Annual global forest gain maps from 1984 to 2020

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.受土地利用变化（主要为多数热带地区的森林砍伐与多数温带地区的人工造林）与气候变化影响，全球森林覆盖正快速发生变化。然而，目前仍缺乏具备精细空间与时间分辨率的全球森林新增覆盖详细图谱。本研究基于Landsat时间序列数据、LandTrendr算法与谷歌地球引擎（Google Earth Engine, GEE）平台，构建了一套全新的自动化框架，用于绘制全球逐年森林新增覆盖图谱。首先，本研究利用基于全球森林变化产品（Global Forest Change, GFC）采集的稳定森林样本，确定了用于森林新增覆盖识别的年度归一化燃烧比（Normalized Burn Ratio, NBR）阈值。其次，结合1982年至2020年的NBR时间序列数据与LandTrendr算法，本研究基于一系列决策规则，生成了1984年至2020年的全球逐年森林新增覆盖数据集。研究结果显示，中国、俄罗斯、巴西与北美地区发生了大面积的森林新增覆盖，且全球绝大多数森林新增覆盖均发生于2000年之后。该全新数据集在空间范围与森林新增覆盖发生年份两方面，均与野外调查数据及其他遥感产品结果保持一致。本数据集可为探讨森林覆盖变化对全球碳循环的净影响这一政策相关研究提供支撑，同时也为监测其他类型土地覆盖动态提供了一套高效且可迁移的方法。