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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)
[article]
Titre : Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data Type de document : Article/Communication Auteurs : Piotr Tompalski, Auteur ; Joanne C. White, Auteur ; Nicholas C. Coops, Auteur ; Michael A. Wulder, Auteur Année de publication : 2019 Article en page(s) : pp 110 - 124 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] méthode des moindres carrés
[Termes IGN] méthode robuste
[Termes IGN] modèle mathématique
[Termes IGN] régression
[Termes IGN] semis de points
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Airborne laser scanning (ALS) is a reliable source of accurate information for forest stand inventory attributes including height, cover, basal area, and volume. The commonly applied area-based approach (ABA) allows the derivation of wall-to-wall geospatial coverages representing each of the modeled attributes at a grid-cell level, with spatial resolutions typically between 20 and 30 m. The ABA predictive models are developed using stratified inventory data from field plots, the requirement for which can increase the overall cost of the ALS-based inventory. Parsimonious use of ground plots is a key means to control variable costs in the operational implementation of the ABA. In this paper, we demonstrate how the prediction accuracy of Lorey's height (HL, m), quadratic mean diameter (QMD, cm), and gross volume (V, m3) vary when existing ABA models are transferred to different areas or are applied to point cloud data with different characteristics than those on which the original model was developed. Specifically, we consider three scenarios of model transferability: (i) same point cloud characteristics, different areas; (ii) different point cloud characteristics, same areas; and (iii) different point cloud characteristics, different areas. We generated area-based models using three modeling approaches: linear regression (OLS), random forests (RF), and k-nearest neighbour (kNN) imputation. Results indicated that the prediction accuracy of area-based models varied by attribute and by modeling approach. We found that when the models were transferred their prediction accuracy decreased, with an average increase in relative bias up to 22.04%, and increase in relative RMSE up to 29.31%. Prediction accuracies for HL were higher than those of QMD or V when models were transferred, and had the lowest average increase in relative bias and relative RMSE of Numéro de notice : A2019-227 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.04.006 Date de publication en ligne : 13/04/2019 En ligne : https://doi.org/10.1016/j.rse.2019.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92741
in Remote sensing of environment > vol 227 (15 June 2019) . - pp 110 - 124[article]Automatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels / Loïc Elsholz in XYZ, n° 159 (juin 2019)
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Titre : Automatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels Type de document : Article/Communication Auteurs : Loïc Elsholz, Auteur Année de publication : 2019 Article en page(s) : pp 37 - 43 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] contrôle qualité
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image terrestre
[Termes IGN] objet géographique linéaire
[Termes IGN] objet géographique ponctuel
[Termes IGN] SAGA GIS
[Termes IGN] signalisation routièreRésumé : (auteur) Par le passé, la compensation du réseau géodésique classique national se faisait par petits blocs et par fuseau suivant la projection UTM. Cela a engendré une propagation d’erreurs entre les blocs et des altérations linéaires au niveau des zones de jonction entre les fuseaux de la projection UTM. Cet article présente la démarche de traitement et calcul du réseau géodésique classique national Algérien, par un ajustement global (utilisant le programme CHABAKA) suivant un fuseau étendu (utilisant le programme TRANMERCAFE). L’application a concerné un réseau échantillon du réseau géodésique primordial de l’Algérie (réalisé par l’IGN, en 1955). Les résultats obtenus sont illustrés et discutés. Numéro de notice : A2019-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans En ligne : http://www.aftopo.org/FR/xyz-4.html Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93156
in XYZ > n° 159 (juin 2019) . - pp 37 - 43[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2019021 RAB Revue Centre de documentation En réserve L003 Disponible Combining 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)
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Titre : Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest Type de document : Article/Communication Auteurs : Angela Blázquez-Casado, Auteur ; Rafael Calama, Auteur ; Manuel Valbuena, Auteur ; Marta Vergarechea, Auteur ; Francisco Rodriguez, Auteur Année de publication : 2019 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse discriminante
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt méditerranéenne
[Termes IGN] houppier
[Termes IGN] image Pléiades-HR
[Termes IGN] Pinus pinaster
[Termes IGN] Pinus pineaRésumé : (Auteur) Context : The discrimination of tree species at individual level in mixed Mediterranean forest based on remote sensing is a field which has gained greater importance. In these stands, the capacity to predict the quality and quantity of non-wood forest products is particularly important due to the very different goods the two species produce.
Aims : To assess the potential of using low-density airborne LiDAR data combined with high-resolution Pleiades images to discriminate two different pine species in mixed Mediterranean forest (Pinus pinea L. and Pinus pinaster Ait.) at individual tree level.
Methods : A Random Forest model was trained using plots from the pure stand dataset, determining which LiDAR and satellite variables allow us to obtain better discrimination between groups. The model constructed was then validated by classifying individuals in an independent set of pure and mixed stands.
Results : The model combining LiDAR and Pleiades data provided greater accuracy (83.3% and 63% in pure and mixed validation stands, respectively) than the models which only use one type of covariables.
Conclusion : The automatic crown delineation tool developed allows two very similar species in mixed Mediterranean conifer forest to be discriminated using continuous spatial information at the surface: Pleiades images and open source LiDAR data. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products and aiding forest managers to accurately predict production.Numéro de notice : A2019-180 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0835-x Date de publication en ligne : 17/05/2019 En ligne : https://doi.org/10.1007/s13595-019-0835-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92700
in Annals of Forest Science > vol 76 n° 2 (June 2019)[article]Estimating 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)
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Titre : Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching Type de document : Article/Communication Auteurs : Kasper Kansanen, Auteur ; Jari Vauhkonen, Auteur ; Timo Lähivaara, Auteur ; Aku Seppänen, Auteur ; Matti Maltamo, Auteur ; Lauri Mehtätalo, Auteur Année de publication : 2019 Article en page(s) : pp 66 - 78 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement d'histogramme
[Termes IGN] chaîne de traitement
[Termes IGN] détection d'arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier local
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] placette d'échantillonnage
[Termes IGN] surface terrière
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) Errors in individual tree detection and delineation affect diameter distribution predictions based on crown attributes extracted from the detected trees. We develop a methodology for circumventing these problems. The method is based on matching cumulative distribution functions of field measured tree diameter distributions and crown radii distributions extracted from airborne laser scanning data through individual tree detection presented by Vauhkonen and Mehtätalo (2015). In this study, empirical distribution functions and a monotonic, nonlinear model curve are introduced. Tree crown radius distribution produced by individual tree detection is corrected by a method taking into account that all trees cannot be detected. The evaluation is based on the ability of the developed model sequence to predict quadratic mean diameter and total basal area. The studied data consists of 36 field plots in a typical boreal managed forest area in eastern Finland. The suggested enhancements to the model sequence produce improved results in most of the test cases. Most notably, in leave-one-out cross-validation experiments the modified models improve RMSE of basal area 13% in the full data and RMSE of quadratic mean diameter and basal area 69% and 11%, respectively, in pure pine plots. Better modeling of the crown radius distribution and improved matching between crown radii and stem diameters add the operational premises of the full distribution matching. Numéro de notice : A2019-455 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.007 Date de publication en ligne : 15/04/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92868
in ISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 66 - 78[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt RegisTree: 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)PermalinkiTowns, le nouveau moteur de visualisation 3D de données géospatiales du Géoportail / Mirela Konini in Responsabilité et environnement, n° 94 (Avril 2019)PermalinkLearning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkOrléans monte sa maquette virtuelle / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)PermalinkRobust external calibration of terrestrial laser scanner and digital camera for structural monitoring / Mohammad Omidalizarandi in Journal of applied geodesy, vol 13 n° 2 (April 2019)Permalink