International Institute for Applied Systems Analysis, Laxenburg, A-2361, Austria.
V.N. Sukachev Institute of Forest, Siberian Branch of the Russian Academy of Science, Akademgorodok 50(28), Krasnoyarsk, 660036, Russia.
Sci Data. 2022 May 10;9(1):199. doi: 10.1038/s41597-022-01332-3.
Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki ( https://www.geo-wiki.org/ ). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.
在全球范围内获取关于森林管理的具体空间信息对于了解森林的现状、规划可持续的森林管理和恢复以及保护活动至关重要。在这里，我们首次制作了参考数据集和具有高空间细节的全球一致森林管理图原型，涵盖了最常见的森林管理类别，如原始森林、具有天然更新的管理森林、人工林、种植林（轮伐期长达 15 年）、油棕种植园和农林复合经营。我们通过一系列使用 Geo-Wiki（https://www.geo-wiki.org/）的专家和众包活动，开发了 226K 个独特位置的参考数据集。然后，我们将参考样本与 PROBA-V 卫星图像的时间序列相结合，为 2015 年创建了一个分辨率为 100m 的全球全覆盖的森林管理图，森林管理类别精度范围为 58%至 80%。参考数据集和地图呈现了森林生态系统的现状，可用于调查森林对物种、生态系统及其服务的价值。
