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

CHEN Huixin; CHEN Chao; ZHANG Zili; WANG Liyan; LIANG Jintao

doi:10.6046/zrzyyg.2022308

2022 Vol. 34, No. 4

Article Contents

Article navigation > Remote Sensing for Natural Resources > 2022 > 34(4): 60-67

Next Article Previous Article

CHEN Huixin, CHEN Chao, ZHANG Zili, WANG Liyan, LIANG Jintao. 2022. A remote sensing information extraction method for intertidal zones based on Google Earth Engine. Remote Sensing for Natural Resources, 34(4): 60-67. doi: 10.6046/zrzyyg.2022308

Citation:

CHEN Huixin, CHEN Chao, ZHANG Zili, WANG Liyan, LIANG Jintao. 2022. A remote sensing information extraction method for intertidal zones based on Google Earth Engine. Remote Sensing for Natural Resources, 34(4): 60-67. doi: 10.6046/zrzyyg.2022308

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

1. Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China；
2. Zhejiang Province Ecological Environment Monitoring Centre (Zhejiang Key Laboratory of Ecological and Environmental Monitoring,Forewarning and Quality Control), Hangzhou 310012, China

Received Date: 27 July 2022

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

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

FullText(HTML)

References

[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. Google Scholar
[2]	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. Google Scholar
[3]	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. Google Scholar
[4]	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. Google Scholar
[5]	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. Google Scholar
[6]	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. Google Scholar
[7]	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. Google Scholar
[8]	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. Google Scholar
[9]	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. Google Scholar
[10]	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. Google Scholar
[11]	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. Google Scholar
[12]	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. Google Scholar
[13]	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. Google Scholar
[14]	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. Google Scholar
[15]	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. Google Scholar
[16]	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. Google Scholar
[17]	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. Google Scholar
[18]	Kumar L, Mutanga O. Google Earth Engine applications since inception: Usage,trends,and potential[J]. Remote Sensing, 2018, 10(10):1509. Google Scholar
[19]	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. Google Scholar
[20]	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. Google Scholar
[21]	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. Google Scholar
[22]	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. Google Scholar
[23]	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. Google Scholar
[24]	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. Google Scholar
[25]	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. Google Scholar
[26]	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. Google Scholar
[27]	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. Google Scholar
[28]	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. Google Scholar
[29]	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. Google Scholar
[30]	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. Google Scholar
[31]	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. Google Scholar
[32]	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. Google Scholar
[33]	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. Google Scholar
[34]	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. Google Scholar
[35]	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. Google Scholar
[36]	Catalao J, Nico G. Multitemporal backscattering logistic analysis for intertidal bathymetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2):1066-1073. Google Scholar
[37]	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. Google Scholar
[38]	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. Google Scholar
[39]	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. Google Scholar
[40]	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. Google Scholar
[41]	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. Google Scholar
[42]	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. Google Scholar
[43]	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. Google Scholar
[44]	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. Google Scholar
[45]	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. Google Scholar
[46]	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. Google Scholar
[47]	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. Google Scholar
[48]	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. Google Scholar
[49]	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. Google Scholar
[50]	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. Google Scholar
[51]	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. Google Scholar
[52]	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. Google Scholar
[53]	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. Google Scholar
[54]	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. Google Scholar
[55]	Lu X P. DEM data acquisition and slope analysis application based on Google Earth[J]. Sichuan Water Resources, 2018, 39(5):100-104. Google Scholar
[56]	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. Google Scholar
[57]	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. Google Scholar
[58]	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. Google Scholar
[59]	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. Google Scholar
[60]	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. Google Scholar
[61]	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. Google Scholar
[62]	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. Google Scholar
[63]	Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8(2): 127-150. Google Scholar
[64]	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. Google Scholar
[65]	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. Google Scholar
[66]	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. Google Scholar
[67]	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. Google Scholar
[68]	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. Google Scholar
[69]	Gao Y. Study on accuracy assessment of global 30 m land cover products[D]. Xi’an: Xi’an University of Science and Technology, 2021. Google Scholar