Hansen Dataset on Global Forest Change
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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.
马里兰大学全球土地分析与探索（Global Land Analysis and Discovery, GLAD）实验室与全球森林观察（Global Forest Watch, GFW）合作，发布基于陆地卫星（Landsat）时序影像生成的年度更新全球尺度森林损失数据集。本数据集可在此处获取，是表征全球森林损失动态时空趋势的相对指标。该数据集依托2000年至2022年的陆地卫星影像时序分析结果，用于刻画全球森林覆盖范围及其动态变化。数据将树木定义为高度超过5米的植被，以输出网格单元的百分比形式进行表达，即「2000年树木覆盖百分比（2000 Percent Tree Cover）」。「森林覆盖损失（Forest Cover Loss）」被定义为2000年至2022年间发生的林分更替干扰，或森林向非森林状态的转变。「森林覆盖增益（Forest Cover Gain）」为森林损失的反向过程，即2000年至2012年间非森林区域向森林区域的转变。「森林损失年份（Forest Loss Year）」是将总森林损失按年度时间尺度拆分得到的细分指标。2000年与2022年的参考影像为通过质量评估的生长季观测影像集合的中值合成结果。但受诸多因素影响，该数据集仍存在不一致性。
