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
你的大佬9996 小时前
阿里云百炼ai模型
人工智能·阿里云·云计算
一只栖枝7 小时前
备考华为HCIA - 云计算,培训与自学到底该怎么选?
云计算·华为认证·hcia·考证·职业规划
数据与人工智能律师13 小时前
AI的法治迷宫:技术层、模型层、应用层的法律痛点
大数据·网络·人工智能·云计算·区块链
荣光波比21 小时前
Docker(三)—— Docker Compose 编排与 Harbor 私有仓库实战指南
运维·docker·容器·云计算
企鹅侠客1 天前
mysqldump导入备份数据到阿里云RDS会报错吗
阿里云·adb·云计算
iHero1 天前
【Jitsi Meet】阿里云Docker安装Jitsi Meet后的调整
阿里云·docker·云计算
荣光波比1 天前
Ansible(三)—— 使用Ansible自动化部署LNMP环境实战指南
运维·自动化·云计算·ansible
荣光波比1 天前
Docker(五)—— Docker Compose 一键搭建 LNMP 架构并部署 WordPress
运维·docker·容器·云计算
mjhcsp2 天前
深入解析 IDM 插件开发挑战赛:技术要点与实践指南
服务器·阿里云·云计算
荣光波比2 天前
MySQL数据库(八)—— MySQL全量+增量备份方案:从脚本开发到连锁餐饮场景落地
运维·数据库·mysql·云计算