{"id":82489,"date":"2022-11-07T14:47:23","date_gmt":"2022-11-07T19:47:23","guid":{"rendered":"https:\/\/mrnf.gouv.qc.ca\/nos-publications\/method-reduce-bias-digital-terrain-model-canopy-height-model-lidar-data\/"},"modified":"2022-11-07T14:47:23","modified_gmt":"2022-11-07T19:47:23","slug":"method-reduce-bias-digital-terrain-model-canopy-height-model-lidar-data","status":"publish","type":"cpt_publication","link":"https:\/\/mrnf.gouv.qc.ca\/en\/our-publications\/method-reduce-bias-digital-terrain-model-canopy-height-model-lidar-data\/","title":{"rendered":"Method to reduce the bias on digital terrain model and canopy height model from Lidar data"},"content":{"rendered":"\n<p>Published in <strong>Remote Sensing, vol 11(7), p. 863.<\/strong><\/p>\n<p>Underestimation of LiDAR heights, which is linked to the pulse\u2019s probability of reaching the top of vegetation and the ground, is widely known but has never been evaluated for different sensors and for diverse types of ecological conditions. The main causes for this underestimation are: pulse density, scan pattern (sensors), scan angles, and specific contract parameters (flight altitude, pulse repetition frequency), together with territory characteristics (slope, stand density, species composition). This study, which was conducted at a 1 \u00d7 1 m resolution, calculated the bias of both the digital terrain model (DTM) and canopy height model (CHM) by subtracting two LiDAR datasets: high-density pixels with 21 pulses\/m\u00b2 (first return) and more (DTM or CHM reference value pixels); and low-density pixels (DTM or CHM value to correct). After preliminary analyses, we concluded that the DTM did not need any specific adjustment; only CHM needed it. Among the variables studied, three were selected for the final CHM adjustment model and an empirical equation using a non-linear mixed model was developed. CHM underestimation correction could be applied before using the CHM for volume calculations, in forest growth models or for multi-temporal analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Published in Remote Sensing, vol 11(7), p. 863. Underestimation of LiDAR heights, which is linked to the pulse\u2019s probability of reaching the top of vegetation and the ground, is widely known but has never been evaluated for different sensors and for diverse types of ecological conditions. The main causes for this underestimation are: pulse density, [&hellip;]<\/p>\n","protected":false},"template":"","format":"standard","cptt_secteur":[1179],"cptt_theme":[874,1190],"cptt_categ_publi":[1149],"cptt_auteurs_ministeriels":[1096],"class_list":["post-82489","cpt_publication","type-cpt_publication","status-publish","format-standard","hentry","cptt_secteur-forests","cptt_theme-cartography","cptt_theme-imaging-and-lidar","cptt_categ_publi-scientific-article","cptt_auteurs_ministeriels-a-leboeuf"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Method to reduce the bias on digital terrain model and canopy height model from Lidar data - Minist\u00e8re des Ressources naturelles et des For\u00eats<\/title>\n<meta name=\"description\" content=\"Scientific article about a method to reduce the bias on digital terrain model and canopy height model from Lidar data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mrnf.gouv.qc.ca\/en\/our-publications\/method-reduce-bias-digital-terrain-model-canopy-height-model-lidar-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Method to reduce the bias on digital terrain model and canopy height model from Lidar data - 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