数据集为全球100万海岸线数据,包括海岸线空间分布,海海岸线名称及所在国家名称等属性数据。数据的时间范围为2012年
比例尺为 1:100万
字段1:Eng_Name (海岸线名称) 数据类型:Text(100)
字段2:CNTRY_CODE (城市简称) 数据类型:Text(10)
字段3:CONTINENT (城市名称) 数据类型:Text(50)
字段4:TYPE (类型) 数据类型:Text(75)
字段5:Shape_Leng (长度) 数据类型:Double(0,0)
采集时间 | 2012/01/01 - 2012/12/31 |
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采集地点 | 全球范围 |
数据量 | 108.0 MiB |
数据格式 | shp |
坐标系 | WGS84 |
购买自ADC WorldMap7.1数据库
用arcmap对数据库进行导出及整理,做了几何、拓扑和属性检查,对所有多边形都是用ArcGIS Repair Geometry工具进行检查。
数据质量良好
# | 标题 | 文件大小 |
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1 | 全球1:100万海岸线数据集.zip | 108.0 MiB |
# | 时间 | 姓名 | 用途 |
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1 | 2024/03/26 23:58 | 谭*鹏 |
论文题目:全球树木多样性研究
数据在研究中的作用:与树木多样性进行相关分析
论文类型:SCI
导师姓名:郭文永
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2 | 2024/03/03 04:48 | 丘*国 |
硕士论文:基于MGWR的道路网络与生态质量的多尺度效应
作用:计算欧氏距离
导师:胡喜生教授
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3 | 2024/02/28 22:35 | 耿* |
论文题目:福建省沿海地区水产养殖研究
数据在研究中的作用:用于确定海陆分割位置
论文类型:本科论文
导师姓名:焦华富
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4 | 2024/01/31 00:48 | 庆* |
为了进行有关风电站潜力估计和选址的研究,
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5 | 2024/01/30 23:39 | 岳*龙 |
绘制全球土地利用变化的图时,制图需要海岸线数据
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6 | 2024/01/10 18:12 | 孙*婷 |
用于亚太地区各个国家的海岸线绘图以及计算热带气旋登陆个数等相关工作
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7 | 2023/12/19 01:00 | 杨*鑫 |
计划用于研究全球基本地理要素、专题要素、社会经济要素的专题信息
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8 | 2023/12/14 23:50 | 张* |
数据用于提取沿海岸新石器时代遗址,探讨沿海史前人群的生业模式
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9 | 2023/11/14 22:24 | 万* |
自身是一个GIS爱好者,想用这个海岸线数据做一个专题数据。
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10 | 2023/10/30 18:46 | 张*然 |
论文题目:Mapping multi-depth soil salinity using remote sensing-enabled machine learning in the Yellow River Delta, China
论文摘要:Soil salinization is a crucial type in the degradation of coastal land, but its spatial distribution and drivers have not been sufficiently explored especially at the depth scale owing to its multi-dimensional nature. In this study, we proposed a novel multi-depth soil salinity reconstruction model (0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm) fully using the advantages of satellite image data and field sampling to rapidly estimate the multi-depth soil salinity in the Yellow River Del-ta, China. Firstly, a multi-depth soil salinity predictive factor system was developed through correlation analysis of soil sample electrical conductivity with a series of remote-sensing pa-rameters containing heat, moisture, salinity, vegetation indices, spectral value, and spatial loca-tion. Then, three machine learning methods including back propagation neural network (BP), support vector machine (SVM), and random forest (RF) were adopted to construct a coastal soil salinity inversion model. By using the best inversion model, we obtain the spatial distribution of soil salinity in the Yellow River Delta. The results show that (1) the vegetation index had a strong indication for the spatial distribution of soil salinity at various depths, with correlations above 0.350; (2) The RF model was chosen as the optimal approach for predicting and mapping soil salinity based on performance at the four soil depths; (3) The soil salinity profiles exhibited intricate coexistence of two distinct types: surface aggregated and homogeneous. The former was dominant in the east, where salinity was higher. The central and southwestern parts were mostly homogeneous, with lower soil salinity. (4) the soil salinity throughout the four depths examined were found to be most elevated in saltern and bare land and lowest in wetland vegetation and farmland, according to land-cover type.
论文类型:期刊论文
导师姓名:傅新
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