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An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)  

Public [article]  inGeocarto international > vol 33 n° 5 (May 2018) . - pp 443 - 457 Titre : An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery Type de document : Article/Communication Auteurs : Luis Angel Ruiz, Auteur ; Jorge Abel Recio, Auteur ; Pablo Crespo-Peremarch, Auteur ; Marta Sapena Moll, Auteur Année de publication : 2018 Article en page(s) : pp 443 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arbre de décision [Termes IGN] biomasse (combustible) [Termes IGN] carte forestière [Termes IGN] classification barycentrique [Termes IGN] classification orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt méditerranéenne [Termes IGN] image Sentinel-MSI [Termes IGN] image Worldview [Termes IGN] modèle de simulation [Termes IGN] structure d'un peuplement forestier Résumé : (Auteur) Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata. Numéro de notice : A2018-140 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265595 Date de publication en ligne : 28/11/2016 En ligne : https://doi.org/10.1080/10106049.2016.1265595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89690 [article]

Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud / Cigdem Serifoglu Yilmaz in Geocarto international, vol 33 n° 5 (May 2018)  

Public [article]  inGeocarto international > vol 33 n° 5 (May 2018) . - pp 522 - 537 Titre : Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud Type de document : Article/Communication Auteurs : Cigdem Serifoglu Yilmaz, Auteur ; Oguz Gungor, Auteur Année de publication : 2018 Article en page(s) : pp 522 - 537 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage de points [Termes IGN] interpolation [Termes IGN] Matlab [Termes IGN] modèle numérique de terrain [Termes IGN] performance [Termes IGN] semis de points [Termes IGN] Triangulated Irregular Network [Termes IGN] université Résumé : (Auteur) Ground filtering algorithms mainly focus on filtering LiDAR (Light Detection and Ranging) point clouds owing to their intrinsic characteristics to classify ground and non-ground points. However, the acquisition and processing of LiDAR data is still costly. Compared to LiDAR technology, UAVs (Unmanned Aerial Vehicle) are cheap and easy to use. In this study, the performances of five widely used ground filtering algorithms (Progressive Morphological 1D/2D, Maximum Local Slope, Elevation Threshold with Expand Window, and Adaptive TIN) were investigated by conducting qualitative and quantitative evaluations on UAV-based point clouds. Evaluation results indicated that the Adaptive TIN algorithm presented the best performance. The result of the Adaptive TIN algorithm was interpolated by using a MATLAB script to generate the DTM (Digital Terrain Model). Field measurements indicated that using UAV-based point clouds may be a reasonable alternative for LiDAR data, depending on the characteristics of the study area. Numéro de notice : A2018-141 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265599 Date de publication en ligne : 07/12/2016 En ligne : https://doi.org/10.1080/10106049.2016.1265599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89691 [article]