GEE数据集：1996 年到 2020 年全球红树林观测数据集（JAXA）（更新）

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发布于 2024-10-01 09:03:16

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发布于 2024-10-01 09:03:16

简介

全球红树林观测

这项研究使用了日本宇宙航空研究开发机构（JAXA）提供的 L 波段合成孔径雷达（SAR）全球mask数据集，从 1996 年到 2020 年的 11 个时间段，建立了全球红树林范围和变化的长期时间序列。该研究采用 "从地图到图像 "的方法进行变化检测，其中基线地图（GMW v2.5）使用阈值化和上下文红树林变化掩码进行更新。这种方法适用于所有图像-日期对，每个时间段生成 10 幅地图，汇总后生成全球红树林时间序列。所绘制的红树林范围图的准确度估计为 87.4%（95th conf. int.: 86.2 - 88.6%），但单个增益和损失变化类别的准确度较低，分别为 58.1%（52.4 - 63.9%）和 60.6%（56.1 - 64.8%）。误差来源包括合成孔径雷达镶嵌数据集的错误登记（只能部分纠正），以及红树林破碎区域（如水产养殖池塘周围）的混淆。总体而言，1996 年确定的红树林面积为 152,604 平方公里（133,996 - 176,910），到 2020 年将减少-5,245 平方公里（-13,587 - 3686），总面积为 147,359 平方公里（127,925 - 168,895），估计 24 年间损失 3.4%。全球红树林观测 3.0 版是迄今为止最全面的全球红树林变化记录，预计将支持广泛的活动，包括对全球沿海环境的持续监测、保护目标进展情况的界定和评估、保护区规划以及全球红树林生态系统的风险评估。

数据集说明

免责声明：数据集说明的全部或部分内容由作者或其作品提供。预处理¶ 对栅格图块进行镶嵌，以便将所有外延和相关栅格图块整合到单一集合中。日期范围随后被添加到栅格和矢量图层中。

数据集

全球红树林观测：年度红树林范围 4.0.19

为提高全球红树林观测（GMW）基线的分辨率和地方相关性，为 2020 年创建了一个新图层。利用哥白尼哨兵-2 卫星图像（像素分辨率为 10 米），对红树林范围进行了全面重新绘制和修订，将许多以前未绘制的区域纳入了新地图。这将绘图的空间分辨率从 25 米像素分辨率提高到 10 米，从而能够绘制出更精细的特征，如边缘红树林和河岸红树林。

代码

var extent_raster = ee.ImageCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/GMW_V3");
var extent_1996 = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_1996_vec");
var extent_2020 = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/extent/gmw_v3_2020_vec");

Map.addLayer(ee.Image().paint(extent_1996,0,3), {"palette":["228B22"]}, 'Extent Vector 1996',false)
Map.addLayer(ee.Image().paint(extent_2020,0,3), {"palette":["228B22"]}, 'Extent Vector 2020',false)

Map.addLayer(extent_raster.filterDate('1996-01-01','1996-12-31').first(),{"opacity":1,"bands":["b1"],"min":1,"max":1,"palette":["228B22"]},'Extent Raster 1996',false)
Map.addLayer(extent_raster.filterDate('2020-01-01','2020-12-31').first(),{"opacity":1,"bands":["b1"],"min":1,"max":1,"palette":["228B22"]},'Extent Raster 2020',false)

var change_f1996_raster = ee.ImageCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/change_f1996");
var change_f1996_2007 = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2007_vec");
var change_f1996_2020 = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/change/gmw_v3_f1996_t2020_vec");

Map.addLayer(ee.Image().paint(change_f1996_2007,0,3), {"palette":["228B22"]}, 'Change vector 1996-2007',false)
Map.addLayer(ee.Image().paint(change_f1996_2020,0,3), {"palette":["228B22"]}, 'Change Vector 1996-2007',false)

Map.addLayer(change_f1996_raster.sort('system:time_end').first(),{"opacity":1,"bands":["b1"],"min":1,"max":2,"palette":["#ff0000","#0000ff"]},'Change Loss:Gain Raster 1996-2007')
Map.addLayer(change_f1996_raster.sort('system:time_end',false).first(),{"opacity":1,"bands":["b1"],"min":1,"max":2,"palette":["#ff0000","#0000ff"]},'Change Loss:Gain Raster 1996-2020')

//Union: Single layer of pixels which were mangroves at any date in the time series
var gmw_union_raster = ee.Image("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/union/gmw_v3_mng_union");
var gmw_union_vector = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/union/gmw_v3_union_vec");

Map.addLayer(gmw_union_raster,{"opacity":1,"bands":["b1"],"min":1,"max":1,"palette":["228B22"]},'GMW Union raster',false)

//Core: Single layer of pixels which were mangroves at all dates within the time series
var gmw_core_raster = ee.Image("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/core/gmw_v3_mng_core");
var gmw_core_vector = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/core/gmw_v3_core_vec");

Map.addLayer(gmw_core_raster,{"opacity":1,"bands":["b1"],"min":1,"max":1,"palette":["228B22"]},'GMW Core raster',false)

//Tiles
var tiles = ee.FeatureCollection("projects/earthengine-legacy/assets/projects/sat-io/open-datasets/GMW/gmw_v3_tiles");
Map.addLayer(ee.Image().paint(tiles,0,3), {"palette":["#000000"]}, 'Tiles')

//Extent v4
var raster_extent = ee.ImageCollection("projects/sat-io/open-datasets/GMW/annual-extent/GMW_MNG_2020");
var vector_extent = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/annual-extent/GMW_MNG_VEC_2020");
Map.addLayer(raster_extent.median(),{"opacity":1,"bands":["b1"],"min":1,"max":1,"palette":["228B22"]},'GMW Raster Extent 2020  v4.0.19')
Map.addLayer(ee.Image().paint(vector_extent,0,3), {"palette":["red"]}, 'GMW Vector Extent 2020 v4.0.19')

代码链接

https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLOBAL-MANGROVE-WATCH

结果

引用

Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, T.; Tadono, T.; Worthington, T.A.; Spalding, M.; Murray, N.J.; Rebelo, L-M. Global Mangrove Extent Change 1996 – 2020: Global Mangrove Watch Version 3.0. Remote Sensing. 2022

Bunting, Pete, Rosenqvist, Ake, Hilarides, Lammert, Lucas, Richard, Thomas, Nathan, Tadono , Takeo, Worthington, Thomas, Spalding , Mark, Murray, Nicholas, & Rebelo, Lisa-Maria. (2022). Global Mangrove Watch (1996 - 2020) Version 3.0 Dataset (3.0) [Data set]. Zenodo. https://doi.org/10.5281/ zenodo.6894273