{"id":6890,"date":"2026-05-13T11:31:17","date_gmt":"2026-05-13T09:31:17","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=6890"},"modified":"2026-05-13T12:10:20","modified_gmt":"2026-05-13T10:10:20","slug":"global-forest-attribute-dataset-gfad-high-resolution-spatial-layers-and-statistics-for-monitoring-forest-morphology-connectivity-and-accounting","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2026\/05\/13\/global-forest-attribute-dataset-gfad-high-resolution-spatial-layers-and-statistics-for-monitoring-forest-morphology-connectivity-and-accounting\/","title":{"rendered":"Global Forest Attribute Dataset (GFAD): high-resolution spatial layers and statistics for monitoring forest morphology, connectivity, and accounting"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p><span style=\"color: #0d5c06;font-size: 10pt\"><strong>Data paper<\/strong><\/span><\/p>\n<p><strong><a href=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/05\/26-05-13_Caudullo-G.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-6918 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/05\/26-05-13_Caudullo-G-251x300.png\" alt=\"\" width=\"235\" height=\"281\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/05\/26-05-13_Caudullo-G-251x300.png 251w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/05\/26-05-13_Caudullo-G-640x766.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/05\/26-05-13_Caudullo-G.png 685w\" sizes=\"auto, (max-width: 235px) 100vw, 235px\" \/><\/a>Key message<\/strong><\/p>\n<p align=\"justify\">We present the Global Forest Attribute Dataset (GFAD), a comprehensive set of spatial layers and quantitative indicators derived from the \u201cGlobal Forest Cover 2020\u201d (GFC2020). GFAD characterizes the connectivity, morphology, and spatial arrangement of forest land cover at a global scale. Central to the development of this dataset is the introduction of\u00a0<span class=\"u-monospace\">pyguidos<\/span>, a new open-source Python module designed to automate complex image processing for landscape analysis within a Python environment. By leveraging this automated processing chain, we generated global spatial forest attribute layers, which are color-coded with standard ramps. The layers allow for intuitive visualization as well as precise identification of spatial forest attributes. All indicators are normalized on a scale of [0\u2013100] and aggregated by country, facilitating neutral reporting and direct cross-country comparisons of forest conditions. By providing a standardized set of layers and indicators, GFAD and the underlying\u00a0<span class=\"u-monospace\">pyguidos<\/span> framework address key forest monitoring components that can be applied in various fields, such as conservation, management, and policymaking. The versatility of these tools makes them a robust reference for a wide range of applications, allowing end-users to adapt the analysis to their specific reporting priorities.<\/p>\n<p><strong>Keywords<\/strong><br \/>\nForest monitoring; Forest pattern; Forest structure; Global forest cover<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1186\/s13595-026-01332-y'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nCaudullo, G., Vogt, P. Global Forest Attribute Dataset (GFAD): high-resolution spatial layers and statistics for monitoring forest morphology, connectivity, and accounting.\u00a0<i>Annals of Forest Science<\/i>\u00a083, 13 (2026). <a href=\"https:\/\/doi.org\/10.1186\/s13595-026-01332-y\">https:\/\/doi.org\/10.1186\/s13595-026-01332-y<\/a><\/p>\n<p><strong>Data availability<\/strong><br \/>\nThe entire dataset is publicly available on Zenodo at: <a href=\"https:\/\/doi.org\/10.5281\/zenodo.18924625\">https:\/\/doi.org\/10.5281\/zenodo.18924625<\/a>, and the metadata are available at:\u00a0<a href=\"https:\/\/metadata-afs.nancy.inra.fr\/geonetwork\/srv\/fre\/catalog.search#\/metadata\/7937a918-c80c-4879-b20e-24081c56ae0b\">https:\/\/metadata-afs.nancy.inra.fr\/geonetwork\/srv\/fre\/catalog.search#\/metadata\/7937a918-c80c-4879-b20e-24081c56ae0b<\/a>.<br \/>\nThe dataset generated during and\/or analyzed during the current study is available in the FTP-like HTTP server:\u00a0<a href=\"https:\/\/jeodpp.jrc.ec.europa.eu\/ftp\/jrc-opendata\/FOREST\/FAL\/GFAD\/VER3-0\">https:\/\/jeodpp.jrc.ec.europa.eu\/ftp\/jrc-opendata\/FOREST\/FAL\/GFAD\/VER3-0<\/a>. The sequence of Python notebooks to reproduce the dataset is available on the git repository:\u00a0<a href=\"https:\/\/code.europa.eu\/jrc-forest\/reproducibility\/gfad-analysis\">https:\/\/code.europa.eu\/jrc-forest\/reproducibility\/gfad-analysis<\/a>.<\/p>\n<p><strong>Handling editor<\/strong><br \/>\nErwin Dreyer<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data paper Key message We present the Global Forest Attribute Dataset (GFAD), a comprehensive set of spatial layers and quantitative indicators derived from the \u201cGlobal Forest Cover 2020\u201d (GFC2020). GFAD characterizes the connectivity, morphology, and spatial arrangement of forest land cover at a global scale. Central to the development of this dataset is the introduction [&hellip;]<\/p>\n","protected":false},"author":240,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,100,1,109],"tags":[],"class_list":["post-6890","post","type-post","status-publish","format-standard","hentry","category-article-type","category-data-paper","category-non-classe","category-open-access","cat-14-id","cat-100-id","cat-1-id","cat-109-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/6890","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/users\/240"}],"replies":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/comments?post=6890"}],"version-history":[{"count":5,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/6890\/revisions"}],"predecessor-version":[{"id":6919,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/6890\/revisions\/6919"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=6890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=6890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=6890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}