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Riparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds / Elena Belcore in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)  

Public [article]  inRemote sensing in ecology and conservation > vol 8 n° 5 (October 2022) . - pp 644 - 655 Titre : Riparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds Type de document : Article/Communication Auteurs : Elena Belcore, Auteur ; Melissa Latella, Auteur Année de publication : 2022 Article en page(s) : pp 644 - 655 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] carte de la végétation [Termes IGN] densité de la végétation [Termes IGN] détection d'objet [Termes IGN] forêt ripicole [Termes IGN] houppier [Termes IGN] image captée par drone [Termes IGN] Italie [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] orthophotoplan numérique [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) In recent years, numerous directives worldwide have addressed the conservation and restoration of riparian corridors, activities that rely on continuous vegetation mapping to understand its volumetric features and health status. Mapping riparian corridors requires not only fine-scale resolution but also the coverage of relatively large areas. The use of Unmanned Aerial Vehicles (UAV) allows for meeting both conditions, although the cost-effectiveness of their use is highly influenced by the type of sensor mounted on them. Few works have so far investigated the use of photogrammetric sensors for individual tree crown detection, despite being cheaper than the most common Light Detection and Ranging (LiDAR) ones. This work aims to improve the individual crown detection from UAV-photogrammetric datasets in a two fold way. Firstly, the effectiveness of a new approach that has already achieved interesting results in LiDAR applications was tested for photogrammetric point clouds. The test was carried out by comparing the accuracy achieved by the new approach, which is based on the point density features of the analysed dataset, with those related to the more common local maxima and textural methods. The results indicated the potentiality of the density-based method, which achieved accuracy values (0.76F-score) consistent with the traditional methods (0.49–0.80F-score range) but was less affected by under- and over-fitting. Secondly, the potential improvement of working on intra-annual multi-temporal datasets was assessed by applying the density-based approach to seven different scenarios, three of which were constituted by single-epoch datasets and the remaining given by the joining of the others. The F-score increased from 0.67 to 0.76 when passing from single- to multi-epoch datasets, aligning with the accuracy achieved by the new method when applied to LiDAR data. The results demonstrate the potential of multi-temporal acquisitions when performing individual crown detection from photogrammetric data. Numéro de notice : A2022-879 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.267 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.1002/rse2.267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102193 [article]