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Historical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India / M. Dhananjayan in Marine geodesy, vol 45 n° 1 (January 2022)
[article]
Titre : Historical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India Type de document : Article/Communication Auteurs : M. Dhananjayan, Auteur ; S. Vasanthakumar, Auteur ; S.A. Sannasiraj, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 47 - 74 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] détection de changement
[Termes IGN] érosion côtière
[Termes IGN] image Landsat
[Termes IGN] Inde
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] régression linéaire
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) A shoreline change analysis has been carried out for the coastal stretch from Ennore creek to Karungali village located along the southeast coast of India. This 15 km-long coastal stretch had undergone significant changes such as erosion and accretion concerning infrastructure developments and leading to large impact on the livelihood of the community. To assess the shoreline changes, the analysis of multi-temporal satellite images has been carried out. A historical trend is established for the study period from 1991 to 2019. The analysis has been made in three timelines considering various developing activities. There was no significant coastal infrastructure development during 1991 to 1999; however, between 1999 and 2009, a major port, pier, and a groyne field were constructed. Additionally, a port was established between 2009 and 2019. Erosion was observed on the coast from Kattupalli to Karungali at a rate of −16.85 m/yr since 2009, while the coast on the south of Ennore port is accreting at the rate of +12.43 m/yr during the same period. The near-future projection using a linear regression model shows further erosion in the coast under similar conditions. The results of this study provide a baseline data for future anthropogenic activities along this coast. Numéro de notice : A2022-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2021.1992546 Date de publication en ligne : 08/11/2021 En ligne : https://doi.org/10.1080/01490419.2021.1992546 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99370
in Marine geodesy > vol 45 n° 1 (January 2022) . - pp 47 - 74[article]Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021. An automated machine learning-based approach for structural novelty detection based on SHM / Nicolas Manzini (2022)
Titre de série : Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021 Titre : An automated machine learning-based approach for structural novelty detection based on SHM Type de document : Article/Communication Auteurs : Nicolas Manzini, Auteur ; Ndeye Mar, Auteur ; Franziska Schmidt, Auteur ; Jean-François Bercher, Auteur ; André Orcesi, Auteur ; Pierre Marchand, Auteur ; Julien Gazeaux , Auteur ; Christian Thom , Auteur Editeur : Springer Nature Année de publication : 2022 Collection : Lecture Notes in Civil Engineering num. 200 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : EUROSTRUCT 2021, 1st Conference of the European Association on Quality Control of Bridges and Structures 29/08/2021 01/09/2021 Padoue Italie Proceedings Springer Importance : pp 1180 - 1189 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] détection d'anomalie
[Termes IGN] ouvrage d'art
[Termes IGN] pont
[Termes IGN] régression multiple
[Termes IGN] réseau de capteurs
[Termes IGN] résidu
[Termes IGN] surveillance d'ouvrageRésumé : (auteur) One major goal of structural health monitoring (SHM) is to detect, and possibly locate, quantify or predict damage on structures. Without detailed knowledge of structural mechanical behavior, data analysis is a complex task and operational monitoring is often limited to the use of more or less arbitrary thresholds. Data-driven techniques, which rely on a statistical analysis of data, have encountered a growing interest over the past two decades. In parallel, SHM is now increasingly considered for several types of structures with the development of low-cost sensors and IoT. In this context, this paper proposes an approach based on multiple automated machine learning-based models for novelty detection and location in monitoring data. This study focuses on the monitoring of large structures with multiple sensors. For each sensor, multiple regression models (based on neural networks) are generated using the same training set, with various input data: internal temperature, environmental conditions, or data from other sensors deployed on the structure. Anomalies are then identified in the dataset based on residuals between model outputs and in situ data. For a given sensor, residuals of all models are then compiled to produce an anomaly indicator. This paper presents some of the results obtained on data acquired from the monitoring of a large concrete bridge. Some anomalies are simulated and added to the dataset to demonstrate the detection performance of the proposed approach. Numéro de notice : C2021-086 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-030-91877-4_134 Date de publication en ligne : 12/12/2021 En ligne : https://doi.org/10.1007/978-3-030-91877-4_134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99378 Understory plant community responses to widespread spruce mortality in a subalpine forest / Trevor A. Carter in Journal of vegetation science, vol 33 n° 1 (January 2022)
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Titre : Understory plant community responses to widespread spruce mortality in a subalpine forest Type de document : Article/Communication Auteurs : Trevor A. Carter, Auteur ; Paula J. Fornwalt, Auteur ; Katleen A. Dwire, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba
[Termes IGN] forêt subalpine
[Termes IGN] insecte nuisible
[Termes IGN] Leaf Area Index
[Termes IGN] maladie phytosanitaire
[Termes IGN] mortalité
[Termes IGN] Picea abies
[Termes IGN] régression linéaire
[Termes IGN] semis (sylviculture)
[Termes IGN] sous-étage
[Termes IGN] Wyoming (Etats-Unis)
[Vedettes matières IGN] ForesterieRésumé : (auteur) Aims: Spruce beetles (Dendroctonus rufipennis) are causing widespread spruce (Picea spp.) mortality in subalpine forests in western North America. Spruce beetles are changing forest structure and composition by killing a dominant overstory species, but we know little about how the understory community responds to the increase in resource availability brought about by spruce mortality, what mechanisms drive its response, or how its response affects other forest properties and processes.
Location: Glacier Lakes Ecosystem Experiments Site, Medicine Bow Mountains, Wyoming, USA.
Methods: We measured understory community cover and richness in 75 permanent plots during and 10 years after an epidemic spruce beetle outbreak, and measured trait values for 46 common understory species. We used linear regression to determine how the understory community has changed over time and along a gradient of spruce mortality, and to evaluate the relative support for two mechanisms contributing to species responses.
Results: Understory cover nearly doubled between sampling periods and increased the most where spruce mortality was most severe. Understory richness doubled and showed a weak positive trend with spruce mortality. Understory species with the largest increases in cover were the most frequent across the landscape before the disturbance, were the tallest at maturity and had the lowest leaf turgor loss points. Fir seedling density decreased over time, with decreases lessening with increases in understory cover. Changes in spruce seedling density were not predicted by changes in understory cover.
Conclusions: Our findings highlight some of the diverse ways in which understory communities can be altered by spruce beetle outbreaks, and how the direction and magnitude of change can depend on the amount of spruce mortality as well as on priority effects and traits of pre-disturbance species. Our findings also highlight how understory community changes can have implications for other forest properties and processes, such as tree regeneration and forest recovery.Numéro de notice : A2022-173 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/jvs.13109 Date de publication en ligne : 14/12/2021 En ligne : https://doi.org/10.1111/jvs.13109 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99809
in Journal of vegetation science > vol 33 n° 1 (January 2022) . - 15 p.[article]Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])
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Titre : Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran Titre original : Estimation multi-capteurs de la biomasse aérienne de la forêt de feuillus hyrcanienne d’Iran Type de document : Article/Communication Auteurs : Ghasem Ronoud, Auteur ; Parviz Fatehi, Auteur ; Ali Asghar Darvishsefat, Auteur Année de publication : 2021 Article en page(s) : pp 818 - 834 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] estimation statistique
[Termes IGN] Fagus orientalis
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Iran
[Termes IGN] régression multiple
[Vedettes matières IGN] Inventaire forestierMots-clés libres : Support Vector Regression Résumé : (auteur) In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation. Numéro de notice : A2021-956 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1968811 Date de publication en ligne : 07/09/2021 En ligne : https://doi.org/10.1080/07038992.2021.1968811 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99982
in Canadian journal of remote sensing > vol 47 n° 6 [01/11/2021] . - pp 818 - 834[article]Linear regression and lines intersecting as a method of extracting punctual entities in a lidar point cloud / Marlo Antonio Ribeiro Martins in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])
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Titre : Linear regression and lines intersecting as a method of extracting punctual entities in a lidar point cloud Type de document : Article/Communication Auteurs : Marlo Antonio Ribeiro Martins, Auteur ; Edson Aparecido Mitishita, Auteur Année de publication : 2021 Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] intersection spatiale
[Termes IGN] régression linéaire
[Termes IGN] semis de points
[Termes IGN] toitRésumé : (auteur) The characteristics of data points obtained by laser scanning (LiDAR) and images have been considered complementary in the field of photogrammetric applications, and research to improve their integrated use have recently intensified. This study aim to verify the performance of determining punctual entities in a LiDAR point cloud using linear regression and intersecting lines obtained from buildings with square rooftop containing four planes (hip roof), as well as compare punctual entities three-dimensional coordinates determined by planes intersection. Our results show that the proposed method was more accurate in determining three-dimensional coordinates than plan intersection method. The obtained coordinates were evaluated and framed into the map accuracy standard for digital cartographic products (PEC-PCD), besides being analyzed for trend and precision. Accuracy analysis results frame punctual entities three-dimensional coordinates into the 1:2,000 or lower scale for Class A of PEC-PCD. Numéro de notice : A2021-959 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1590/s1982-21702021000300022 En ligne : https://doi.org/10.1590/s1982-21702021000300022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100074
in Boletim de Ciências Geodésicas > vol 27 n° 3 [01/10/2021] . - 21 p.[article]Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm / Sumitra Iyer in Journal of applied geodesy, vol 15 n° 4 (October 2021)PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)PermalinkUnsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network / Fengpeng Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 8 (August 2021)PermalinkRetrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean / Kunpeng Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkEstimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkCloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkAnalysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science, vol 78 n° 1 (March 2021)PermalinkGeographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships / Sensen Wu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkMachine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)PermalinkDeveloping a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data / Juan Guerra-Hernández in Forest ecology and management, vol 481 (February 2021)Permalink