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Horvitz-Thompson–like estimation with distance-based detection probabilities for circular plot sampling of forests / Kasper Kansanen in Biometrics, vol 77 n° 2 (June 2021)
Titre : Horvitz-Thompson–like estimation with distance-based detection probabilities for circular plot sampling of forests Type de document : Article/Communication Auteurs : Kasper Kansanen, Auteur ; Petteri Packalen, Auteur ; Matti Maltamo, Auteur ; Lauri Mehtätalo, Auteur Année de publication : 2021 Article en page(s) : pp 715 - 728 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] distribution de Poisson
[Termes IGN] erreur systématique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] placette d'échantillonnage
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) In circular plot sampling, trees within a given distance from the sample plot location constitute a sample, which is used to infer characteristics of interest for the forest area. If the sample is collected using a technical device located at the sampling point, eg, a terrestrial laser scanner, all trees of the sample plot cannot be observed because they hide behind each other. We propose a Horvitz-Thompson–like estimator with distance-based detection probabilities derived from stochastic geometry for estimation of population totals such as stem density and basal area in such situation. We show that our estimator is unbiased for Poisson forests and give estimates of variance and approximate confidence intervals for the estimator, unlike any previous methods. We compare the estimator to two previously published benchmark methods. The comparison is done through a simulation study where several plots are simulated either from field measured data or different marked point processes. The simulations show that the estimator produces lower or comparable error values than the other methods. In the sample plots based on the field measured data, the bias is relatively small—relative mean of errors for stem density, for example, varying from 0.3% to 2.2%, depending on the detection condition. The empirical coverage probabilities of the approximate confidence intervals are either similar to the nominal levels or conservative in these sample plots. Numéro de notice : A2021-987 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/biom.13312 Date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1111/biom.13312 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103237
in Biometrics > vol 77 n° 2 (June 2021) . - pp 715 - 728[article]
Titre : Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain Type de document : Article/Communication Auteurs : R. Niederheiser, Auteur ; M. Winkler, Auteur ; V. Di Cecco, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 120 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre
[Termes IGN] Alpes
[Termes IGN] caméra numérique
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification semi-dirigée
[Termes IGN] couvert végétal
[Termes IGN] distribution de Poisson
[Termes IGN] données topographiques
[Termes IGN] indice de végétation
[Termes IGN] module linéaire
[Termes IGN] montagne
[Termes IGN] occupation du sol
[Termes IGN] photogrammétrie métrologique
[Termes IGN] semis de pointsRésumé : (auteur) In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance. Numéro de notice : A2021-258 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2020.1859264 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1080/15481603.2020.1859264 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97295
in GIScience and remote sensing > vol 58 n° 1 (February 2021) . - pp 120 - 137[article]
Titre : A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data Type de document : Article/Communication Auteurs : Haiyan Tao, Auteur ; Keli Wang, Auteur ; Li Zhuo, Auteur Année de publication : 2020 Article en page(s) : pp 604 - 624 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] Chine
[Termes IGN] diffusion spatiale
[Termes IGN] distribution de Poisson
[Termes IGN] données socio-économiques
[Termes IGN] hétérogénéité environnementale
[Termes IGN] hétérogénéité spatiale
[Termes IGN] maladie infectieuse
[Termes IGN] migration humaine
[Termes IGN] mobilité territoriale
[Termes IGN] modèle de régression
[Termes IGN] modèle mathématique
[Termes IGN] origine - destination
[Termes IGN] point d'intérêt
[Termes IGN] risque sanitaire
[Termes IGN] urbanisationRésumé : (auteur) International communication and global cooperation have greatly accelerated the worldwide spread of dengue fever, increasing the impact of imported cases on dengue outbreaks in non-naturally endemic areas. Existing studies mostly focus on describing the quantitative relationship between imported cases and local transmission but ignore the space-time diffusion mode of imported cases under the influence of individual mobility. In this paper, we propose a comprehensive framework at a fine scale to establish the disease transmission network and a mathematical model, which constructs ‘source-sink’ links between the imported and indigenous cases on a regular grid with a spatial resolution of 1 km to explore the diffusion pattern and spatiotemporal heterogeneity of imported cases. An application to Guangzhou, China, reveals the main flow and transmission path of imported cases under the influence of human movement and identifies the spatiotemporal distribution of transmission speed according to the time lag of each source-sink link. In addition, we demonstrate that using individual-based movement data and socio-economic factors to study human mobility and imported cases can help to understand the driving forces of dengue spread. Our research provides a comprehensive framework for the analysis of early dengue transmission patterns with benefits to similar urban applications. Numéro de notice : A2020-107 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1684497 Date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1684497 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94707
in International journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 604 - 624[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020031 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Surface reconstruction based on forest terrestrial LiDAR data Type de document : Thèse/HDR Auteurs : Jules Morel, Auteur ; Marc Daniel, Directeur de thèse ; Cédric Vega Editeur : Aix-en-Provence : Université d'Aix-Marseille Année de publication : 2017 Importance : 178 p. Format : 21 x 30 cm Note générale : bibliographie
A dissertation presented to the Department of Mathématique et Informatique in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Computer ScienceLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] distribution de Poisson
[Termes IGN] données lidar
[Termes IGN] données TLS (télémétrie)
[Termes IGN] fonction de base radiale
[Termes IGN] interpolation
[Termes IGN] modélisation de la forêt
[Termes IGN] placette d'échantillonnage
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] structure de la végétationIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In recent years, the capacity of LiDAR technology to capture detailed information about forests structure has attracted increasing attention in the field of forest science. In particular, the terrestrial LiDAR arises as a promising tool to retrieve geometrical characteristics of trees at a millimeter level. This thesis studies the surface reconstruction problem from scattered and unorganized point
clouds, captured in forested environment by a terrestrial LiDAR. We propose a sequence of algorithms dedicated to the reconstruction of forests plot attributes model: the ground and the woody structure of trees (i.e. the trunk and the main branches). In practice, our approaches model the surface with implicit function build with radial basis functions to manage the homogeneity and handle the noise of the sample data points. Our first focus is on the reconstruction of the ground surface whose level of detail is based on local complexity, through alternation between scale refinement, filtering and reconstruction. The result arises from the polygonization of the implicit function expressed as the merging of local approximations by compactly supported radial basis function used as partition of unity. Once the ground is modeled, the topology effects can be ignored in the following computation steps that focus on the modeling of trees. Traditionally, the processing of the woody part is achieved by a discrete reconstruction in the form of a stack of independent building blocks. From such a model, our approach developed for the ground is adapted to approximate the woody part of the tree by a more flexible continuous surface. Expressed as an implicit function, the tree model can be refined by an additional computational step in order to describe precisely the geometry. With this in mind, we propose a method dedicated to the fine reconstruction of occluded objects: from 3D samples presenting occlusions,
we use the previously described continuous model to guide a Poisson surface reconstruction. Thus, we guarantee the production of a watertight surface that approximates sharply the point cloud in the visible areas and extrapolates consistently the tree shape in the occlusions.Note de contenu : 1- Introduction
2- Terrestrial LiDAR scanning in forests
3- Survey on surface reconstruction
4- Reconstruction of open surface
5- Geometric model of trees
6- Reconstruction of partially occluded objects
7- Conclusion and perspectivesNuméro de notice : 25855 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : IMAGERIE Nature : Thèse française Note de thèse : PhD Thesis: Computer Science : Marseille : 2017 Organisme de stage : Institut Français de Pondichéri (Inde) nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2017AIXM0039 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95472
contenu dans Proceedings of SilviLaser 2015, 14th conference on Lidar Applications for Assessing and Managing Forest Ecosystems, September 28-30, 2015 - La Grande Motte, France / Sylvie Durrieu (2015)
Titre : Computation of tree volume from terrestrial LiDAR data Type de document : Article/Communication Auteurs : Jules Morel, Auteur ; Alexandra Bac, Auteur ; Cédric Vega Editeur : Antony [France] : Institut national de recherche en sciences et technologies pour l’environnement et l’agriculture IRSTEA (2012-2019) Année de publication : 2015 Projets : 1-Pas de projet / Conférence : SilviLaser 2015, 14th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2015 30/09/2015 La Grande Motte France open access proceedings Importance : pp 308 - 310 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] distribution de Poisson
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fonction de base radiale
[Termes IGN] placette d'échantillonnage
[Termes IGN] reconstruction d'objet
[Termes IGN] volume en boisRésumé : (auteur) This paper introduces a new method for the detailed estimation of volumes of individual trees from terrestrial laser scanner data. This method is based on adapted compactly supported radial basis function models together with a deformable models approach. Numéro de notice : C2015-043 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83731 Documents numériques
en open access
Computation of tree volumeAdobe Acrobat PDF
