Centre de documentation
scientifique

Dépouillements

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]

The impact of landscape characteristics on the performance of upscaled maps / Peijun Sun in Geocarto international, vol 36 n° 17 ([15/09/2021])  

Public [article]  inGeocarto international > vol 36 n° 17 [15/09/2021] . - pp 1905 - 1922 Titre : The impact of landscape characteristics on the performance of upscaled maps Type de document : Article/Communication Auteurs : Peijun Sun, Auteur ; Russell G. Congalton, Auteur Année de publication : 2021 Article en page(s) : pp 1905 - 1922 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] analyse du paysage [Termes IGN] carte de base [Termes IGN] logiciel de simulation [Termes IGN] mise à l'échelle [Termes IGN] paysage [Termes IGN] précision cartographique Résumé : (auteur) Upscaled maps, as necessary data sources, have drawn much attention to fill data gaps or match the spatial resolution of pre-existing projects. Nevertheless, it remains a challenging task to quantitatively assess the impact of landscape characteristics on the upscaled maps. To simplify the investigation, three characteristics: fragmentation, number of classes and major class impact factor (MCIF), were selected. We utilized SIMMAP to produce categorical maps for generating base maps with different landscape characteristics. The Majority Rule Based algorithm was then used to produce upscaled maps at 11 different spatial resolutions. The findings indicate that the combined effect of landscape patterns greatly impacts upscaling accuracy. This important result should be carefully considered when developing the next generation of upscaling techniques. Overall, extending our understanding of the impacts of landscape characteristics is a critical step forward improving upscaling accuracy and therefore, our use of these maps. Numéro de notice : A2021-693 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1678681 Date de publication en ligne : 18/10/2019 En ligne : https://doi.org/10.1080/10106049.2019.1678681 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98511 [article]