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Modeling multifrequency GPS multipath fading in land vehicle environments / Vicente Carvalho Lima Filho in GPS solutions, vol 25 n° 1 (January 2021)  

Public [article]  inGPS solutions > vol 25 n° 1 (January 2021) . - 14 p. Titre : Modeling multifrequency GPS multipath fading in land vehicle environments Type de document : Article/Communication Auteurs : Vicente Carvalho Lima Filho, Auteur ; Alison Moraes, Auteur Année de publication : 2021 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] densité de probabilité [Termes descripteurs IGN] interférence [Termes descripteurs IGN] propagation du signal [Termes descripteurs IGN] qualité du signal [Termes descripteurs IGN] signal GPS [Termes descripteurs IGN] simulation de signal [Termes descripteurs IGN] trajet multiple [Termes descripteurs IGN] véhicule Résumé : (auteur) The reliability and performance of GPS receivers depend on the quality of the signal received, which can be largely affected by the interference caused by buildings, trees, and other obstacles. Since obstacles are always present in practical applications, several statistical representations have been developed along the years to measure, predict, and compensate errors induced by interferences. Two of the most used models to characterize GPS signal fading are the Nakagami-m and Rice, but in this work, we present evidence that supports the κ–μ distribution as the best fit to deal with multifrequency GPS multipath channels inside urban, rural, and forest areas. A synthetic signal simulator was developed to create propagation cases involving scattering clusters and specular reflections. Additionally, experimental measurements are presented to confirm the κ–μ distribution as the best distribution to characterize different situations on the available three GPS frequencies. We then present typical values of fading coefficients in L1, L2C, and L5 signals, for cases involving urban canyons, regular urban, rural, and dense vegetation areas. These coefficients can also be used to evaluate the receiver performance under similar cases or may be applied in weights measurement methods for positioning computation improvement. Numéro de notice : A2021-002 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01040-8 date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1007/s10291-020-01040-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96080 [article]

Topographic, edaphic and climate influences on aspen (Populus tremuloides) drought stress on an intermountain bunchgrass prairie / Andrew Neary in Forest ecology and management, vol 479 ([01/01/2021])  

Public [article]  inForest ecology and management > vol 479 [01/01/2021] . - 12 p. Titre : Topographic, edaphic and climate influences on aspen (Populus tremuloides) drought stress on an intermountain bunchgrass prairie Type de document : Article/Communication Auteurs : Andrew Neary, Auteur ; Ricardo Mata-González, Auteur ; Heidi Schmalz, Auteur Année de publication : 2021 Article en page(s) : 12 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] climat [Termes descripteurs IGN] écophysiologie [Termes descripteurs IGN] état du sol [Termes descripteurs IGN] facteur édaphique [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] manteau neigeux [Termes descripteurs IGN] Oregon (Etats-Unis) [Termes descripteurs IGN] Poaceae [Termes descripteurs IGN] populus tremuloides [Termes descripteurs IGN] prairie [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] stress hydrique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Quaking aspen, Populus tremuloides, has experienced severe declines in recent years in part due to the effects of changing climate and extreme drought. This study set out to investigate these effects by assessing associations of climatic, edaphic and topographic variables with physiological drought stress in aspen. The study took place on the Zumwalt Prairie in northeastern Oregon, a semi-arid bunchgrass prairie where aspen occur in isolated stands associated with riparian areas and late-season persistence of snow drifts. Using a 33-year time series of Landsat imagery to detect associations of aspen stands late-season snow cover and field measurements of soil moisture in aspen stands during 2017, we found while snow dominated stands were associated with greater soil moisture during spring, levels had equilibrated to those of other upland stands by summer. Measurements of predawn and midday stem Ψ in multiple height classes of aspen ramets revealed associations of both shallow soil moisture and vapor pressure deficit with physiological drought stress in aspen. Analysis of soil texture class revealed an important association with midday stem Ψ, with finer textured soils associated with decreased stem Ψ in comparison to coarser textured soils. While neither topographical characteristics nor snow cover were found to be important drivers of drought stress, topographical curvature was found to have a strong influence on summer soil moisture in upland stands. These findings contribute to our understanding of aspen physiology, drought ecology and landscape hydrology toward the xeric margin of aspen’s range. This information can help land managers anticipate and adapt to changing climates and understand their effects on key plant species such as aspen. Numéro de notice : A2021-001 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118530 date de publication en ligne : 08/09/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118530 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96028 [article]

Climate sensitive single tree growth modeling using a hierarchical Bayes approach and integrated nested Laplace approximations (INLA) for a distributed lag model / Arne Nothdurft in Forest ecology and management, vol 478 ([15/12/2020])  

Public [article]  inForest ecology and management > vol 478 [15/12/2020] . - 14 p. Titre : Climate sensitive single tree growth modeling using a hierarchical Bayes approach and integrated nested Laplace approximations (INLA) for a distributed lag model Type de document : Article/Communication Auteurs : Arne Nothdurft, Auteur Année de publication : 2020 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] approximation [Termes descripteurs IGN] Autriche [Termes descripteurs IGN] bioclimatologie [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] dendrochronologie [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] larix decidua [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] quercus sessiliflora [Termes descripteurs IGN] série temporelle [Vedettes matières IGN] Sylviculture [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) A novel methodological framework is presented for climate-sensitive modeling of annual radial stem increments using tree-ring width time series. The approach is based on a hierarchical Bayes model together with a distributed time lag model that take into account the effects of a series of monthly temperature and precipitation values, as well as their interactions. By using a set of random walk priors, the hierarchical Bayes model allows both the detrending of the individual time series and the regression modeling to be performed simultaneously in a single model step. The approach was applied to comprehensive tree-ring width data from Austria collected on sample plots arranged in triplets representing different mixture types. Bayesian predictions revealed that European larch (Larix decidua Mill.), Norway spruce (Picea abies (L.) H. Karst.), and Scots pine (Pinus sylvestris L.) show positive climate-related growth trends throughout higher elevation sites in Tyrol, and these trends remain unchanged under a mixed-stand scenario. At the lower Austrian sites, Norway spruce was found to show a severely negative growth trend under both the pure- and mixed-stand scenario. The increment rates of European beech (Fagus sylvatica L.) were found to have a negative climate-related trend in pure stands, and the trend diminished through an admixture of spruce or larch. The trends of European larch and sessile oak (Quercus petraea (Matt.) Liebl.) showed stationary behavior, irrespective of the mixture scenario. Scots pine data showed a positive trend at the lower elevation sites under both the pure- and mixed-stand scenario. These findings indicate that species mixing does not lower the climate-related increment fluctuations of beech, oak, pine, and spruce at lower elevation sites. Numéro de notice : A2020-625 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118497 date de publication en ligne : 07/09/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118497 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96025 [article]

Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates / Robert E. Keane in Forest ecology and management, vol 477 ([01/12/2020])  

Public [article]  inForest ecology and management > vol 477 [01/12/2020] . - 12 p. Titre : Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates Type de document : Article/Communication Auteurs : Robert E. Keane, Auteur ; Lisa M. Holsinger, Auteur ; Rachel Loehman, Auteur Année de publication : 2020 Article en page(s) : 12 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] habitat forestier [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] modélisation de la forêt [Termes descripteurs IGN] Montana (Etats-Unis) [Termes descripteurs IGN] substitution [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Land managers need new tools for planning novel futures due to climate change. Species distribution modeling (SDM) has been used extensively to predict future distributions of species under different climates, but their map products are often too coarse for fine-scale operational use. In this study we developed a flexible, efficient, and robust method for mapping current and future distributions and abundances of vegetation species and communities at the fine spatial resolutions that are germane to land management. First, we mapped Potential Vegetation Types (PVTs) using conventional statistical modeling techniques (Random Forests) that used bioclimatic ecosystem process and climate variables as predictors. We obtained over 50% accuracy across 13 mapped PVTs for our study area. We then applied future climate projections as climate input to the Random Forest model to generate future PVT maps, and used field data describing the occurrence of tree and non-tree species in each PVT category to model and map species distribution for current and future climate. These maps were then compared to two previous SDM mapping efforts with over 80% agreement and equivalent accuracy. Because PVTs represent the biophysical potential of the landscape to support vegetation communities as opposed to the vegetation that currently exists, they can be readily linked to climate forecasts and correlated with other, climate-sensitive ecological processes significant in land management, such as fire regimes and site productivity. Numéro de notice : A2020-624 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118498 date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96022 [article]

Active and incremental learning for semantic ALS point cloud segmentation / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 73 - 92 Titre : Active and incremental learning for semantic ALS point cloud segmentation Type de document : Article/Communication Auteurs : Yaping Lin, Auteur ; M. George Vosselman, Auteur ; Yanpeng Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 73 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] entropie [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] itération [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (auteur) Supervised training of a deep neural network for semantic segmentation of point clouds requires a large amount of labelled data. Nowadays, it is easy to acquire a huge number of points with high density in large-scale areas using current LiDAR and photogrammetric techniques. However it is extremely time-consuming to manually label point clouds for model training. In this paper, we propose an active and incremental learning strategy to iteratively query informative point cloud data for manual annotation and the model is continuously trained to adapt to the newly labelled samples in each iteration. We evaluate the data informativeness step by step and effectively and incrementally enrich the model knowledge. The data informativeness is estimated by two data dependent uncertainty metrics (point entropy and segment entropy) and one model dependent metric (mutual information). The proposed methods are tested on two datasets. The results indicate the proposed uncertainty metrics can enrich current model knowledge by selecting informative samples, such as considering points with difficult class labels and choosing target objects with various geometries in the labelled training pool. Compared to random selection, our metrics provide valuable information to significantly reduce the labelled training samples. In contrast with training from scratch, the incremental fine-tuning strategy significantly save the training time. Numéro de notice : A2020-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.003 date de publication en ligne : 14/09/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96061 [article]

A comparison of neighbourhood relations based on ordinary Delaunay diagrams and area Delaunay diagrams: an application to define the neighbourhood relations of buildings / Hiroyuki Usui in International journal of geographical information science IJGIS, vol 34 n°11 (November 2020)  

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Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data / Wai Yeung Yan in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

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Geostatistical analysis and mitigation of the atmospheric phase screens in Ku-band terrestrial radar interferometric observations of an alpine glacier / Simone Baffelli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

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Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest / Luka Jurjević in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

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Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea / Sunmin Lee in Geocarto international, vol 35 n° 15 ([01/11/2020])  

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Mapping uncertain geographical attributes: incorporating robustness into choropleth classification design / Wangshu Mu in International journal of geographical information science IJGIS, vol 34 n°11 (November 2020)  

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A multi-scale representation model of polyline based on head/tail breaks / Pengcheng Liu in International journal of geographical information science IJGIS, vol 34 n°11 (November 2020)  

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River ice segmentation with deep learning / Abhineet Singh in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

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Soil erosion assessment using RUSLE model and its validation by FR probability model / Amiya Gayen in Geocarto international, vol 35 n° 15 ([01/11/2020])  

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Drought stress detection in juvenile oilseed rape using hyperspectral imaging with a focus on spectra variability / Wiktor R. Żelazny in Remote sensing, vol 12 n° 20 (October - 2 2020)  

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