1 data and object-oriented wetland information extraction method[J]. Beijing Surveying and Mapping, 2020, 34(3):365-370.
[9]	徐怡波. 基于ENVISAT ASAR数据的洞庭湖湿地遥感监测研究[D]. 南京: 南京林业大学, 2010.
[9]	Xu Y B. Study on the remote sensing monitoring of wetland in the Dongting Lake using ENVISAT ASAR data[D]. Nanjing: Nanjing Forestry University, 2010.
[10]	付波霖, 蓝斐芜, 邓腾芳, 等. 基于DInSAR的洪河国家级自然保护区沼泽水位相对变化量监测研究[J]. 湿地科学, 2021, 19(1):27-39.
[10]	Fu B L, Lan F W, Deng T F, et al. Monitoring of relative variation of water level of marshes in Honghe National Nature Reserve using DInSAR technique[J]. Wetland Science, 2021, 19(1): 27-39.
[11]	贺广均. 联合SAR与光学遥感数据的山区积雪识别研究[D]. 南京: 南京大学, 2015.
[11]	He G J. Snow recognition in mountain areas based on SAR and optical remote sensing data[D]. Nanjing: Nanjing University, 2015.
[12]	关韵桐. 基于SAR与光学数据的高原湿地土壤水分反演研究[D]. 昆明: 云南师范大学, 2019.
[12]	Guan Y T. Inversion of soil moisture in plateau wetland based on SAR and optical data[D]. Kunming: Yunnan Normal University, 2019.
[13]	Mizuochia H, Hiyama T, Ohta T, et al. Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: An integrated use of AMSR series,MODIS,and Landsat[J]. Remote Sensing of Environment, 2017, 199: 370-388. doi: 10.1016/j.rse.2017.07.026 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717303425
[14]	常文涛, 王浩, 宁晓刚, 等. 融合Sentinel-2红边波段和Sentinel-1雷达波段影像的扎龙湿地信息提取[J]. 湿地科学, 2020, 18(1):10-19.
[14]	Chang W T, Wang H, Ning X G, et al. Extraction of Zhalong wetlands information based on images of Sentinel-2 red-edge bands and Sentinel-1 Radar bands[J]. Wetland Science, 2020, 18(1): 10-19.
[15]	李哲, 宫兆宁, 刘先林, 等. 基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究[J]. 遥感技术与应用, 2018, 33(6):1149-1158.
[15]	Li Z, Gong Z N, Liu X L, et al. Vegetation coverage retrieval and spatio-temporal distribution based on object-oriented multi-terminal mixed model[J]. Remote Sensing Technology and Application, 2018, 33(6):1149-1158.
[16]	刘瑶, 余自强, 范杰平, 等. 鄱阳湖丰水期水体后向散射特性研究[J]. 华中师范大学学报(自然科学版), 2019, 53(2):283-289.
[16]	Liu Y, Yu Z Q, Fan J P, et al. The characters of backscattering coefficient during flood period in Poyang Lake[J]. Journal of Central China Normal University (Natural Science Edition), 2019, 53(2):283-289.
[17]	贾亮亮, 汪小钦, 王峰. 基于波段运算和纹理特征的高分一号多光谱数据云检测[J]. 遥感信息, 2018, 33(5):62-68.
[17]	Jia L L, Wang X Q, Wang F. Cloud detection based on band operation texture feature for GF-1 multispectral data[J]. Remote Sensing Information, 2018, 33(5):62-68.
[18]	王婕. 基于分类导向的三维联合头部姿态估计与人脸关键点定位[D]. 合肥: 中国科学技术大学, 2017.
[18]	Wang J. Joint 3D head pose and face landmarks regression based on classification-guided method[D]. Hefei: University of Science and Technology of China, 2017.
[19]	Gudina L F, Henrik M, Rasmus F, 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. doi: 10.1016/j.rse.2013.08.029 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425713002873
[20]	王一帆, 徐涵秋. 基于客观阈值与随机森林Gini指标的水体遥感指数对比[J]. 遥感技术与应用, 2020, 35(5):1089-1098.
[20]	Wang Y F, Xu H Q. Comparison of remote sensing water indices based on objective threshold value and the random forest gini coefficient[J]. Remote Sensing Technology and Application, 2020, 35(5):1089-1098.

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