A remote sensing information extraction method for intertidal zones based on Google Earth Engine

doi:10.6046/zrzyyg.2022308

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Abstract

Intertidal zones, as important parts of coastal wetlands play a significant role in ecological and economic development. However, the dynamic interaction between seawater and land makes it difficult to accurately determine the tidal flat area using the remote sensing information extraction method based on instant remote sensing images. To solve this problem, this study developed an intertidal information extraction method based on Google Earth Engine (GEE) platform and remote sensing index. This proposed method was applied to study the coastal zone of Zhoushan Islands. First, a decision tree algorithm based on the fusion of the digital elevation model (DEM) data was built using the Landsat8 time series image data in 2021. Then, a multi-layer automatic decision tree classification model was formed using the maximum spectral index composite (MSIC) and the Otsu algorithm (OTSU). Based on this, the DEM data were fused to extract and calculate the area of the intertidal zone in Zhoushan Islands. The results show that the area of the intertidal zone in Zhoushan Islands is 35.19 km² in 2021. The evaluation based on the Google Earth high-resolution images shows that this proposed method has a general precision of 97.7% and a Kappa coefficient of 0.95, indicating good extraction precision and practical effects. This method can provide data support for sustainable management and utilization of coastal zone resources through automatic and rapid extraction of intertidal information, thus promoting regional high-quality development.

Keywords intertidal zone Landsat8 imagery Google Earth Engine maximum spectral index composite (MSIC) Otsu algorithm (OTSU)

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Issue Date: 27 December 2022

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	Huixin CHEN
	Chao CHEN
	Zili ZHANG
	Liyan WANG
	Jintao LIANG

Cite this article:

Huixin CHEN,Chao CHEN,Zili ZHANG, et al. 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.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022308 OR https://www.gtzyyg.com/EN/Y2022/V34/I4/60

Fig.1 Location of study area

Fig.2 Workflow of intertidal information extraction

Tab.1 Confusion matrix and precision analysis

Fig.3 Extraction result of intertidal zone in Zhoushan Islands

Fig.4 Local extraction results of intertidal zone in Zhoushan Islands

[1]	王小龙, 张杰, 初佳兰. 基于光学遥感的海岛潮间带和湿地信息提取——以东沙岛(礁)为例[J]. 海洋科学进展, 2005(4):477-481.
[1]	Wang X L, Zhang J, Chu J L. Extraction of remotely sensed information of island intertidal zone and wetland:Taking the Dongsha Island as an example[J]. Advances Marine Science, 2005(4):477-481.
[2]	刘瑞清, 李加林, 孙超, 等. 基于Sentinel-2遥感时间序列植被物候特征的盐城滨海湿地植被分类[J]. 地理学报, 2021, 76(7):1680-1692. doi: 10.11821/dlxb202107008
[2]	Liu R Q, Li J L, Sun C, et al. Classification of Yancheng coastal wetland vegetation based on vegetation phenological characteristics derived from Sentinel-2 time-series[J]. Acta Geographica Sinica, 2021, 76(7):1680-1692. doi: 10.11821/dlxb202107008
[3]	李晶, 雷茵茹, 崔丽娟, 等. 我国滨海滩涂湿地现状及研究进展[J]. 林业资源管理, 2018(2):24-28,137.
[3]	Li J, Lei Y R, Cui L J, et al. Current status and research progress of coastal tidal flat wetlands in China[J]. Forest Resources Management, 2018(2):24-28,137.
[4]	吴威, 李彩霞, 陈雪初. 基于生态系统服务的海岸带生态修复工程成效评估——以鹦鹉洲湿地为例[J]. 华东师范大学学报(自然科学版), 2020(3):98-108.
[4]	Wu W, Li C X, Chen X C. Evaluation of the effectiveness of a coastal ecological restoration project based on ecosystem services: A case study on Yingwuzhou Wetland,China[J]. Journal of East China Normal University (Natural Science), 2020(3): 98-108.
[5]	王雨岚, 王远东, 郑衡泌. 基于多时相遥感的泉州湾滨海湿地变化监测[J]. 赣南师范大学学报, 2017, 38(3):102-107.
[5]	Wang Y L, Wang Y D, Zheng H M. Monitoring dynamic changes of coastal wetlands and its surrounding areas in Quanzhou Bay based on multi-temporal remote sensing images[J]. Journal of Gannan Normal University, 2017, 38(3):102-107.
[6]	Ren C, Wang Z, Zhang Y, et al. Rapid expansion of coastal aquaculture ponds in China from Landsat observations during 1984—2016[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 82: 101902. doi: 10.1016/j.jag.2019.101902 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243419304295
[7]	Meng W, Hu B, He M, et al. Temporal-spatial variations and driving factors analysis of coastal reclamation in China[J]. Estuarine,Coastal and Shelf Science, 2017, 191: 39-49. doi: 10.1016/j.ecss.2017.04.008 url: https://linkinghub.elsevier.com/retrieve/pii/S0272771416305807
[8]	程丽娜, 钟才荣, 李晓燕, 等. Sentinel-2密集时间序列数据和Google Earth Engine的潮间带湿地快速自动分类[J]. 遥感学报, 2022, 26(2):348-357.
[8]	Cheng L N, Zhong C R, Li X Y, et al. Rapid and automatic classification of intertidal wetlands based on intensive time series Sentinel-2 images and Google Earth Engine[J]. National Remote Sensing Bulletin, 2022, 26(2):348-357.
[9]	骆永明. 中国海岸带可持续发展中的生态环境问题与海岸科学发展[J]. 中国科学院院刊, 2016, 31(10):1133-1142.
[9]	Luo Y M. Sustainability associated coastal eco-environmental problems and coastal science development in China[J]. Bulletin of Chinese Academy of Sciences, 2016, 31(10): 1133-1142.
[10]	Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202:18-27. doi: 10.1016/j.rse.2017.06.031 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717302900
[11]	Kumar L, Mutanga O. Google Earth Engine applications since inception: Usage,trends,and potential[J]. Remote Sensing, 2018, 10(10):1509. doi: 10.3390/rs10101509 url: http://www.mdpi.com/2072-4292/10/10/1509
[12]	付东杰, 肖寒, 苏奋振, 等. 遥感云计算平台发展及地球科学应用[J]. 遥感学报, 2021, 25(1):220-230.
[12]	Fu D J, Xiao H, Su F Z, et al. Remote sensing cloud computing platform development and Earth science application[J]. National Remote Sensing Bulletin, 2021, 25(1):220-230.
[13]	邹亚东, 何亮, 张晓萍, 等. 基于GEE数据平台的北洛河流域1970—2019年土地利用结构变化特征[J]. 水土保持通报, 2021, 41(6):209-219.
[13]	Zou Y D, He L, Zhang X P, et al. Characteristics of land use structure change in Beiluo River basin during 1970—2019 based on Google Earth Engine[J]. Bulletin of Soil and Water Conservation, 2021, 41(6):209-219.
[14]	戴声佩, 易小平, 罗红霞, 等. 基于GEE和Landsat时间序列数据的海南岛土地利用分类研究[J]. 热带作物学报, 2021, 42(11):3351-3357.
[14]	Dai S P, Yi X P, Luo H X, et al. Mapping land use in Hainan Island based on Google Earth Engine and Landsat time series data[J]. Journal of Tropical Crops, 2021, 42(11):3351-3357.
[15]	陈康明, 朱旭东. 基于Google Earth Engine的南方滨海盐沼植被时空演变特征分析[J]. 遥感技术与应用, 2021, 36(4):751-759.
[15]	Chen K M, Zhu X D. Remote Sensing of spatio-temporal dynamics of saltmarsh vegetation along South China Coast based on Google Earth Engine[J]. Remote Sensing Technology and Application, 2021, 36(4):751-759.
[16]	李若楠, 欧光龙, 代沁伶, 等. 基于GEE和Landsat时间序列数据的香格里拉森林类型分类研究[J]. 西南林业大学学报(自然科学), 2020, 40(5):115-125.
[16]	Li R N, Ou G L, Dai Q L, et al. Forest types classification of Shangri-La based on Google Earth Engine and Landsat time-series data[J]. Journal of Southwest Forest University, 2020, 40(5):115-125.
[17]	Hansen M C, Potapov P V, Moore R, et al. High-resolution global maps of 21st-century forest cover change[J]. Science, 2013, 342(6160):850-853. doi: 10.1126/science.1244693 pmid: 24233722
[18]	Pekel J F, Cottam A, Gorelick N, et al. High-resolution mapping of global surface water and its long-term changes[J]. Nature, 2016, 540(7633):418-422. doi: 10.1038/nature20584 url: https://doi.org/10.1038/nature20584
[19]	闰亚迪, 崔耀平, 秦耀辰, 等. 基于GEE的黄河滩区水土时空动态研究[J]. 人民黄河, 2021, 43(9):85-89,93.
[19]	Run Y D, Cui Y P, Qin Y C, et al. Spatial-temporal dynamic of water and land in the Yellow River beach area based on Google Earth Engine[J]. Yellow River, 2021, 43(9):85-89,93.
[20]	Chen B Q, Xiao X M, Li X P, et al. A mangrove forest map of china in 2015: Analysis of time series Landsat7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 131:104-120. doi: 10.1016/j.isprsjprs.2017.07.011 url: https://linkinghub.elsevier.com/retrieve/pii/S092427161730285X
[21]	Wang X X, Xiao X M, Zou Z H, et al. Tracking annual changes of coastal tidal flats in china during 1986—2016 through analyses of Landsat images with Google Earth Engine[J]. Remote Sensing of Environment, 2018, 238:110987-111002. doi: 10.1016/j.rse.2018.11.030 url: https://linkinghub.elsevier.com/retrieve/pii/S003442571830539X
[22]	Campbell A D, Wang Y Q. Salt marsh monitoring along the Midatlantic Coast by Google Earth Engine enabled time series[J]. PLOS One, 2020, 15(2):229605-229628.
[23]	Catalao J, Nico G. Multitemporal backscattering logistic analysis for intertidal bathymetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2):1066-1073. doi: 10.1109/TGRS.2016.2619067 url: http://ieeexplore.ieee.org/document/7763808/
[24]	Zhang X, Treitz P M, Chen D, et al. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 62: 201-214. doi: 10.1016/j.jag.2017.06.010 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243417301368
[25]	Dhanjal-Adams K L, Hanson J O, Murray N J, et al. The distribution and protection of intertidal habitats in Australia[J]. Emu-Austral Ornithology, 2016, 116(2): 208-214. doi: 10.1071/MU15046 url: https://www.tandfonline.com/doi/full/10.1071/MU15046
[26]	Zhang K Y, Dong X Y, Liu Z G, et al. Mapping tidal flats with Landsat8 images and Google Earth Engine:A case study of the China’s Eastern Coastal Zone circa 2015[J]. Remote Sensing, 2019, 11(8): 924. doi: 10.3390/rs11080924 url: https://www.mdpi.com/2072-4292/11/8/924
[27]	Wang X X, Xiao X M, Zou Z H, et al. Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163: 312-326. doi: 10.1016/j.isprsjprs.2020.03.014 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271620300848
[28]	Murray N J, Phinn S R, Clemens R S, et al. Continental scale mapping of tidal flats across East Asia using the Landsat archive[J]. Remote Sensing, 2012, 4(11):3417-3426. doi: 10.3390/rs4113417 url: http://www.mdpi.com/2072-4292/4/11/3417
[29]	Murray N J, Phinn S R, DeWitt M, et al. The global distribution and trajectory of tidal flats[J]. Nature, 2019, 565(7738): 222-225. doi: 10.1038/s41586-018-0805-8 url: https://doi.org/10.1038/s41586-018-0805-8
[30]	Jia M, Wang Z, Mao D, et al. Rapid,robust,and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine[J]. Remote Sensing of Environment, 2021, 255: 112285. doi: 10.1016/j.rse.2021.112285 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425721000031
[31]	Sagar S, Roberts D, Bala B, et al. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations[J]. Remote Sensing of Environment, 2017, 195: 153-169. doi: 10.1016/j.rse.2017.04.009 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717301591
[32]	Liu Y, Li M, Cheng L, et al. Topographic mapping of offshore sandbank tidal flats using the waterline detection method: A case study on the Dongsha Sandbank of Jiangsu Radial Tidal Sand Ridges,China[J]. Marine Geodesy, 2012, 35(4): 362-378. doi: 10.1080/01490419.2012.699501 url: http://www.tandfonline.com/doi/abs/10.1080/01490419.2012.699501
[33]	Wang X, Gao X, Zhang Y, et al. Land-cover classification of coastal wetlands using the RF algorithm for Worldview-2 and Landsat8 images[J]. Remote Sensing, 2019, 11(16): 1927. doi: 10.3390/rs11161927 url: https://www.mdpi.com/2072-4292/11/16/1927
[34]	Zhang H, Jiang Q, Xu J. Coastline extraction using support vector machine from remote sensing image[J]. Journal of Multimedia, 2013, 8(2): 175-182.
[35]	陈超, 陈慧欣, 陈东, 等. 舟山群岛海岸线遥感信息提取及时空演变分析[J]. 国土资源遥感, 2021, 33(2):141-152.doi: 10.6046/gtzyyg.2020248. doi: 10.6046/gtzyyg.2020248
[35]	Chen C, Chen H X, Chen D, et al. Coastline extraction and spatial-temporal variations using remote sensing technology in Zhoushan Islands[J]. Remote Sensing for Land and Resources, 2021, 33(2):141-152.doi: 10.6046/gtzyyg.2020248. doi: 10.6046/gtzyyg.2020248
[36]	Chen H, Chen C, Zhang Z, et al. Changes of the spatial and temporal characteristics of land-use landscape patterns using multi-temporal Landsat satellite data: A case study of Zhoushan Island,China[J]. Ocean and Coastal Management, 2021, 213: 105842. doi: 10.1016/j.ocecoaman.2021.105842 url: https://linkinghub.elsevier.com/retrieve/pii/S0964569121003252
[37]	Chen C, Chen H, Liao W, et al. Dynamic monitoring and analysis of land-use and land-cover change using Landsat multitemporal data in the Zhoushan Archipelago,China[J]. IEEE Access, 2020, 8: 210360-210369. doi: 10.1109/ACCESS.2020.3036128 url: https://ieeexplore.ieee.org/document/9248989/
[38]	张欢, 李弘毅, 李浩杰, 等. 基于机载LiDAR的高寒山区遥感高程数据精度评估[J]. 遥感技术与应用, 2021, 36(6):1311-1320.
[38]	Zhang H, Li H Y, Li H J, et al. Accuracy evaluation of remote sensing elevation data in alpine mountains based on airborne LiDAR[J]. Remote Sensing Technology and Application, 2021, 36(6):1311-1320.
[39]	卢喜平. 基于Google Earth的DEM数据获取及坡度分析应用[J]. 四川水利, 2018, 39(5):100-104.
[39]	Lu X P. DEM data acquisition and slope analysis application based on Google Earth[J]. Sichuan Water Resources, 2018, 39(5):100-104.
[40]	饶萍, 王建力. 最优分区与最优指数联合的水体信息提取[J]. 地球信息科学学报, 2017, 19(5):702-712. doi: 10.3724/SP.J.1047.2017.00702
[40]	Rao P, Wang J L. Water extraction based on the optimal subregion and the optimal indexes combined[J]. Journal of Geo-Information Science, 2017, 19(5):702-712.
[41]	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. doi: 10.1080/01431169608948714 url: https://www.tandfonline.com/doi/full/10.1080/01431169608948714
[42]	Rogers A S, Kearney M S. Reducing signature variability in unmixing coastal marsh thematic mapper scenes using spectral indices[J]. International Journal of Remote Sensing, 2004, 25(12):2317-2335. doi: 10.1080/01431160310001618103 url: https://www.tandfonline.com/doi/full/10.1080/01431160310001618103
[43]	Xu H Q. Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006, 27(14): 3025-3033. doi: 10.1080/01431160600589179 url: https://www.tandfonline.com/doi/full/10.1080/01431160600589179
[44]	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. doi: 10.1016/j.rse.2013.08.029 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425713002873
[45]	Ji L, Zhang L, Wylie B. Analysis of dynamic thresholds for the normalized difference water index[J]. Photogrammetric Engineering and Remote Sensing, 2009, 75(11):1307-1317. doi: 10.14358/PERS.75.11.1307 url: http://openurl.ingenta.com/content/xref?genre=article&issn=0099-1112&volume=75&issue=11&spage=1307
[46]	Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8(2): 127-150. doi: 10.1016/0034-4257(79)90013-0 url: https://linkinghub.elsevier.com/retrieve/pii/0034425779900130
[47]	吴冰, 秦志远. 自动确定图像二值化最佳阈值的新方法[J]. 测绘学院学报, 2001(4):283-286.
[47]	Wu B, Qin Z Y. New approaches for the automatic selection of the optimal threshold in image binarization[J]. Journal of Institute of Surveying and Mapping, 2001(4):283-286.
[48]	汪政辉, 辛存林, 孙喆, 等. Sentinel-2数据的小型湖泊水生植被类群自动提取方法——以翠屏湖为例[J]. 遥感信息, 2019, 34(5):132-141.
[48]	Wang Z H, Xin C L, Sun Z, et al. Automatic extraction method of aquatic vegetation types in small shallow lakes research based on Sentinel-2 data: A case study of Cuiping Lake[J]. Remote Sensing Information, 2019, 34(5):132-141.
[49]	高媛. 全球30米土地覆盖产品的精度评估研究[D]. 西安: 西安科技大学, 2021.
[49]	Gao Y. Study on accuracy assessment of global 30 m land cover products[D]. Xi’an: Xi’an University of Science and Technology, 2021.

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