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Affiner la recherche Interroger des sources externesOn the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters / Cédric Vega in Remote sensing of environment, vol 175 (15 March 2016)
Titre : On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters Type de document : Article/Communication Auteurs : Cédric Vega Année de publication : 2016 Projets : FORESEE / Mallet, Clément Article en page(s) : pp 32 - 42 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] biomasse aérienne
[Termes descripteurs IGN] classification ascendante hiérarchique
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données localisées 3D
[Termes descripteurs IGN] feuillu
[Termes descripteurs IGN] inventaire forestier (techniques et méthodes)
[Termes descripteurs IGN] métrique
[Termes descripteurs IGN] modèle numérique de surface de la canopée
[Termes descripteurs IGN] modèle numérique de terrain
[Termes descripteurs IGN] semis de points
[Termes descripteurs IGN] surface terrièreRésumé : (auteur) We proposed a new area-based approach to process Lidar point clouds and develop new sets of metrics to improve models dedicated to predict forest parameters. First, we introduced point normalization based on penetration depth below the outer canopy layer to avoid biases introduced by ground normalization and canopy surface heterogeneity during metric computation. Second, we proposed computation of area and volume metrics from canopy surface models computed from both first and last returns to better characterize the 3D plot heterogeneity. The set of proposed metrics were combined with traditional ones, based on point height above ground level, to measure their contribution to models of basal area (BA) and aboveground volume (AGV). The modeling framework included a wide range of forest types, canopy structures and Lidar characteristics. Models were developed for all sites grouped together or separately. In each case, the set of metrics was submitted to a hierarchical clustering process to select the best variables to be included in the models that were further established using a best-subset method. Overall, the introduction of the proposed metrics allowed a reduction in models root mean squared error from − 0.06% to 19.58% according to forest types and target forest parameters. Best improvements were achieved for broadleaved forests, showing the potential of the proposed metrics to efficiently characterize the structure of such porous forest canopies. Numéro de notice : A2016--089 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2015.12.039 date de publication en ligne : 07/01/2016 En ligne : http://doi.org/10.1016/j.rse.2015.12.039 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84582
in Remote sensing of environment > vol 175 (15 March 2016) . - pp 32 - 42[article]
Titre : Large-scale road detection in forested mountainous areas using airborne topographic lidar data Type de document : Article/Communication Auteurs : António Ferraz Année de publication : 2016 Projets : FORESEE / Mallet, Clément Article en page(s) : pp 23 - 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] analyse d'image orientée objet
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] extraction du réseau routier
[Termes descripteurs IGN] MNS lidar
[Termes descripteurs IGN] modèle numérique de surface
[Termes descripteurs IGN] montagneRésumé : (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]
Titre : Combining LIDAR and growth and yield models to assess standing biomass in various forest ecosystems Type de document : Article/Communication Auteurs : Laurent Saint André, Auteur ; Jean-Pierre Renaud, Auteur ; Jérôme Bock, Auteur ; Cédric Vega Congrès : Congrès: FORESEE Workshop 2014 Forestry applications of remote sensing technologies (8 - 10 octobre 2014; Champenoux, France), Auteur Editeur : [s.l.] : [s.n.] Année de publication : 2015 Projets : FORESEE / Mallet, Clément Langues : Anglais (eng) Numéro de notice : C2015-063 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93471 Documents numériques
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Combining LIDAR and growth and yield models to assess standing biomass in various forest ecosystems - ppt auteurAdobe Acrobat PDF
Titre : PTrees: A point-based approach to forest tree extraction from lidar data Type de document : Article/Communication Auteurs : Cédric Vega Année de publication : 2014 Projets : FORESEE / Mallet, Clément Article en page(s) : pp 98 - 108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données localisées 3D
[Termes descripteurs IGN] extraction d'arbres
[Termes descripteurs IGN] houppier
[Termes descripteurs IGN] inventaire forestier national (données France)
[Termes descripteurs IGN] segmentation dynamique
[Termes descripteurs IGN] semis de points
[Termes descripteurs IGN] traitement de données localiséesRésumé : (auteur) This paper introduces PTrees, a multi-scale dynamic point cloud segmentation dedicated to forest tree extraction from lidar point clouds. The method process the point data using the raw elevation values (Z) and compute height (H = Z − ground elevation) during post-processing using an innovative procedure allowing to preserve the geometry of crown points. Multiple segmentations are done at different scales. Segmentation criteria are then applied to dynamically select the best set of apices from the tree segments extracted at the various scales. The selected set of apices is then used to generate a final segmentation. PTrees has been tested in 3 different forest types, allowing to detect 82% of the trees with under 10% of false detection rate. Future development will integrate crown profile estimation during the segmentation process in order to both maximize the detection of suppressed trees and minimize false detections. Numéro de notice : A2014-800 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2014.05.001 date de publication en ligne : 28/05/2014 En ligne : http://dx.doi.org/10.1016/j.jag.2014.05.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83462
in International journal of applied Earth observation and geoinformation > vol 33 (December 2014) . - pp 98 - 108[article]
