سابِق|تال

Rapid and automatic classification of intertidal wetlands based on intensive time series Sentinel-2 images and Google Earth Engine

  • role： First author第一作者
  • Affiliation：

    College of Earth Science, Jilin University, Changchun 130061, China

    Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

  • Email：chengln20@mails.jlu.edu.cn
  • Introduction：程丽娜，1996年生，女，硕士研究生，研究方向为滨海湿地遥感。E-mail： chengln20@mails.jlu.edu.cn
CHENG Lina13,  
  • Affiliation：

    Hainan Academy of Forestry (Hainan Academy of Mangrove), Haikou 571100, China

ZHONG Cairong2,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Earth Science, Jilin University, Changchun 130061, China

  • Email：lxyan@jlu.edu.cn
  • Introduction：李晓燕，1975年生，女，教授，研究方向为资源环境遥感与土地信息系统。E-mail： lxyan@jlu.edu.cn
LI Xiaoyan1*,  
  • Affiliation：

    Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

JIA Mingming3,  
  • Affiliation：

    Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

    National Earth System Science Data Center of China, Beijing 100101, China

WANG Zongming34,  
  • Affiliation：

    Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

MAO Dehua3

ملخص

Intertidal wetlands are an important part of coastal wetlands and have crucial ecological functions, such as maintaining biodiversity and promoting carbon sink. However, intertidal wetlands are severely threatened by coastal erosion, sea level rise, and human activities. Timely and accurately monitoring the status of intertidal wetlands is the basis for achieving the goal of sustainable management of intertidal wetlands. Periodic tidal inundation is one of the largest challenges in mapping intertidal wetlands. The key is to obtain the remote sensing images at the time with the lowest and highest tides rapidly and accurately to accurately extract information of intertidal wetlands. At present, the dense temporal resolution of Sentinel-2 images with revisit interval of 3—5 days offers a great opportunity to capture the lowest and highest tides, which is vital to conduct accurate and robust delineation of intertidal wetlands. Previous efforts on intertidal wetland classification relied on either training samples, manual intervened thresholds or pre and postprocessing. This work aimed to set up an automatic, rapid, and high precision procedure that uses time series Sentinel-2 images to derive intertidal wetland information based on the Google Earth Engine platform.The methodology includes four steps: (1) building a high-quality dense time series image stack; (2) deeply analyzing the time series remote sensing characteristics of different wetland types and selecting appropriate spectral indexes; (3) creating the maximal water surface image, the minimal water surface image, and vegetation difference enhanced image on the basis of the maximum spectral index composite algorithm; and (4) establishing a multilayer automatic decision tree classification model to extract different intertidal wetlands from simple types to complex types by using the Otsu algorithm.The procedure was utilized to classify the intertidal wetlands in the Fujian Zhangjiangkou National Mangrove Nature Reserve in 2020 with an overall accuracy of 96.5% and a kappa coefficient of 0.95. The intertidal wetlands in the Zhangjiangkou Reserve consist of mangrove forest, Spartina alterniflora, and tidal flat, with an area of 82.46, 218.26, and 496.84 hm2, respectively. Abundant tidal flat resources were mainly located on the outer edge of mangrove forest and S. alterniflora. Mangroves were mainly concentrated on the southwest coast of the Zhangjiang River. S. alterniflora was mainly distributed on the south of Zhangjiang River with good integrity, whereas part of them grew on the north side of Zhangjiang River with a banding distribution.The high-quality Sentinel-2 dense time series image stack increases the opportunity to obtain the lowest and highest tide images and provides sufficient phenological information for the classification of mangrove and S. alterniflora. The maximal intertidal water surface can be easily obtained by combining with the modified normalized difference water index maximum value composite image and the method of extracting the largest patch area. The Normalized Difference Vegetation Index (NDVI) maximum value composite image well highlights the difference between tidal flats, water bodies, and vegetated areas. The negative NDVI maximum value composite image plays a positive role in enhancing the characteristic difference between mangrove forest and S. alterniflora. The proposed method can realize the automatic, rapid, and accurate classification of intertidal wetlands, which has important reference value for the accurate classification research of intertidal and other inland wetlands.

مفهوم

tidal flat;wetland;Sentinel-2 imagery;Maximum Spectral Index Composite (MSIC);otsu algorithm (Otsu);Google Earth Engine (GEE)

References

  1. 1.
    Bao Y X, Ge B M, Zheng X, Cheng H Y and Hu Y Z. 2007. Seasonal variation of the macrobenthic community at east tidal flat of Lingkun Island, Wenzhou Bay. Acta Hydrobiologica Sinica, 31(3): 437-444
  2. 2.
    Dong D, Zeng J S, Wei Z and Yan J H. 2020. Integrating spaceborne optical and SAR imagery for monitoring mangroves and Spartina alterniflora in Zhangjiang Estuary. Journal of Tropical Oceanography, 39(2): 107-117
  3. 3.
    Dong J W, Xiao X M, Menarguez M A, Zhang G L, Qin Y W, Thau D, Biradar C and Moore III B. 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185: 142-154
  4. 4.
    Duan Y Q, Li X, Zhang L P, Chen D, Liu S and Ji H Y. 2020. Mapping national-scale aquaculture ponds based on the Google Earth Engine in the Chinese coastal zone. Aquaculture, 520: 734666
  5. 5.
    Gong P. 2021. Intelligent mapping with remote sensing, iMap. Journal of Remote Sensing, 25(2): 527-529
  6. 6.
    Gong P, Zhang W, Yu L, Li C C, Wang J, Liang L, Li X C, Ji L Y and Bai Y Q. 2016. New research paradigm for global land cover mapping. Journal of Remote Sensing, 20(5): 1002-1016
  7. 7.
    Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27
  8. 8.
    Hu J D, Zheng B H and Wan J. 2009. Studies and application of an assessment model of the habitat function degradation in the intertidal wetland. Research of Environmental Sciences, 22(2): 171-175
  9. 9.
    Jia M M, Wang Z M, Mao D H, Ren C Y, Wang C and Wang Y Q. 2021. Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 255: 112285
  10. 10.
    Li H Y, Jia M M, Zhang R, Ren Y X and Wen X. 2019. Incorporating the plant Phenological trajectory into mangrove species mapping with dense time series sentinel-2 imagery and the Google earth engine platform. Remote Sensing, 11(21): 2479
  11. 11.
    Meng W, Wan J and Lei K. 2009. Comparison of substance exchange function of the intertidal wetland in Bohai Bay. Marine Science Bulletin, 28(5): 7-12
  12. 12.
    Murray N J, Phinn S R, Clemens R S, Roelfsema C M and Fuller R A. 2012. Continental scale mapping of tidal flats across East Asia using the Landsat archive. Remote Sensing, 4(11): 3417-3426
  13. 13.
    Murray N J, Phinn S R, DeWitt M, Ferrari R, Johnston R, Lyons M B, Clinton N, Thau D and Fuller R A. 2019. The global distribution and trajectory of tidal flats. Nature, 565(7738): 222-225
  14. 14.
    Neukermans G, Dahdouh-Guebas F, Kairo J G and Koedam N. 2008. Mangrove species and stand mapping in Gazi bay (Kenya) using quickbird satellite imagery. Journal of Spatial Science, 53(1): 75-86
  15. 15.
    Otsu N. 1975. A threshold selection method from gray-level histograms. Automatica, 11(285-296): 23-27
  16. 16.
    Shi B W. 2012. Sediment Dynamic Processes Over Transitional Zone of Salt Marsh-Mudflat on Eastern Chongming Island, Yangtze Estuary. Shanghai: East China Normal University
  17. 17.
    Tian J Y, Wang L, Yin D M, Li X J, Diao C Y, Gong H L, Shi C, Menenti M, Ge Y, Nie S, Ou Y, Song X N and Liu X M. 2020. Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion. Remote Sensing of Environment, 242: 111745
  18. 18.
    Wan J, Meng W and Zheng B H. 2010. Study on assessment method of function degeneration of stable shoreline in inter-tidal wetland. Marine Environmental Science, 29(4): 594-598
  19. 19.
    Wang L, Sousa W P, Gong P and Biging G S. 2004. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment, 91(3/-4): 432-440
  20. 20.
    Wang X X, Xiao X M, Zou Z H, Chen B Q, Ma J, Dong J W, Doughty R B, Zhong Q Y, Qin Y W, Dai S Q, Li X P, Zhao B and Li B. 2020. Tracking annual changes of coastal tidal flats in China during 1986-2016 through analyses of Landsat images with Google Earth Engine. Remote Sensing of Environment, 238: 110987
  21. 21.
    Wang Z H, Xin C L, Sun Z, Luo J H and Ma R H. 2019. Automatic extraction method of aquatic vegetation types in small shallow lakes based on sentinel-2 data: a case study of Cuiping Lake. Remote Sensing Information, 34(5): 132-141
  22. 22.
    Wu Y Q, Xiao X M, Chen B Q, Wang X X and Li X P. 2018. Phenological remote sensing monitoring of salt marsh vegetation in Yancheng intertidal wetland in recent thirty years. Jiangsu Agricultural Sciences, 46(16): 264-270
  23. 23.
    Xu R L, Zhao S Q and Ke Y H. 2021. A simple phenology-based vegetation index for mapping invasive Spartina alterniflora using Google earth engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 190-201
  24. 24.
    Xu W. 2017. Classification of the Intertidal Saltmarsh Using Sentinel-1 and Landsat-8 Data. Shanghai: East China Normal University
  25. 25.
    Xu Y, Zhen J N, Jiang X P and Wang J J. 2021. Mangrove species classification with UAV-based remote sensing data and XGBoost. Journal of Remote Sensing, 25(3): 737-752
  26. 26.
    Yao Y C, Ren C Y, Wang Z M, Wang C and Deng P Y. 2016. Monitoring of Salt Ponds and Aquaculture Ponds in the Coastal Zone of China in 1985 and 2010. ScienceWetland, 14(6): 874-882
  27. 27.
    Zhang W K. 2001. A study on exploitation and utilization of tideland resources in Fujian province. Resources Science, 23(3): 29-32
  28. 28.
    Zhong Q C, Wang K Y, Zhou K and Lai Q F. 2015. Research advances on carbon cycling and its environmental controlling mechanisms in intertidal wetlands. Ecology and Environmental Sciences, 24(1): 174-182

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website