{"id":7123,"date":"2026-10-19T13:37:28","date_gmt":"2026-10-19T11:37:28","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=7123"},"modified":"2026-10-14T09:48:32","modified_gmt":"2026-10-14T07:48:32","slug":"bi-temporal-lidar-based-estimates-of-stand-variables-reduce-growth-modelling-costs-in-even-aged-larix-olgensis-a-henry-plantations","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2026\/10\/19\/bi-temporal-lidar-based-estimates-of-stand-variables-reduce-growth-modelling-costs-in-even-aged-larix-olgensis-a-henry-plantations\/","title":{"rendered":"Bi-temporal LiDAR-based estimates of stand variables reduce growth modelling costs in even-aged Larix olgensis A. Henry plantations"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p><span style=\"color: #0d5c06\"><span style=\"font-size: 13.3333px\"><b><a href=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/10\/13595_2026_1365_Fig1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-7124 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/10\/13595_2026_1365_Fig1-300x256.png\" alt=\"\" width=\"300\" height=\"256\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/10\/13595_2026_1365_Fig1-300x256.png 300w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/10\/13595_2026_1365_Fig1-768x655.png 768w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/10\/13595_2026_1365_Fig1-640x546.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/10\/13595_2026_1365_Fig1.png 844w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>Research paper<\/b><\/span><\/span><\/p>\n<p><strong>Key message<\/strong><\/p>\n<p align=\"justify\">We developed a dynamic stand-level growth and yield model using bi-temporal UAV LiDAR data to support forest management and planning. This approach aims to reduce the cost and intensity of ground-based inventories by using low-cost UAV LiDAR as an alternative data source.<\/p>\n<section class=\"c-article-recommendations\" aria-labelledby=\"inline-recommendations\" data-title=\"Inline Recommendations\" data-track-component=\"inline-recommendations\"><\/section>\n<section class=\"c-article-recommendations\" aria-labelledby=\"inline-recommendations\" data-title=\"Inline Recommendations\" data-track-component=\"inline-recommendations\"><\/section>\n<p><strong>Keywords<br \/>\n<\/strong>KNN imputation; UAV LiDAR; SUR model<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1186\/s13595-026-01365-3'  style='float: right; ' ><\/div>\n<p><strong>Publication<br \/>\n<\/strong>Zhu, B., Pukkala, T., Hao, Y.\u00a0<i>et al.<\/i>\u00a0Bi-temporal LiDAR-based estimates of stand variables reduce growth modelling costs in even-aged\u00a0<i>Larix olgensis<\/i>\u00a0A. Henry plantations.\u00a0<i>Annals of Forest Science<\/i>\u00a0<b>83<\/b>, 48 (2026). <a href=\"https:\/\/doi.org\/10.1186\/s13595-026-01365-3\">https:\/\/doi.org\/10.1186\/s13595-026-01365-3<\/a><\/p>\n<p><strong>Data availability<\/strong><br \/>\nThe dataset used in this study has been deposited in the Zenodo repository and is available at <a href=\"https:\/\/doi.org\/10.5281\/zenodo.21561586\">https:\/\/doi.org\/10.5281\/zenodo.21561586<\/a>. To protect the precise locations of the study area, the released point-cloud data were transformed from their original georeferenced coordinate system into a local relative coordinate system.<\/p>\n<p><strong>Handling editor<br \/>\n<\/strong>Shengli Tao<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Research paper Key message We developed a dynamic stand-level growth and yield model using bi-temporal UAV LiDAR data to support forest management and planning. This approach aims to reduce the cost and intensity of ground-based inventories by using low-cost UAV LiDAR as an alternative data source. Keywords KNN imputation; UAV LiDAR; SUR model Publication Zhu, [&hellip;]<\/p>\n","protected":false},"author":240,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,1,109,15],"tags":[],"class_list":["post-7123","post","type-post","status-publish","format-standard","hentry","category-article-type","category-non-classe","category-open-access","category-research-paper","cat-14-id","cat-1-id","cat-109-id","cat-15-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/7123","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=7123"}],"version-history":[{"count":2,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/7123\/revisions"}],"predecessor-version":[{"id":7128,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/7123\/revisions\/7128"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=7123"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=7123"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=7123"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}