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Large-scale road detection in forested mountainous areas using airborne topographic lidar data / António Ferraz in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)
[article]
Titre : Large-scale road detection in forested mountainous areas using airborne topographic lidar data Type de document : Article/Communication Auteurs : António Ferraz , Auteur ; Clément Mallet , Auteur ; Nesrine Chehata , Auteur Année de publication : 2016 Projets : FORESEE / Bigot-de-Morogues, Francis Article en page(s) : pp 23 - 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] extraction du réseau routier
[Termes IGN] MNS lidar
[Termes IGN] modèle numérique de surface
[Termes IGN] montagne
[Termes IGN] semis de pointsRésumé : (auteur) In forested mountainous areas, the road location and characterization are invaluable inputs for various purposes such as forest management, wood harvesting industry, wildfire protection and fighting. Airborne topographic lidar has become an established technique to characterize the Earth surface. Lidar provides 3D point clouds allowing for fine reconstruction of ground topography while preserving high frequencies of the relief: fine Digital Terrain Models (DTMs) is the key product.
This paper addresses the problem of road detection and characterization in forested environments over large scales (>1000 km2). For that purpose, an efficient pipeline is proposed, which assumes that main forest roads can be modeled as planar elongated features in the road direction with relief variation in orthogonal direction. DTMs are the only input and no complex 3D point cloud processing methods are involved. First, a restricted but carefully designed set of morphological features is defined as input for a supervised Random Forest classification of potential road patches. Then, a graph is built over these candidate regions: vertices are selected using stochastic geometry tools and edges are created in order to fill gaps in the DTM created by vegetation occlusion. The graph is pruned using morphological criteria derived from the input road model. Finally, once the road is located in 2D, its width and slope are retrieved using an object-based image analysis. We demonstrate that our road model is valid for most forest roads and that roads are correctly retrieved (>80%) with few erroneously detected pathways (10–15%) using fully automatic methods. The full pipeline takes less than 2 min per km2 and higher planimetric accuracy than 2D existing topographic databases are achieved. Compared to these databases, additional roads can be detected with the ability of lidar sensors to penetrate the understory. In case of very dense vegetation and insufficient relief in the DTM, gaps may exist in the results resulting in local incompleteness (∼15%).Numéro de notice : A2016-137 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.12.002 Date de publication en ligne : 29/12/2015 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.12.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80309
in ISPRS Journal of photogrammetry and remote sensing > vol 112 (February 2016) . - pp 23 - 36[article]Object classification and recognition from mobile laser scanning point clouds in a road environment / Matti Lehtomäki in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
[article]
Titre : Object classification and recognition from mobile laser scanning point clouds in a road environment Type de document : Article/Communication Auteurs : Matti Lehtomäki, Auteur ; Anttoni Jaakkola, Auteur ; Juha Hyyppä, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1226 - 1239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] classification automatique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] histogramme
[Termes IGN] reconnaissance d'objets
[Termes IGN] réseau routier
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobile
[Termes IGN] télémétrie laser terrestreRésumé : (Auteur) Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future. Numéro de notice : A2016-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2476502 En ligne : https://doi.org/10.1109/TGRS.2015.2476502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80000
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 1226 - 1239[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible Pan-tropical hinterland forests: mapping minimally disturbed forests / Alexandra Tyukavina in Global ecology and biogeography, vol 25 n° 2 (February 2016)
[article]
Titre : Pan-tropical hinterland forests: mapping minimally disturbed forests Type de document : Article/Communication Auteurs : Alexandra Tyukavina, Auteur ; Matthew C. Hansen, Auteur ; P.V. Potapov, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 151 - 163 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte forestière
[Termes IGN] dégradation de la flore
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] image LandsatRésumé : (auteur) Aim : Tropical forest degradation is a significant source of carbon emissions due to selective logging, fragmentation and other disturbance factors. However, methods for mapping and monitoring pan-tropical forest degradation are still in their infancy. Here we present a new and automated approach to differentiate forests likely to be affected by degradation dynamics from more structurally intact forests, referred to as hinterland forests.
Location : Pan-tropical.
Methods : Inputs required for hinterland forest mapping include the extent of the initial forest cover and subsequent forest cover loss data, in this case global-scale Landsat-derived tree cover and stand-replacement disturbance maps. User-defined parameters employed to generate the extent and change of hinterland forest include: (1) minimum size of hinterland forest patch, (2) minimum corridor width, (3) distance from disturbance, and (4) extant history.
Results : Hinterland forest extent was mapped using forest cover loss data from 2000 to 2012 and hinterland forest loss was quantified from 2007 to 2013. Lidar-modelled forest height data were shown to be different within and outside hinterland forests, demonstrating the biophysical basis of the hinterland concept in discriminating likely degradation. Overall, hinterland forests experienced an 18% decline from 2007 to 2013. Regional variation in hinterland forest extent and loss was high. Data on 2013 pan-tropical hinterland forest extent can be downloaded from http://glad.geog.umd.edu/hinterland/index.html and viewed online at http://earthenginepartners.appspot.com/science-2013-global-forest.
Main conclusions : The largest extent of hinterland forests and of hinterland forest loss was found in Latin America, followed by Africa and Southeast Asia, respectively. The highest proportional loss of hinterland forest occurred in Southeast Asia, followed by Africa and Latin America, respectively. Nearly 95% of all 2013 hinterland forests were found in 17 of the 69 tropical forest countries studied. The extent and loss of hinterland forest can be an input to national monitoring and management programmes focused on forest carbon stocks, biodiversity conservation and other ecosystem services.Numéro de notice : A2016--199 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/geb.12394 En ligne : http://dx.doi.org/10.1111/geb.12394 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80340
in Global ecology and biogeography > vol 25 n° 2 (February 2016) . - pp 151 - 163[article]Reconstructing a church in 3D / Matthias Naumann in GIM international, vol 30 n° 2 (February 2016)
[article]
Titre : Reconstructing a church in 3D Type de document : Article/Communication Auteurs : Matthias Naumann, Auteur ; Gôrres Grenidôrffer, Auteur Année de publication : 2016 Article en page(s) : pp 12 - 15 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement de données localisées
[Termes IGN] données lidar
[Termes IGN] drone
[Termes IGN] église
[Termes IGN] photogrammétrie architecturale
[Termes IGN] photogrammétrie terrestre
[Termes IGN] précision des données
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de pointsRésumé : (éditeur) Reconstruction and maintenance work in historical buildings such as churches requires detailed and accurate information about them, but it can be difficult and expensive to acquire such data efficiently. The combination of terrestrial Lidar and UAS-based photogrammetry provides an adequate approach for gathering a full model of the outside of a church. Additionally, it allows for accuracy evaluation by comparing areas with overlap between terrestrial Lidar and the point cloud derived from the UAS images. Numéro de notice : A2016-081 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79916
in GIM international > vol 30 n° 2 (February 2016) . - pp 12 - 15[article]Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass / Timothy G. Gregoire in Remote sensing of environment, vol 173 (February 2016)
[article]
Titre : Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass Type de document : Article/Communication Auteurs : Timothy G. Gregoire, Auteur ; Erik Naesset, Auteur ; Ronald E. McRoberts, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 98 - 108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] biomasse aérienne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] incertitude géométrique
[Termes IGN] inférence statistique
[Termes IGN] varianceRésumé : (auteur) For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of above-ground forest biomass. This technology is now employed routinely in forest management inventories of some Nordic countries, and there is eager anticipation for its application to assess changes in standing biomass in vast tropical regions of the globe in concert with the UN REDD program to limit C emissions. In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc. In many instances, too, there appears to be no recognition of different bases of statistical inference which bear importantly on uncertainty estimation. Statistically rigorous assessment of uncertainty for four large LiDAR-assisted surveys is expounded. Numéro de notice : A2016--160 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2015.11. En ligne : https://doi.org/10.1016/j.rse.2015.11.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87012
in Remote sensing of environment > vol 173 (February 2016) . - pp 98 - 108[article]A wavelet-based echo detector for waveform LiDAR data / Cheng-Kai Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkPermalinkAssessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area / Phil Wilkes (2016)PermalinkPermalinkAutonomous navigation in complex nonplanar environments based on laser ranging / Philipp Andreas Krüsi (2016)PermalinkA comprehensive cartographic approach to evacuation map creation for Hurricane Ike in Galveston County, Texas / Yin-Hsuen Chen in Cartography and Geographic Information Science, Vol 43 n° 1 (January 2016)PermalinkCorrection de nuages de points lidar embarqué sur véhicule pour la reconstruction d’environnement 3D vaste / Pierre Merriaux (2016)PermalinkDétection à haute résolution spatiale de la desserte forestière en milieu montagneux par lidar aéroporté / Clément Mallet in Forêt entreprise, n° 226 (janvier/février 2016)PermalinkDetection, segmentation and localization of individual trees from MMS point cloud data / Martin Weinmann (2016)PermalinkPermalink