数字地球澳大利亚海岸线
澳大利亚数字地球海岸线是一个大陆数据集，包括从1988年至今整个澳大利亚海岸线的年度海岸线和海岸变化率。该产品将澳大利亚地球科学组织的澳大利亚数字地球计划的卫星数据与潮汐模型相结合，绘制出每年平均海平面上最具代表性的海岸线位置。该产品使得每年都可以在地方和大陆范围内检查海岸线的退缩和增长趋势，并且可以绘制历史上的海岸变化模式，并随着数据的不断获取而定期更新。这样就可以把目前的海岸变化率与前几年或几十年的观察结果进行比较。前言 – 床长人工智能教程
通过绘制每年的海岸线位置图，可以了解到我们的海岸线变化是特定事件或行动的结果，还是一个随着时间推移而逐渐变化的过程，这一点很有价值。这些信息可以使科学家、管理者和政策制定者评估影响我们海岸线的一系列驱动因素的影响，并有可能帮助规划和预测未来的情况。你可以在这里找到更多的细节，你可以在这里下载数据集。
Digital Earth Australia - Public Data (DEV)
免责声明：数据集的全部或部分描述是由作者或其作品提供的。
Citation¶
Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. 2021. Digital Earth Australia
Coastlines. Geoscience Australia, Canberra. https://doi.org/10.26186/116268
Publications¶
Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic
coastline at mean sea level using three decades of Landsat imagery. Remote Sensing of
Environment, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734
Nanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into
Australia's coastal change using a national dataset derived from the multi-decadal Landsat
archive. Estuarine, Coastal and Shelf Science, 265, p.107712. Available: https://doi.org/10.1016/
j.ecss.2021.107712
Bishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., & Sixsmith, J. (2019). Sub-pixel
waterline extraction: Characterising accuracy and sensitivity to indices and spectra. Remote
Sensing, 11(24), 2984. Available: https://www.mdpi.com/2072-4292/11/24/2984
Earth Engine Snippet¶
var shoreline_annual = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v200_shorelines_annual");
var hotspot_zoom_1 = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v200_hotspots_zoom_1");
var hotspot_zoom_2 = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v200_hotspots_zoom_2");
var hotspot_zoom_3 = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v200_hotspots_zoom_3");
var rate_of_change = ee.FeatureCollection("projects/sat-io/open-datasets/DEA/COASTLINES/coastlines_v200_rates_of_change");
Map.centerObject(shoreline_annual.first(),18)
// Get a color for a year
var yearColor = ee.Dictionary({
'1988': '#4d4d7a',
'1989': '#535a88',
'1990': '#5a608d',
'1991': '#616692',
'1992': '#686c97',
'1993': '#6f719c',
'1994': '#7677a1',
'1995': '#7d7ca6',
'1996': '#8482ab',
'1997': '#8b87b0',
'1998': '#922db9',
'1999': '#9922b3',
'2000': '#a01db0',
'2001': '#a717aa',
'2002': '#af11a4',
'2003': '#b70d9e',
'2004': '#bf0898',
'2005': '#c70292',
'2006': '#ce027e',
'2007': '#d5016a',
'2008': '#dc0156',
'2009': '#e40042',
'2010': '#ec003e',
'2011': '#f3003a',
'2012': '#fa0036',
'2013': '#ff0033',
'2014': '#ff4733',
'2015': '#ff6d33',
'2016': '#ff9333',
'2017': '#ffb933',
'2018': '#ffd033',
'2019': '#ffe54c',
'2020': '#ffe54c',
'2021': '#ffe54c'
}
)
function addStyle(ft) {
var color = yearColor.get(ft.get('year'));
return ft.set('styleProperty', ee.Dictionary({'color': color}));
}
// Make a FeatureCollection out of years database
var pp = ee.FeatureCollection(shoreline_annual).map(addStyle);
function addLayer(year) {
Map.addLayer(pp.filter(ee.Filter.eq('year', ee.Number.parse(year))).style({styleProperty: 'styleProperty', neighborhood: 50}), {}, year, true, 0.8);
}
// Apply `addLayer` to each record in years
yearColor.keys().getInfo().map(addLayer);
// Define a dictionary which will be used to make legend and visualize image on map
var dict = {
"names": [
"1988",
"1989",
"1990",
"1991",
"1992",
"1993",
"1994",
"1995",
"1996",
"1997",
"1998",
"1999",
"2000",
"2001",
"2002",
"2003",
"2004",
"2005",
"2006",
"2007",
"2008",
"2009",
"2010",
"2011",
"2012",
"2013",
"2014",
"2015",
"2016",
"2017",
"2018",
"2019",
"2020",
"2021"
],
"colors": [
'#4d4d7a',
'#535a88',
'#5a608d',
'#616692',
'#686c97',
'#6f719c',
'#7677a1',
'#7d7ca6',
'#8482ab',
'#8b87b0',
'#922db9',
'#9922b3',
'#a01db0',
'#a717aa',
'#af11a4',
'#b70d9e',
'#bf0898',
'#c70292',
'#ce027e',
'#d5016a',
'#dc0156',
'#e40042',
'#ec003e',
'#f3003a',
'#fa0036',
'#ff0033',
'#ff4733',
'#ff6d33',
'#ff9333',
'#ffb933',
'#ffd033',
'#ffe54c',
'#ffe54c',
'#ffe54c'
]};
var legend = ui.Panel({
style: {
position: 'middle-right',
padding: '8px 15px'
}
});
// Create and add the legend title.
var legendTitle = ui.Label({
value: 'Shoreline Year',
style: {
fontWeight: 'bold',
fontSize: '18px',
margin: '0 0 4px 0',
padding: '0'
}
});
legend.add(legendTitle);
var loading = ui.Label('Loading legend...', {margin: '2px 0 4px 0'});
legend.add(loading);
// Creates and styles 1 row of the legend.
var makeRow = function(color, name) {
// Create the label that is actually the colored box.
var colorBox = ui.Label({
style: {
backgroundColor: color,
// Use padding to give the box height and width.
padding: '8px',
margin: '0 0 4px 0'
}
});
// Create the label filled with the description text.
var description = ui.Label({
value: name,
style: {margin: '0 0 4px 6px'}
});
return ui.Panel({
widgets: [colorBox, description],
layout: ui.Panel.Layout.Flow('horizontal')
});
};
var palette = dict['colors'];
var names = dict['names'];
loading.style().set('shown', false);
for (var i = 0; i < names.length; i++) {
legend.add(makeRow(palette[i], names[i]));
}
// Print the panel containing the legend
print(legend);
License¶
These datasets are made available under the CC BY 4.0 Attribution 4.0 International license. This license allows users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.前言 – 床长人工智能教程
Created by: Digital Earth Australia
Curated in GEE by : Samapriya Roy
Keywords : Sea, ocean and coast, marine and coastal, coast, erosion, waterline extraction, subpixel waterlines, coastal change, DEA CoastLines, coastline data, coastal erosion
Last updated : 2023-03-26