默里全球潮汐湿地变化数据集包含全球潮汐湿地范围及其变化的地图。这些地图是根据三个阶段的分类法绘制的,旨在(i)估计潮汐湿地(定义为潮汐沼泽、潮汐平地或红树林生态系统)的全球分布,(ii)检测它们在研究期间的变化,以及(iii)估计生态系统类型和潮汐湿地变化事件的时间。
该数据集是通过将Landsat 5至8号卫星获取的1,166,385张卫星图像的观测数据与已知影响每种生态系统类型分布的变量的环境数据相结合而产生的,包括温度、坡度和海拔。该图像包含研究期开始和结束时间段的潮汐湿地范围产品(潮汐湿地出现的随机森林概率)和整个研究期的潮汐湿地变化产品(潮汐湿地的损失和增加)的波段。
请见项目网站上的使用说明。关于该软件产生的数据的方法、验证和局限性的完整描述可在相关科学论文中找到。前言 – 人工智能教程
也可参见UQ/murray/Intertidal/v1_1/global_intertidal,了解潮滩生态系统的全球分布图。
Dataset Availability
1999-01-01T00:00:00 - 2019-12-31T00:00:00
Dataset Provider
Murray/JCU
Collection Snippet
Copied
ee.Image("JCU/Murray/GIC/global_tidal_wetland_change/2019")
Resolution
30 meters
Bands Table
| Name |
Description |
| loss |
Set to 1 for loss locations, masked out otherwise. |
| lossYear |
Integer representing the end year of the time-step of loss analysis (e.g., 19 = 2017-2019). |
| lossType |
Loss type * 2 - Tidal Flat * 3 - Mangrove * 5 - Tidal Marsh |
| gain |
Set to 1 for gain locations, masked out otherwise. |
| gainYear |
Integer representing the end year of the time-step of gain analysis (e.g., 19 = 2017-2019). |
| gainType |
Gain type: * 2 - Tidal Flat * 3 - Mangrove * 5 - Tidal Marsh |
| twprobabilityStart |
Random forest agreement of the overarching tidal wetland class for the first time step (1999-2001). Integer between 0 and 100. |
| twprobabilityEnd |
Random forest agreement of the overarching tidal wetland class for the last time step (2017-2019). Integer between 0 and 100. |
Murray, N.J., Worthington, T.A., Bunting, P., Duce, S., Hagger, V., Lovelock, C.E., Lucas, R., Saunders, M.I., Sheaves, M., Spalding, M., Waltham, N.J., Lyons, M.B., 2022. High-resolution mapping of losses and gains of Earth's tidal wetlands. Science. doi:10.1126/science.abm9583
https://doi.org/10.1126/science.abm9583
代码:
var dataset = ee.Image('JCU/Murray/GIC/global_tidal_wetland_change/2019');
Map.setCenter(103.7, 1.3, 12);
Map.setOptions('SATELLITE');
var plasma = [
'0d0887', '3d049b', '6903a5', '8d0fa1', 'ae2891', 'cb4679', 'df6363',
'f0844c', 'faa638', 'fbcc27', 'f0f921'
];
Map.addLayer(
dataset.select('twprobabilityStart'), {palette: plasma, min: 0, max: 100},
'twprobabilityStart', false, 1);
Map.addLayer(
dataset.select('twprobabilityEnd'), {palette: plasma, min: 0, max: 100},
'twprobabilityEnd', false, 1);
var lossPalette = ['fe4a49'];
var gainPalette = ['2ab7ca'];
Map.addLayer(
dataset.select('loss'), {palette: lossPalette, min: 1, max: 1},
'Tidal wetland loss', true, 1);
Map.addLayer(
dataset.select('gain'), {palette: gainPalette, min: 1, max: 1},
'Tidal wetland gain', true, 1);
var viridis = ['440154', '414487', '2a788e', '22a884', '7ad151', 'fde725'];
Map.addLayer(
dataset.select('lossYear'), {palette: viridis, min: 4, max: 19},
'Year of loss', false, 0.9);
Map.addLayer(
dataset.select('gainYear'), {palette: viridis, min: 4, max: 19},
'Year of gain', false, 0.9);
// Ecosystem type.
var classPalette = ['9e9d9d', 'ededed', 'ff9900', '009966', '960000', '006699'];
var classNames =
['null', 'null', 'Tidal flat', 'Mangrove', 'null', 'Tidal marsh'];
Map.addLayer(
dataset.select('lossType'), {palette: classPalette, min: 0, max: 5},
'Loss type', false, 0.9);
Map.addLayer(
dataset.select('gainType'), {palette: classPalette, min: 0, max: 5},
'Gain type', false, 0.9);
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