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Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 375 - 396 Titre : Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis Type de document : Article/Communication Auteurs : Marta Sapena, Auteur ; Luis Angel Ruiz, Auteur Année de publication : 2021 Article en page(s) : pp 375 - 396 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse discriminante [Termes IGN] analyse spatio-temporelle [Termes IGN] carte d'occupation du sol [Termes IGN] carte d'utilisation du sol [Termes IGN] changement d'occupation du sol [Termes IGN] croissance urbaine [Termes IGN] distance euclidienne [Termes IGN] modèle de croissance végétale [Termes IGN] pondération Résumé : (auteur) The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas. Numéro de notice : A2021-040 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1817463 date de publication en ligne : 08/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1817463 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96752 [article]

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)  

Public [article]  inGeocarto international > vol 33 n° 5 (May 2018) . - pp 443 - 457 Titre : An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery Type de document : Article/Communication Auteurs : Luis Angel Ruiz, Auteur ; Jorge Abel Recio, Auteur ; Pablo Crespo-Peremarch, Auteur ; Marta Sapena, Auteur Année de publication : 2018 Article en page(s) : pp 443 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arbre de décision [Termes IGN] biomasse (combustible) [Termes IGN] carte forestière [Termes IGN] classification barycentrique [Termes IGN] classification orientée objet [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt méditerranéenne [Termes IGN] image Sentinel-MSI [Termes IGN] image Worldview [Termes IGN] modèle de simulation [Termes IGN] structure d'un peuplement forestier Résumé : (Auteur) Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata. Numéro de notice : A2018-140 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265595 date de publication en ligne : 28/11/2016 En ligne : https://doi.org/10.1080/10106049.2016.1265595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89690 [article]