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Partager le résultat de cette rechercheLand cover and vegetation carbon stock changes in Greece: a 29-year assessment based on CORINE and Landsat land cover data / A. Gemitzi in Science of the Total Environment, vol. 786 (10 September 2021)
Titre : Land cover and vegetation carbon stock changes in Greece: a 29-year assessment based on CORINE and Landsat land cover data Type de document : objet à 3 dimensions, artefacts, ... Auteurs : A. Gemitzi ; F. Kratouna ; R. Albarakat ; A. Gemitzi ; V. Lakshmi Année de publication : 2021 Article en page(s) : p. 1-12 Langues : Anglais (eng) Langues originales : Anglais (eng) Catégories : A HISTOIRE - Pays et ensemble de pays:Histoire de l'Europe:Europe occidentale:Grèce ; F POPULATIONS - ETUDES DE CAS:D SOCIOLOGIE - ETHNOLOGIE - ANTHROPOLOGIE :4.45 Etablissements humains et utilisation des terres:Utilisation des terres ; S SCIENCES ET TECHNIQUES:Pollution, catastrophes et sécurité:Dégradation de l'environnement:Changement climatique ; S SCIENCES ET TECHNIQUES:Sciences de la chimie:Élément chimique:Carbone Mots-clés : 07 - ENVIRONNEMENT 7.1 - Généralités. Situation Environnementale LAND COVER COUVERTURE DU SOL LAND USE CARBON CARBON SEQUESTRATION SEQUESTRATION DU CARBONE CLIMATIC CHANGE GREECE Résumé : Evaluation of carbon sequestration in various land cover types is a valuable tool for environmental policies targeting towards minimization of CO2 emissions and climate change impacts. For the past few decades, remotely sensed information on land cover has been used as useful alternative to ground observations and has proved to be a robust tool for studying land use / land cover (LULC) changes. The present work deals with the assessment of land-cover changes in a Mediterranean country - Greece, where expected climate change impacts and desertification risk are stated to be severe. This work focused on the CORINE land cover inventory at a spatial resolution of 100 m from 1990 to 2018 and selected Landsat images at 30 m spatial resolution for 1990, 2000 and 2018. Results indicated that the dominant land-cover changes in Greece over the predefined 29-year period, are related to land transformation from Non-irrigated arable land to Irrigated areas, implying an intensification of agricultural practices. Natural grasslands lose a substantial part of their areas transforming into Sclerophyllus vegetation and Sparsely vegetated areas. Forests gain areas from Transitional woodland-shrub and Olive groves increase their extent indicating an overall transition to woody vegetation. Estimation of Vegetation Carbon Stocks indicated a moderate decrease in the 1990 decade followed by a significant increase up to 2012 and a slight decrease thereafter. Forests of all types are by far the most important carbon sinks. Possible implications of country's recent economic crisis were examined and results indicated that economic welfare of the country seems to favor certain land cover types such as Mixed Forests and Permanently Irrigated land, but also preservation of the Vegetation Carbon Stocks. En ligne : https://doi.org/10.1016/j.scitotenv.2021.147408 Permalink : https://cs.iut.univ-tours.fr/index.php?lvl=notice_display&id=272793
in Science of the Total Environment > vol. 786 (10 September 2021) . - p. 1-12[article] Land cover and vegetation carbon stock changes in Greece: a 29-year assessment based on CORINE and Landsat land cover data [objet à 3 dimensions, artefacts, ...] / A. Gemitzi ; F. Kratouna ; R. Albarakat ; A. Gemitzi ; V. Lakshmi . - 2021 . - p. 1-12.
Langues : Anglais (eng) Langues originales : Anglais (eng)
in Science of the Total Environment > vol. 786 (10 September 2021) . - p. 1-12
Catégories : A HISTOIRE - Pays et ensemble de pays:Histoire de l'Europe:Europe occidentale:Grèce ; F POPULATIONS - ETUDES DE CAS:D SOCIOLOGIE - ETHNOLOGIE - ANTHROPOLOGIE :4.45 Etablissements humains et utilisation des terres:Utilisation des terres ; S SCIENCES ET TECHNIQUES:Pollution, catastrophes et sécurité:Dégradation de l'environnement:Changement climatique ; S SCIENCES ET TECHNIQUES:Sciences de la chimie:Élément chimique:Carbone Mots-clés : 07 - ENVIRONNEMENT 7.1 - Généralités. Situation Environnementale LAND COVER COUVERTURE DU SOL LAND USE CARBON CARBON SEQUESTRATION SEQUESTRATION DU CARBONE CLIMATIC CHANGE GREECE Résumé : Evaluation of carbon sequestration in various land cover types is a valuable tool for environmental policies targeting towards minimization of CO2 emissions and climate change impacts. For the past few decades, remotely sensed information on land cover has been used as useful alternative to ground observations and has proved to be a robust tool for studying land use / land cover (LULC) changes. The present work deals with the assessment of land-cover changes in a Mediterranean country - Greece, where expected climate change impacts and desertification risk are stated to be severe. This work focused on the CORINE land cover inventory at a spatial resolution of 100 m from 1990 to 2018 and selected Landsat images at 30 m spatial resolution for 1990, 2000 and 2018. Results indicated that the dominant land-cover changes in Greece over the predefined 29-year period, are related to land transformation from Non-irrigated arable land to Irrigated areas, implying an intensification of agricultural practices. Natural grasslands lose a substantial part of their areas transforming into Sclerophyllus vegetation and Sparsely vegetated areas. Forests gain areas from Transitional woodland-shrub and Olive groves increase their extent indicating an overall transition to woody vegetation. Estimation of Vegetation Carbon Stocks indicated a moderate decrease in the 1990 decade followed by a significant increase up to 2012 and a slight decrease thereafter. Forests of all types are by far the most important carbon sinks. Possible implications of country's recent economic crisis were examined and results indicated that economic welfare of the country seems to favor certain land cover types such as Mixed Forests and Permanently Irrigated land, but also preservation of the Vegetation Carbon Stocks. En ligne : https://doi.org/10.1016/j.scitotenv.2021.147408 Permalink : https://cs.iut.univ-tours.fr/index.php?lvl=notice_display&id=272793
Titre : Machine learning in modelling land-use and land cover-change (LULCC): current status, challenges and prospects Type de document : objet à 3 dimensions, artefacts, ... Auteurs : J. Wang ; M.A. Delavar ; M.A.A. Dewan ; M. Bretz Année de publication : 2022 Article en page(s) : p. 1-17 Langues : Anglais (eng) Langues originales : Anglais (eng) Catégories : F POPULATIONS - ETUDES DE CAS:D SOCIOLOGIE - ETHNOLOGIE - ANTHROPOLOGIE :4.45 Etablissements humains et utilisation des terres:Utilisation des terres ; F POPULATIONS - ETUDES DE CAS:Models ; F POPULATIONS - ETUDES DE CAS:Natural resources management ; J CULTURE - ART - LOISIRS - ANIMATION:J.26 Technologie de l'information (logiciels):Traitement des données:Codage:Télédétection An advanced system of information gathering to monitor and forecast developments on the surface of the earth and identify an area's natural resources by looking at the world from aircraft, balloons, or satellites and evaluating the data gathered.Mots-clés : 07 - ENVIRONNEMENT 7.4 - Ressources Naturelles : Paysage, Biodiversité, Patrimoine naturel LAND USE LAND COVER COUVERTURE DU SOL MODELE REMOTE SENSING RESEAU DE NEURONES GESTION DES RESSOURCES NATURELLES APPRENTISSAGE AUTOMATIQUE Résumé : Land-use and land-cover change (LULCC) are of importance in natural resource management, environmental modelling and assessment, and agricultural production management. However, LULCC detection and modelling is a complex, data-driven process in the remote sensing field due to the processing of massive historical and current data, real-time interaction of scenario data, and spatial environmental data. In this paper, we review principles and methods of LULCC modelling, using machine learning and beyond, such as traditional cellular automata (CA). Then, we examine the characteristics, capabilities, limitations, and perspectives of machine learning. Machine learning has not yet been dramatic in modelling LULCC, such as urbanization prediction and crop yield prediction because competition and transition between land cover types are dynamic at a local scale under varying natural drivers and human activities. Upcoming challenges of machine learning in modelling LULCC remain in the detection and prediction of LULC evolutionary processes if considering their applicability and feasibility, such as the spatio-temporal transition mechanisms to describe occurrence, transition, spreading, and spatial patterns of changes, availability of training data of all the change drivers, particularly sequence data, and identification and inclusion of local ecological, hydrological, and social-economic drivers in addressing the spectral feature change. This review points out the need for multidisciplinary research beyond image processing and pattern recognition of machine learning in accelerating and advancing studies of LULCC modelling. Despite this, we believe that machine learning has strong potentials to incorporate new exploratory variables in modelling LULCC through expanding remote sensing big data and advancing transient algorithms. En ligne : https://doi.org/10.1016/j.scitotenv.2022.153559 Permalink : https://cs.iut.univ-tours.fr/index.php?lvl=notice_display&id=284595
in Science of the Total Environment > vol. 822 (20 May 2022) . - p. 1-17[article] Machine learning in modelling land-use and land cover-change (LULCC): current status, challenges and prospects [objet à 3 dimensions, artefacts, ...] / J. Wang ; M.A. Delavar ; M.A.A. Dewan ; M. Bretz . - 2022 . - p. 1-17.
Langues : Anglais (eng) Langues originales : Anglais (eng)
in Science of the Total Environment > vol. 822 (20 May 2022) . - p. 1-17
Catégories : F POPULATIONS - ETUDES DE CAS:D SOCIOLOGIE - ETHNOLOGIE - ANTHROPOLOGIE :4.45 Etablissements humains et utilisation des terres:Utilisation des terres ; F POPULATIONS - ETUDES DE CAS:Models ; F POPULATIONS - ETUDES DE CAS:Natural resources management ; J CULTURE - ART - LOISIRS - ANIMATION:J.26 Technologie de l'information (logiciels):Traitement des données:Codage:Télédétection An advanced system of information gathering to monitor and forecast developments on the surface of the earth and identify an area's natural resources by looking at the world from aircraft, balloons, or satellites and evaluating the data gathered.Mots-clés : 07 - ENVIRONNEMENT 7.4 - Ressources Naturelles : Paysage, Biodiversité, Patrimoine naturel LAND USE LAND COVER COUVERTURE DU SOL MODELE REMOTE SENSING RESEAU DE NEURONES GESTION DES RESSOURCES NATURELLES APPRENTISSAGE AUTOMATIQUE Résumé : Land-use and land-cover change (LULCC) are of importance in natural resource management, environmental modelling and assessment, and agricultural production management. However, LULCC detection and modelling is a complex, data-driven process in the remote sensing field due to the processing of massive historical and current data, real-time interaction of scenario data, and spatial environmental data. In this paper, we review principles and methods of LULCC modelling, using machine learning and beyond, such as traditional cellular automata (CA). Then, we examine the characteristics, capabilities, limitations, and perspectives of machine learning. Machine learning has not yet been dramatic in modelling LULCC, such as urbanization prediction and crop yield prediction because competition and transition between land cover types are dynamic at a local scale under varying natural drivers and human activities. Upcoming challenges of machine learning in modelling LULCC remain in the detection and prediction of LULC evolutionary processes if considering their applicability and feasibility, such as the spatio-temporal transition mechanisms to describe occurrence, transition, spreading, and spatial patterns of changes, availability of training data of all the change drivers, particularly sequence data, and identification and inclusion of local ecological, hydrological, and social-economic drivers in addressing the spectral feature change. This review points out the need for multidisciplinary research beyond image processing and pattern recognition of machine learning in accelerating and advancing studies of LULCC modelling. Despite this, we believe that machine learning has strong potentials to incorporate new exploratory variables in modelling LULCC through expanding remote sensing big data and advancing transient algorithms. En ligne : https://doi.org/10.1016/j.scitotenv.2022.153559 Permalink : https://cs.iut.univ-tours.fr/index.php?lvl=notice_display&id=284595
