ADVANCING FOREST LAND MONITORING IN ISTANBUL REGIONAL DIRECTORATE OF FORESTRY: INTEGRATING U-NET DEEP LEARNING
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| Názov: | ADVANCING FOREST LAND MONITORING IN ISTANBUL REGIONAL DIRECTORATE OF FORESTRY: INTEGRATING U-NET DEEP LEARNING |
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| Autori: | Ergin Çağatay Çankaya, Burhan Gencal, Turan Sönmez |
| Zdroj: | Volume: 7, Issue: 126-44 ArtGRID-Journal of Architecture Engineering and Fine Arts |
| Informácie o vydavateľovi: | ArtGRID - Journal of Architecture Engineering and Fine Arts, 2025. |
| Rok vydania: | 2025 |
| Predmety: | Artificial Intelligence (Other), Forest Management, Remote Sensing, Deep Learning, Istanbul, U-Net, Fotogrametri ve Uzaktan Algılama, Modelling and Simulation, Yapay Zeka (Diğer), Photogrammetry and Remote Sensing, Modelleme ve Simülasyon, Orman Amenajmanı, Uzaktan Algılama, Derin Öğrenme, İstanbul |
| Popis: | This study presents a comprehensive analysis of land use and land cover change within the Istanbul Regional Directorate of Forestry (RDF) utilizing semantic segmentation referred to as pixel-based classification. Focusing particularly on forest land dynamics, Sentinel-2 satellite imagery spanning five years from 2019 to 2023 was processed using a U-Net architecture. The study area encompasses diverse forest ecosystems, urban/built-up areas, water bodies, rangelands, wetlands, and agricultural lands. Through the application of advanced remote sensing techniques, significant changes in forest and rangeland were identified and quantified, 15.250 and 13.226 hectares of area decreased in five years, shedding light on the drivers and implications of land use transformations in this critical region. Controversially, built area and agricultural lands were increased by 13.878 and 15.953 hectares over 5 years. The findings contribute to a deeper understanding of forest dynamics and inform sustainable management strategies for preserving the ecological integrity and socio-economic value of forested landscapes within the Istanbul RDF. Additionally, the results reveal the average F-1 Score for each land cover class is approximately 90% for each year, with forested areas achieving an average F-1 score of about 92%, demonstrating the robustness and accuracy of the classification approach. |
| Druh dokumentu: | Article |
| Popis súboru: | application/pdf |
| ISSN: | 2717-879X |
| DOI: | 10.57165/artgrid.1709260 |
| Prístupová URL adresa: | https://dergipark.org.tr/tr/pub/artgrid/issue/93375/1709260 |
| Prístupové číslo: | edsair.doi.dedup.....f4658e41fa000ceb5f8246f902b24b2c |
| Databáza: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | This study presents a comprehensive analysis of land use and land cover change within the Istanbul Regional Directorate of Forestry (RDF) utilizing semantic segmentation referred to as pixel-based classification. Focusing particularly on forest land dynamics, Sentinel-2 satellite imagery spanning five years from 2019 to 2023 was processed using a U-Net architecture. The study area encompasses diverse forest ecosystems, urban/built-up areas, water bodies, rangelands, wetlands, and agricultural lands. Through the application of advanced remote sensing techniques, significant changes in forest and rangeland were identified and quantified, 15.250 and 13.226 hectares of area decreased in five years, shedding light on the drivers and implications of land use transformations in this critical region. Controversially, built area and agricultural lands were increased by 13.878 and 15.953 hectares over 5 years. The findings contribute to a deeper understanding of forest dynamics and inform sustainable management strategies for preserving the ecological integrity and socio-economic value of forested landscapes within the Istanbul RDF. Additionally, the results reveal the average F-1 Score for each land cover class is approximately 90% for each year, with forested areas achieving an average F-1 score of about 92%, demonstrating the robustness and accuracy of the classification approach. |
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| ISSN: | 2717879X |
| DOI: | 10.57165/artgrid.1709260 |