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Aerial and UAV images for photogrammetric analysis of Belvedere Glacier evolution in the period 1977–2019 / Carlo Lapige De Gaetani in Remote sensing, vol 13 n° 18 (September-2 2021)
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Titre : Aerial and UAV images for photogrammetric analysis of Belvedere Glacier evolution in the period 1977–2019 Type de document : Article/Communication Auteurs : Carlo Lapige De Gaetani, Auteur ; Francesco Loli, Auteur ; Livio Pinto, Auteur Année de publication : 2021 Article en page(s) : n° 3787 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] changement climatique
[Termes IGN] données maillées
[Termes IGN] glacier
[Termes IGN] glaciologie
[Termes IGN] historique des données
[Termes IGN] image aérienne
[Termes IGN] image captée par drone
[Termes IGN] masque
[Termes IGN] modèle numérique de surface
[Termes IGN] Piémont (Italie)
[Termes IGN] point d'appui
[Termes IGN] restitution analogique
[Termes IGN] structure-from-motionRésumé : (auteur) Alpine glaciers are strongly suffering the consequences of the temperature rising and monitoring them over long periods is of particular interest for climate change tracking. A wide range of techniques can be successfully applied to survey and monitor glaciers with different spatial and temporal resolutions. However, going back in time to retrace the evolution of a glacier is still a challenging task. Historical aerial images, e.g., those acquired for regional cartographic purposes, are extremely valuable resources for studying the evolution and movement of a glacier in the past. This work analyzed the evolution of the Belvedere Glacier by means of structure from motion techniques applied to digitalized historical aerial images combined with more recent digital surveys, either from aerial platforms or UAVs. This allowed the monitoring of an Alpine glacier with high resolution and geometrical accuracy over a long span of time, covering the period 1977–2019. In this context, digital surface models of the area at different epochs were computed and jointly analyzed, retrieving the morphological dynamics of the Belvedere Glacier. The integration of datasets dating back to earlier times with those referring to surveys carried out with more modern technologies exploits at its full potential the information that at first glance could be thought obsolete, proving how historical photogrammetric datasets are a remarkable heritage for glaciological studies. Numéro de notice : A2021-753 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13183787 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.3390/rs13183787 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98745
in Remote sensing > vol 13 n° 18 (September-2 2021) . - n° 3787[article]Mapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)
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Titre : Mapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches Type de document : Article/Communication Auteurs : He Zhang, Auteur ; Marijn Bauters, Auteur ; Pascal Boeckx, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 3777 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] Congo (bassin)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle numérique de terrain
[Termes IGN] photogrammétrie aérienne
[Termes IGN] point d'appui
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] surveillance forestièreRésumé : (auteur) Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests. Numéro de notice : A2021-754 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13183777 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.3390/rs13183777 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98746
in Remote sensing > vol 13 n° 18 (September-2 2021) . - n° 3777[article]