Google Earth Engine ——数据全解析专辑(COPERNICUS/CORINE/V20/100m)欧洲土地利用数据集1986-2018

简介: Google Earth Engine ——数据全解析专辑(COPERNICUS/CORINE/V20/100m)欧洲土地利用数据集1986-2018

The CORINE (coordination of information on the environment) Land Cover (CLC) inventory was initiated in 1985 to standardize data collection on land in Europe to support environmental policy development. The project is coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme and implemented by national teams. The number of participating countries has increased over time currently including 33 (EEA) member countries and six cooperating countries (EEA39) with a total area of over 5.8 Mkm2.


CLC2018 is one of the datasets produced within the frame the Corine Land Cover programme referring to land cover / land use status of year 2018. The reference year of the first CLC inventory was 1990 and the first update created in 2000. Later, the update cycle has become 6 years. Satellite imagery provides the geometrical and thematic basis for mapping with in-situ data as essential ancillary information. The basic technical parameters of CLC (i.e. 44 classes in nomenclature, 25 hectares minimum mapping unit (MMU), and 100 meters minimum mapping width) have not changed since the beginning, therefore the results of the different inventories are comparable.


CORINE(环境信息协调)土地覆盖 (CLC) 清单于 1985 年启动,旨在标准化欧洲土地数据收集,以支持环境政策制定。该项目由欧洲环境署 (EEA) 在欧盟哥白尼计划的框架内协调,并由国家团队实施。随着时间的推移,参与国的数量不断增加,目前包括 33 个(欧洲经济区)成员国和六个合作国家(EEA39),总面积超过 5.8 平方公里。 CLC2018 是 Corine Land Cover 计划框架内生成的数据集之一,涉及 2018 年的土地覆盖/土地利用状况。第一次 CLC 清单的参考年份是 1990 年,第一次更新创建于 2000 年。后来,更新周期已经变成6年了。卫星图像为以实地数据作为基本辅助信息的制图提供了几何和专题基础。 CLC的基本技术参数(即44类命名、25公顷最小制图单位(MMU)、100米最小制图宽度)自开始以来没有变化,因此不同清单的结果具有可比性。

The time period covered by each asset is:

  • 1990 asset: 1989 to 1998
  • 2000 asset: 1999 to 2001
  • 2006 asset: 2005 to 2007
  • 2012 asset: 2011 to 2012
  • 2018 asset: 2017 to 2018

每项影像涵盖的时间段为:


1990年资产:1989年至1998年

2000年资产:1999年至2001年

2006年资产:2005年至2007年

2012年资产:2011年至2012年

2018年资产:2017年至2018年

Dataset Availability

1986-01-01T00:00:00 - 2018-12-31T00:00:00

Dataset Provider

EEA/Copernicus

Collection Snippet

ee.ImageCollection("COPERNICUS/CORINE/V20/100m")

Resolution

100 meters

Bands Table

Name Description
landcover Land cover

Class Table: landcover

Value Color Color Value Description
111 #E6004D Artificial surfaces > Urban fabric > Continuous urban fabric
112 #FF0000 Artificial surfaces > Urban fabric > Discontinuous urban fabric
121 #CC4DF2 Artificial surfaces > Industrial, commercial, and transport units > Industrial or commercial units
122 #CC0000 Artificial surfaces > Industrial, commercial, and transport units > Road and rail networks and associated land
123 #E6CCCC Artificial surfaces > Industrial, commercial, and transport units > Port areas
124 #E6CCE6 Artificial surfaces > Industrial, commercial, and transport units > Airports
131 #A600CC Artificial surfaces > Mine, dump, and construction sites > Mineral extraction sites
132 #A64DCC Artificial surfaces > Mine, dump, and construction sites > Dump sites
133 #FF4DFF Artificial surfaces > Mine, dump, and construction sites > Construction sites
141 #FFA6FF Artificial surfaces > Artificial, non-agricultural vegetated areas > Green urban areas
142 #FFE6FF Artificial surfaces > Artificial, non-agricultural vegetated areas > Sport and leisure facilities
211 #FFFFA8 Agricultural areas > Arable land > Non-irrigated arable land
212 #FFFF00 Agricultural areas > Arable land > Permanently irrigated land
213 #E6E600 Agricultural areas > Arable land > Rice fields
221 #E68000 Agricultural areas > Permanent crops > Vineyards
222 #F2A64D Agricultural areas > Permanent crops > Fruit trees and berry plantations
223 #E6A600 Agricultural areas > Permanent crops > Olive groves
231 #E6E64D Agricultural areas > Pastures > Pastures
241 #FFE6A6 Agricultural areas > Heterogeneous agricultural areas > Annual crops associated with permanent crops
242 #FFE64D Agricultural areas > Heterogeneous agricultural areas > Complex cultivation patterns
243 #E6CC4D Agricultural areas > Heterogeneous agricultural areas > Land principally occupied by agriculture, with significant areas of natural vegetation
244 #F2CCA6 Agricultural areas > Heterogeneous agricultural areas > Agro-forestry areas
311 #80FF00 Forest and semi natural areas > Forests > Broad-leaved forest
312 #00A600 Forest and semi natural areas > Forests > Coniferous forest
313 #4DFF00 Forest and semi natural areas > Forests > Mixed forest
321 #CCF24D Forest and semi natural areas > Scrub and/or herbaceous vegetation associations > Natural grasslands
322 #A6FF80 Forest and semi natural areas > Scrub and/or herbaceous vegetation associations > Moors and heathland
323 #A6E64D Forest and semi natural areas > Scrub and/or herbaceous vegetation associations > Sclerophyllous vegetation
324 #A6F200 Forest and semi natural areas > Scrub and/or herbaceous vegetation associations > Transitional woodland-shrub
331 #E6E6E6 Forest and semi natural areas > Open spaces with little or no vegetation > Beaches, dunes, sands
332 #CCCCCC Forest and semi natural areas > Open spaces with little or no vegetation > Bare rocks
333 #CCFFCC Forest and semi natural areas > Open spaces with little or no vegetation > Sparsely vegetated areas
334 #000000 Forest and semi natural areas > Open spaces with little or no vegetation > Burnt areas
335 #A6E6CC Forest and semi natural areas > Open spaces with little or no vegetation > Glaciers and perpetual snow
411 #A6A6FF Wetlands > Inland wetlands > Inland marshes
412 #4D4DFF Wetlands > Inland wetlands > Peat bogs
421 #CCCCFF Wetlands > Maritime wetlands > Salt marshes
422 #E6E6FF Wetlands > Maritime wetlands > Salines
423 #A6A6E6 Wetlands > Maritime wetlands > Intertidal flats
511 #00CCF2 Water bodies > Inland waters > Water courses
512 #80F2E6 Water bodies > Inland waters > Water bodies
521 #00FFA6 Water bodies > Marine waters > Coastal lagoons
522 #A6FFE6 Water bodies > Marine waters > Estuaries
523 #E6F2FF Water bodies > Marine waters > Sea and ocean

 

影像属性

Name Type Description
landcover_class_names List of Strings Land cover class names
landcover_class_palette List of Strings Land cover class palette
landcover_class_values List of Ints Value of the land cover classification.


代码:

var dataset = ee.Image('COPERNICUS/CORINE/V20/100m/2012');
var landCover = dataset.select('landcover');
Map.setCenter(16.436, 39.825, 6);
Map.addLayer(landCover, {}, 'Land Cover');


相关文章
|
6月前
|
存储 传感器 数据可视化
3D目标检测数据集 KITTI(标签格式解析、3D框可视化、点云转图像、BEV鸟瞰图)
本文介绍在3D目标检测中,理解和使用KITTI 数据集,包括KITTI 的基本情况、下载数据集、标签格式解析、3D框可视化、点云转图像、画BEV鸟瞰图等,并配有实现代码。
518 0
|
7月前
|
编解码 网络协议
Google-CTF-2016-Stego.pcap数据包解析
Google-CTF-2016-Stego.pcap数据包解析
29 0
|
存储 编解码 数据安全/隐私保护
ISPRS Vaihingen 数据集解析
ISPRS Vaihingen 数据集解析
709 0
ISPRS Vaihingen 数据集解析
|
9月前
|
API 计算机视觉 索引
【COCO数据集】COCO API 解析图像数据和目标标签,vision-transformer DETR的相关transforms操作实现
【COCO数据集】COCO API 解析图像数据和目标标签,vision-transformer DETR的相关transforms操作实现
289 0
|
9月前
|
存储 JSON 测试技术
【COCO数据集】Annotations标注解析
【COCO数据集】Annotations标注解析
767 0
|
自然语言处理 JavaScript 前端开发
图解 Google V8 # 12:延迟解析:V8是如何实现闭包的?
图解 Google V8 # 12:延迟解析:V8是如何实现闭包的?
114 0
图解 Google V8 # 12:延迟解析:V8是如何实现闭包的?
|
机器学习/深度学习 计算机视觉
使用paddle搭建多种卷积神经网络实现Cifar10数据集 解析
本项目把几大重要的卷积神经网络进行了解析使用了Cifar10 项目是陆平老师的,解析采取了由上至下的方式,上面的解析详细,下面的可能没有标注 如果有疑问可以留言或私聊我都可以。
352 0
使用paddle搭建多种卷积神经网络实现Cifar10数据集 解析
|
4天前
|
Java Android开发
Android12 双击power键启动相机源码解析
Android12 双击power键启动相机源码解析
13 0
|
1天前
PandasTA 源码解析(一)(2)
PandasTA 源码解析(一)
7 0
|
1天前
PandasTA 源码解析(一)(1)
PandasTA 源码解析(一)
10 0

推荐镜像

更多