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A novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures / Alice Ahlem Othmani in Machine Vision and Applications, vol 27 n° 5 (July 2016)
[article]
Titre : A novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures Type de document : Article/Communication Auteurs : Alice Ahlem Othmani, Auteur ; Cansen Jiang, Auteur ; Nicolas Lomenie, Auteur ; Jean-Marie Favreau, Auteur ; Alexandre Piboule, Auteur ; Lew F. C. Lew Yan Voon, Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] arbre (flore)
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] écorce
[Termes IGN] extraction d'arbres
[Termes IGN] forêt tempérée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation
[Termes IGN] texture d'image
[Termes IGN] zone saillante 3DRésumé : (auteur) Terrestrial Laser Scanning (TLS) systems have gained increasing popularity in the forestry domain and are today widely used for the automatic measurement of forest inventory attributes. Nevertheless, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. It is in this context that we present a novel Computer-Aided Tree Species Identification method based on 3D bark texture analysis. The novelty of our approach resides in the following three key points: (1) 3D salient regions extraction using a new morphological segmentation method that we have called Burst Wind Segmentation, (2) the extraction and pre-annotation of a collection of typical 3D bark patterns, known as scars, from each of the tree species. The pre-annotated scars are stored in a dictionary that we have called ScarBook and they are used as a reference for the comparison of the 3D salient segmented regions, (3) a wide variety of advanced shape, saliency, curvature and roughness features are extracted from the 3D salient segmented regions. To study the performance of our method, an experiment has been carried out on a dataset composed of 969 patches which correspond to 30 cm long segments of the trunk at breast height. Six species among the most dominant species in European forests have been tested with patches of different diameter at breast height values so as to study the identification accuracy with respect to age. The results obtained are very encouraging and promising and they confirm the possibility of identifying tree species using TLS data. Numéro de notice : A2016--134 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s00138-015-0738-2 Date de publication en ligne : 28/11/2015 En ligne : https://doi.org/10.1007/s00138-015-0738-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85267
in Machine Vision and Applications > vol 27 n° 5 (July 2016)[article]Object-based image mapping of conifer tree mortality in San Diego county based on multitemporal aerial ortho-imagery / Mary Pyott Freeman in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)
[article]
Titre : Object-based image mapping of conifer tree mortality in San Diego county based on multitemporal aerial ortho-imagery Type de document : Article/Communication Auteurs : Mary Pyott Freeman, Auteur ; Douglas A. Stow, Auteur ; Dar A. Roberts, Auteur Année de publication : 2016 Article en page(s) : pp 571 - 580 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] arbre mort
[Termes IGN] carte de la végétation
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] image aérienne
[Termes IGN] image multitemporelle
[Termes IGN] orthoimage
[Termes IGN] Pinophyta
[Termes IGN] San DiegoRésumé : (Auteur) Two GEOBIA approaches are compared for their effectiveness in mapping dead trees within island montane forests of Southern California: a spatial contextual approach using an artificial neural network classifier, and a segmentation and multi-pixel classification approach. Both approaches are tested with multitemporal aerial orthoimagery having varying spatial resolutions. Spectral transformation inputs are also tested. An object-based accuracy assessment is conducted. Accuracies range between 30 percent to 90 percent for the dead tree class and are significantly higher for the spatial-contextual approach. Inclusion of spectral transforms increased accuracies by 5 percent for the true object-based approach, up to 13 percent for the spatial contextual approach, and reduced commission error up to 10 percent for both approaches. Masking techniques increased accuracies of the spatial contextual approach by 20 percent. With manual editing, the most accurate maps of individual live and dead trees from the spatial contextual approach are suitable for studying spatio-temporal trends in montane conifer mortality. Numéro de notice : A2016-518 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.7.571 En ligne : http://dx.doi.org/10.14358/PERS.82.7.571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81589
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 7 (juillet 2016) . - pp 571 - 580[article]Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa / Romano Lottering in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)
[article]
Titre : Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa Type de document : Article/Communication Auteurs : Romano Lottering, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2016 Article en page(s) : pp 870 - 880 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] classification dirigée
[Termes IGN] espèce végétale
[Termes IGN] image Worldview
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] rééchantillonnage
[Termes IGN] sous-étage
[Termes IGN] surface forestière
[Termes IGN] varianceRésumé : (Auteur) The objective of this study was to identify an appropriate spatial resolution for discriminating forest vegetation at subspecies level. WorldView-2 imagery was progressively resampled to coarser spatial resolutions. At a compartment level, 30 × 30-m subsets were generated across forest compartments to represent the five forest subspecies investigated in this study. From the centre of each subset, the spatial resolution of the original WorldView-2 image was resampled from 6 to 34-m, with increments of 4-m. The variance was then calculated at every resampled spatial resolution using each of the eight WorldView-2 bands. Based on the sampling theorem, the 3-m spatial resolution provided an appropriate resolution for all subspecies investigated. The WorldView-2 image was subsequently classified using the partial least squares linear discriminant analysis algorithm and the appropriate spatial resolution. An overall classification accuracy of 90% was established with an allocation disagreement of 9 and a quantity disagreement of 1. Numéro de notice : A2016-458 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1094519 Date de publication en ligne : 26/10/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1094519 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81382
in Geocarto international > vol 31 n° 7 - 8 (July - August 2016) . - pp 870 - 880[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016041 RAB Revue Centre de documentation En réserve L003 Disponible Wildlife management using aiborne Lidar / Joan Hagar in GIM international [en ligne], vol 30 n° 7 (July 2016)
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Titre : Wildlife management using aiborne Lidar Type de document : Article/Communication Auteurs : Joan Hagar, Auteur ; Dave Vesely, Auteur ; Patricia Haggerty, Auteur Année de publication : 2016 Article en page(s) : pp 31 - 33 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Aves
[Termes IGN] comptage
[Termes IGN] données lidar
[Termes IGN] gestion de la vie sauvage
[Termes IGN] habitat forestier
[Termes IGN] lasergrammétrie
[Termes IGN] sous-étageRésumé : (éditeur) The traditional sampling protocol for quantifying nesting habitat for murrelet require an observer to peer up through the tree crown from the forest floor, a task resulting in data of dubious quality. Two dimensional feature of the canopy surface can be quantifyied using satellite imagery, but the three-dimensional attributes of the inaccessible middle strata of the canopy are the most critical to understanding and mapping the habitat. The development of lidar as a tool for quantifying forest structure offers great promise of new solutions to existing habitat measurement problems and opens up a new realm of possibilities for exploring wildlife-habitat relationships. Numéro de notice : A2016-491 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81509
in GIM international [en ligne] > vol 30 n° 7 (July 2016) . - pp 31 - 33[article]Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach / Marco Andrew Njana in Annals of Forest Science, vol 73 n° 2 (June 2016)
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Titre : Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach Type de document : Article/Communication Auteurs : Marco Andrew Njana, Auteur ; Ole Martin Bollandsås, Auteur ; Tron Eid, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 353 - 369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] biomasse forestière
[Termes IGN] changement climatique
[Termes IGN] état de surface du sol
[Termes IGN] mangrove
[Termes IGN] sol forestier
[Termes IGN] sous-sol
[Termes IGN] surveillance de la végétation
[Termes IGN] Tanzanie
[Termes IGN] teneur en carboneRésumé : (auteur) Key message: Tested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above- and belowground biomass models for three mangrove species were therefore developed. The species-specific models fitted better to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania.
Context: Mangroves are essential for climate change mitigation through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and carbon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented.
Aims: The aims of the study were to develop above- and belowground biomass models and to evaluate the predictive accuracy of existing aboveground biomass models developed for mangroves in other regions and neighboring countries when applied on data from Tanzania.
Methods: Data was collected through destructive sampling of 120 trees (aboveground biomass), among these 30 trees were sampled for belowground biomass. The data originated from four sites along the Tanzanian coastline covering three dominant species: Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith, and Rhizophora mucronata Lam. The biomass models were developed through mixed modelling leading to fixed effects/common models and random effects/species-specific models.
Results: Both the above- and belowground biomass models improved when random effects (species) were considered. Inclusion of total tree height as predictor variable, in addition to diameter at breast height alone, further improved the model predictive accuracy. The tests of existing models from other regions on our data generally showed large and significant prediction errors for aboveground tree biomass.
Conclusion: Inclusion of random effects resulted into improved goodness of fit for both above- and belowground biomass models. Species-specific models therefore are recommended for accurate biomass estimation of mangrove forests in Tanzania for both management and ecological applications. For belowground biomass (S. alba) however, the fixed effects/common model is recommended.Numéro de notice : A2016-352 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-015-0524-3 Date de publication en ligne : 14/10/2015 En ligne : https://doi.org/10.1007/s13595-015-0524-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81063
in Annals of Forest Science > vol 73 n° 2 (June 2016) . - pp 353 - 369[article]Cork oak pests: a review of insect damage and management / Riziero Tiberi in Annals of Forest Science, vol 73 n° 2 (June 2016)PermalinkDeveloping a dynamic growth model for maritime pine in Asturias (NW Spain): comparison with nearby regions / Manuel Arias-Rodil in Annals of Forest Science, vol 73 n° 2 (June 2016)PermalinkEffects of experimental warming on soil respiration and biomass in Quercus variabilis Blume and Pinus densiflora Sieb. et Zucc. seedlings / Nam Jin Noh in Annals of Forest Science, vol 73 n° 2 (June 2016)PermalinkEstimations dendrométriques pour l’aménagement forestier à l’aide de LiDAR aéroporté : premier démonstrateur en forêts littorales dunaires / Alain Munoz in Rendez-vous techniques, n° 50 (Hiver 2016)PermalinkExpérience pratique de la réalisation du projet démonstrateur « LiDAR forestier » / Didier Canteloup in Rendez-vous techniques, n° 50 (Hiver 2016)PermalinkForest vegetation in western Romania in relation to climate variables: Does community composition reflect modelled tree species distribution? / S. Heinrichs in Annals of forest research, vol 59 n° 2 (July - December 2016)PermalinkInventory-based estimation of forest biomass in Shitai County, China: A comparison of five methods / X. Tang in Annals of forest research, vol 59 n° 2 (July - December 2016)PermalinkLinked Forests: Semantic similarity of geographical concepts “forest” / Otakar Cerba in Open geosciences, vol 8 n° 1 (January - July 2016)PermalinkRelationship between landform classification and vegetation (case study: southwest of Fars province, Iran) / Marzieh Mokarram in Open geosciences, vol 8 n° 1 (January - July 2016)PermalinkA simple method for detecting phenological change from time series of vegetation index / Jin Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)Permalink