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UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment / Katerina Trepekli in Natural Hazards, vol 113 n° 1 (August 2022)  

Public [article]  inNatural Hazards > vol 113 n° 1 (August 2022) . - pp 423 - 451 Titre : UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment Type de document : Article/Communication Auteurs : Katerina Trepekli, Auteur ; Thomas Balstrøm, Auteur ; Thomas Friborg, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 423 - 451 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] antenne GNSS [Termes IGN] centrale inertielle [Termes IGN] faisceau laser [Termes IGN] Ghana [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] risque naturel [Termes IGN] semis de points [Termes IGN] Triangulated Irregular Network [Termes IGN] zone urbaine Résumé : (auteur) In this study, we present the first findings of the potential utility of miniaturized light and detection ranging (LiDAR) scanners mounted on unmanned aerial vehicles (UAVs) for improving urban flood modelling and assessments at the local scale. This is done by generating ultra-high spatial resolution digital terrain models (DTMs) featuring buildings and urban microtopographic structures that may affect floodwater pathways (DTMbs). The accuracy and level of detail of the flooded areas, simulated by a hydrologic screening model (Arc-Malstrøm), were vastly improved when DTMbs of 0.3 m resolution representing three urban sites surveyed by a UAV-LiDAR in Accra, Ghana, were used to supplement a 10 m resolution DTM covering the region’s entire catchment area. The generation of DTMbs necessitated the effective classification of UAV-LiDAR point clouds using a morphological and a triangulated irregular network method for hilly and flat landscapes, respectively. The UAV-LiDAR data enabled the identification of archways, boundary walls and bridges that were critical when predicting precise run-off courses that could not be projected using the coarser DTM only. Variations in a stream’s geometry due to a one-year time gap between the satellite-based and UAV-LiDAR data sets were also observed. The application of the coarser DTM produced an overestimate of water flows equal to 15% for sloping terrain and up to 62.5% for flat areas when compared to the respective run-offs simulated from the DTMbs. The application of UAV-LiDAR may enhance the effectiveness of urban planning by projecting precisely the locations, extents and run-offs of flooded areas in dynamic urban settings. Numéro de notice : A2022-704 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s11069-022-05308-9 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.1007/s11069-022-05308-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101567 [article]

Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102686 Titre : Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach Type de document : Article/Communication Auteurs : Linyuan Li, Auteur ; Xihan Mu, Auteur ; Francesco Chianucci, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102686 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme SLIC [Termes IGN] apprentissage profond [Termes IGN] canopée [Termes IGN] carte forestière [Termes IGN] Chine [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couvert forestier [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] faisceau laser [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] sous-étage [Termes IGN] structure-from-motion Résumé : (auteur) Accurate wall-to-wall estimation of forest crown cover is critical for a wide range of ecological studies. Notwithstanding the increasing use of UAVs in forest canopy mapping, the ultrahigh-resolution UAV imagery requires an appropriate procedure to separate the contribution of understorey from overstorey vegetation, which is complicated by the spectral similarity between the two forest components and the illumination environment. In this study, we investigated the integration of deep learning and the combined data of imagery and photogrammetric point clouds for boreal forest canopy mapping. The procedure enables the automatic creation of training sets of tree crown (overstorey) and background (understorey) data via the combination of UAV images and their associated photogrammetric point clouds and expands the applicability of deep learning models with self-supervision. Based on the UAV images with different overlap levels of 12 conifer forest plots that are categorized into “I”, “II” and “III” complexity levels according to illumination environment, we compared the self-supervised deep learning-predicted canopy maps from original images with manual delineation data and found an average intersection of union (IoU) larger than 0.9 for “complexity I” and “complexity II” plots and larger than 0.75 for “complexity III” plots. The proposed method was then compared with three classical image segmentation methods (i.e., maximum likelihood, Kmeans, and Otsu) in the plot-level crown cover estimation, showing outperformance in overstorey canopy extraction against other methods. The proposed method was also validated against wall-to-wall and pointwise crown cover estimates using UAV LiDAR and in situ digital cover photography (DCP) benchmarking methods. The results showed that the model-predicted crown cover was in line with the UAV LiDAR method (RMSE of 0.06) and deviate from the DCP method (RMSE of 0.18). We subsequently compared the new method and the commonly used UAV structure-from-motion (SfM) method at varying forward and lateral overlaps over all plots and a rugged terrain region, yielding results showing that the method-predicted crown cover was relatively insensitive to varying overlap (largest bias of less than 0.15), whereas the UAV SfM-estimated crown cover was seriously affected by overlap and decreased with decreasing overlap. In addition, canopy mapping over rugged terrain verified the merits of the new method, with no need for a detailed digital terrain model (DTM). The new method is recommended to be used in various image overlaps, illuminations, and terrains due to its robustness and high accuracy. This study offers opportunities to promote forest ecological applications (e.g., leaf area index estimation) and sustainable management (e.g., deforestation). Numéro de notice : A2022-192 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102686 Date de publication en ligne : 05/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102686 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99951 [article]

Impact of beam diameter and scanning approach on point cloud quality of terrestrial laser scanning in forests / Meinrad Abegg in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8153 - 8167 Titre : Impact of beam diameter and scanning approach on point cloud quality of terrestrial laser scanning in forests Type de document : Article/Communication Auteurs : Meinrad Abegg, Auteur ; Ruedi Boesch, Auteur ; Michael E. Schaepman, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8153 - 8167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] densité du peuplement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] faisceau laser [Termes IGN] forêt [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modélisation de la forêt [Termes IGN] qualité des données [Termes IGN] semis de points [Termes IGN] signal lidar [Termes IGN] Suisse [Termes IGN] télémétrie laser terrestre Résumé : (auteur) In recent years, portable laser scanning devices and their applications in the context of forest mensuration have undergone rapid methodological and technological developments. Devices have become smaller, lighter, and more affordable, whereas new data-driven methods and software packages have facilitated the derivation of information from point clouds. Thus, terrestrial laser scanning (TLS) is now well established, and laser–object interactions have been studied using theoretical, modeling, and experimental approaches. The representation of scanned objects in terms of accuracy and completeness is a key factor for successful feature extraction. Still, little is known about the influence of TLS and survey properties on point clouds in complex scattering environments, such as forests. In this study, we investigate the influence of laser beam diameter and signal triggering on the quality of point clouds in forested environments. Based on the Swiss National Forest Inventory data, we simulate the TLS measurements in 684 virtual forest stands using a 3-D content creation suite. We show that small objects lack sufficient representation in the point cloud and they are further negatively influenced by large laser beam diameters, dense stands, and large distances from the scanning device. We provide simulations that make it possible to derive a rationale for decisions regarding the appropriate choice of TLS device and survey configuration for forest inventories. Numéro de notice : A2021-709 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1109/TGRS.2020.3037763 Date de publication en ligne : 08/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3037763 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98608 [article]

Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070 Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] atmosphère terrestre [Termes IGN] canopée [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] faisceau laser [Termes IGN] feuille (végétation) [Termes IGN] foresterie [Termes IGN] forêt de feuillus [Termes IGN] forêt tropicale [Termes IGN] plus proche voisin, algorithme du [Termes IGN] précision de la classification [Termes IGN] Python (langage de programmation) [Termes IGN] semis de points [Termes IGN] transfert radiatif Résumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 Date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970 [article]

Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios / Klemen Istenič in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 13 - 25 Titre : Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios Type de document : Article/Communication Auteurs : Klemen Istenič, Auteur ; Nuno Gracias, Auteur ; Aurélien Arnaubec, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 13 - 25 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] estimation de pose [Termes IGN] étalonnage [Termes IGN] faisceau laser [Termes IGN] image à haute résolution [Termes IGN] image sous-marine [Termes IGN] photogrammétrie sous-marine [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D [Termes IGN] structure-from-motion Résumé : (Auteur) Improvements in structure-from-motion techniques are enabling many scientific fields to benefit from the routine creation of detailed 3D models. However, for a large number of applications, only a single camera is available for the image acquisition, due to cost or space constraints in the survey platforms. Monocular structure-from-motion raises the issue of properly estimating the scale of the 3D models, in order to later use those models for metrology. The scale can be determined from the presence of visible objects of known dimensions, or from information on the magnitude of the camera motion provided by other sensors, such as GPS. This paper addresses the problem of accurately scaling 3D models created from monocular cameras in GPS-denied environments, such as in underwater applications. Motivated by the common availability of underwater laser scalers, we present two novel approaches which are suitable for different laser scaler configurations. A fully unconstrained method enables the use of arbitrary laser setups, while a partially constrained method reduces the need for calibration by only assuming parallelism on the laser beams and equidistance with the camera. The proposed methods have several advantages with respect to existing methods. By using the known geometry of the scene represented by the 3D model, along with some parameters of the laser scaler geometry, the need for laser alignment with the optical axis of the camera is eliminated. Furthermore, the extremely error-prone manual identification of image points on the 3D model, currently required in image-scaling methods, is dispensed with. The performance of the methods and their applicability was evaluated both on data generated from a realistic 3D model and on data collected during an oceanographic cruise in 2017. Three separate laser configurations have been tested, encompassing nearly all possible laser setups, to evaluate the effects of terrain roughness, noise, camera perspective angle and camera-scene distance on the final estimates of scale. In the real scenario, the computation of 6 independent model scale estimates using our fully unconstrained approach, produced values with a standard deviation of 0,3 %. By comparing the values to the only other possible method currently usable for this dataset, we showed that the consistency of scales obtained for individual lasers is much higher for our approach (0,6 % compared to 4 %). Numéro de notice : A2020-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.10.007 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.10.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94397 [article]

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Manuel d'optique / Germain Chartier (2020)

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Lidars with narrow FOV for daylight measurements / Ronald Eixmann in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)  

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Small-footprint laser scanning simulator for system validation, error assessment, and algorithm development / Antero Kukko in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 10 (October 2009)  

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Development of a simulation model to predict Lidar interception in forested environments / N.R. Goodwin in Remote sensing of environment, vol 111 n° 4 (28/12/2007)  

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Initial analysis and visualization of waveform laser scanner data / Johanna Töpel (2005)

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Entwicklung und Erprobung eines abbildenden Laseraltimeters für den Flugeinsatz unter Verwendung des Mehrfrequenz- Phasenvergleichsverfahrens / C. Hug (1996)

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Lunette de Galilée pour contrôle du faisceau laser / L. Oterma (1976)

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