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Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain / Convadonga Prendes in iForest, biogeosciences and forestry, vol 12 n° 4 (July 2019)
[article]
Titre : Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain Type de document : Article/Communication Auteurs : Convadonga Prendes, Auteur ; Sandra Bujan, Auteur ; Celestino Ordóñez, Auteur ; Elena Canga, Auteur Année de publication : 2019 Article en page(s) : pp 366 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse d'image orientée objet
[Termes IGN] axe médian
[Termes IGN] chemin forestier
[Termes IGN] classification pixellaire
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Espagne
[Termes IGN] montagneRésumé : (auteur) While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-automatic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis. Numéro de notice : A2019-659 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3832/ifor2989-012 Date de publication en ligne : 05/07/2019 En ligne : https://doi.org/10.3832/ifor2989-012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98528
in iForest, biogeosciences and forestry > vol 12 n° 4 (July 2019) . - pp 366 - 374[article]Monitoring the structure of forest restoration plantations with a drone-lidar system / D.R.A. Almeida in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)
[article]
Titre : Monitoring the structure of forest restoration plantations with a drone-lidar system Type de document : Article/Communication Auteurs : D.R.A. Almeida, Auteur ; E.N. Broadbent, Auteur ; A.M.A. Zambrano, Auteur ; Benjamin E. Wilkinson, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 192-198 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Brésil
[Termes IGN] canopée
[Termes IGN] densité du feuillage
[Termes IGN] données lidar
[Termes IGN] forêt tropicale
[Termes IGN] gestion forestière durable
[Termes IGN] image captée par drone
[Termes IGN] indice foliaire
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] reboisement
[Termes IGN] surveillance forestièreRésumé : (auteur) We are in an unprecedented moment for promoting forest restoration globally, with international and regional pledges to restore at least 350 million hectares by 2030. To achieve these ambitious goals, it is necessary to go beyond traditional plot-scale assessments and develop cost-effective technologies that can monitor the structure and function of restored forests at much broader scales. Lidar remote sensing in unmanned aerial vehicle (UAV) platforms can be an agile and autonomous method for monitoring forest restoration projects, especially under conditions when information updates are frequently needed in relatively small areas or, when using an airplane-borne lidar system may be not financially viable. Here, we explored the potential of an UAV-borne lidar system to assess the outcomes of a mixed-species restoration plantation experiment, designed to maximize aboveground biomass (AGB) accumulation. The experiment was established in Brazil’s Atlantic Forest, with 20 native tree species, by combining two levels of planting density and two management levels, totaling four treatment combinations and one control (plots left over for natural regeneration). We analyzed three structural variables from lidar data (canopy height, gap fraction and leaf area index) and one from field inventory data (AGB). Structural differences between the treatments and the control plots were reliably distinguished by the UAV-borne lidar system. AGB was strongly correlated with canopy height, allowing us to elaborate a predictive equation to use the UAV-borne lidar system for monitoring structural features in other restoration plantations in the region. UAV-borne lidar systems showed enormous potential for monitoring relatively broad-scale (thousands of hectares) forest restoration projects, providing an important tool to aid decision making and accountability in forest landscape restoration. Numéro de notice : A2019-468 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.03.014 Date de publication en ligne : 04/04/2019 En ligne : https://doi.org/10.1016/j.jag.2019.03.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93604
in International journal of applied Earth observation and geoinformation > vol 79 (July 2019) . - pp 192-198[article]Shadow detection and correction using a combined 3D GIS and image processing approach / Safa Ridene in Revue internationale de géomatique, vol 29 n° 3 - 4 (juillet - décembre 2019)
[article]
Titre : Shadow detection and correction using a combined 3D GIS and image processing approach Type de document : Article/Communication Auteurs : Safa Ridene, Auteur ; Reda Yaagoubi, Auteur ; Imane Sebari, Auteur ; Audrey Alajouanine, Auteur Année de publication : 2019 Article en page(s) : pp 241 - 253 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'ombre
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Matlab
[Termes IGN] ModelBuilder
[Termes IGN] orthophotographie
[Termes IGN] SIG 3D
[Termes IGN] ToulouseRésumé : (Auteur) While shadow can give useful information about size and shape of objects, it can pose problems in feature detection and object detection, thereby, it represents one of the major perturbator phenomenons frequently occurring on images and unfortunately, it is inevitable. “Shadows may lead to the failure of image analysis processes and also cause a poor quality of information which in turn leads to problems in implementation of algorithms.” (Mahajan and Bajpayee, 2015). It also affects multiple image analysis applications, whereby shadow cast by buildings deteriorate the spectral values of the surfaces. Therefore, its presence causes a deterioration in the visual image's quality and limits the information that the former could give. Ignoring the existence of shadows in images may cause serious problems in various visual processing applications such as false objects detection. In this context, many researches have been conducted through years. However, it is still a challenge for analysts all over the world to find a fully automated and efficient method for shadow removal from images. Numéro de notice : A2019-640 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3166/rig.2019.00091 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.3166/rig.2019.00091 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95649
in Revue internationale de géomatique > vol 29 n° 3 - 4 (juillet - décembre 2019) . - pp 241 - 253[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 047-2019031 SL Revue Centre de documentation Revues en salle Disponible Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)
[article]
Titre : Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Bo Wu, Auteur ; Xuming Ge, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification
[Termes IGN] classification basée sur les régions
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Hong-Kong
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] Paris (75)
[Termes IGN] scène urbaine
[Termes IGN] segmentation en régions
[Termes IGN] segmentation hiérarchique
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) Objects are formed by various structures and such structural information is essential for the identification of objects, especially for street facilities presented by mobile laser scanning (MLS) data with abundant details. However, due to the large volume of data, large variations in point density, noise and complexity of scanned scenes, the achievement of effective decomposition of objects into physical meaningful structures remains a challenge issue. And structural information has been rarely considered to improve the accuracy of distinguishing between objects with global or local similarity, such as traffic signs and traffic lights. Therefore, we propose a structural segmentation and classification method for MLS point clouds that is efficient and robust to variations in point density and complex urban scenes. During the segmentation stage, a novel region growing approach and a multi-size supervoxel segmentation algorithm robust to noise and varying density are combined to extract effective local shape descriptors. Structural components with physically meaningful labels are generated via structural labelling and clustering. During the classification stage, we consider the structural information at various scales and locations and encode it into a conditional random-field model for unary and pairwise inferences. High-order potentials are also introduced into the conditional random field to eliminate regional label noise. These high-order potentials are defined upon regions independent of connection relationships and can therefore take effect on isolated nodes. Experiments with two MLS datasets of typical urban scenes in Paris and Hong Kong were used to evaluate the performance of the proposed method. Nine and eleven different object classes were recognized from these two datasets with overall accuracies of 97.13% and 95.79%, respectively, indicating the effectiveness of the proposed method of interpreting complex urban scenes from point clouds with large variations in point density. Compared with previous studies on the Paris dataset, our method was able to recognize more classes and obtained a mean F1-score of 72.70% of seven common classes, being higher than the best of previous results. Numéro de notice : A2019-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.007 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93075
in ISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)
[article]
Titre : Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland Type de document : Article/Communication Auteurs : Cheikh Mohamedou, Auteur ; Lauri Korhonen, Auteur ; Kalle Eerikäinen, Auteur ; Timo Tokola, Auteur Année de publication : 2019 Article en page(s) : pp 253 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] croissance des arbres
[Termes IGN] diamètre des arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur systématique
[Termes IGN] Finlande
[Termes IGN] humidité du sol
[Termes IGN] indice d'humidité
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de croissance végétale
[Termes IGN] Perceptron multicoucheRésumé : (Auteur) Tree growth information is crucial in forest management and planning. Terrain-derived attributes such as the topographic wetness index (TWI), in addition to leaf area index (LAI) are closely related to tree growth, but are not commonly used in empirical growth models. In this study, we examined if modified TWI and LAI estimated from airborne light detection and ranging (LiDAR) data could be used to improve the predictions of a national single-tree diameter growth model. Altogether 1118 sample trees were selected within 197 subjectively placed plots in randomly selected forest stands in south-eastern Finland. Linear mixed effect (LME) and multilayer perceptron models were used to model the bias of 5-year growth predictions of the model and thus ultimately improve its predictions. The root mean square error (RMSE) of the national model was 0.604 cm. LME modelling reduced this value to 0.404 cm and MLP to 0.568 cm. The predictors included in the best-performing LME model were modified TWI, LAI estimated from LiDAR intensities, and elevation. Without an LAI estimate, the best RMSE was 0.436 cm. When applied as such, original and modified TWIs produced similar accuracy. We conclude that both TWI and LAI obtained from LiDAR data improve the diameter growth predictions of the national model. Numéro de notice : A2019-293 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpz010 Date de publication en ligne : 28/02/2019 En ligne : https://doi.org/10.1093/forestry/cpz010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93184
in Forestry, an international journal of forest research > vol 92 n° 3 (July 2019) . - pp 253 - 263[article]Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)PermalinkAutomatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels / Loïc Elsholz in XYZ, n° 159 (juin 2019)PermalinkCombining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest / Angela Blázquez-Casado in Annals of Forest Science, vol 76 n° 2 (June 2019)PermalinkEstimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching / Kasper Kansanen in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)PermalinkRegisTree: a registration algorithm to enhance forest inventory plot georeferencing / Maryem Fadili in Annals of Forest Science, vol 76 n° 2 (June 2019)PermalinkPiecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)PermalinkDetecting and characterizing downed dead wood using terrestrial laser scanning / Tuomas Yrttimaa in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkEconomic losses caused by tree species proportions and site type errors in forest management planning / Arto Haara in Silva fennica, vol 53 n° 2 (2019)PermalinkEstimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling / Alvaro Lau in Forest ecology and management, vol 439 (1 May 2019)PermalinkPairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets / Yusheng Xu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)Permalink