{"id":4368,"date":"2020-07-30T11:46:24","date_gmt":"2020-07-30T09:46:24","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=4368"},"modified":"2020-07-30T11:46:24","modified_gmt":"2020-07-30T09:46:24","slug":"sample-strategies-for-bias-correction-of-regional-lidar-assisted-forest-inventory-estimates-on-small-woodlots","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2020\/07\/30\/sample-strategies-for-bias-correction-of-regional-lidar-assisted-forest-inventory-estimates-on-small-woodlots\/","title":{"rendered":"Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p align=\"justify\"><strong>Key message<\/strong><br \/>\nThis study presents an easy-to-apply variable probability sample design that is an efficient and cost-effective method to correct for local bias in regional LiDAR-assisted forest inventory estimates. This design is especially useful for small woodlot owners.<\/p>\n<p align=\"justify\"><strong>Abstract<\/strong><br \/>\n<strong>Context<\/strong> Light detection and ranging (LiDAR)-derived forest inventory estimates are generally unbiased at landscape levels but may be biased locally. One solution to correct local bias is to use ground-based double sampling with ratio estimation where the LiDAR estimates form the large sample covariate and the ground plots are used to estimate a correction or calibration ratio.<br \/>\n<strong>Aims<\/strong> Our objectives were to test the performance of different sample strategies, to correct for local bias, and to determine the most efficient and cost-effective sampling design.<br \/>\n<strong>Methods<\/strong> We compared five sample selection methods and four plot types using simulation. Sample sizes and inventory costs required to achieve 5% standard error were calculated to assess sampling efficiency.<br \/>\n<strong>Results<\/strong> The results showed that bias can be corrected successfully using a doubling sampling approach with ratio estimation, and that variable probability selection methods were more efficient than equal probability selection methods. A big basal area factor (BAF) plot was the most cost-effective on-the-ground plot type.<br \/>\n<strong>Conclusion<\/strong> The most efficient and cost-effective sampling design was list sampling with big BAF plots. This combination can be used to calibrate LiDAR-derived forest inventory estimates for a variety of forest attributes.<\/p>\n<p><strong>Keywords<\/strong><br \/>\nLiDAR-assisted inventory, Variable probability sampling, Big BAF sampling, Ratio estimation, Sampling with covariates, Sampling to correct<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1007\/s13595-020-00976-8'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nHsu, Y., Chen, Y., Yang, T. et al. Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots. Annals of Forest Science 77, 75 (2020). <a href=\"https:\/\/doi.org\/10.1007\/s13595-020-00976-8\">https:\/\/doi.org\/10.1007\/s13595-020-00976-8<\/a><\/p>\n<p><strong>For the read-only version of the full text:<\/strong><br \/>\n<a href=\"https:\/\/rdcu.be\/b5YmZ\">https:\/\/rdcu.be\/b5YmZ<\/a><\/p>\n<p><strong>Data availability<\/strong><br \/>\nThe datasets generated during and\/or analyzed during the current study are available in the University of New Brunswick\u2019s DataVerse data archive, <a href=\"https:\/\/doi.org\/10.25545\/Z8WRBJ\">https:\/\/doi.org\/10.25545\/Z8WRBJ<\/a><\/p>\n<p><strong>Handling Editor<\/strong><br \/>\nJean-Michel Leban<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key message This study presents an easy-to-apply variable probability sample design that is an efficient and cost-effective method to correct for local bias in regional LiDAR-assisted forest inventory estimates. This design is especially useful for small woodlot owners. Abstract Context Light detection and ranging (LiDAR)-derived forest inventory estimates are generally unbiased at landscape levels but [&hellip;]<\/p>\n","protected":false},"author":109,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,110,15],"tags":[],"class_list":["post-4368","post","type-post","status-publish","format-standard","hentry","category-article-type","category-data-in-repository","category-research-paper","cat-14-id","cat-110-id","cat-15-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/4368","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=4368"}],"version-history":[{"count":0,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/4368\/revisions"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=4368"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=4368"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=4368"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}