{"id":5840,"date":"2023-10-23T14:02:02","date_gmt":"2023-10-23T12:02:02","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=5840"},"modified":"2023-10-23T14:02:02","modified_gmt":"2023-10-23T12:02:02","slug":"effect-of-sample-size-on-the-estimation-of-forest-inventory-attributes-using-airborne-lidar-data-in-large-scale-subtropical-areas","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2023\/10\/23\/effect-of-sample-size-on-the-estimation-of-forest-inventory-attributes-using-airborne-lidar-data-in-large-scale-subtropical-areas\/","title":{"rendered":"Effect of sample size on the estimation of forest inventory attributes using airborne LiDAR data in large-scale subtropical areas"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p><strong><a href=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/10\/Li-C-2023.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-5843 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/10\/Li-C-2023-300x191.png\" alt=\"\" width=\"357\" height=\"227\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/10\/Li-C-2023-300x191.png 300w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/10\/Li-C-2023-768x488.png 768w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/10\/Li-C-2023-640x407.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2023\/10\/Li-C-2023.png 947w\" sizes=\"auto, (max-width: 357px) 100vw, 357px\" \/><\/a>Key message<\/strong><\/p>\n<p align=\"justify\">Sample size (number of plots) may significantly affect the accuracy of forest attribute estimations using airborne LiDAR data in large-scale subtropical areas. In general, the accuracy of all models improves with increasing sample size. However, the improvement in estimation accuracy varies across forest attributes and forest types. Overall, a larger sample size is required to estimate the stand volume (VOL), while a smaller sample size is required to estimate the mean diameter at breast height (DBH). Broad-leaved forests require a smaller sample size than Chinese fir forests.<\/p>\n<p><strong>Keywords<\/strong><br \/>\nAirborne LiDAR; Forest attributes; Multivariate power model; Sample size<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1186\/s13595-023-01209-4'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nLi, C., Yu, Z., Dai, H. et al. Effect of sample size on the estimation of forest inventory attributes using airborne LiDAR data in large-scale subtropical areas. Annals of Forest Science 80, 40 (2023). <a href=\"https:\/\/doi.org\/10.1186\/s13595-023-01209-4\">https:\/\/doi.org\/10.1186\/s13595-023-01209-4<\/a><\/p>\n<p><strong>Share this article<\/strong><br \/>\n<a href=\"https:\/\/rdcu.be\/dpfRD\">https:\/\/rdcu.be\/dpfRD<\/a><\/p>\n<p><strong>Data and\/or Code availability<\/strong><br \/>\nThe data that support the findings of this study are available at <a href=\"https:\/\/doi.org\/10.57760\/sciencedb.11884\">https:\/\/doi.org\/10.57760\/sciencedb.11884<\/a><\/p>\n<p><strong>Handling Editor<\/strong><br \/>\nBarry A. Gardiner<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key message Sample size (number of plots) may significantly affect the accuracy of forest attribute estimations using airborne LiDAR data in large-scale subtropical areas. In general, the accuracy of all models improves with increasing sample size. However, the improvement in estimation accuracy varies across forest attributes and forest types. Overall, a larger sample size is [&hellip;]<\/p>\n","protected":false},"author":109,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,110,109,15],"tags":[],"class_list":["post-5840","post","type-post","status-publish","format-standard","hentry","category-article-type","category-data-in-repository","category-open-access","category-research-paper","cat-14-id","cat-110-id","cat-109-id","cat-15-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5840","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\/109"}],"replies":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/comments?post=5840"}],"version-history":[{"count":4,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5840\/revisions"}],"predecessor-version":[{"id":5845,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5840\/revisions\/5845"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=5840"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=5840"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=5840"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}