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scientifique

Tree position estimation from TLS data using hough transform and robust least-squares circle fitting / Maja Michałowska in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)  

Public [article]  inRemote Sensing Applications: Society and Environment, RSASE > vol 29 (January 2023) . - n° 100863 Titre : Tree position estimation from TLS data using hough transform and robust least-squares circle fitting Type de document : Article/Communication Auteurs : Maja Michałowska, Auteur ; Jacek Rapinski, Auteur ; Joanna Janicka, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 100863 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] branche (arbre) [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage du bruit [Termes IGN] méthode des moindres carrés [Termes IGN] modèle numérique de terrain [Termes IGN] Pologne [Termes IGN] semis de points [Termes IGN] transformation de Hough Résumé : (auteur) Forest management and planning require information regarding the current state of the forest. Remote sensing techniques allow to obtain geospatial data, also for the forestry sector. As one of the remote-sensed technologies datasets, Terrestrial Laser Scanning data is widely used to derive detailed information about tree and forest stand parameters. This article presents the combination of circular Hough transform, denoising procedure, and robust least-square circle fitting method to extract stem positions from Terrestrial Laser Scanning data. In the proposed approach, initial tree stems position was detected with circular Hough transform. Then, obtained results were denoised to exclude most non-tree trunk points and analyze three-dimensional data from laser scanning to find exact circular tree stems with a robust least-square circle fitting method. The developed algorithm is effective in obtaining the trees’ geodetic positions from laser scanning data. The results generated in this study can be used as basics for further automatic determination of tree characteristics, such as tree species, height, or crown range. In this study, 94.8% tree stems delineation was generated with a mean accuracy of 87.2%, 1.64 cm of root mean square error for stem position, and 1.15 cm for tree radius measured at ground level. The process conducted in this research can be used to detect other circle-shaped objects, such as lamps or power towers, for which obtaining dense Terrestrial Laser Scanning data is available. The detected positions of these objects can power the geographic information systems or thematic industry systems, where it is necessary to determine the geodetic object position results from legal regulations. Numéro de notice : A2023-018 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rsase.2022.100863 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.1016/j.rsase.2022.100863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102183 [article]

Interactive effects of abiotic factors and biotic agents on Scots pine dieback: A multivariate modeling approach in southeast France / Jean Lemaire in Forest ecology and management, vol 526 (December-15 2022)  

Public [article]  inForest ecology and management > vol 526 (December-15 2022) . - n° 120543 Titre : Interactive effects of abiotic factors and biotic agents on Scots pine dieback: A multivariate modeling approach in southeast France Type de document : Article/Communication Auteurs : Jean Lemaire, Auteur ; Michel Vennetier, Auteur ; Bernard Prévosto, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120543 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bilan hydrique [Termes IGN] climat méditerranéen [Termes IGN] croissance des arbres [Termes IGN] dépérissement [Termes IGN] diagnostic foliaire [Termes IGN] facteur édaphique [Termes IGN] France (administrative) [Termes IGN] indice foliaire [Termes IGN] insecte nuisible [Termes IGN] Pinus sylvestris [Termes IGN] régression des moindres carrés partiels [Termes IGN] Viscum album [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forest dieback is a high risk factor for the sustainability of these ecosystems in the climate change context. Productivity losses and increased defoliation and mortality rates have already been recorded for many tree species worldwide. However, dieback is a process that depends on complex interactions between many biotic and environmental factors acting at different scales, and is thus difficult to address and predict. Our aim was to build tree- and stand-level foliar deficit models integrating biotic and abiotic factors for Scots pine (Pinus sylvestris), a species particularly threatened in Europe, and especially in the southeastern part of France. To this end, we quantified foliar deficit in 1740 trees from 87 plots distributed along an environmental gradient. We also measured tree annual radial growth and the abundance of two parasites: the pine processionary moth (Thaumetopoea pityocampa Den. & Schiff.) and mistletoe (Viscum album L.). Topographic, soil, climate and water balance indices were assessed for each plot, together with the stand dendrometric characteristics. Given the large number of environmental factors and the strong correlations between many of them, models were developed using a partial least squares (PLS) regression approach. All the models pointed to a preponderance of the biotic factors (processionary moth and mistletoe) in explaining the intensity of foliar deficit at both tree- and stand- levels. We also show that strong interactions between climate, soil, water balance and biotic factors help to explain the intensity of dieback. Dieback was thus greater in the driest topoedaphic and climatic conditions where the mistletoe and processionary moth were present. This study highlights the need to account for a wide range of biotic and abiotic factors to explain the complex process of forest dieback, and especially the environmental variables that contribute to the water balance on the local scale. The phenomenological modeling approach presented here can be used in other regions and for other species, after a re-calibration and some adaptations to local constraints considering the limited distribution area of some biotic agents. Numéro de notice : A2022-825 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120543 Date de publication en ligne : 20/10/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120543 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102003 [article]

Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid / Zhen Dai in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 11 (November 2022) . - pp 2248 - 2269 Titre : Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid Type de document : Article/Communication Auteurs : Zhen Dai, Auteur ; Sensen Wu, Auteur ; Yuanyuan Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2248 - 2269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] modèle de régression [Termes IGN] régression géographiquement pondérée [Termes IGN] régression linéaire [Termes IGN] réseau neuronal convolutif Résumé : (auteur) Geographically weighted regression (GWR) is a classical method of modeling spatially non-stationary relationships. The geographically neural network weighted regression (GNNWR) model solves the problem of the inaccurate construction of spatial weight kernels using a spatially weighted neural network. However, when the spatial distribution of observations is uneven, the spatial proximity expression in the input of GWR and GNNWR models does not fully represent the impact of the whole research space on the estimating point. Therefore, we established a global spatial proximity grid (GSPG) to express the spatial proximity of each estimating point and proposed a spatially weighted convolutional neural network (SWCNN) to extract the relationship between the GSPG and spatial weights. Finally, we proposed a geographically convolutional neural network weighted regression (GCNNWR) model combining SWCNN and ordinary linear regression (OLR) model to estimate spatial non-stationarity. We used two case studies of simulated data and real environment data to demonstrate the advancements of the GCNNWR model. The GCNNWR model achieved higher estimation accuracy and greater predictive power than the OLR, GWR, multi-scale GWR (MGWR), and GNNWR models. Moreover, the GCNNWR model maintained its better stability and accuracy in estimating spatially non-stationary relationships when the distribution of observations was uneven. Numéro de notice : A2022-773 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2100892 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2100892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101954 [article]

Human mobility and COVID-19 transmission: a systematic review and future directions / Mengxi Zhang in Annals of GIS, vol 28 n° 4 (November 2022)  

Public [article]  inAnnals of GIS > vol 28 n° 4 (November 2022) . - pp 501 - 514 Titre : Human mobility and COVID-19 transmission: a systematic review and future directions Type de document : Article/Communication Auteurs : Mengxi Zhang, Auteur ; Siqin Wang, Auteur ; Tao Hu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 501 - 514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] corrélation automatique de points homologues [Termes IGN] données spatiotemporelles [Termes IGN] épidémie [Termes IGN] hétérogénéité spatiale [Termes IGN] littérature [Termes IGN] maladie virale [Termes IGN] mobilité territoriale [Termes IGN] modèle dynamique [Termes IGN] modélisation spatio-temporelle [Termes IGN] régression linéaire Résumé : (auteur) Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability. Numéro de notice : A2022-863 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2022.2041725 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1080/19475683.2022.2041725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102153 [article]

Improving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height / Mosbeh R. Kaloop in Survey review, vol 54 n° 387 (November 2022)  

Public [article]  inSurvey review > vol 54 n° 387 (November 2022) . - pp 505 - 518 Titre : Improving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height Type de document : Article/Communication Auteurs : Mosbeh R. Kaloop, Auteur ; Samui Pijush, Auteur ; Mostafa Rabah, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 505 - 518 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] apprentissage automatique [Termes IGN] géoïde gravimétrique [Termes IGN] géoïde local [Termes IGN] Koweit [Termes IGN] MNS SRTM [Termes IGN] modèle de géopotentiel [Termes IGN] nivellement avec assistance GPS [Termes IGN] processus gaussien [Termes IGN] régression [Termes IGN] régression multivariée par spline adaptative [Termes IGN] résidu Résumé : (auteur) This study aims to use GPS/Levelling data and machine learning techniques (MLs) to model a high precision local geoid for Kuwait. To improve the accuracy of a local geoid the global geopotential model and local terrain effect should be incorporated. The geoid model was improved based on the modelling of geoid residuals using three MLs. Minimax Probability Machine Regression (MPMR), Gaussian Process Regression (GPR), and Multivariate Adaptive Regression Splines (MARS) MLs were developed for modelling the calculated geoid residuals. The results show that the accuracy of the three MLs was improved compared to previous studies, and the accuracy of the GPR model was better than the other models. The standard deviations of Kuwait geoid undulation determined by GPS/Levelling, gravimetric, and developed GPR models were 1.377, 1.375, 1.375 m, respectively. Thus, the developed GPR model has successfully predicted an accurate geoid height of Kuwait with maximum variation approaches ±0.02 m. Numéro de notice : A2022-829 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1970918 Date de publication en ligne : 27/08/2021 En ligne : https://doi.org/10.1080/00396265.2021.1970918 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102013 [article]

Machine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)  

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Driving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression / Ines Grigorescu in Geocarto international, vol 37 n° 24 ([20/10/2022])  

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Comparison of change and static state as the dependent variable for modeling urban growth / Yongjiu Feng in Geocarto international, vol 37 n° 23 ([15/10/2022])  

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Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)  

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Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks / Abdelkrim Bouasria in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

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GNSS best integer equivariant estimation combining with integer least squares estimation: an integrated ambiguity resolution method with optimal integer aperture test / Liye Ma in GPS solutions, vol 26 n° 4 (October 2022)  

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Machine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)  

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Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions / Di Zhu in Geoinformatica [en ligne], vol 26 n° 4 (October 2022)  

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The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)  

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Prediction of suspended sediment concentration using hybrid SVM-WOA approaches / Sandeep Samantaray in Geocarto international, vol 37 n° 19 ([15/09/2022])  

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