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Google Earth Engine——Murray全球潮间带变化数据集在潮滩分类,用于开发陆地卫星协变量层的陆地卫星图像的数量

Google变量数据开发 图像 分类 全球 Engine
2023-09-11 14:15:12 时间

The Murray Global Intertidal Change Dataset contains global maps of tidal flat ecosystems produced via a supervised classification of 707,528 Landsat Archive images. Each pixel was classified into tidal flat, permanent water or other with reference to a globally distributed set of training data.

The classification was implemented along the entire global coastline between 60° North and 60° South from 1 January 1984 to 31 December 2016. The image collection consists consists of a time-series of 11 global maps of tidal flats at 30m pixel resolution for set time-periods (1984−1986; 1987−1989; 1990−1992; 1993−1995; 1996−1998; 1999−2001; 2002−2004; 2005−2007; 2008−2010; 2011−2013; 2014−2016)

The number of Landsat images used to develop the Landsat covariate layers in each time step of the tidal flat classification. Each image in the image collection refers to a single time step.

Murray全球潮间带变化数据集包含了通过对707,528张Landsat Archive图像进行监督分类而产生的全球潮间带生态系统地图。参照全球分布的训练数据集,每个像素都被划分为潮滩、永久水域或其他。

1984年1月1日至2016年12月31日,分类工作沿着北纬60°和南纬60°之间的整个全球海岸线进行。图像收集包括11张全球潮汐滩涂地图的时间序列,分辨率为30米,时间段为1984-1986;1987-1989;1990-1992;1993-1995;1996-1998;1999-2001;2002-2004;2005-2007;2008-2010;2011-2013;2014-2016)

在潮滩分类的每个时间步骤中,用于开发陆地卫星协变量层的陆地卫星图像的数量。图像集合中的每张图像指的是一个时间步骤。

Dataset Availability

1984-01-01T00:00:00 - 2017-01-01T00:00:00

Dataset Provider

Murray/UQ/Google/USGS/NASA

Collection Snippet

ee.ImageCollection("UQ/murray/Intertidal/v1_1/qa_pixel_count")

Resolution

30 meters

Bands Table

NameDescriptionMinMaxUnits
pixel_countInput image count.0400count

 

引用:

Murray, N.J., Phinn, S.R., DeWitt, M., Ferrari, R., Johnston, R., Lyons, M.B., Clinton, N., Thau, D. & Fuller, R.A. (2019) The global distribution and trajectory of tidal flats. Nature, 565, 222-225.

代码:

var dataset = ee.ImageCollection('UQ/murray/Intertidal/v1_1/qa_pixel_count');

var visualization = {
  bands: ['pixel_count'],
  min: 0.0,
  max: 300.0,
  palette: ['000000', 'FFFFFF']
};

Map.setCenter(126.6339, 37.4394, 10);

Map.addLayer(dataset, visualization, 'QA Pixel Count');