Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 2: Georgia/South Carolina border to North Carolina/Delaware border

Data file: E_USA_SouthCarolina_NorthCarolina_ref_shoreline.geojson Region: Georgia/South Carolina border to North Carolina/Delaware border Data fields: MEAN_SIG_WAVEHEIGHT TIDAL_RANGE CHLOROPHYLL TURBIDITY TEMP_MOISTURE EMU_PHYSICAL REGIONAL_SINUOSITY GHM MAX_SLOPE % OUTFLOW_DENSITY ERODIBILITY LENGTH_GEO ch_label river_label sinuosity_label slope_label tidal_label turbid_label wave_label CSU_Descriptor CSU_ID The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk The data are described in the following publication Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: 10.1080/1755876X.2018.1529714 ABSTRACT A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km² (21,818), and islands smaller than 1 km² (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet. https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714数据文件：E_USA_SouthCarolina_NorthCarolina_ref_shoreline.geojson 研究区域：佐治亚州/南卡罗来纳州边界至北卡罗来纳州/特拉华州边界 数据字段包含：MEAN_SIG_WAVEHEIGHT（平均有效波高）、TIDAL_RANGE（潮差）、CHLOROPHYLL（叶绿素浓度）、TURBIDITY（浊度）、TEMP_MOISTURE（温度与湿度）、EMU_PHYSICAL、REGIONAL_SINUOSITY（区域弯曲度）、GHM、MAX_SLOPE（最大坡度）、OUTFLOW_DENSITY %（径流量密度百分比）、ERODIBILITY（可侵蚀性）、LENGTH_GEO（地理长度）、ch_label（类别标签）、river_label（河流标签）、sinuosity_label（弯曲度标签）、slope_label（坡度标签）、tidal_label（潮汐标签）、turbid_label（浊度标签）、wave_label（波高标签）、CSU_Descriptor（海岸单元描述符）、CSU_ID（海岸单元标识符） 本数据源自：https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk 该数据的相关描述见于以下学术论文： Roger Sayre、Suzanne Noble、Sharon Hamann、Rebecca Smith、Dawn Wright、Sean Breyer、Kevin Butler、Keith Van Graafeiland、Charlie Frye、Deniz Karagulle、Dabney Hopkins、Drew Stephens、Kevin Kelly、Zeenatul Basher、Devon Burton、Jill Cress、Karina Atkins、D. Paco Van Sistine、Beverly Friesen、Rebecca Allee、Tom Allen、Peter Aniello、Irawan Asaad、Mark John Costello、Kathy Goodin、Peter Harris、Maria Kavanaugh、Helen Lillis、Eleonora Manca、Frank Muller-Karger、Bjorn Nyberg、Rost Parsons、Justin Saarinen、Jac Steiner与Adam Reed（2019年）。论文题为《用于构建标准化生态海岸单元的新型30米分辨率全球岸线矢量数据及配套全球岛屿数据库》，发表于《作业海洋学杂志（Journal of Operational Oceanography）》，卷12（增刊2），页码范围S47-S56，DOI：10.1080/1755876X.2018.1529714 摘要：本研究基于2014年陆地卫星（Landsat）遥感影像年度合成数据，构建了全新的30米空间分辨率全球岸线矢量数据（Global Shoreline Vector, GSV）。通过手动选取覆盖全球所有海岸线的水体与非水体类别训练样本点，完成了影像的半自动分类。对全球岸线矢量数据应用多边形拓扑关系后，实现了全球岛屿数量与规模的全新表征。本研究共划定三类岛屿规模等级：大陆主体（5个）、面积大于1平方千米的岛屿（21818个）以及面积小于1平方千米的岛屿（318868个）。全球岸线矢量数据代表了岸带水陆界面边界，是区分陆地与海洋环境的空间显性生态域分隔界线。本文详述了全球岸线矢量数据的构建流程与特征，并提出了一种划分标准化、高空间分辨率全球生态海岸单元（Ecological Coastal Units, ECUs）的方法。在本次海岸生态系统制图工作中，将借助全球岸线矢量数据区分近岸海域与陆上沿岸陆地。本全球岸线矢量数据与生态海岸单元的构建工作由地球观测组织（Group on Earth Observations, GEO）委托开展，并关联了多项地球观测组织相关倡议，包括GEO生态系统计划、GEO海洋生物多样性观测网络（Marine Biodiversity Observation Network, MBON）以及GEO蓝色星球计划。 论文链接：https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714