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Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)
[article]
Titre : Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data Type de document : Article/Communication Auteurs : Ying Quan, Auteur ; Mingze Li, Auteur ; Yuanshuo Hao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2171706 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] espèce végétale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forêt secondaire
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) Recent growth in unmanned aerial vehicle (UAV) technology have promoted the detailed mapping of individual tree species. However, the in-depth mining and comprehending of the significance of features derived from high-resolution UAV data for tree species discrimination remains a difficult task. In this study, a state-of-the-art approach combining UAV-borne light detection and ranging (LiDAR) and hyperspectral was used to classify 11 common tree species in a typical natural secondary forest in Northeast China. First, comprehensive relevant structural and spectral features were extracted. Then, the most valuable feature sets were selected by using a hybrid approach combining correlation-based feature selection with the optimized recursive feature elimination algorithm. The random forest algorithm was used to assess feature importance and perform the classification. Finally, the robustness of features derived from point clouds with different structures and hyperspectral images with different spatial resolutions was tested. Our results showed that the best classification accuracy was obtained by combining LiDAR and hyperspectral data (75.7%) compared to that based on LiDAR (60.0%) and hyperspectral (64.8%) data alone. The mean intensity of single returns and the visible atmospherically resistant index for red-edge band were the most influential LiDAR and hyperspectral derived features, respectively. The selected features were robust in point clouds with a density not lower than 5% (~5 pts/m2) and a resolution not lower than 0.3 m in hyperspectral data. Although canopy surface features were slightly different from original LiDAR features, canopy surface information was also important for tree species classification. This study proved the capabilities of UAV-borne LiDAR and hyperspectral data in natural secondary forest tree species discrimination and the potential for this approach to be transferable to other study areas. Numéro de notice : A2023-194 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/15481603.2023.2171706 Date de publication en ligne : 03/02/2023 En ligne : https://doi.org/10.1080/15481603.2023.2171706 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103075
in GIScience and remote sensing > vol 60 n° 1 (2023) . - n° 2171706[article]UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) / Martin Štroner in European journal of remote sensing, vol 56 n° 1 (2023)
[article]
Titre : UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) Type de document : Article/Communication Auteurs : Martin Štroner, Auteur ; Rudolf Urban, Auteur ; Thomas Křemen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2179942 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] densité de la végétation
[Termes IGN] données lidar
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de terrain
[Termes IGN] rugosité du sol
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) In this paper, we evaluated the results in terms of accuracy and coverage of the LiDAR-UAV system DJI Zenmuse L1 and Digital Aerial Photogrammetric system (DAP – UAV) DJI Zenmuse P1 in a forested area under leaf-off conditions on three sites with varying terrain ruggedness/tree type combinations. Detailed reference clouds were obtained using terrestrial scanning by Leica P40. Our results show that branches pose no problem to the accuracy of LiDAR-UAV and DAP-UAV derived terrain clouds. Elevation accuracies for photogrammetric data were even better than for LiDAR data – as low as 0.015 m on all sites. However, the LiDAR system provided better coverage, with almost full coverage at all sites, while the DAP-UAV coverage declined with the increasing density of branches (being worst in the young forest). In the very dense young forest (Site 1), the coverage by photogrammetrically extracted terrain cloud using high calculation quality and no filtering achieved 80.7% coverage, while LiDAR-UAV reached almost 100% coverage. The importance of the use of multiple (or last) returns when using LiDAR-UAV systems was demonstrated by the fact that on the site with the densest vegetation, only 11% of the ground points were represented by first returns. Numéro de notice : A2023-219 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/22797254.2023.2179942 Date de publication en ligne : 01/03/2023 En ligne : https://doi.org/10.1080/22797254.2023.2179942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103161
in European journal of remote sensing > vol 56 n° 1 (2023) . - n° 2179942[article]Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)
[article]
Titre : Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy Type de document : Article/Communication Auteurs : Mauro Maesano, Auteur ; Giovanni Santopuoli, Auteur ; Federico Valerio Moresi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 451-457 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] Calabre
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] gestion forestière durable
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] régression
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha-1 and from 34.2 to 22.1 Mg ha-1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model. Numéro de notice : A2022-903 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3832/ifor3781-015 Date de publication en ligne : 03/11/2022 En ligne : https://doi.org/10.3832/ifor3781-015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102299
in iForest, biogeosciences and forestry > vol 15 n° 6 (December 2022) . - pp 451-457[article]Assessment of camera focal length influence on canopy reconstruction quality / Martin Denter in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)
[article]
Titre : Assessment of camera focal length influence on canopy reconstruction quality Type de document : Article/Communication Auteurs : Martin Denter, Auteur ; Julian Frey, Auteur ; Teja Kattenborn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100025 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] Abies alba
[Termes IGN] Acer pseudoplatanus
[Termes IGN] Allemagne
[Termes IGN] canopée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Fagus sylvatica
[Termes IGN] image captée par drone
[Termes IGN] Larix decidua
[Termes IGN] longueur focale
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] parcelle forestière
[Termes IGN] Picea abies
[Termes IGN] reconstruction d'image
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) Unoccupied aerial vehicles (UAV) with RGB-cameras are affordable and versatile devices for the generation of a series of remote sensing products that can be used for forest inventory tasks, such as creating high-resolution orthomosaics and canopy height models. The latter may serve purposes including tree species identification, forest damage assessments, canopy height or timber stock assessments. Besides flight and image acquisition parameters such as image overlap, flight height, and weather conditions, the focal length, which determines the opening angle of the camera lens, is a parameter that influences the reconstruction quality. Despite its importance, the effect of focal length on the quality of 3D reconstructions of forests has received little attention in the literature. Shorter focal lengths result in more accurate distance estimates in the nadir direction since small angular errors lead to large positional errors in narrow opening angles. In this study, 3D reconstructions of four UAV-acquisitions with different focal lengths (21, 35, 50, and 85 mm) on a 1 ha mature mixed forest plot were compared to reference point clouds derived from high quality Terrestrial Laser Scans. Shorter focal lengths (21 and 35 mm) led to a higher agreement with the TLS scans and thus better reconstruction quality, while at 50 mm, quality losses were observed, and at 85 mm, the quality was considerably worse. F1-scores calculated from a voxel representation of the point clouds amounted to 0.254 with 35 mm and 0.201 with 85 mm. The precision with 21 mm focal length was 0.466 and 0.302 with 85 mm. We thus recommend a focal length no longer than 35 mm during UAV Structure from Motion (SfM) data acquisition for forest management practices. Numéro de notice : A2022-870 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100025 Date de publication en ligne : 09/11/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102164
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100025[article]Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning / Aboubakar Sani-Mohammed in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)
[article]
Titre : Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning Type de document : Article/Communication Auteurs : Aboubakar Sani-Mohammed, Auteur ; Wei Yao, Auteur ; Marco Heurich, Auteur Année de publication : 2022 Article en page(s) : n° 100024 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre mort
[Termes IGN] Bavière (Allemagne)
[Termes IGN] bois sur pied
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] gestion forestière durable
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image infrarouge couleur
[Termes IGN] peuplement mélangé
[Termes IGN] puits de carbone
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation. Numéro de notice : A2022-871 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100024 Date de publication en ligne : 10/11/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102165
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100024[article]A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)PermalinkRelevé 2D & 3D du marégraphe de Marseille / Emmanuel Clédat in XYZ, n° 173 (décembre 2022)PermalinkA semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) / Masoud Azad in Applied geomatics, vol 14 n° 4 (December 2022)PermalinkGCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK / Morteza Pourreza in Forests, vol 13 n° 11 (November 2022)PermalinkA joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds / Lina Fang in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)PermalinkMapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)PermalinkA deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkIncremental road network update method with trajectory data and UAV remote sensing imagery / Jianxin Qin in ISPRS International journal of geo-information, vol 11 n° 10 (October 2022)PermalinkRiparian 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)PermalinkDiscontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)Permalink