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inventaire forestier (techniques et méthodes)
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Forêts -- Inventaires, Levés forestiers. Inventaire des ressources naturelles, Topographie, Inventaire de la végétation, Sylviculture. >> >>Terme(s) spécifique(s) : Cartographie forestière. Equiv. LCSH : Forest surveys. |
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Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band / Xing Peng in Remote sensing, vol 13 n° 11 (June-1 2021)
[article]
Titre : Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band Type de document : Article/Communication Auteurs : Xing Peng, Auteur ; Xinwu Li, Auteur ; Yanan Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] données localisées 3D
[Termes IGN] forêt boréale
[Termes IGN] hauteur des arbres
[Termes IGN] image 3D
[Termes IGN] image radar moirée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] itération
[Termes IGN] matrice de covariance
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de points
[Termes IGN] Suède
[Termes IGN] tomographie radarRésumé : (auteur) Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m. Numéro de notice : A2021-441 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13112147 Date de publication en ligne : 29/05/2021 En ligne : https://doi.org/10.3390/rs13112147 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97828
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2147[article]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)
[article]
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]Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data / Haijian Liu in Remote sensing of environment, vol 258 (June 2021)
[article]
Titre : Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data Type de document : Article/Communication Auteurs : Haijian Liu, Auteur ; Pinliang Dong, Auteur ; Changshan Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112382 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] arbre (flore)
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] houppier
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] peuplement mélangé
[Termes IGN] semis de points
[Termes IGN] surveillance forestière
[Termes IGN] Wisconsin (Etats-Unis)Résumé : (auteur) Individual tree identification is a key step for forest surveying and monitoring. To identify individual trees with airborne LiDAR data, a local maximum (LM) filter technique is typically performed. With LM, the highest point in a filtering window is generally considered to represent the tree position. This assumption, however, has great limitations, especially for mixed forests. To address this problem, we developed a new approach, the cluster center of higher points (CCHP), for tree position detection with LiDAR data. CCHP assumes that a tree position is located at the clustering center of higher points within a spatial neighborhood, and the center can be detected by a location-based recursive algorithm. The developed CCHP method was applied to a simulated forest and then verified in two real urban forests. In comparison with the variable window-sized LM filter method and layer stacking method, CCHP successfully identified 97% of trees in the simulated forest, while only 78% and 81% of the trees were recognized by LM and layer stacking methods respectively. The average absolute and relative offsets of CCHP are 0.33 m and 6.59%, respectively, and over 80% of the detected trees have an offset of less than 10% of the tree crown radius. CCHP also correctly detected 93.80% and 88.74% of individual trees in the first and second real forests, respectively, but the detection rates from the variable window-sized LM approach and layer stacking were less than 80%. In addition, the tree positions located by CCHP are considerably more accurate than the other two methods. Therefore, CCHP is proven to be promising for detecting individual tree positions from airborne LiDAR data for both simulated and real forests. Numéro de notice : A2021-443 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112382 Date de publication en ligne : 06/03/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97850
in Remote sensing of environment > vol 258 (June 2021) . - n° 112382[article]Model-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing / Michael L. Benson in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
[article]
Titre : Model-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing Type de document : Article/Communication Auteurs : Michael L. Benson, Auteur ; Pierce Leland, Auteur ; Katleen Bergen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 4635 - 4653 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] biomasse aérienne
[Termes IGN] Canada
[Termes IGN] canopée
[Termes IGN] couvert forestier
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt boréale
[Termes IGN] hauteur des arbres
[Termes IGN] image Landsat-TM
[Termes IGN] image radar moirée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] lasergrammétrie
[Termes IGN] Leaf Area Index
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] polarimétrie radar
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) One of the fundamental technical challenges of any new spaceborne vegetation remote sensing mission is the determination of what sensor(s) to place onboard and what, if any, overlapping modes of operation they will employ as each onboard sensor adds significant cost to the overall mission. In this article, the remote sensing of forest parameters using multimodal remote sensing is presented. In particular, polarimetric radar, Light Detection And Ranging (LiDAR), and near-IR passive optical sensing platforms are employed in conjunction with physics-based models. These models are used to accurately estimate forest aboveground biomass as well as canopy height in homogeneous areas. It is shown that this proposed method is capable of achieving high accuracy estimates while using minimal ancillary data in the estimation process. We present a method to combine measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m 2 and 1.68 m respectively. Numéro de notice : A2021-423 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3018638 Date de publication en ligne : 09/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3018638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97778
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4635 - 4653[article]Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops / Nina Kranjec in Geodetski vestnik, vol 65 n° 2 (June - August 2021)
[article]
Titre : Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops Type de document : Article/Communication Auteurs : Nina Kranjec, Auteur ; Mihaela Triglav Cekada, Auteur ; Milan Kobal, Auteur Année de publication : 2021 Article en page(s) : pp 234 - 259 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Acer pseudoplatanus
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] Fagus sylvatica
[Termes IGN] feuillu
[Termes IGN] figure géométrique
[Termes IGN] Fraxinus excelsior
[Termes IGN] houppier
[Termes IGN] identification automatique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Larix decidua
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] Picea abies
[Termes IGN] Pinophyta
[Termes IGN] Pinus sylvestris
[Termes IGN] semis de points
[Termes IGN] SlovénieRésumé : (auteur) Based on the laser point clouds of 240 individual trees that were also identified in the field, we developed decision trees to distinguish deciduous and coniferous trees and individual tree species: Picea abies, Larix decidua, Pinus sylvestris, Fagus sylvatica, Acer pseudoplatanus, Fraxinus excelsior. The volume of the upper part of the tree crown (height of 3 m) and the average intensity of the laser reflections were used as explanatory variables. There were four aerial laser datasets: May 2012, September 2012, March 2013 and July 2015. We found that the combination of the volume and the average intensity of the first three laser datasets was the most reliable for predicting the selected tree species (60% model performance). A slightly poorer model performance was obtained if only the average intensity of the first three datasets was used (54% model performance). The worst model performance was given by the intensities (31 % model performance) or the volumes (21 % model performance) of dataset 4, which represents the national laser scanning of Slovenia (LSS). The best performing was the deciduous and coniferous separation, which achieved 75% and 95% success based on the test data (combination of volume and average intensity of the first three laser datasets). Using only the LSS intensities, deciduous and coniferous trees could be separated with 81% success. Numéro de notice : A2021-559 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2021.02.234-259 Date de publication en ligne : 27/05/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.02.234-259 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98113
in Geodetski vestnik > vol 65 n° 2 (June - August 2021) . - pp 234 - 259[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 139-2021021 RAB Revue Centre de documentation En réserve L003 Disponible Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours / Amir Hossein Safaie in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkVariations in temperate forest biomass ratio along three environmental gradients are dominated by interspecific differences in wood density / Baptiste Kerfriden in Plant ecology, vol 222 n° 3 (March 2021)PermalinkForest height estimation using a single-pass airborne L-band polarimetric and interferometric SAR system and tomographic techniques / Yue Huang in Remote sensing, Vol 13 n° 3 (February 2021)PermalinkA density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January-2 2021)PermalinkEvaluation de la qualité des mesures de croissance pluriannuelles des données d’inventaire forestier national en vue de leur utilisation en monitoring à haute fréquence de la production forestière / Félix Altenhoven (2021)PermalinkFOSTER - An R package for forest structure extrapolation / Martin Queinnec in Plos one, vol 16 n° 1 (January 2021)PermalinkUne généralisation de la méthode de partage des poids dans le cas où la base de sondage est continue / Philippe Brion (2021)PermalinkHigh resolution mapping of forest resources and prediction reliability using multisource inventory approach / Ankit Sagar (2021)PermalinkLes inventaires forestiers nationaux : des méthodes dynamiques pour un sujet dynamique / Olivier Bouriaud (2021)PermalinkThe role of net ecosystem productivity and of inventories in climate change research: the need for “net ecosystem productivity with harvest”, NEPH / E.D. Schulze in Forest ecosystems, vol 8 (2021)Permalink