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Markov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image / L. K. Tiwari in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)  

Public [article]  inGeocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 428 - 445 Titre : Markov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image Type de document : Article/Communication Auteurs : L. K. Tiwari, Auteur ; S.K. Sinha, Auteur ; S. Saran, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 428 - 445 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte d'occupation du sol [Termes IGN] champ aléatoire de Markov [Termes IGN] changement d'utilisation du sol [Termes IGN] classification par maximum de vraisemblance [Termes IGN] forêt Résumé : (Auteur) Forest encroachment (FE) is a problem in Andaman and Nicobar Islands (ANI) in India for environment and planning. Small gaps created in the forest slowly expand its periphery disturbing the biodiversity. Therefore, intrusion of poachers, slash and burn and other factors causing FE must be carefully detected and monitored. Remote sensing offers a great opportunity to accomplish this task because of its synoptic view. Conventional classification methods with remotely sensed images are problematic because of small size of FE and mixed landcover composition. This study presents an application of super-resolution mapping (SRM) based on Markov random field for detection of FE using ASTER (15 m) images. The SRM results were validated using multispectral IRS LISS-IV (5.8 m) image. Non-contiguous FE patches of various sizes and shapes are characterized using the spatial contextual information. The novelty of this approach lies in the identification and separability of small FE pockets which could not be achieved with pixel-based maximum likelihood classifier (MLC). The SRM parameters were optimized and found comparable to previous studies. Classification accuracy obtained with SRM at scale factor 3 is κ = 0.62 that is superior to accuracy of MLC (κ = 0.51). SRM is a promising tool for detection and monitoring of FE at Rutland Island in ANI, India. Numéro de notice : A2016-157 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1054441 Date de publication en ligne : 01/07/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1054441 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80401 [article]

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