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Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 106 - 119 Titre : Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar Type de document : Article/Communication Auteurs : Melissa Fedrigo, Auteur ; Glenn J. Newnham, Auteur ; Nicholas C. Coops, Auteur ; Darius S. Culvenor, Auteur ; Douglas K. Bolton, Auteur ; Craig R. Nitschke, Auteur Année de publication : 2018 Article en page(s) : pp 106 - 119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse en composantes principales [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] Australie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Eucalyptus (genre) [Termes IGN] forêt tempérée [Termes IGN] peuplement forestier [Termes IGN] prédiction [Termes IGN] strate végétale Résumé : (Auteur) Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen’s kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the ‘ecotone’) between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation. Numéro de notice : A2018-074 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.018 Date de publication en ligne : 29/12/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89438 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018023	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018022	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans / Pyare Pueschel in ISPRS Journal of photogrammetry and remote sensing, vol 77 (March 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 77 (March 2013) . - pp 44 - 56 Titre : The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans Type de document : Article/Communication Auteurs : Pyare Pueschel, Auteur ; Glenn J. Newnham, Auteur ; Gilles Rock, Auteur ; Thomas Udelhoven, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 44 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection automatique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Pseudotsuga menziesii [Termes IGN] semis de points [Termes IGN] tronc [Termes IGN] volume (grandeur) Résumé : (Auteur) Terrestrial laser scanning (TLS) has been used to estimate a number of biophysical and structural vegetation parameters. Of these stem diameter is a primary input to traditional forest inventory. While many experimental studies have confirmed the potential for TLS to successfully extract stem diameter, the estimation accuracies differ strongly for these studies – due to differences in experimental design, data processing and test plot characteristics. In order to provide consistency and maximize estimation accuracy, a systematic study into the impact of these variables is required. To contribute to such an approach, 12 scans were acquired with a FARO photon 120 at two test plots (Beech, Douglas fir) to assess the effects of scan mode and circle fitting on the extraction of stem diameter and volume. An automated tree stem detection algorithm based on the range images of single scans was developed and applied to the data. Extraction of stem diameter was achieved by slicing the point cloud and fitting circles to the slices using three different algorithms (Lemen, Pratt and Taubin), resulting in diameter profiles for each detected tree. Diameter at breast height (DBH) was determined using both the single value for the diameter fitted at the nominal breast height and by a linear fit of the stem diameter vertical profile. The latter is intended to reduce the influence of outliers and errors in the ground level determination. TLS-extracted DBH was compared to tape-measured DBH. Results show that tree stems with an unobstructed view to the scanner can be successfully extracted automatically from range images of the TLS data with detection rates of 94% for Beech and 96% for Douglas fir. If occlusion of trees is accounted for stem detection rates decrease to 85% (Beech) and 84% (Douglas fir). As far as the DBH estimation is concerned, both DBH extraction methods yield estimates which agree with reference measurements, however, the linear fit based approach proved to be more robust for the single scan DBH extraction (RMSE range 1.39–1.74 cm compared to 1.47–2.43 cm). With regard to the different circle fit algorithms applied, the algorithm by Lemen showed the best overall performance (RMSE range 1.39–1.65 cm compared to 1.49–2.43 cm). The Lemen algorithm was also found to be more robust in case of noisy data. Compared to the single scans, the DBH extraction from the merged scan data proved to be superior with significant lower RMSE’s (0.66–1.21 cm). The influence of scan mode and circle fitting is reflected in the stem volume estimates, too. Stem volumes extracted from the single scans exhibit a large variability with deviations from the reference volumes ranging from -34% to 44%. By contrast volumes extracted from the merged scans only vary weakly (-2% to 6%) and show a marginal influence of circle fitting. Numéro de notice : A2013-114 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.12.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.12.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32252 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013031	RAB	Revue	Centre de documentation	En réserve L003	Disponible