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GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya / Satish Kumar in Geocarto international, vol 32 n° 11 (November 2017)  

Public [article]  inGeocarto international > vol 32 n° 11 (November 2017) . - pp 1254 - 1267 Titre : GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya Type de document : Article/Communication Auteurs : Satish Kumar, Auteur ; Pankaj Kumar Srivastava, Auteur ; Snehmani, Auteur Année de publication : 2017 Article en page(s) : pp 1254 - 1267 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] analyse multicritère [Termes IGN] avalanche [Termes IGN] cartographie des risques [Termes IGN] Himalaya [Termes IGN] image Landsat-8 [Termes IGN] image Terra-ASTER [Termes IGN] Inde [Termes IGN] outil d'aide à la décision [Termes IGN] plan de prévention des risques [Termes IGN] processus de hiérarchisation analytique [Termes IGN] risque naturel [Termes IGN] vulnérabilité Résumé : (Auteur) Avalanches are behind the majority of fatalities and heavy damage to property in snow-covered mountainous terrain like Himalaya. Recognizing avalanche susceptible areas and publication of avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk. The present study is an attempt to prepare an avalanche susceptibility map of the Nubra valley region using multi-criteria decision analysis–analytical hierarchy process model in GIS environment. The most prominent avalanche occurrence factors used in this model are slope, aspect, curvature, elevation, terrain roughness and ground cover. ASTER GDEM V2 and Landsat 8 satellite imagery were used to generate considered factors. For validation of the results, prediction rate/accuracy is calculated using the avalanche inventory map of documented avalanche locations. To calculate the prediction accuracy, area under the ROC curve (ROC-AUC) method has been used. The prediction accuracy of the validation results using ROC-AUC shows 91%. Numéro de notice : A2017-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1206626 Date de publication en ligne : 13/07/2016 En ligne : https://doi.org/10.1080/10106049.2016.1206626 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87157 [article]

Interferometric SAR for characterization of ravines as a function of their density, depth, and surface cover / R.S. Chatterjee in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 5 (September - October 2009)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 64 n° 5 (September - October 2009) . - pp 472 - 481 Titre : Interferometric SAR for characterization of ravines as a function of their density, depth, and surface cover Type de document : Article/Communication Auteurs : R.S. Chatterjee, Auteur ; S. Saha, Auteur ; Satish Kumar, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 472 - 481 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] caractérisation [Termes IGN] érosion hydrique [Termes IGN] image ERS-SAR [Termes IGN] image radar moirée [Termes IGN] Inde [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] profondeur Résumé : (Auteur) In recent years, the problem of ravine erosion with consequent loss of usable land has received much attention worldwide. The Chambal ravine zone in India is well known for being an extremely intricate, deeply incised network of ravines in a 10 km wide zone on the flanks of the Chambal River. It occupies an area of not, vert, similar0.5 million hectares at the expense of fertile agricultural land of the Chambal Valley. The broad grouping of the ravines considering their reclamation potential, as carried out by previous workers based on visual interpretation of optical remote sensing data, is mostly descriptive in nature. In the present study, characterization of the ravines as a function of their erosion potential expressed through ravine density, ravine depth, and ravine surface cover was made in quantitative terms exploiting the preferential characteristics of side-looking, long-wavelength, coherent SAR signal and precision measurements associated with the InSAR technique. The outlines of ravines appear remarkably prominent in SAR backscattered amplitude images due to the high sensitivity of the SAR signal to terrain ruggedness. Using local statistics-based meso and macro textural information of SAR backscattered amplitude images in 7*7 pixel windows (the pixel size being 20 m*20 m), the ravine-affected area has been classified into three density classes, namely low, moderate, and high density ravine classes. C-band InSAR digital elevation models (DEMs) of sparsely vegetated ravine areas essentially give the terrain height. From the pixel-by-pixel terrain height, the ravine depth was calculated by differencing the maximum and minimum terrain heights of the pixels in a 100 m distance range. Considering the vertical precision of the ERS InSAR DEMs of not, vert, similar5 m and ravine depth classification by previous workers [Sharma, H.S., 1968. Genesis and pattern of ravines of the Lower Chambal Valley, India. Special Issue. 21st International Geographical Union Congress 30(4), 14–24; Seth, S.P., Bhatnagar, R.K., Chauhan, S.S., 1969. Reclamability classification and nature of ravines of Chambal Command Areas. Journal of Soil and Water Conservation in India 17 (3–4), 39–44.], three depth classes, namely shallow (20 m) ravines, were made. Using the temporal decorrelation property of the close time interval InSAR data pair, namely the ERS SAR tandem pair, four ravine surface cover classes, namely barren land, grass/scrub/crop land, sparse vegetation, and wet land/dense vegetation, could be delineated, which was corroborated by the spectral signatures in the optical range and selective ground truths. Copyright ISPRS Numéro de notice : A2009-400 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2008.12.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2008.12.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30031 [article]

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081-09051	SL	Revue	Centre de documentation	Revues en salle	Disponible

Global fire monitoring: use of MODIS near-real-time satellite data / D.J. Davies in GIM international, vol 18 n° 4 (April 2004)

Public [article]  inGIM international > vol 18 n° 4 (April 2004) . - pp 41 - 43 Titre : Global fire monitoring: use of MODIS near-real-time satellite data Type de document : Article/Communication Auteurs : D.J. Davies, Auteur ; Satish Kumar, Auteur ; J. Descloitres, Auteur Année de publication : 2004 Article en page(s) : pp 41 - 43 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] gaz [Termes IGN] image Terra-MODIS [Termes IGN] incendie [Termes IGN] risque naturel [Termes IGN] surveillance météorologique Résumé : (Auteur) Global fire detection, derived from MODIS data, is available on the Internet approximately 4-6 hours after satellite overpass through a partnership between the NASA Goddard Space Flight Centre and the University of Maryland. This active fire data is provided as overlays on MODIS true colour imagery and through a series of searchable, interactive web-mapping sites. This article provides a description of MODIS Rapid Response and Web Fire Mapper, the key components of this collaborative effort. Numéro de notice : A2004-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26673 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
061-04041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Best-bases feature extraction algorithms for classification of hyperspectral data / Satish Kumar in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1368 - 1379 Titre : Best-bases feature extraction algorithms for classification of hyperspectral data Type de document : Article/Communication Auteurs : Satish Kumar, Auteur ; J. Ghosh, Auteur ; Melba M. Crawford, Auteur Année de publication : 2001 Article en page(s) : pp 1368 - 1379 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) Due to advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in hundreds of bands. Algorithms that both reduce the dimensionality of the data sets and handle highly correlated bands are required to exploit the information in these data sets effectively. the authors propose a set of best-bases feature extraction algorithms that are simple, fast, and highly effective for classification of hyperspectral data. These techniques intelligently combine subsets of adjacent bands into a smaller number of features. Both top-down and bottom-up algorithms are proposed. The top-down algorithm recursively partitions the bands into two (not necessarily equal) sets of bands and then replaces each final set of bands by its mean value. The bottom-up algorithm builds an agglomerative tree by merging highly correlated adjacent bands and projecting them onto their Fisher direction, yielding high discrimination among classes. Both these algorithms are used in a pairwise classifier framework where the original C-class problem is divided into a set of (2C) two-class problems. The new algorithms (1) find variable length bases localized in wavelength, (2) favor grouping highly correlated adjacent bands that, when merged either by taking their mean or Fisher linear projection, yield maximum discrimination, and (3) seek orthogonal bases for each of the (2C) two-class problems into which a C-class problem can be decomposed. Experiments on an AVIRIS data set for a 12-class problem show significant improvements in classification accuracies while using a much smaller number of features Numéro de notice : A2001-197 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934070 En ligne : https://ieeexplore.ieee.org/document/934070 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21891 [article]

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065-01071	RAB	Revue	Centre de documentation	En réserve L003	Disponible