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Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon / Tamer ElGharbawi in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)
[article]
Titre : Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon Type de document : Article/Communication Auteurs : Tamer ElGharbawi, Auteur ; Fawzi Zarzoura, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Beyrouth
[Termes IGN] corrélation
[Termes IGN] décorrélation
[Termes IGN] dommage matériel
[Termes IGN] étude d'impact
[Termes IGN] filtre passe-haut
[Termes IGN] image radar moirée
[Termes IGN] risque technologiqueRésumé : (auteur) Early well-coordinated response during unexpected catastrophes can define the near future of the stricken regions. Beirut city, Lebanon, was one of the unfortunate regions to endure the horrific ordeal of an unexpected explosion that caused thousands of human casualties, billions of dollars’ worth of property damage, and destroyed its main maritime entry point. In this paper, we identify damaged regions and classify their severity using a simple and robust SAR correlation technique. We employ phase coherence and amplitude correlation of a SAR stack to estimate pixels’ damage probability using hypothesis testing. We use a spatial phase filter applied in the frequency domain to improve the estimated coherence by removing the spatial decorrelation component of the total estimated coherence. Using this filter improved the coherence of nearly 44.2% of pixels identified with coherence less than 0.25 in our study area. The estimated damaged regions are presented and compared against a damage map issued by Advanced Rapid Imaging and Analysis (ARIA) which shows an average agreement of 68.3%. Also, a fine agreement was observed when compared to optical satellite images. Numéro de notice : A2021-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.00 Date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96871
in ISPRS Journal of photogrammetry and remote sensing > vol 173 (March 2021) . - pp 1 - 9[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021031 SL Revue Centre de documentation Revues en salle Disponible 081-2021033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Development and assessment of rainwater harvesting suitability map using analytical hierarchy process, GIS and RS techniques / Khaled S. Balkhair in Geocarto international, vol 36 n° 4 ([01/03/2021])
[article]
Titre : Development and assessment of rainwater harvesting suitability map using analytical hierarchy process, GIS and RS techniques Type de document : Article/Communication Auteurs : Khaled S. Balkhair, Auteur ; Khalil Ur Rahman, Auteur Année de publication : 2021 Article en page(s) : pp 421 - 448 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] aide à la décision
[Termes IGN] analyse de sensibilité
[Termes IGN] Arabie Saoudite
[Termes IGN] bassin hydrographique
[Termes IGN] carte hydrographique
[Termes IGN] eau pluviale
[Termes IGN] écoulement des eaux
[Termes IGN] gestion de l'eau
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] système d'information géographiqueRésumé : (auteur) Rainwater harvesting (RWH), which is the collection and storage of rainwater for multiple purposes, is gaining recognition in water supply issues. Selection of harvesting sites is the most critical factor in RWH projects. The objective of this study is to develop a suitability map of RWH sites for a basin in Saudi Arabia. The method used, constitute the identification and assigning weights to criteria, and generation of suitability map using Analytical Hierarchy Process (AHP). Eight appropriate criteria were considered. Results showed that excellent and good sites covered about 40.6% of the total available sites. Sensitivity analysis showed that the curve number (CN), slope, rainfall and soil were the most influential criteria. The maximum increase in the percentage area of excellent sites was 92% while good and moderate classes decreased by 43 and 53%, respectively. The developed suitability maps provide useful information to the decision maker for use in water management. Numéro de notice : A2021-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.160859 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1608591 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97082
in Geocarto international > vol 36 n° 4 [01/03/2021] . - pp 421 - 448[article]Geographically 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)
[article]
Titre : Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships Type de document : Article/Communication Auteurs : Sensen Wu, Auteur ; Zhongyi Wang, Auteur ; Zhenhong Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 582 - 608 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal
[Termes IGN] espace-temps
[Termes IGN] estimation par noyau
[Termes IGN] littoral
[Termes IGN] modélisation environnementale
[Termes IGN] raisonnement spatiotemporel
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression linéaireRésumé : (auteur) Geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) are classic methods for estimating non-stationary relationships. Although these methods have been widely used in geographical modeling and spatiotemporal analysis, they face challenges in adequately expressing space-time proximity and constructing a kernel with optimal weights. This probably results in an insufficient estimation of spatiotemporal non-stationarity. To address complex non-linear interactions between time and space, a spatiotemporal proximity neural network (STPNN) is proposed in this paper to accurately generate space-time distance. A geographically and temporally neural network weighted regression (GTNNWR) model that extends geographically neural network weighted regression (GNNWR) with the proposed STPNN is then developed to effectively model spatiotemporal non-stationary relationships. To examine its performance, we conducted two case studies of simulated datasets and environmental modeling in coastal areas of Zhejiang, China. The GTNNWR model was fully evaluated by comparing with ordinary linear regression (OLR), GWR, GNNWR, and GTWR models. The results demonstrated that GTNNWR not only achieved the best fitting and prediction performance but also exactly quantified spatiotemporal non-stationary relationships. Further, GTNNWR has the potential to handle complex spatiotemporal non-stationarity in various geographical processes and environmental phenomena. Numéro de notice : A2021-167 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1775836 Date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1080/13658816.2020.1775836 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97102
in International journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 582 - 608[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021031 SL Revue Centre de documentation Revues en salle Disponible GIS-based spatial landslide distribution analysis of district Neelum, AJ&K, Pakistan / Shah Naseer in Natural Hazards, vol 106 n° 1 (March 2021)
[article]
Titre : GIS-based spatial landslide distribution analysis of district Neelum, AJ&K, Pakistan Type de document : Article/Communication Auteurs : Shah Naseer, Auteur ; Tanveer Ul Haq, Auteur ; Abdullah Khan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 965 - 989 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] ArcGIS
[Termes IGN] distribution spatiale
[Termes IGN] effondrement de terrain
[Termes IGN] fréquence
[Termes IGN] Google Earth
[Termes IGN] lit majeur
[Termes IGN] modèle numérique de surface
[Termes IGN] Pakistan
[Termes IGN] réseau hydrographique
[Termes IGN] réseau routier
[Termes IGN] sismicitéRésumé : (auteur) The Landslide happens in mountainous regions due to the catastrophe of slope through intensive rain and seismicity. The Himalayas is one of the susceptible parts of the world in the perspective of slope catastrophe hazard; i.e., Mass Movement, especially Neelum valley is considerable destruction of community infrastructure, highway, and critically disturbed the tourism segment. Landslide is a common and recurrent phenomenon in the northern mountainous terrain of Pakistan such as District Neelum. After the 2005 Kashmir earthquake, the importance of landslide investigation is increasing. The purpose of this research is to establish a brief landslide inventory and to determine the relationship of landslides with causative factors by spatial distribution analysis. With the aid of Google Earth imageries and field visits, a total of 618 landslides were identified in the study area of 3621 km. These landslide localities compared with causative factors. Finally, distribution maps are generated and analyse their feature class through Digital Elevation Model and ArcGIS. Landslide intensity is calculated in terms of landslide concentration. Landslide concentration (LC) is significantly found very high in slope gradient less than 30 (1.21) and the first 100 m zone around the road network (15.06). A bit higher landslide frequency is noted in east orienting slopes. In the first 100 m, zone road network and drainage networks are 83.49% and 62.78% of the total landslide occurs having LC value 4.6, respectively. The analysis shows that the steep slopes, an area closer to the road network, drainage network, barren lands, and Quaternary alluvium of loose material are more susceptible to landslides. In addition, a landslide classification map is also prepared on the basis of field observation that shows that debris slides are more dominating. Numéro de notice : A2021-420 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-021-04502-5 Date de publication en ligne : 21/01/2021 En ligne : https://doi.org/10.1007/s11069-021-04502-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97770
in Natural Hazards > vol 106 n° 1 (March 2021) . - pp 965 - 989[article]Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
[article]
Titre : Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions Type de document : Article/Communication Auteurs : Xin-Ming Zhu, Auteur ; Xiao-Ning Song, Auteur ; Pei Leng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2680 - 2697 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image Landsat-8
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] température au sol
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases. Numéro de notice : A2021-219 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3002821 Date de publication en ligne : 26/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3002821 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97211
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2680 - 2697[article]Integration of an InSAR and ANN for sinkhole susceptibility mapping: A case study from Kirikkale-Delice (Turkey) / Hakan Nefeslioglu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkLandslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkLearning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkUne nouvelle détermination de l'altitude de l'Everest par le Népal et la Chine / Gavin Schrock in XYZ, n° 166 (mars 2021)PermalinkPBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery / Xian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkSaline-soil deformation extraction based on an improved time-series InSAR approach / Wei Xiang in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkSpace-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach / Bisong Hu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkSuitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkUrban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkAssessing spatial-temporal evolution processes and driving forces of karst rocky desertification / Fei Chen in Geocarto international, vol 36 n° 3 ([15/02/2021])Permalink