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Estimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image / Sinan Kaptan in Geocarto international, vol 37 n° 5 ([01/03/2022])  

Public [article]  inGeocarto international > vol 37 n° 5 [01/03/2022] . - pp 1408 - 1425 Titre : Estimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image Type de document : Article/Communication Auteurs : Sinan Kaptan, Auteur ; Hasan Aksoy, Auteur Année de publication : 2022 Article en page(s) : pp 1408 - 1425 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification dirigée [Termes IGN] correction géométrique [Termes IGN] forêt inéquienne [Termes IGN] houppier [Termes IGN] image Landsat-OLI [Termes IGN] occupation du sol [Termes IGN] peuplement forestier [Termes IGN] Turquie [Termes IGN] utilisation du sol Résumé : (Auteur) This study used the Landsat 8 OLI satellite image and the supervised classification method to estimate uneven-aged forest stand parameters and land use/cover. The spatial success of classification was also investigated. The overall success rates and Kappa values of the classification were, respectively, 74.7% and 0.75 for actual structural type, 84.6% and 0.80 for crown closure, and 88.35% and 0.81 for land use class, whereas the spatial success of classification on the forest cover type map was 36.91% for actual structural type, 64.74% for crown closure, and 41.78% for land use/cover class. The results revealed that the Landsat 8 OLI image can be used to identify stand parameters and land use/cover class. However, because the spatial success rates were below 50% for the actual structural type and land use/cover class of the stand types, it is not suitable for use in spatial classification determination for these classes. Numéro de notice : A2022-277 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1765888 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1080/10106049.2020.1765888 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100795 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
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Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])  

Public [article]  inGeocarto international > vol 37 n° 4 [15/02/2022] . - pp 1183 - 1202 Titre : Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model Type de document : Article/Communication Auteurs : Hasan Aksoy, Auteur ; Sinan Kaptan, Auteur Année de publication : 2022 Article en page(s) : pp 1183 - 1202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] automate cellulaire [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] gestion forestière [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-TM [Termes IGN] modèle de Markov [Termes IGN] occupation du sol [Termes IGN] surface cultivée [Termes IGN] surface forestière [Termes IGN] Turquie [Termes IGN] utilisation du sol Résumé : (auteur) This study aimed to simulate and assess forest cover and land use/land cover (LULC) changes between 2019 and 2039 using the cellular automata-Markov model. The performance of the model was evaluated by comparing the 2019 simulation map with the 2019 supervised classified map, and it was found to be reliable, with a similarity rate of 85.43%. The LULC analysis and estimates were carried out for a total of six classes: coniferous, broad-leaf, mixed forest, settlement, water and agriculture. Between 1999 and 2019, the areas of total forest increased by 17.4%, settlement by 84.6% and water by 20.1%, whereas the agriculture area decreased by 33.2%. According to 2019‒2039 land use/cover simulation results, there were decreases of 2.4% in total forest area and 3.7% in residential and water surface areas, but a 6.9% decrease in the agriculture class. Tracking these changes will contribute to decision making and strategy development efforts of forest planners and managers. Numéro de notice : A2022-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1778102 Date de publication en ligne : 22/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1778102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100691 [article]