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A comparative assessment of the statistical methods based on urban population density estimation / Merve Yılmaz in Geocarto international, vol 38 n° 1 ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° 1 [01/01/2023] . - n° 2152494 Titre : A comparative assessment of the statistical methods based on urban population density estimation Type de document : Article/Communication Auteurs : Merve Yılmaz, Auteur Année de publication : 2023 Article en page(s) : n° 2152494 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de population [Termes IGN] planification urbaine [Termes IGN] population urbaine [Termes IGN] régression géographiquement pondérée [Termes IGN] régression multiple [Termes IGN] Turquie Résumé : (auteur) Population density is important spatial information for addressing the use and access to land resources in cities under the Sustainable Development Goals. This is because the spatial data support appropriate spatial policies at the spatial scale and predicts how much land will be consumed in the future. The study aims to compare and evaluate the regression tools in the context of estimating the population density difference. The three analysis tools used are Random Forest-Based Classification, Multiple Linear Regression, and Geographically Weighted Regression. The sampling area covers cities around Türkiye. Comparative results showed that the two most important descriptive variables in the Random Forest-Based Classification model are the density difference of the new developed area and the connectivity. The three main explanatory variables of the Multiple Linear Regression model are centrality, vehicle ownership, and accessibility. The results of the Multiple Linear Regression model (a non-spatial model) and the Geographically Weighted Regression model (a spatial model), were found to be quite similar. The importance of accessibility and connectivity is more evident in the Multiple Linear Regression model when the Random Forest-Based Classification model highlights the density values in the new development areas. Numéro de notice : A2023-055 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2152494 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.1080/10106049.2022.2152494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102388 [article]

Rapid mapping of seismic intensity assessment using ground motion data calculated from early aftershocks selected by GIS spatial analysis / Huaiqun Zhao in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - pp 1 - 21 Titre : Rapid mapping of seismic intensity assessment using ground motion data calculated from early aftershocks selected by GIS spatial analysis Type de document : Article/Communication Auteurs : Huaiqun Zhao, Auteur ; Yijiao Jia, Auteur ; Wenkai Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1 - 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] dommage [Termes IGN] régression [Termes IGN] sismologie [Termes IGN] zone sinistrée [Termes IGN] zone tampon Résumé : (auteur) Following a major earthquake, disaster information services must deliver accurate damage assessment results during the emergency ‘black box’ phase when data is scarce. Seismic intensity maps contain crucial information for determining the damage in the affected area. For earthquakes with Mw between 5.5 and 7, this study proposes using GIS analysis to mine aftershock events in early aftershock sequences that are closely related to the mainshock fault, and then using these events to generate seismic intensity assessment maps. Regression curves were first obtained using a nonparametric method (rLowess) to analyse the geographical coordinates of early aftershocks. Then, a buffer of 1 or 1.5 km radius was made for the curve, and the aftershocks in the buffer were used to calculate the predicted peak ground velocity (PGV) values over a specific km-grid range. Finally, rapid mapping of seismic intensity was assessed based on the intensity scale. This straightforward and repeatable method employs seismic station data obtained shortly after the mainshock. The assessed seismic intensity accurately reflects the location and extent of the hardest hit areas and can be cross-referenced with geophysical results to accurately assess the damage in the affected areas. Numéro de notice : A2023-035 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1080/19475705.2022.2160663 Date de publication en ligne : 02/01/2023 En ligne : https://doi.org/10.1080/19475705.2022.2160663 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102304 [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]

Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)  

Public [article]  iniForest, biogeosciences and forestry > vol 15 n° 6 (December 2022) . - pp 451-457 Titre : Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy Type de document : Article/Communication Auteurs : Mauro Maesano, Auteur ; Giovanni Santopuoli, Auteur ; Federico Valerio Moresi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 451-457 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] Calabre [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] gestion forestière durable [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] régression [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha-1 and from 34.2 to 22.1 Mg ha-1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model. Numéro de notice : A2022-903 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3832/ifor3781-015 Date de publication en ligne : 03/11/2022 En ligne : https://doi.org/10.3832/ifor3781-015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102299 [article]

Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia / Mitiku Badasa Moisa in Applied geomatics, vol 14 n° 4 (December 2022)  

Public [article]  inApplied geomatics > vol 14 n° 4 (December 2022) . - pp 653 - 667 Titre : Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia Type de document : Article/Communication Auteurs : Mitiku Badasa Moisa, Auteur ; Indale Niguse Dejene, Auteur ; Dessalegn Obsi Gemeda, Auteur Année de publication : 2022 Article en page(s) : pp 653 - 667 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] changement d'occupation du sol [Termes IGN] climat urbain [Termes IGN] espace vert [Termes IGN] étalement urbain [Termes IGN] Ethiopie [Termes IGN] flore urbaine [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] modèle de régression [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression multiple [Termes IGN] surface imperméable [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (auteur) Rapid urbanization and population growth are the main problems faced by developing countries that lead to natural resource depletion in the periphery of the city. This research attempts to analyze the impacts of urban land use land cover (LULC) change on land surface temperature (LST) from 1991 to 2021 in Jimma city, southwestern Ethiopia. Landsat Thematic Mapper (TM) 1991, Landsat Enhanced Thematic Mapper Plus (ETM +) 2005, and Landsat-8 Operational land imagery (OLI)/Thermal Infrared Sensor (TIRS) 2021 were used in this study. Multispectral bands and thermal infrared bands of Landsat images were used to calculate LULC change, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and LST. The LULC of the study area was classified using a supervised classification method with the maximum likelihood algorithm. The results of this study clearly showed that there is a negative correlation between vegetation cover and LST. The decrease in vegetation coverage and expansion of impervious surfaces lead to elevated LST in urban areas. The loss of vegetation cover contributed to the increasing trend of LST. Moreover, the conversion of vegetation cover to impervious surfaces aggravates the problem of LST. The results revealed that the built-up area was increased at a rate of 0.4 km2/year from 1991 to 2021. The vegetation cover in the city declined due to urban expansion to the periphery of the city. Consequently, the dense vegetation and sparse vegetation were converted into built-up areas by approximately 5.2 km2 during the study period. The mean LST of the study area increased by 10.3 °C from 1991 to 2021 during the winter season in daytime. To improve the problems of climate change around urban areas, all stakeholders should work together to increase the urban green space coverage, which will contribute a significant role in mitigating LST and the urban heat island effect. More specifically, all residents could be accessible to public green spaces around big cities. Numéro de notice : A2022-893 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-022-00463-x Date de publication en ligne : 22/08/2022 En ligne : https://doi.org/10.1007/s12518-022-00463-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102241 [article]

Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)  

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Urban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis / Das Subhasis in Geocarto international, vol 37 n° 25 ([01/12/2022])  

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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)  

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

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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)  

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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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