滩涂作为潮间带生态系统的重要组成部分，具有维持海岸线稳定，加速物质交换和促进碳循环等独特的环境调节服务功能和生态效益。对潮间带湿地现状进行准确、及时的评估对实现可持续管理目标至关重要。本文借助Google Earth Engine （GEE）云计算平台，选用2020年Sentinel-2密集时间序列遥感影像，集成最大光谱指数合成算法（Maximum spectral index composite，MSIC）和大津算法（Otsu）构建多层决策树分类模型，实现澳大利亚潮间带滩涂的快速自动化提取。经过矢量化处理得到2020年澳大利亚高分辨率潮间带滩涂空间分布数据集，提取的滩涂面积为10708.22 km2，总体精度为95.32%，Kappa系数为0.94。该数据集存储格式为.shp，时间分辨率为年，空间分辨率为10 m，数据量为87.8 M。", "ds_source": "
借助Google Earth Engine （GEE）云计算平台，选用2020年Sentinel-2密集时间序列遥感影像，集成最大光谱指数合成算法（Maximum spectral index composite，MSIC）和大津算法（Otsu）构建多层决策树分类模型，实现澳大利亚潮间带滩涂的快速自动化提取。", "ds_process_way": "
本研究基于GEE用2020年密集时间序列Sentinel-2卫星影像，集成MSIC和Otsu算法绘制了2020年澳大利亚潮间带滩涂空间分布图，（1）筛选并调用澳大利亚地区2020年所有满足条件的Sentinel-2 L2A系列产品，利用QA60波段剔除大量卷云层和厚云层的云像素，以获取高质量观测像素，即有效观测像素。计算归一化差异植被指数和改正后的归一化差异水指数并将其作为影像的两个新波段插入到影像集合中，以构建Sentinel-2高质量密集时序影像集合。（2）选取mNDWI和NDVI波段进行MSIC影像合成，分别生成最高潮影像和最低潮影像。将Otsu算法应用于最高潮影像，得到水体与非水体。鉴于滩涂的上限是人工岸线（堤坝、道路等），且与海水密切相连，通过保留最大水体斑块的思想获取潮间带最大水面。（3）利用潮间带最大水面掩膜提取最低潮影像，得到一幅包含海水、滩涂和潮间带植被的最大潮水淹没区域低潮影像。应用Otsu算法移除高NDVI像素以避免潮间带植被对滩涂提取的影响，得到一幅海水滩涂图像。将Otsu应用于海水滩涂影像，实现滩涂的快速、高精度、稳健提取。通过矢量化处理和叠加分析，最终得到2020年澳大利亚潮间带滩涂空间分布数据。", "ds_quality": "
2020年澳大利亚高分辨率潮间带滩涂空间分布数据集，提取的滩涂面积为10708.22 km2，总体精度为95.32%，Kappa系数为0.94。该数据集存储格式为.shp，时间分辨率为年，空间分辨率为10 m，数据量为87.8 MB。", "ds_acq_start_time": "2020-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "澳大利亚地区，包括西澳大利亚州，北部地区，南澳大利亚州，昆士兰州，新南威尔士州，维多利亚州，塔斯马尼亚州在内的沿海区域", "ds_acq_lon_east": 112.93, "ds_acq_lat_south": 43.64, "ds_acq_lon_west": 153.64, "ds_acq_lat_north": 9.54, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 161901671, "ds_files_count": 9, "ds_format": ".shp、.xml、.shx、.sbx、.sbn、.prj、.dbf、.cpg", "ds_space_res": "10 meters", "ds_time_res": "1 year", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "478d8f90-1714-4abd-8a78-efc788fe91e6.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.40" ], "quality_level": 3, "publish_time": "2023-06-25 18:12:26", "last_updated": "2025-04-24 16:15:05", "protected": false, "protected_to": null, "lang": "null", "cstr": "11738.11.NCDC.RS.DB2883.2023", "i18n": { "en": { "title": "Australian Intertidal mudflat Spatial Distribution Data Set (2020)", "ds_format": ".shp、.xml、.shx、.sbx、.sbn、.prj、.dbf、.cpg", "ds_source": "
With the help of Google Earth Engine (GEE) cloud computing platform, the remote sensing images of Sentinel-2 dense time series in 2020 are selected, and the multi-level decision tree classification model is built by integrating the Maximum spectral index composite (MSIC) algorithm and Otsu algorithm to achieve rapid and automatic extraction of mudflat in the intertidal zone of Australia.", "ds_quality": "
In the 2020 Australian high-resolution intertidal zone mudflat spatial distribution data set, the extracted mudflat area is 10708.22 km2, the overall accuracy is 95.32%, and the Kappa coefficient is 0.94. The storage format of this dataset is. shp, with a time resolution of years and a spatial resolution of 10m, and a data volume of 87.8 MB.", "ds_ref_way": "", "ds_abstract": "
As an important part of intertidal ecosystem, mudflat have unique environmental regulation services and ecological benefits such as maintaining the stability of coastline, accelerating material exchange and promoting carbon cycle. Accurate and timely assessment of the current status of intertidal wetlands is crucial for achieving sustainable management goals. This paper uses Google Earth Engine (GEE) cloud computing platform, selects Sentinel-2 dense time series remote sensing images in 2020, integrates the Maximum spectral index composite (MSIC) algorithm and Otsu algorithm to build a multi-level decision tree classification model, and realizes the rapid and automatic extraction of mudflat in the intertidal zone of Australia. The spatial distribution data set of mudflat in Australia's high resolution intertidal zone in 2020 was obtained through vectorization. The extracted mudflat area was 10708.22 km2, the overall accuracy was 95.32%, and the Kappa coefficient was 0.94. The storage format of this dataset is. shp, with a time resolution of years and a spatial resolution of 10m, and a data volume of 87.8M.
", "ds_time_res": "1 year", "ds_acq_place": "Australia, including Western Australia, Northern Territory, South Australia, Queensland, New South Wales, Victoria, Tasmania and other coastal areas", "ds_space_res": "10 meters", "ds_projection": "WGS_1984_Albers", "ds_process_way": "This research is based on GEE's 2020 intensive time series Sentinel-2 Satellite imagery, integrated with MSIC and Otsu algorithms, and mapped the spatial distribution map of Australia's Intertidal zone mudflat in 2020. Calculate the normalized differential vegetation index and the corrected normalized differential water index, and insert them as two new bands of the image into the image set to construct the Sentinel-2 high-quality dense temporal image set. (2) Select the mNDWI and NDVI bands for MSIC image synthesis, and generate the highest and lowest tide images, respectively. Apply the Otsu algorithm to the climax image to obtain water and non water bodies. Since the upper limit of mudflat is artificial shoreline (dike, road, etc.), and it is closely connected with seawater, the maximum water surface of Intertidal zone can be obtained by retaining the idea of the largest water body patch. (3) Using the maximum water surface mask of Intertidal zone to extract the lowest tide image, a low tide image of the maximum tidal submerged area containing seawater, mudflat and Intertidal zone vegetation is obtained. Otsu algorithm is applied to remove high NDVI pixels to avoid the impact of Intertidal zone vegetation on mudflat extraction, and a seawater mudflat image is obtained. Otsu is applied to seawater mudflat image to realize fast, high-precision and robust extraction of mudflat. Through vectorization and superposition analysis, the spatial distribution data of Intertidal zone mudflat in Australia in 2020 is finally obtained.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "滩涂" ], "ds_subject_tags": [ "地图学" ], "ds_class_tags": [], "ds_locus_tags": [ "澳大利亚" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ { "true_name": "贾明明", "email": "jiamingming@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" }, { "true_name": "李慧颖", "email": "Lihy@qut.edu.cn", "work_for": "青岛理工大学", "country": "中国" }, { "true_name": "于皓", "email": "yuhao@jlju.edu.cn", "work_for": "吉林建筑大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈繁", "email": "Changchunqch@163.com", "work_for": "吉林建筑大学", "country": "中国" } ], "ds_managers": [ { "true_name": "程丽娜", "email": "chengln20@mails.jlu.edu.cn", "work_for": "吉林大学", "country": "中国" } ], "category": "遥感及产品" }