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Linear regression and lines intersecting as a method of extracting punctual entities in a lidar point cloud / Marlo Antonio Ribeiro Martins in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])  

Public [article]  inBoletim de Ciências Geodésicas > vol 27 n° 3 [01/10/2021] . - 21 p. Titre : Linear regression and lines intersecting as a method of extracting punctual entities in a lidar point cloud Type de document : Article/Communication Auteurs : Marlo Antonio Ribeiro Martins, Auteur ; Edson Aparecido Mitishita, Auteur Année de publication : 2021 Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] intersection spatiale [Termes IGN] régression linéaire [Termes IGN] semis de points [Termes IGN] toit Résumé : (auteur) The characteristics of data points obtained by laser scanning (LiDAR) and images have been considered complementary in the field of photogrammetric applications, and research to improve their integrated use have recently intensified. This study aim to verify the performance of determining punctual entities in a LiDAR point cloud using linear regression and intersecting lines obtained from buildings with square rooftop containing four planes (hip roof), as well as compare punctual entities three-dimensional coordinates determined by planes intersection. Our results show that the proposed method was more accurate in determining three-dimensional coordinates than plan intersection method. The obtained coordinates were evaluated and framed into the map accuracy standard for digital cartographic products (PEC-PCD), besides being analyzed for trend and precision. Accuracy analysis results frame punctual entities three-dimensional coordinates into the 1:2,000 or lower scale for Class A of PEC-PCD. Numéro de notice : A2021-959 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1590/s1982-21702021000300022 En ligne : https://doi.org/10.1590/s1982-21702021000300022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100074 [article]

Automatic detection of planted trees and their heights using photogrammetric rpa point clouds / Kênia Samara Mourão Santos in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])  

Public [article]  inBoletim de Ciências Geodésicas > vol 27 n° 3 [01/10/2021] Titre : Automatic detection of planted trees and their heights using photogrammetric rpa point clouds Type de document : Article/Communication Auteurs : Kênia Samara Mourão Santos, Auteur ; Christel Lingnau, Auteur ; Daniel Rodrigues dos Santos, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] détection d'arbres [Termes IGN] hauteur des arbres [Termes IGN] image aérienne [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Parana (Brésil) [Termes IGN] Pinus taeda [Termes IGN] plantation forestière [Termes IGN] semis de points Résumé : (auteur) This work aims to analyze the potential of the Photogrammetric Point Cloud (PPC) obtained from Remote Piloted Aircraft (RPA) optical images for detecting and obtaining tree heights in a loblolly pine plantation using a global maximum filter. The enhanced algorithm used in this study is then named STD (Single Tree Detection). Field surveys were conducted to count all the trees in the field (Forest Census) and measure the trees’ height with a vertex hypsometer. The results were faced to PCC outcomes. The detection rate (r) was equal to the precision rate (p), indicating that the algorithm reaches a high tree detection performance. In summary, the STD algorithm segmented 2,192 trees, representing 89% of trees recorded in the forest census. The retrieved tree height reached, on average, a height of 17.05 m, whereas slightly higher by the traditional forest inventory (17.42 m). The root-mean-square error (RMSE) and Bias were 47 cm (2.8%) and -37 cm (-2.2%), respectively. The Dunnett test showed that the tree height did not significantly differ between the results obtained by traditional forest inventory from those generated by the STD. It confirms the potential use of PPC for forest inventory procedures. Numéro de notice : A2021-958 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1590/s1982-21702021000300025 En ligne : https://doi.org/10.1590/s1982-21702021000300025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100075 [article]