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Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model / Gaurav Talukdar in Natural Hazards, vol 109 n°1 (October 2021)  

Public [article]  inNatural Hazards > vol 109 n°1 (October 2021) . - pp 389 - 403 Titre : Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model Type de document : Article/Communication Auteurs : Gaurav Talukdar, Auteur ; Janaki Ballav Swain, Auteur ; Kanhu Charan Patra, Auteur Année de publication : 2021 Article en page(s) : pp 389 - 403 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] cartographie automatique [Termes IGN] cartographie des risques [Termes IGN] Inde [Termes IGN] inondation [Termes IGN] littoral [Termes IGN] modèle hydrographique [Termes IGN] modèle numérique de surface [Termes IGN] occupation du sol [Termes IGN] précipitation [Termes IGN] ruissellement [Termes IGN] texture du sol Résumé : (auteur) Frequent flood is a concern for most of the coastal regions of India. The importance of flood maps in governing strategies for flood risk management is of prime importance. Flood inundation maps are considered dependable output generated from simulation results from hydraulic models in evaluating flood risks. In the present work, a continuous hydrologic-hydraulic model has been implemented for mapping the flood, caused by the Baitarani River of Odisha, India. A rainfall time-series data were fed into the hydrologic model and the runoff generated from the model was given as an input into the hydraulic model. The study was performed using the HEC-HMS model and the FLO-2D model to map the extent of flooding in the area. Shuttle Radar Topographic Mission (SRTM) 90 m Digital Elevation Model (DEM) data, Land use/Land cover map (LULC), soil texture data of the basin area were used to compute the topographic and hydraulic parameters. Flood inundation was simulated using the FLO-2D model and based on the flow depth, hazard zones were specified using the MAPPER tool of the hydraulic model. Bhadrak District was found to be the most hazard-prone district affected by the flood of the Baitarani River. The result of the study exhibited the hydraulic model as a utile tool for generating inundation maps. An approach for assessing the risk of flooding and proper management could help in mitigating the flood. The automated procedure for mapping and the details of the study can be used for planning flood disaster preparedness in the worst affected area. Numéro de notice : A2021-751 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-021-04841-3 En ligne : https://doi.org/10.1007/s11069-021-04841-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98736 [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)  

Public [article]  inJournal of applied geodesy > vol 15 n° 4 (October 2021) . - pp 279 - 291 Titre : Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm Type de document : Article/Communication Auteurs : Sumitra Iyer, Auteur ; Alka Mahajan, Auteur Année de publication : 2021 Article en page(s) : pp 279 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] autorégression [Termes IGN] déformation temporelle dynamique [Termes IGN] format RINEX [Termes IGN] Inde [Termes IGN] modèle de simulation [Termes IGN] modèle ionosphérique [Termes IGN] série temporelle [Termes IGN] signal GPS [Termes IGN] tempête magnétique [Termes IGN] teneur totale en électrons [Termes IGN] teneur verticale totale en électrons Résumé : (auteur) The ionospheric total electron content (TEC) severely impacts the positional accuracy of a single frequency Global Positioning System (GPS) receiver at the equatorial latitudes. The ionosphere causes a frequency-dependent group delay in the GPS-ranging signals, which reduces the receiver’s accuracy. Further, the variations in TEC due to various space weather phenomena make the ionosphere’s behaviour nonhomogeneous and complex. Hence, developing an accurate forecast model that can track the dynamic behaviour of the ionosphere remains a challenge. However, advances in emerging data-driven algorithms have been found helpful in tracking non-stationary behavior in TEC. These models help forecast the delays in advance. The multivariate Vector Autoregression model (VAR) predicts the Ionospheric TEC in the proposed model. The prediction model uses input data compiled in real-time from the lag values of incoming TEC data and features extracted from TEC. The TEC is predicted in real-time and tested for different prediction intervals. The metrics – Mean Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used for testing and validating the accuracy of the model statistically. Testing the predicted output accuracy is also done with the dynamic time warping (DTW) algorithm by comparing it with the actual value obtained from the dual-frequency receiver. The model is tested for storm days of the year 2015 for Bangalore and Hyderabad stations and found to be reliable and accurate. A prediction interval of twenty-minute shows the highest accuracy with an error within 10 TECU for all the storm days. Numéro de notice : A2021-749 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2021-0015 date de publication en ligne : 23/06/2021 En ligne : https://doi.org/10.1515/jag-2021-0015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98717 [article]

Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques / Rajkumar Dhakar in Geocarto international, vol 36 n° 18 (November 2021)  

Public [article]  inGeocarto international > vol 36 n° 18 (November 2021) . - pp 2044 - 2064 Titre : Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques Type de document : Article/Communication Auteurs : Rajkumar Dhakar, Auteur ; Vinay Kumar Sehgal, Auteur ; Debasish Chakraborty, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2044 - 2064 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] blé (céréale) [Termes IGN] correction atmosphérique [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] Inde [Termes IGN] indice foliaire [Termes IGN] Leaf Area Index [Termes IGN] pouvoir de résolution géométrique [Termes IGN] réseau neuronal artificiel Résumé : (auteur) This study assessed the effect of atmospheric correction algorithms, inversion techniques and image spatial and spectral resolution on wheat crop LAI retrieval using Sentinel-2 MSI and Landsat-8 OLI imagery. The LAI retrievals were validated with in-situ measurements collected in farmers’ fields. The MSI-based LAI retrievals improved significantly when images were atmospherically corrected using MODTRAN than using the libRadtran code. Among the two PROSAIL inversion approaches, look-up table outperforms artificial neural network for LAI retrievals. Using the best strategy of atmospheric correction and inversion, the effect of spatial resolution from 20 m (MSI) to 30 m (OLI) while using common six bands, showed non-significant improvement in LAI retrievals. The inclusion of additional two red-edge bands as available in MSI significantly reduced the uncertainly in LAI retrievals over that obtained by using six bands, while inclusion of only additional VNIR band did not show any significant effect on LAI retrievals. Numéro de notice : A2021-742 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1687591 date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98666 [article]

Spatial biodiversity modeling using high-performance computing cluster: A case study to access biological richness in Indian landscape / Hariom Singh in Geocarto international, vol 36 n° 18 (November 2021)  

Public [article]  inGeocarto international > vol 36 n° 18 (November 2021) . - pp 2023 - 2043 Titre : Spatial biodiversity modeling using high-performance computing cluster: A case study to access biological richness in Indian landscape Type de document : Article/Communication Auteurs : Hariom Singh, Auteur ; R.D. Garg, Auteur ; Harish Chandra Karnatak, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2023 - 2043 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] autocorrélation spatiale [Termes IGN] biodiversité [Termes IGN] coefficient de corrélation [Termes IGN] distribution spatiale [Termes IGN] Inde [Termes IGN] processus d'analyse hiérarchique [Termes IGN] regroupement de données [Termes IGN] relevé phytosociologique [Termes IGN] SIG participatif Résumé : (auteur) The parallel processing and distributed GIServices provide an efficient approach to address the geocomputation challenges in biodiversity modeling. Using the widely applied Spatial Biodiversity Model (SBM) as an illustration, this study demonstrates parallelization of the spatial landscape algorithms based on Message Passing Interface (MPI) in cluster computing. The geocomputation based on MPI is performed to characterize the spatial distribution of Biological Richness (BR) for Indian landscape using developed high-performance cluster computing-based model named as SBM-HPC. In performance analysis, the execution time is reduced by 56.42%–81.41% (or the speedups of 2.29–5.38) using the parallel and cluster computing environment. Also, the spatial landscape algorithms of the model are extended to integrate large-scale geodata from online map services archives using distributed GIServices. To validate BR map, the phytosociological data is collected using participatory GIS approach. Furthermore, regression analysis between derived BR map and Shannon-Wiener index (Hˈ) represents high correlation coefficient R2 values. Highlights : - Development of spatial biodiversity model using parallel computing on the cluster. - Geocomputation of spatial landscape indices using large-scale geospatial datasets. - Distributed GIService integration in model to compute distributed data archives. - Prediction of biological richness pattern and validation using participatory GIS. - Characterize correlations between biological richness and bioclimatic patterns. Numéro de notice : A2021-763 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1678679 date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1080/10106049.2019.1678679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98798 [article]

Assessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, Vol 36 n° 17 ([21/09/2021])  

Public [article]  inGeocarto international > Vol 36 n° 17 [21/09/2021] . - pp 1960 - 1992 Titre : Assessment and prediction of urban growth for a mega-city using CA-Markov model Type de document : Article/Communication Auteurs : Veerendra Yadav, Auteur ; Sanjay Kumar Ghosh, Auteur Année de publication : 2021 Article en page(s) : pp 1960 - 1992 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] changement d'occupation du sol [Termes IGN] classification par maximum de vraisemblance [Termes IGN] coefficient de corrélation [Termes IGN] croissance urbaine [Termes IGN] mégalopole [Termes IGN] modèle de Markov [Termes IGN] modèle de simulation [Termes IGN] OpenStreetMap [Termes IGN] Tamil Nadu (Inde ; état) [Termes IGN] urbanisation Résumé : (auteur) Most of World’s mega-cities are facing high population growth. To accommodate the increased population, new built-up areas are emerging at the periphery or fringe area of cities. New urbanisation has an adverse impact on the existing Land Use Land Cover (LULC). To monitor and analyse the impact of urbanisation, LULC change analysis has become the primary concern for LULC monitoring agencies. In this study, LULC change of Chennai has been assessed during 1981–2011 using temporal Landsat data. All the dataset has been classified using Maximum Likelihood Classifier (MLC). Quantitative change in LULC has been carried out using Pearson’s Correlation Coefficient, Transition Potential Matrix, Land Use Dynamic Degree and MLC. Further, spatio-temporal change analysis has been performed using Post-classification comparison technique. Cellular Automata-Markov (CA-Markov) Model used for LULC prediction for 2021–2051. The urban area of Chennai has increased from 40.74 to 103.52 km2 during 1981–2011. Further, LULC prediction using the CA-Markov model shows that the urban area of Chennai district may increase from 103.52 to 140.79 km2 during 2011–2051. During the period 1981–2051, the prediction model indicates that mostly vegetation and barren land will be converted into urban land class. Numéro de notice : A2021-692 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2019.1690054 date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1690054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98507 [article]

Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, Vol 36 n° 15 ([10/08/2021])  

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Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery / S. Vigneshwaran in Geocarto international, vol 36 n° 13 ([15/07/2021])  

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A cellular-automata model for assessing the sensitivity of the street network to natural terrain / Jeeno Soa George in Annals of GIS, vol 27 n° 3 (July 2021)  

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Electrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed / H.S. Virupaksha in Geocarto international, vol 36 n° 8 ([15/04/2021])  

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Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data / Vijay Pratap Yadav in Geocarto international, vol 36 n° 7 ([01/04/2021])  

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Urban expansion in the megacity since 1970s: a case study in Mumbai / Sisi Yu in Geocarto international, vol 36 n° 6 ([30/03/2021])  

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Landslide 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)  

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Geospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data / C.M. Bhatt in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

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Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India / Sunil Saha in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

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Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) / Mirza Razi Imam Baig in Annals of GIS, vol 26 n° 4 (December 2020)  

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