浏览全部资源
扫码关注微信
纸质出版日期： 2022-02-07 ，
扫 描 看 全 文
程丽娜，钟才荣，李晓燕，贾明明，王宗明，毛德华.2022.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.遥感学报，26（2）： 348-357
Cheng L N，Zhong C R，Li X Y，Jia M M，Wang Z M and Mao D H. 2022. Rapid and automatic classification of intertidal wetlands based on intensive time series Sentinel-2 images and Google Earth Engine. National Remote Sensing Bulletin， 26（2）：348-357
潮间带湿地是滨海湿地的重要组成部分，具有维持生物多样性、促进碳汇等重要生态功能。及时、准确地掌握潮间带湿地现状是实现潮间带湿地可持续管理目标的基础。先前的潮间带湿地分类研究依赖于训练样本、人工设定阈值或后处理等，本研究基于GEE（Google Earth Engine）平台开发一种自动、快速、高精度的潮间带湿地分类方法。该方法首先构建高质量密集时序Sentinel-2影像堆栈；然后，分析不同潮间带湿地的遥感特征，基于最大光谱指数合成算法（MSIC）和大津算法（Otsu）建立多层自动决策树分类模型。应用该方法对2020年福建漳江口红树林自然保护区的潮间带湿地进行分类，得到的总体精度为96.5%，Kappa系数为0.95。漳江口红树林保护区内潮间带湿地包括红树林、互花米草和滩涂3种类型，面积分别为82.46 hm
2
、218.26 hm
2
和496.84 hm
2
。本研究的方法能够实现潮间带湿地的自动、快速、高精度分类，对潮间带和其他内陆湿地的精准分类研究具有重要的借鉴价值。
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 hm
2
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.
滩涂湿地Sentinel-2影像最大光谱指数合成算法（MSIC）大津算法（Otsu）Google Earth Engine（GEE）
tidal flatwetlandSentinel-2 imageryMaximum Spectral Index Composite (MSIC)otsu algorithm (Otsu)Google Earth Engine (GEE)
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
鲍毅新, 葛宝明, 郑祥, 程宏毅, 胡一中. 2007. 温州湾灵昆岛东滩潮间带大型底栖动物群落的季节动态. 水生生物学报, 31(3): 437-444 [DOI: 10.3321/j.issn:1000-3207.2007.03.021http://dx.doi.org/10.3321/j.issn:1000-3207.2007.03.021]
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
董迪, 曾纪胜, 魏征, 严金辉. 2020. 联合星载光学和SAR影像的漳江口红树林与互花米草遥感监测. 热带海洋学报, 39(2): 107-117 [DOI: 10.11978/2019063http://dx.doi.org/10.11978/2019063]
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 [DOI: 10.1016/j.rse.2016.02.016http://dx.doi.org/10.1016/j.rse.2016.02.016]
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 [DOI: 10.1016/j.aquaculture.2019.734666http://dx.doi.org/10.1016/j.aquaculture.2019.734666]
Gong P. 2021. Intelligent mapping with remote sensing, iMap. Journal of Remote Sensing, 25(2): 527-529
宫鹏. 2021. 智慧遥感制图(iMap). 遥感学报, 25(2): 527-529 [DOI: 10.11834/jrs.20211010http://dx.doi.org/10.11834/jrs.20211010]
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
宫鹏, 张伟, 俞乐, 李丛丛, 王杰, 梁璐, 李雪草, 计璐艳, 白玉琪. 2016. 全球地表覆盖制图研究新范式. 遥感学报, 20(5): 1002-1016 [DOI: 10.11834/jrs.20166138http://dx.doi.org/10.11834/jrs.20166138]
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 [DOI: 10.1016/j.rse.2017.06.031http://dx.doi.org/10.1016/j.rse.2017.06.031]
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
胡嘉东, 郑丙辉, 万峻. 2009. 潮间带湿地栖息地功能退化评价方法研究与应用. 环境科学研究, 22(2): 171-175 [DOI: 10.13198/j.res.2009.02.49.hujd.011http://dx.doi.org/10.13198/j.res.2009.02.49.hujd.011]
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 [DOI: 10.1016/j.rse.2021.112285http://dx.doi.org/10.1016/j.rse.2021.112285]
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 [DOI: 10.3390/rs11212479http://dx.doi.org/10.3390/rs11212479]
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
孟伟, 万峻, 雷坤. 2009. 渤海湾潮间带湿地物质交换功能的历史比较. 海洋通报, 28(5): 7-12 [DOI: 10.3969/j.issn.1001-6392.2009.05.002http://dx.doi.org/10.3969/j.issn.1001-6392.2009.05.002]
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 [DOI: 10.3390/rs4113417http://dx.doi.org/10.3390/rs4113417]
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 [DOI: 10.1038/s41586-018-0805-8http://dx.doi.org/10.1038/s41586-018-0805-8]
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 [DOI: 10.1080/14498596.2008.9635137http://dx.doi.org/10.1080/14498596.2008.9635137]
Otsu N. 1975. A threshold selection method from gray-level histograms. Automatica, 11(285-296): 23-27
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
史本伟. 2012. 长江口崇明东滩盐沼—光滩过渡带沉积动力过程研究. 上海: 华东师范大学
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 [DOI: 10.1016/j.rse.2020.111745http://dx.doi.org/10.1016/j.rse.2020.111745]
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
万峻, 孟伟, 郑丙辉. 2010. 潮间带湿地稳定岸线功能退化评价方法研究. 海洋环境科学, 29(4): 594-598 [DOI: 10.3969/j.issn.1007-6336.2010.04.032http://dx.doi.org/10.3969/j.issn.1007-6336.2010.04.032]
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 [DOI: 10.1016/j.rse.2004.04.005http://dx.doi.org/10.1016/j.rse.2004.04.005]
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 [DOI: 10.1016/j.rse.2018.11.030http://dx.doi.org/10.1016/j.rse.2018.11.030]
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
汪政辉, 辛存林, 孙喆, 罗菊花, 马荣华. 2019. 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. 遥感信息, 34(5): 132-141 [DOI: 10.3969/j.issn.1000-3177.2019.05.022http://dx.doi.org/10.3969/j.issn.1000-3177.2019.05.022]
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
吴亚茜, 肖向明, 陈帮乾, 王新新, 李香萍. 2018. 近30年来盐城潮间带湿地盐沼植被物候遥感监测. 江苏农业科学, 46(16): 264-270 [DOI: 10.15889/j.issn.1002-1302.2018.16.063http://dx.doi.org/10.15889/j.issn.1002-1302.2018.16.063]
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 [DOI: 10.1109/JSTARS.2020.3038648http://dx.doi.org/10.1109/JSTARS.2020.3038648]
Xu W. 2017. Classification of the Intertidal Saltmarsh Using Sentinel-1 and Landsat-8 Data. Shanghai: East China Normal University
胥为. 2017. 基于Sentinel-1和Landsat 8数据的潮间带盐沼湿地分类研究. 上海: 华东师范大学
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
徐逸, 甄佳宁, 蒋侠朋, 王俊杰. 2021. 无人机遥感与XGBoost的红树林物种分类. 遥感学报, 25(3): 737-752 [DOI: 10.11834/jrs.20210281http://dx.doi.org/10.11834/jrs.20210281]
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
姚云长, 任春颖, 王宗明, 王灿, 邓培银. 2016. 1985年和2010年中国沿海盐田和养殖池遥感监测. 湿地科学, 14(6): 874-882 [DOI: 10.13248/j.cnki.wetlandsci.2016.06.016http://dx.doi.org/10.13248/j.cnki.wetlandsci.2016.06.016]
Zhang W K. 2001. A study on exploitation and utilization of tideland resources in Fujian province. Resources Science, 23(3): 29-32
张文开. 2001. 福建省潮间带滩涂资源的开发利用研究. 资源科学, 23(3): 29-32 [DOI: 10.3321/j.issn:1007-7588.2001.03.007http://dx.doi.org/10.3321/j.issn:1007-7588.2001.03.007]
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
仲启铖, 王开运, 周凯, 来琦芳. 2015. 潮间带湿地碳循环及其环境控制机制研究进展. 生态环境学报, 24(1): 174-182 [DOI: 10.16258/j.cnki.1674-5906.2015.01.025http://dx.doi.org/10.16258/j.cnki.1674-5906.2015.01.025]
相关作者
相关机构
京公网安备11010802024621