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Auteur Damjan Strnad |
Documents disponibles écrits par cet auteur (2)



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)
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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] SlovénieRésumé : (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
in International journal of applied Earth observation and geoinformation > vol 111 (July 2022) . - n° 102844[article]Fuzzy modelling of growth potential in forest development simulation / Damjan Strnad in Ecological Informatics, vol 48 (November 2018)
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Titre : Fuzzy modelling of growth potential in forest development simulation Type de document : Article/Communication Auteurs : Damjan Strnad, Auteur ; Štefan Kohek, Auteur ; Simon Kolmanič, Auteur Année de publication : 2018 Article en page(s) : pp 80 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] analyse de sensibilité
[Termes IGN] biodiversité
[Termes IGN] composition floristique
[Termes IGN] croissance des arbres
[Termes IGN] écosystème forestier
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] Slovénie
[Termes IGN] sous ensemble flou
[Termes IGN] surveillance écologiqueRésumé : (Auteur) In the paper, we introduce a new fuzzy-based model for calculation of plant growth potential in the context of forest development simulation, which is an important tool for prediction and monitoring of forest biodiversity. When modelling a forest ecosystem, one needs to account for a significant amount of ambiguity in the specification of plant requirements and environmental conditions, whose overlap determines the competitive potential of co-occurring species. The proposed fuzzy model addresses the imprecision and uncertainty about proper interpretation of numerically estimated growth conditions with respect to linguistically specified plant requirements. Individual requirement levels are represented as fuzzy sets to which estimated growth conditions are mapped, while plant needs are modelled as fuzzy numbers with adjustable tolerance radii. The growth potential with respect to a particular resource is then calculated as a membership of condition mean in a fuzzy set of plant demand. We validate the model operation within the ForestMAS simulator on real data obtained from six decades of observations registered at a forest fire recovery site in northern Slovenia. We show that the enhanced expressiveness about the tolerance of tree species to deviations of growth conditions allows the fuzzy model to improve the accuracy of forest composition prediction with respect to the crisp model. Sensitivity analysis also shows that, in many cases, the fuzzy model increases simulation robustness with respect to vaguely defined plant needs and estimated site conditions. Numéro de notice : A2019-229 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecoinf.2018.08.002 Date de publication en ligne : 11/08/2018 En ligne : https://doi.org/10.1016/j.ecoinf.2018.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92744
in Ecological Informatics > vol 48 (November 2018) . - pp 80 - 88[article]