Global planted forest data for timber species.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Sarah B Richardson, Lauralee An, Sarah E Pollack, Hemalatha Velappan, Ruth Nogueron, Jessica Richter, Shelley L Gardner, Karen L Williams, John C Hermanson, Elizabeth Dow Goldman, Suzanne M Peyer
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Abstract

Discerning whether certain timber species were harvested from natural forests versus often less restricted planted forests can help ascertain the legality of wood products that enter the global market. However, readily available global planted forest data to the species level have been scarce. We confronted the need for such data by developing a two-pronged dataset, consisting of 'polygon' and 'non-polygon' location-based data, collectively, Planted Forest Timber Data. We obtained the polygon data from the World Resources Institute's Spatial Database of Planted Trees v2.0, extracting data specific to traded timber species. We derived the non-polygon data from peer-reviewed literature and government documents. The polygon dataset encompasses 27 countries and 253 species and the non-polygon dataset spans 91 countries and 447 species. We envision that the more these two living datasets grow, the more they will mutually benefit from one another for data cross-validation. This assembled information is meant to equip global leaders in forest governance, policy, enforcement, and research with vetted data for promoting legal timber trade and protecting biodiversity.

全球人工林木材物种数据。
辨别某些木材树种是采伐自天然林,还是采伐自限制较少的人工林,有助于确定进入全球市场的木材产品的合法性。然而,现成的全球人工林树种数据一直很少。为了满足对此类数据的需求,我们开发了一个双管齐下的数据集,包括基于 "多边形 "和 "非多边形 "位置的数据,统称为 "人工林木材数据"。我们从世界资源研究所的人工林空间数据库 v2.0 中获取了多边形数据,并提取了交易木材物种的特定数据。我们从同行评议的文献和政府文件中获得了非多边形数据。多边形数据集涵盖 27 个国家和 253 个物种,非多边形数据集涵盖 91 个国家和 447 个物种。我们预计,这两个活的数据集越壮大,就越能在数据交叉验证方面相互受益。这些汇集起来的信息旨在为森林治理、政策、执法和研究领域的全球领导者提供经过审核的数据,以促进合法木材贸易和保护生物多样性。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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