Descripteur
Documents disponibles dans cette catégorie (906)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data / Shichao Jin in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
[article]
Titre : Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data Type de document : Article/Communication Auteurs : Shichao Jin, Auteur ; Yanjun Su, Auteur ; Fangfang Wu, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1336 - 1346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] maïs (céréale)
[Termes IGN] phénologie
[Termes IGN] segmentation en régionsRésumé : (Auteur) Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem-leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem-leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem-leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture. Numéro de notice : A2019-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2866056 Date de publication en ligne : 19/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2866056 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92454
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1336 - 1346[article]UAS lidar for ecological restoration of wetlands / Marie de Boisvilliers in GIM international, Vol 33 n° 2 (March - April 2019)
[article]
Titre : UAS lidar for ecological restoration of wetlands Type de document : Article/Communication Auteurs : Marie de Boisvilliers, Auteur ; Morgane Selve, Auteur Année de publication : 2019 Article en page(s) : pp 29 - 31 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] drone
[Termes IGN] eau de surface
[Termes IGN] écosystème
[Termes IGN] marais
[Termes IGN] modèle numérique de terrain
[Termes IGN] QGIS
[Termes IGN] semis de points
[Termes IGN] Terrascan
[Termes IGN] zone humideRésumé : (Auteur) Wetlands are essential ecosystems which provide numerous benefits to society as a whole. But their functionality strongly depends on the hydrology and topography of the watershed, thus creating the need for monitoring. The use of terrestrial topographical survey methods can be a challenging task in wetlands, however. Flooded areas, muddy terrain and low vegetation can substantially slow down or even prevent the movement of surveyors, while tall vegetation can obstruct GPS reception. As this article outlines, advanced technologies such as airborne or UAS Lidar offer interesting alternatives for surveying the hydrology and topography of wetlands. Numéro de notice : A2019-162 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article DOI : sans En ligne : https://www.gim-international.com/content/article/uas-lidar-for-ecological-resto [...] Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92567
in GIM international > Vol 33 n° 2 (March - April 2019) . - pp 29 - 31[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 061-2019021 RAB Revue Centre de documentation En réserve L003 Disponible Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning / Qing Xu in Forest ecology and management, vol 434 (28 February 2019)
[article]
Titre : Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning Type de document : Article/Communication Auteurs : Qing Xu, Auteur ; Bo Li, Auteur ; Matti Maltamo, Auteur ; Timo Tokola, Auteur ; Zhengyang Hou, Auteur Année de publication : 2019 Article en page(s) : pp 205 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] allométrie
[Termes IGN] analyse comparative
[Termes IGN] détection d'arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] méthode des moindres carrés
[Termes IGN] télédétection par lidar
[Termes IGN] théorie des probabilitésRésumé : (auteur) Biomass inventories that employ airborne laser scanning (ALS) require models that can predict tree diameter at breast height (DBH) from ALS-derived tree dimensions, as ALS can usually not directly measure DBH due to scanning angle, inadequate point density and canopy obstruction. Although some work has been done in using correlation as a measure of dependence to describe the linear relationship between variable means, none has investigated the copula-based measure of dependence for the prediction of DBH from ALS-derived height and crown diameter. Following the application of a locally-estimated copula method to 79 sample plots in eastern Finland, we compared the performance of the copula method with a baseline local regression (LOESS) model and an ordinary least squares (OLS) model. We found that the copula method outperformed the OLS model by decreasing 30% of the root-mean-squared error (RMSE). The copula method performed slightly better than the LOESS model for the original sample, but the results of the bootstrap samples showed that the variance in RMSE was sixteen times lower in the copula method than the LOESS model, suggesting that the copula had a more consistent and robust model performance across the 10,000 bootstrap samples. Moreover, while the LOESS model only predicts the conditional mean of the response variable, the copula method can also predict median and other quantiles. Numéro de notice : A2019 - 012 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2018.12.020 Date de publication en ligne : 19/12/2018 En ligne : https://doi.org/10.1016/j.foreco.2018.12.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91615
in Forest ecology and management > vol 434 (28 February 2019) . - pp 205 - 212[article]Using LiDAR to develop high-resolution reference models of forest structure and spatial pattern / Haley L. Wiggins in Forest ecology and management, vol 434 (28 February 2019)
[article]
Titre : Using LiDAR to develop high-resolution reference models of forest structure and spatial pattern Type de document : Article/Communication Auteurs : Haley L. Wiggins, Auteur ; Cara R. Nelson, Auteur ; Andrew J. Larson, Auteur ; Hugh D. Safford, Auteur Année de publication : 2019 Article en page(s) : pp 318 - 330 Note générale : bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] caractérisation
[Termes IGN] coupe (sylviculture)
[Termes IGN] détection d'arbres
[Termes IGN] diamètre des arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] Mexique
[Termes IGN] parc naturel national
[Termes IGN] restauration écologique
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] topographie localeRésumé : (auteur) Successful restoration of degraded forest landscapes requires reference models that adequately capture structural heterogeneity at multiple spatial scales and for specific landforms. Despite this need, managers often lack access to reliable reference information, in large part because field-based methods for assessing variation in forest structure are costly and inherently suffer from limited replication and spatial coverage and, therefore, yield limited insights about the ecological structure of reference forests at landscape scales. LiDAR is a cost-effective alternative that can provide high-resolution characterizations of variation in forest structure among landform types. However, managers and researchers have been reluctant to use LiDAR for characterizing structure because of low confidence in its capacity to approximate actual tree distributions. By calculating bias in LiDAR estimates for a range of tree-height cutoffs, we improved LiDAR’s ability to capture structural variability in terms of individual trees. We assessed bias in the processed LiDAR data by comparing datasets of field-measured and LiDAR-detected trees of various height classes in terms of overall number of trees and estimates of structure and spatial pattern in an important contemporary reference forest, the Sierra de San Pedro Martir National Park, Baja California, Mexico. Agreement between LiDAR- and field-based estimates of tree density, as well as estimates of forest structure and spatial pattern, was maximized by removing trees less than 12 m tall. We applied this height cutoff to LiDAR-detected trees of our study landscape, and asked if forest structure and spatial pattern varied across topographic settings. We found that canyons, shallow northerly, and shallow southerly slopes were structurally similar; each had a greater number of all trees, large trees, and large tree clumps than steep southerly slopes and ridges. Steep northerly slopes supported unique structures, with taller trees than ridges and shorter trees than canyons and shallow southerly slopes. Our results show that characterizations of forest structure based on LiDAR-detected trees are reasonably accurate when the focus is narrowed to the overstory. In addition, our finding of strong variation of forest structure and spatial pattern across topographic settings demonstrates the importance of developing reference models at the landscape scale, and highlights the need for replicated sampling among stands and landforms. Methods developed here should be useful to managers interested in using LiDAR to characterize distributions of medium and large overstory trees, particularly for the development of landscape-scale reference models. Numéro de notice : A2019-013 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2018.12.012 Date de publication en ligne : 24/12/2018 En ligne : https://doi.org/10.1016/j.foreco.2018.12.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91616
in Forest ecology and management > vol 434 (28 February 2019) . - pp 318 - 330[article]Leaf area density from airborne LiDAR: Comparing sensors and resolutions in a temperate broadleaf forest ecosystem / Aaron G. Kamoske in Forest ecology and management, vol 433 (15 February 2019)
[article]
Titre : Leaf area density from airborne LiDAR: Comparing sensors and resolutions in a temperate broadleaf forest ecosystem Type de document : Article/Communication Auteurs : Aaron G. Kamoske, Auteur ; Kyla M. Dahlin, Auteur ; Scott C. Stark, Auteur ; Shawn P. Serbin, Auteur Année de publication : 2019 Article en page(s) : pp 364 - 375 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] densité du feuillage
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] feuillu
[Termes IGN] forêt tempérée
[Termes IGN] Leaf Area Index
[Termes IGN] R (langage)
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) Forest processes that play an essential role in carbon sequestration, such as light use efficiency, photosynthetic capacity, and trace gas exchange, are closely tied to the three-dimensional structure of forest canopies. However, the vertical distribution of leaf traits is not uniform; leaves at varying vertical positions within the canopy are physiologically unique due to differing light and environmental conditions, which leads to higher carbon storage than if light conditions were constant throughout the canopy. Due to this within-canopy variation, three-dimensional structural traits are critical to improving our estimates of global carbon cycling and storage by Earth system models and to better understanding the effects of disturbances on carbon sequestration in forested ecosystems. In this study, we describe a reproducible and open-source methodology using the R programming language for estimating leaf area density (LAD; the total leaf area per unit of volume) from airborne LiDAR. Using this approach, we compare LAD estimates at the Smithsonian Environmental Research Center in Maryland, USA, from two airborne LiDAR systems, NEON AOP and NASA G-LiHT, which differ in survey and instrument specifications, collections goals, and laser pulse densities. Furthermore, we address the impacts of the spatial scale of analysis as well as differences in canopy penetration and pulse density on LAD and leaf area index (LAI) estimates, while offering potential solutions to enhance the accuracy of these estimates. LAD estimates from airborne LiDAR can be used to describe the three-dimensional structure of forests across entire landscapes. This information can help inform forest management and conservation decisions related to the estimation of aboveground biomass and productivity, the response of forests to large-scale disturbances, the impacts of drought on forest health, the conservation of bird habitat, as well as a host of other important forest processes and responses. Numéro de notice : A2019-008 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2018.11.017 Date de publication en ligne : 21/11/2018 En ligne : https://doi.org/10.1016/j.foreco.2018.11.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91601
in Forest ecology and management > vol 433 (15 February 2019) . - pp 364 - 375[article]A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions / Syed Adnan in Forest ecology and management, vol 433 (15 February 2019)PermalinkA derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds / Fan Xue in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkImproving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkA local projection-based approach to individual tree detection and 3-D crown delineation in multistoried coniferous forests using high-density airborne LiDAR data / Aravind Harikumar in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkModelling forest canopy gaps using LiDAR-derived variables / Leighton Lombard in Geocarto international, vol 34 n° 2 ([01/02/2019])PermalinkQuantification of airborne lidar accuracy in coastal dunes (Fire Island, New York) / William J. Schmelz in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)PermalinkRepeated structure detection for 3D reconstruction of building façade from mobile lidar data / Yanming Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)PermalinkVariation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkPermalink3D radiative transfer modeling over complex vegetation canopies and forest reconstruction from LIDAR measurements / Jianbo Qi (2019)Permalink