Google Earth Engine（GEE）——墨累全球潮汐湿地变化 v1 (1999-2019) 数据集

The Murray Global Tidal Wetland Change Dataset contains maps of the global extent of tidal wetlands and their change. The maps were developed from a three stage classification that sought to (i) estimate the global distribution of tidal wetlands (defined as either tidal marsh, tidal flat or mangrove ecosystems), (ii) detect their change over the study period, and (iii) estimate the ecosystem type and timing of tidal wetland change events.

The dataset was produced by combining observations from 1,166,385 satellite images acquired by Landsat 5 to 8 with environmental data of variables known to influence the distributions of each ecosystem type, including temperature, slope, and elevation. The image contains bands for a tidal wetland extent product (random forest probability of tidal wetland occurrence) for the start and end time-steps of the study period and a tidal wetland change product over the full study period (loss and gain of tidal wetlands).

Please see the ​​usage notes​​​ on the ​​project website​​. A full description of the methods, validation, and limitations of the data produced by this software is available in the associated scientific paper.

See also ​​UQ/murray/Intertidal/v1_1/global_intertidal​​ for global maps of the distribution of tidal flat ecosystems.

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。这些地图是根据三阶段分类开发的，旨在 (i) 估计潮汐湿地（定义为潮汐沼泽、潮滩或红树林生态系统）的全球分布，(ii) 检测它们在研究期间的变化，以及 (iii) ) 估计潮汐湿地变化事件的生态系统类型和时间。

该数据集是通过将 Landsat 5 到 8 获取的 1,166,385 幅卫星图像的观测结果与已知会影响每种生态系统类型分布的变量的环境数据（包括温度、坡度和海拔）相结合而生成的。该图像包含研究期间开始和结束时间步长的潮汐湿地范围产品（潮汐湿地发生的随机森林概率）和整个研究期间的潮汐湿地变化产品的波段（潮汐湿地的损失和增益） .

请参阅​​项目网站​​​ 上的​​使用说明​​。相关科学论文中提供了对该软件产生的数据的方法、验证和限制的完整描述。

另请参阅 ​​UQ/murray/Intertidal/v1_1/global_intertidal​​ 了解全球潮滩生态系统分布图。

数据集可用性

1999-01-01T00:00:00Z - 2019-12-31T00:00:00

数据集提供者

​​默里/JCU​​

地球引擎

ee.ImageCollection("JCU/Murray/GIC/global_tidal_wetland_change/2019"

分辨率
30 米

波段

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);

Terms of Use

CC-BY-4.0

Citations:

• 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​​