GEE数据集——汉森全球森林变化数据集Hansen Global Forest Change v1.11 (2000-2023)

Hansen Global Forest Change v1.11 (2000-2023)

对大地遥感卫星图像进行时间序列分析以确定全球森林范围和变化特征的结果。

第一个 "和 "最后一个 "波段是大地遥感卫星光谱波段(红、近红外、SWIR1 和 SWIR2)的第一个和最后一个可用年份的参考多光谱图像。参考复合图像代表了这些波段中每个波段的生长季节质量评估观测数据集的中值观测数据。

请参阅 1.11 版更新的用户说明以及相关期刊文章:Hansen、Potapov、Moore、Hancher 等:"21 世纪森林覆盖变化的高分辨率全球地图"。科学》342.6160 (2013):850-853.

Dataset Availability

2000-01-01T00:00:00 - 2023-12-31T00:00:00

Dataset Provider

Hansen/UMD/Google/USGS/NASA

Collection Snippet

Copied

ee.Image("UMD/hansen/global_forest_change_2023_v1_11")

Resolution

30.92 meters

Bands Table
Name Description Min Max Units Wavelength
treecover2000 Tree canopy cover for year 2000, defined as canopy closure for all vegetation taller than 5m in height. 0 100 %
loss Forest loss during the study period, defined as a stand-replacement disturbance (a change from a forest to non-forest state).
loss Bitmask * Bit 0: Forest loss during the study period. * 0: Not loss * 1: Loss
gain Forest gain during the period 2000-2012, defined as the inverse of loss (a non-forest to forest change entirely within the study period). Note that this has not been updated in subsequent versions.
gain Bitmask * Bit 0: Forest gain during the period 2000-2012. * 0: No gain * 1: Gain
first_b30 Landsat Red cloud-free image composite (corresponding to Landsat 5/7 band 3 and Landsat 8/9 band 4). Reference multispectral imagery from the first available year, typically 2000. 0.63-0.69µm
first_b40 Landsat NIR cloud-free image composite (corresponding to Landsat 5/7 band 4 and Landsat 8/9 band 5). Reference multispectral imagery from the first available year, typically 2000. 0.77-0.90µm
first_b50 Landsat SWIR1 cloud-free image composite (corresponding to Landsat 5/7 band 5 and Landsat 8/9 band 6). Reference multispectral imagery from the first available year, typically 2000. 1.55-1.75µm
first_b70 Landsat SWIR2 cloud-free image composite (corresponding to Landsat 5/7 band 7 and Landsat 8/9 band 7). Reference multispectral imagery from the first available year, typically 2000. 2.09-2.35µm
last_b30 Landsat Red cloud-free image composite (corresponding to Landsat 5/7 band 3 and Landsat 8/9 band 4). Reference multispectral imagery from the last available year, typically the last year of the study period. 0.63-0.69µm
last_b40 Landsat NIR cloud-free image composite (corresponding to Landsat 5/7 band 4 and Landsat 8/9 band 5). Reference multispectral imagery from the last available year, typically the last year of the study period. 0.77-0.90µm
last_b50 Landsat SWIR1 cloud-free image composite (corresponding to Landsat 5/7 band 5 and Landsat 8/9 band 6). Reference multispectral imagery from the last available year, typically the last year of the study period. 1.55-1.75µm
last_b70 Landsat SWIR2 cloud-free image composite (corresponding to Landsat 5/7 band 7 and Landsat 8/9 band 7). Reference multispectral imagery from the last available year, typically the last year of the study period. 2.09-2.35µm
datamask Three values representing areas of no data, mapped land surface, and permanent water bodies.
datamask Bitmask * Bits 0-1: Three values representing areas of no data, mapped land surface, and permanent water bodies. * 0: No data * 1: Mapped land surface * 2: Permanent water bodies
lossyear Year of gross forest cover loss event. Forest loss during the study period, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. Encoded as either 0 (no loss) or else a value in the range 1-23, representing loss detected primarily in the year 2001-2023, respectively. 0 23

代码

javascript 复制代码
var geometry = 
    /* color: #d63000 */
    /* displayProperties: [
      {
        "type": "rectangle"
      }
    ] */
    ee.Geometry.Polygon(
        [[[-111.37186963558197, 41.621164801215464],
          [-111.37186963558197, 34.14087733236979],
          [-100.12186963558197, 34.14087733236979],
          [-100.12186963558197, 41.621164801215464]]], null, false);
var image = ee.Image("UMD/hansen/global_forest_change_2023_v1_11")
print(image)

Map.addLayer(image.clip(geometry),{},'sss')

数据引用

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend.

  1. "High-Resolution Global Maps of 21st-Century Forest Cover Change." Science 342 (15 November): 850-53. 10.1126/science.1244693 Data available on-line at: Global Forest Change.

网址推荐

0代码在线构建地图应用

https://sso.mapmost.com/#/login?source_inviter=nClSZANO

机器学习

https://www.cbedai.net/xg

相关推荐
独自破碎E13 小时前
Windows系统Codex 接入阿里云百炼的国产大模型
阿里云·云计算
uesowys17 小时前
腾讯云大模型服务平台 TokenHub
云计算·腾讯云·tokenhub
华万通信king17 小时前
腾讯云ADP企业智能体开发入门:从零搭建你的第一个Agent应用
云计算·腾讯云·adp
TG_yunshuguoji18 小时前
腾讯云代理商:腾讯云如何部署DeepSeek版 Claude Code?
人工智能·云计算·腾讯云·ai智能体
Sss_Ass18 小时前
CodeBuddy IDE(腾讯云代码助手)介绍及下载安装
ide·云计算·腾讯云
TG_yunshuguoji20 小时前
阿里云代理商:阿里云部署 WordPress的3 种方案
人工智能·阿里云·云计算·wordpress·ai智能体
云边云科技_云网融合20 小时前
云边云科技受邀出席 2026 亚马逊云科技中国合作伙伴峰会
大数据·网络·人工智能·科技·云计算
容器魔方21 小时前
KubeEdge SIG AI: 基于KubeEdge-Ianvs的大模型联邦微调算法
大数据·人工智能·算法·云原生·容器·云计算
dog2501 天前
把确定性交给统计-浅析 AWS RNG
云计算·aws
ZStack开发者社区1 天前
ZStack Cloud 5.5.22正式发布
阿里云·云计算