Hansen Dataset on Global Forest Change

The Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland, in partnership with Global Forest Watch (GFW), provides annually updated global-scale forest loss data, derived using Landsat time-series imagery. These data, available here, are a relative indicator of spatiotemporal trends in forest loss dynamics globally. Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2022. Trees are defined as vegetation taller than 5m in height and are expressed as a percentage per output grid cell as ‘2000 Percent Tree Cover’. ‘Forest Cover Loss’ is defined as a stand-replacement disturbance, or a change from a forest to non-forest state, during the period 2000–2022. ‘Forest Cover Gain’ is defined as the inverse of loss, or a non-forest to forest change entirely within the period 2000–2012. ‘Forest Loss Year’ is a disaggregation of total ‘Forest Loss’ to annual time scales. Reference 2000 and 2022 imagery are median observations from a set of quality assessment-passed growing season observations. However, inconsistencies exist due to many factors.

搜集汇总

背景与挑战

背景概述

Hansen全球森林变化数据集提供了2000-2022年全球森林覆盖变化的年度监测数据，包括树木覆盖百分比、森林损失和增益等指标，基于Landsat影像时间序列分析，是研究全球森林动态的重要资源。