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Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading / Štefan Kohek in International journal of applied Earth observation and geoinformation, vol 111 (July 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 111 (July 2022) . - n° 102844 Titre : Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading Type de document : Article/Communication Auteurs : Štefan Kohek, Auteur ; Borut Žalik, Auteur ; Damjan Strnad, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102844 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse de sensibilité [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] dissymétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] houppier [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation de la forêt [Termes IGN] ombre [Termes IGN] semis de points [Termes IGN] Slovénie Résumé : (auteur) Reliable forest growth forecasting requires detailed tree data for forest simulation, while manual on-site collection of relevant data is work-intensive and unfeasible in larger forests. This paper proposes a complete methodology for fully automated forest growth simulation that relies primarily on airborne topographic Light Detection And Ranging (LiDAR) point clouds of individual trees. The proposed method estimates tree parameters and performs growth of individual trees based on an individual-based forest growth simulator, named BWINPro. In addition, competition and detailed asymmetric tree crown growth are modeled regarding the shading of tree crowns, which is estimated from the surrounding environment and neighbor trees. The result of the proposed approach is a new point cloud for subsequent analyses. The proposed method was validated by comparing canopy height models derived from the point clouds of the simulated trees with canopy height models derived from more recent ground truth point clouds. The results demonstrate the efficacy of the proposed method which achieves a 9.4% higher accuracy than the averaged linear regression model and, in the case of datasets with more distinct self-standing trees, where a tree crown boundary plays major role, a 4.1% higher accuracy than the directly fitted linear regression model. Numéro de notice : A2022-552 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102844 Date de publication en ligne : 04/06/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102844 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101156 [article]

Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR / Denis Horvat in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 1 – 14 Titre : Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR Type de document : Article/Communication Auteurs : Denis Horvat, Auteur ; Borut Žalik, Auteur ; Domen Mongus, Auteur Année de publication : 2016 Article en page(s) : pp 1 – 14 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de sensibilité [Termes IGN] classification dirigée [Termes IGN] détection automatique [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] méthode robuste [Termes IGN] morphologie mathématique [Termes IGN] prise en compte du contexte [Termes IGN] végétation [Termes IGN] zone rurale Résumé : (auteur) This paper proposes a new method for the detection of vegetation in LiDAR data. As vegetation points are characterised by non-linear distributions, they are efficiently recognised based-on large errors obtained when applying the local fitting of planar surfaces. In addition, three contextual filters are introduced capable of dealing with those exceptions that do not conform with previous interpretations. Namely, they are designed for detecting overgrowing vegetation, small objects attached to the planar surfaces (such as balconies, chimneys, and noise within the buildings) and small objects that do not belong to vegetation (vehicles, statues, fences). During the validation, the proposed method achieved over 97% correctness as well as completeness of vegetation recognition in rural areas while its average accuracy in urban settings was 90.7% in terms of F1F1-scores. The method uses only three input parameters and allows for efficient compensation between completeness and correctness, without significantly affecting the F1F1-score. Sensitivity analysis of the method also confirmed the robustness against a sub-optimal definition of the input parameters. Numéro de notice : A2016-576 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.02.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.02.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81706 [article]

Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces / Domen Mongus in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 93 (July 2014) . - pp. 145 - 156 Titre : Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces Type de document : Article/Communication Auteurs : Domen Mongus, Auteur ; Niko Lukač, Auteur ; Borut Žalik, Auteur Année de publication : 2014 Article en page(s) : pp. 145 - 156 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] extraction automatique [Termes IGN] point d'appui Numéro de notice : A2014-333 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.12.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.12.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73700 [article]

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