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Assessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])  

Public [article]  inGeocarto international > vol 36 n° 17 [15/09/2021] . - 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]

Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 10 [01/08/2020] . - pp 1067 - 1087 Titre : Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method Type de document : Article/Communication Auteurs : Vijendra Singh Bramhe, Auteur ; Sanjay Kumar Ghosh, Auteur ; Pradeep Kumar Garg, Auteur Année de publication : 2020 Article en page(s) : pp 1067 - 1087 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse spectrale [Termes IGN] analyse texturale [Termes IGN] bati [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] extraction de traits caractéristiques [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] plus proche voisin, algorithme du [Termes IGN] réseau neuronal artificiel [Termes IGN] texture d'image Résumé : (auteur) Information of built-up area is essential for various applications, such as sustainable development or urban planning. Built-up area extraction using optical data is challenging due to spectral confusion between built-up and other classes (bare land or river sand, etc.). Here an automated approach has been proposed to generate built-up maps using spectral-textural features and feature selection techniques. Eight Grey-Level Co-Occurrence Matrix based texture features are extracted using Landsat-8 Operational Land Imager bands and combined with multispectral data. The most informative features are selected from combined spectral-textural dataset using feature selection techniques. Further, Support Vector Machine (SVM) classifiers are trained on labelled samples using optimal features and results are compared with Back Propagation-Neural Network (BP-NN) and k-Nearest Neighbour (k-NN). The results show that inclusion of textural features and applying feature selection methods increases the highest overall accuracy of Linear-SVM, RBF-SVM, BP-NN, and k-NN by 9.20%, 9.09%, 8.42%, and 7.39%, respectively. Numéro de notice : A2020-425 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1566406 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566406 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95489 [article]

Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data / Prateek Verma in Geocarto international, vol 34 n° 10 ([15/07/2019])  

Public [article]  inGeocarto international > vol 34 n° 10 [15/07/2019] . - pp 1075 - 1088 Titre : Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data Type de document : Article/Communication Auteurs : Prateek Verma, Auteur ; Sanjay Kumar Ghosh, Auteur Année de publication : 2019 Article en page(s) : pp 1075 - 1088 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] changement climatique [Termes IGN] glacier [Termes IGN] Himalaya [Termes IGN] image Sentinel-MSI [Termes IGN] lac glaciaire [Termes IGN] Normalized Difference Water Index [Termes IGN] seuillage d'image [Termes IGN] Uttarakhand (Inde ; état) Résumé : (auteur) It is important to identify and locate glacial lakes for assessing any potential hazard. This study presents a combination of semi-automatic method Double-Window Flexible Pace Search (DFPS) and edge detection technique to identify glacial lakes using Sentinel 2A satellite data. Initially, Normalized Difference Water Index (NDWI) has been used to identify water and non-water areas, while DFPS and Edge detection technique has been used to identify an optimum threshold value to distinguish between water and shadow areas. The optimal threshold from DFPS process is 0.21, while threshold value of gradient magnitude using edge detection process is 0.318. The number of glacial lakes identified using the above algorithm is in close agreement with previously published results on glacial lakes in Gangotri glacier using different techniques. Thus, a combination of DFPS and edge detection process has successfully segregated glacial lakes from other features present in Gangotri glacier. Numéro de notice : A2019-300 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1469677 Date de publication en ligne : 15/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1469677 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93220 [article]

Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data / Siddhartha Khare in Geocarto international, vol 33 n° 7 (July 2018)  

Public [article]  inGeocarto international > vol 33 n° 7 (July 2018) . - pp 681 - 698 Titre : Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data Type de document : Article/Communication Auteurs : Siddhartha Khare, Auteur ; Hooman Latifi, Auteur ; Sanjay Kumar Ghosh, Auteur Année de publication : 2018 Article en page(s) : pp 681 - 698 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] arbre caducifolié [Termes IGN] espèce exotique envahissante [Termes IGN] forêt [Termes IGN] Himalaya [Termes IGN] image Landsat-8 [Termes IGN] image optique [Termes IGN] image Pléiades-HR [Termes IGN] image RapidEye [Termes IGN] réflectance végétale Résumé : (Auteur) We used a full remote sensing-based approach to assess plant species diversity in large and inaccessible areas affected by Lantana camara L., a common invasive species within the deciduous forests of Western Himalayan region of India, using spectral heterogeneity information extracted from optical data. The spread of L. camara was precisely mapped by Pléiades 1A data, followed by comparing Pléiades 1A, RapidEye and Landsat-8 OLI – assessed plant species diversities in invaded areas. The single plant species analysis was improved by Pléiades 1A-based diversity analysis, and higher species diversity values were observed for mixed vegetation cover. Furthermore, lower Coefficient of Variation and Renyi diversity values were observed where L. camara was the only species, while higher variations were observed in areas with a mixed spectral reflectance. This study was concluded to add a crucial baseline to the previous studies on remote sensing-based solutions for rapid estimation of biodiversity attributes. Numéro de notice : A2018-334 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1289562 Date de publication en ligne : 10/02/2017 En ligne : https://doi.org/10.1080/10106049.2017.1289562 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90530 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2018031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach / Priyadarshi Upadhyay in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)  

Public [article]  inGeocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 278 - 295 Titre : Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach Type de document : Article/Communication Auteurs : Priyadarshi Upadhyay, Auteur ; Sanjay Kumar Ghosh, Auteur ; Anil Kumar, Auteur Année de publication : 2016 Article en page(s) : pp 278 - 295 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] blé (céréale) [Termes IGN] bruit rose [Termes IGN] classification automatique [Termes IGN] croissance végétale [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] surveillance agricole Résumé : (Auteur) In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 104 for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely. Numéro de notice : A2016-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047415 Date de publication en ligne : 26/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047415 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80381 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spatial interpolation to predict missing attributes in GIS using semantic kriging / Shrutilipi Bhattacharjee in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)  

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Estimating and accommodating uncertainty through the soft classification of remote sensing data / M.A. Ibrahim in International Journal of Remote Sensing IJRS, vol 26 n° 14 (July 2005)  

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Dynamic problems in satellite tracking simulation : a case study / Sanjay Kumar Ghosh (02/04/1989)

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Photo-scale, map-scale and contour intervals in topographic mapping / Sanjay Kumar Ghosh in Photogrammetria, vol 42 n° 1-2 (November 1987)

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A study of map revision with stereo orthophotos / V. Mahazoasy in Photogrammetria, vol 41 n° 4 (September 1987)

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Panoramic photography / Sanjay Kumar Ghosh (1984)

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Strip photography / Sanjay Kumar Ghosh (1982)

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The 1970 International Symposium on photography and navigation / Sanjay Kumar Ghosh (1979)

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