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Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])  

Public [article]  inGeocarto international > vol 34 n° 14 [30/10/2019] . - pp 1552 - 1567 Titre : Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis Type de document : Article/Communication Auteurs : Divyesh Varade, Auteur ; Anudeep Sure, Auteur ; Onkar Dikshit, Auteur Année de publication : 2019 Article en page(s) : pp 1552 - 1567 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Inde [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) This study proposes the development of a multi-sensor, multi-spectral composite from Landsat-8 and Sentinel-2A imagery referred to as ‘LSC’ for land use land cover (LULC) characterisation and compared with respect to the hyperspectral imagery of the EO1: Hyperion sensor. A three-stage evaluation was implemented based on the similarity observed in the spectral response, supervised classification results and endmember abundance information obtained using linear spectral unmixing. The study was conducted for two areas located around Dhundi and Rohtak in Himachal Pradesh and Haryana, respectively. According to the analysis of the spectral reflectance curves, the spectral response of the LSC is capable of identifying major LULC classes. The kappa accuracy of 0.85 and 0.66 was observed for the classification results from LSC and Hyperion data for Dhundi and Rohtak datasets, respectively. The coefficient of determination was found to be above 0.9 for the LULC classes in both the datasets as compared to Hyperion, indicating a good agreement. Thus, these three-stage results indicated the significant potential of a composite derived from freely available multi-sensor multi-spectral imagery as an alternative to hyperspectral imagery for LULC studies. Numéro de notice : A2019-527 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1497096 date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1497096 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94101 [article]

Evaluation of global geopotential models: a case study for India / Ropesh Goyal in Survey review, vol 51 n° 368 (September 2019)  

Public [article]  inSurvey review > vol 51 n° 368 (September 2019) . - pp 402 - 412 Titre : Evaluation of global geopotential models: a case study for India Type de document : Article/Communication Auteurs : Ropesh Goyal, Auteur ; Onkar Dikshit, Auteur ; Nagarajan Balasubramania, Auteur Année de publication : 2019 Article en page(s) : pp 402 - 412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] Inde [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] modèle de géopotentiel [Termes descripteurs IGN] modèle de géopotentiel local [Termes descripteurs IGN] réseau altimétrique national Résumé : (Auteur) This paper aims to identify most suitable global geopotential model (GGM) for India, by comparing 15 GGMs developed through 1996 to 2017. The GGM derived geoid undulation values are compared with the geometrical undulation values obtained from GNSS/levelling data on Indian vertical datum. A correction term is added to the computed GGM derived geoid undulation value after fitting three-, four-, five- and seven-parameter models to account for bias and tilt between local geometric Indian vertical datum and global gravimetric vertical datum. The results indicate that EGM2008 model is the best GGM available for India with root-mean-square error (RMSE) of 0.28 m, without model fitting. However, after considering the systematic errors in the two datums with seven-parameter model, GECO GGM has shown significantly better results with RMSE of 0.19 m for India. Numéro de notice : A2019-366 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1468537 date de publication en ligne : 11/05/2018 En ligne : https://doi.org/10.1080/00396265.2018.1468537 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93472 [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 descripteurs IGN] changement climatique [Termes descripteurs IGN] glacier [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] lac glaciaire [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] seuillage d'image [Termes descripteurs 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 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]

Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 9 [15/06/2019] . - pp 1022-1041 Titre : Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; A. Choudhary, Auteur ; D. K. Gupta, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1022-1041 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bande C [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] Uttar Pradesh (Inde ; état) Résumé : (auteur) In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R2 (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R2 (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R2 (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models. Numéro de notice : A2019-517 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1464601 date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1464601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93876 [article]

Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans / Tanumi Kumar in Geocarto international, vol 34 n° 4 ([15/03/2019])  

Public [article]  inGeocarto international > vol 34 n° 4 [15/03/2019] . - pp 415 - 442 Titre : Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans Type de document : Article/Communication Auteurs : Tanumi Kumar, Auteur ; Abhishek Mandal, Auteur ; Dibyendu Dutta, Auteur ; R. Nagaraja, Auteur ; Vinay Kumar Dadhwal, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 442 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] Avicennia marina [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification Spectral angle mapper [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] Inde [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] palétuvier Résumé : (Auteur) In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels. Numéro de notice : A2019-451 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1408699 date de publication en ligne : 11/12/2017 En ligne : https://doi.org/10.1080/10106049.2017.1408699 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92839 [article]

An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service / Hariom Singh in Geocarto international, vol 34 n° 2 ([01/02/2019])  

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A multi‐objective framework for analysis of road network vulnerability for relief facility location during flood hazards : A case study of relief location analysis in Bankura District, India / Omprakash Chakraborty in Transactions in GIS, vol 22 n° 5 (October 2018)  

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An open source framework for publishing flood inundation extent libraries in a Web GIS environment using open source technologies / Vinod Kumar Sharma in International journal of cartography, vol 4 n° 1 (March 2018)  

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Identification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques / Tarun Kumar in Geocarto international, vol 32 n° 12 (December 2017)  

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Tracking the relationship between changing skyline and population growth of an Indian megacity using earth observation technology / Joy Sanyal in Geocarto international, vol 32 n° 12 (December 2017)  

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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)  

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Forest canopy height estimation using satellite laser altimetry : a case study in the Western Ghats, India / S.M. Ghosh in Applied geomatics, vol 9 n° 3 (September 2017)  

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Performance evaluation of ionospheric time delay forecasting models using GPS observations at a low-latitude station / G. Sivavaraprasad in Advances in space research, vol 60 n° 2 (15 July 2017)  

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Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)  

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Urban land use/land cover discrimination using image-based reflectance calibration methods for hyperspectral data / Shailesh S. Deshpande in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 5 (May 2017)  

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