{"id":5528,"date":"2022-10-05T15:05:18","date_gmt":"2022-10-05T13:05:18","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=5528"},"modified":"2022-10-05T15:05:18","modified_gmt":"2022-10-05T13:05:18","slug":"combining-weibull-distribution-and-k-nearest-neighbor-imputation-method-to-predict-wall-to-wall-tree-lists-for-the-entire-forest-region-of-northeast-china","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2022\/10\/05\/combining-weibull-distribution-and-k-nearest-neighbor-imputation-method-to-predict-wall-to-wall-tree-lists-for-the-entire-forest-region-of-northeast-china\/","title":{"rendered":"Combining Weibull distribution and k-nearest neighbor imputation method to predict wall-to-wall tree lists for the entire forest region of Northeast China"},"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\/2022\/10\/Fu-Y.-2022.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-5531 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2022\/10\/Fu-Y.-2022-283x300.png\" alt=\"\" width=\"320\" height=\"339\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2022\/10\/Fu-Y.-2022-283x300.png 283w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2022\/10\/Fu-Y.-2022-768x813.png 768w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2022\/10\/Fu-Y.-2022-640x677.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2022\/10\/Fu-Y.-2022.png 941w\" sizes=\"auto, (max-width: 320px) 100vw, 320px\" \/><\/a>Key message<\/strong><\/p>\n<p align=\"justify\">We propose a coupled framework to combine the strengths of the Weibull function in modeling diameter distributions and the ability of the k-nearest neighbor (kNN) method to impute spatially continuous forest stand attributes for the prediction of wall-to-wall tree lists (lists of stems per hectare by species and diameter at breast height (DBH)) at regional scales. The tree lists of entire Northeast China\u2019s forests predicted by the above framework reasonably reflect the species-specific tree density and diameter distributions.<\/p>\n<p><strong>Keywords<\/strong><br \/>\nWeibull function; kNN imputation; Coupled framework; Diameter distribution; Tree density<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1186\/s13595-022-01161-9'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nFu, Y., He, H.S., Wang, S. et al. Combining Weibull distribution and k-nearest neighbor imputation method to predict wall-to-wall tree lists for the entire forest region of Northeast China. Annals of Forest Science 79, 42 (2022). <a href=\"https:\/\/doi.org\/10.1186\/s13595-022-01161-9\">https:\/\/doi.org\/10.1186\/s13595-022-01161-9<\/a><\/p>\n<p><strong>Data and\/or Code availability<\/strong><br \/>\nThe datasets generated during and\/or analyzed during the current study are available from the corresponding author on reasonable request.<\/p>\n<p><strong>Handling Editor<\/strong><br \/>\nAndreas Bolte<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key message We propose a coupled framework to combine the strengths of the Weibull function in modeling diameter distributions and the ability of the k-nearest neighbor (kNN) method to impute spatially continuous forest stand attributes for the prediction of wall-to-wall tree lists (lists of stems per hectare by species and diameter at breast height (DBH)) [&hellip;]<\/p>\n","protected":false},"author":109,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,109,15],"tags":[],"class_list":["post-5528","post","type-post","status-publish","format-standard","hentry","category-article-type","category-open-access","category-research-paper","cat-14-id","cat-109-id","cat-15-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5528","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=5528"}],"version-history":[{"count":5,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5528\/revisions"}],"predecessor-version":[{"id":5534,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/5528\/revisions\/5534"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=5528"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=5528"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=5528"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}