{"id":2748,"date":"2018-03-23T11:02:14","date_gmt":"2018-03-23T10:02:14","guid":{"rendered":"https:\/\/ist.blogs.inra.fr\/afs\/?p=2748"},"modified":"2018-06-22T11:33:31","modified_gmt":"2018-06-22T09:33:31","slug":"modeling-diameter-distributions-in-radiata-pine-plantations-in-spain-with-existing-countrywide-lidar-data","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2018\/03\/23\/modeling-diameter-distributions-in-radiata-pine-plantations-in-spain-with-existing-countrywide-lidar-data\/","title":{"rendered":"Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p>Topical collection: <a title=\"SilviLaser\" href=\"https:\/\/link.springer.com\/journal\/13595\/topicalCollection\/AC_181695fac42fff969ce4e923146622ca\">SilviLaser<\/a><\/p>\n<p>We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated every 6 years (temporal cover planned for countrywide LiDAR flights in Spain).<\/p>\n<p><strong>Context<\/strong> The estimation of stand diameter distribution is informative for forest managers in terms of stand structure, forest growth model inputs, and economic timber value. In this sense, airborne LiDAR may represent an adequate source of information.<br \/>\n<strong>Aims<\/strong> The objective was to evaluate the use of low-density Spanish countrywide LiDAR data for estimating diameter distributions in <em>Pinus radiata<\/em> D. Don stands in NW Spain.<br \/>\n<strong>Methods<\/strong> The empirical distributions were obtained from 25 sample plots. We applied the moment-based parameter recovery method in combination with the Weibull function to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. We evaluated the results by using the Kolmogorov\u2013Smirnov (KS) test and a classification tree and random forest (RF) to relate the KS test result for each plot to stand-level variables.<br \/>\n<strong>Results<\/strong> The models used to estimate average (dm) and quadratic (dg) mean diameters from LiDAR metrics, required for recovery of the Weibull parameters, explained a high percentage of the observed variance (77 and 80%, respectively), with RMSE values of 3.626 and 3.422 cm for the same variables. However, the proportion of plots accepted by the KS was low. This poor performance may be due to the strictness of the KS test and\/or by the characteristics of the LiDAR flight.<br \/>\n<strong>Conclusion<\/strong> The results justify the assessment of this approach over different species and forest types in regional or countrywide surveys.<\/p>\n<p>Keywords<br \/>\nPNOA (Plan Nacional de Ortofotograf\u00eda A\u00e9rea de Espa\u00f1a) project, Airborne laser scanning (ALS), Remote sensing, Weibull, Distribution function, Moment-based parameter recovery method<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1007\/s13595-018-0712-z'  style='float: right; ' ><\/div>\n<p>Publication<br \/>\nArias-Rodil, M., Di\u00e9guez-Aranda, U., \u00c1lvarez-Gonz\u00e1lez, J.G. et al. Annals of Forest Science (2018) 75: 36.<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/s13595-018-0712-z\">https:\/\/doi.org\/10.1007\/s13595-018-0712-z<\/a><\/p>\n<p>For the read-only version of the full text: <a href=\"http:\/\/rdcu.be\/JFmm\">http:\/\/rdcu.be\/JFmm<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Topical collection: SilviLaser We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated [&hellip;]<\/p>\n","protected":false},"author":94,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,15,108],"tags":[],"class_list":["post-2748","post","type-post","status-publish","format-standard","hentry","category-article-type","category-research-paper","category-special-issuetopical-collection","cat-14-id","cat-15-id","cat-108-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/2748","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\/94"}],"replies":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/comments?post=2748"}],"version-history":[{"count":0,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/2748\/revisions"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=2748"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=2748"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=2748"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}