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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)
[article]
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 solRésumé : (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
in Applied geomatics > vol 14 n° 4 (December 2022) . - pp 653 - 667[article]Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)
[article]
Titre : Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Arnaud Le Bris , Auteur ; Ali Asghar Darvishsefat, Auteur ; Fardin Moradi, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 1759 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] aire protégée
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SARRésumé : (auteur) Considering the importance of accurate and up-to-date land use/cover (LULC) maps and in a situation of fast LULC changes, an accurate mapping of complex landscapes requires real-time high-resolution remote sensed data and powerful classification algorithms. The new ESA Copernicus satellites Sentinel-1 (S-1) and Sentinel-2 (S-2) have contributed to the effective monitoring of the Earth’s surface. This paper aims at assessing the potential of mono-temporal S-1 and S-2 satellite images and three common classification algorithms including maximum likelihood (ML), support vector machine (SVM), and random forest (RF) for LULC classification. The research methodology consists of a sequence of tasks including data collection and preprocessing, the extraction of texture and spectral features, the definition of several feature set configurations, classification, and accuracy assessment. Based on the results, using S-1 data alone leads to quite poor results, even though dual polarimetric C-band and texture features increased the classification accuracy. The S-2 data outperformed the S-1 data in terms of overall and class level accuracies. A combined use of S-1 and S-2 satellite images involving extracted features from both sources led to the best result for identifying all classes. This emphasizes the critical importance of using multi-modal datasets and different features in the LULC classification. Among classification algorithms, the SVM led to the highest accuracies irrespective of the dataset. To sum it up, according to the applied methodology and results, S-1 and S-2 data can provide optimal and up-to-date information for LULC mapping using non-parametric classifiers as SVM or RF. Numéro de notice : A2022-699 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12517-022-11035-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s12517-022-11035-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102253
in Arabian Journal of Geosciences > vol 15 n° 24 (December 2022) . - n° 1759[article]Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)
[article]
Titre : Modelling evacuation preparation time prior to floods: A machine learning approach Type de document : Article/Communication Auteurs : R. Sreejith, Auteur ; K.R. Sinimole, Auteur Année de publication : 2022 Article en page(s) : n° 104257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] chronométrie
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion de crise
[Termes IGN] inondation
[Termes IGN] Kerala (Inde ; état)
[Termes IGN] modèle de simulation
[Termes IGN] plan de prévention des risques
[Termes IGN] questionnaire
[Termes IGN] risque naturel
[Termes IGN] secours d'urgenceRésumé : (auteur) Flooding is a significant hazard responsible for substantial damage and risks to human life worldwide. Effective emergency evacuation to a safer location remains a concern even though the crisis can be predicted and warnings were given. During a calamity, most residents cannot quickly and securely flee. As it is crucial to start evacuation at the right time to have a safe evacuation, this study focuses on a machine learning-based model for predicting a household's evacuation preparation time in the incident of a flood. The study is based on the data collected from flood-affected people from Kerala, India, through a questionnaire. The study indicates that people's demographic, geographical and behavioural aspects, awareness of natural hazards and management are the critical components for improved emergency actions. Further, the article also analysed the characteristics of the respondents and successfully created clusters in which the respondents broadly belong, which will help the rescue team operationalize the evacuation process. Numéro de notice : A2022-819 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104257 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101986
in Sustainable Cities and Society > vol 87 (December 2022) . - n° 104257[article]Multi-frequency simulation of ionospheric scintillation using a phase-screen model / Fernando D. Nunes in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)
[article]
Titre : Multi-frequency simulation of ionospheric scintillation using a phase-screen model Type de document : Article/Communication Auteurs : Fernando D. Nunes, Auteur ; Fernando M.G. Sousa, Auteur ; José M.V. Marçal, Auteur Année de publication : 2022 Article en page(s) : n° 545 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] amplitude
[Termes IGN] correction ionosphérique
[Termes IGN] fréquence multiple
[Termes IGN] ionosphère
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] phase
[Termes IGN] scintillation
[Termes IGN] série temporelle
[Termes IGN] signal GNSS
[Termes IGN] teneur totale en électronsRésumé : (auteur) A fast Monte Carlo technique to simulate equatorial ionospheric scintillation on global navigation satellite system signals is proposed. The algorithm uses a single-layer phase-screen model of the ionosphere and the scintillation is expressed as a Huygens-Fresnel integral (HFI). By assuming a specially-tailored random phase screen, the HFI can be expressed in closed form as a combination of Fresnel integrals. We statistically characterize the amplitude and phase computed by the HFI for different values of the scintillation index S4. Results for the L1, L2, and L5 bands were obtained and compared with real data, showing good agreement. Some of the advantages of the proposed technique are: (a) the amplitude and phase of the scintillation process are simultaneously obtained; (b) arbitrarily long ionospheric scintillation time series with pre-defined stationary characteristics are synthesized; and (c) several scintillation time series corresponding to different carrier frequencies are generated using a common phase-screen model. Numéro de notice : A2022-918 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.33012/navi.545 Date de publication en ligne : 18/06/2022 En ligne : https://doi.org/10.33012/navi.545 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102446
in Navigation : journal of the Institute of navigation > vol 69 n° 4 (Fall 2022) . - n° 545[article]Navigation and Ionosphere Characterization Using High-Frequency Signals: A Performance Analysis / Yoav Baumgarten in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)
[article]
Titre : Navigation and Ionosphere Characterization Using High-Frequency Signals: A Performance Analysis Type de document : Article/Communication Auteurs : Yoav Baumgarten, Auteur ; M.L. Psiaki, Auteur ; David L. Hysell, Auteur Année de publication : 2022 Article en page(s) : n° 546 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] algorithme de Gauss-Newton
[Termes IGN] correction du signal
[Termes IGN] correction ionosphérique
[Termes IGN] matrice de covariance
[Termes IGN] mesurage de phase
[Termes IGN] modèle ionosphérique
[Termes IGN] propagation du signal
[Termes IGN] récepteur
[Termes IGN] teneur verticale totale en électronsRésumé : (auteur) The performance of a proposed high-frequency (HF) navigation concept is analyzed using simulated data. The method relies on pseudorange and beat carrier-phase measurements of signals that propagate in the ionosphere along curved trajectories, where signals are refracted back downwards from the ionosphere. It has been demonstrated that the location of a receiver can be determined if several signals, broadcast from beacons at different locations, are received and processed at a user receiver. A challenge of determining exact signal paths is the uncertainty in the ionosphere’s electron density distribution. This is addressed by a batch filter that simultaneously estimates the receiver position along with corrections to a parametric model of the ionosphere. A previous paper developed the theory and batch filter for this concept. The present study examines its potential performance. Total horizontal position errors on the order of tens to hundreds of meters are achieved, depending on the case’s characteristics. Numéro de notice : A2022-919 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.33012/navi.546 Date de publication en ligne : 19/06/2022 En ligne : https://doi.org/10.33012/navi.546 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102448
in Navigation : journal of the Institute of navigation > vol 69 n° 4 (Fall 2022) . - n° 546[article]A new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions / Rasit Ulug in Journal of geodesy, vol 96 n° 12 (December 2022)PermalinkA novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)PermalinkOn study of the Earth topography correction for the GRACE surface mass estimation / Fan Yang in Journal of geodesy, vol 96 n° 12 (December 2022)PermalinkProgressive collapse of dual-line rivers based on river segmentation considering cartographic generalization rules / Fubing Zhang in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)PermalinkReconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)PermalinkRobust modeling of GNSS orbit and clock error dynamics / Elisa Gallon in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)PermalinkSea 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)PermalinkSemantic integration of OpenStreetMap and CityGML with formal concept analysis / Somayeh Ahmadian in Transactions in GIS, vol 26 n° 8 (December 2022)PermalinkSemantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)PermalinkA semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) / Masoud Azad in Applied geomatics, vol 14 n° 4 (December 2022)Permalink