ADVANCING FOREST LAND MONITORING IN ISTANBUL REGIONAL DIRECTORATE OF FORESTRY: INTEGRATING U-NET DEEP LEARNING
Öz
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.
Anahtar Kelimeler
Forest Management Remote Sensing Deep Learning Istanbul U-Net
Kaynakça
- Adepoju, M. O., Millington, A. C., & Tansey, K. T. (2006). Land use/land cover change detection in metropolitan Lagos (Nigeria): 1984–2002. P 1-5 in ASPRS 2006 Annual Conference Reno, Nevada May.
- Atalay, I., Efe, R., & Öztürk, M. (2014). Ecology and classification of forests in Türkiye. Procedia-Social and Behavioral Sciences, 120, 788-805. https://doi.org/10.1016/j.sbspro.2014.02.163
- Atmis, E., Günsen, B. H., Yücedağ, C., & Lise, W. (2012). Status, use and management of urban forests in Türkiye. South-east European Forestry: SEEFOR, 3(2), 69-78. https://doi.org/10.15177/seefor.12-08
- Atmis, E., & Cil, A. (2013). Sustainable forestry in Türkiye. Journal of Sustainable Forestry, 32(4), 354-364. https://doi.org/10.1080/10549811.2013.767210
- Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. Remote Sensing of Environment, 127, 210-222. https://doi.org/10.1016/j.rse.2012.09.011
- Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing, 114, 24-31.
- Bozkurt, S. G., Kuşak, L., & Akkemik, Ü. (2023). Investigation of land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems in Istanbul (Türkiye) metropolitan area between 1990 and 2018. Environmental Monitoring and Assessment, 195(1), 196. https://doi.org/10.1007/s10661-023-10975-7
- Cui, L., & Shi, J. (2012). Urbanization and its environmental effects in Shanghai, China. Urban Climate, 2, 1-15. https://doi.org/10.1016/j.uclim.2012.10.008
- Diakogiannis, F. I., Waldner, F., Caccetta, P., & Wu, C. (2020). ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94-114. https://doi.org/10.1016/j.isprsjprs.2020.01.013
- ESA. (2023, June). Copernicus Open Access Hub. https://scihub.copernicus.eu/dhus/#/home Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote sensing of environment, 80(1), 185-201.
- Farmer, E., Jones, S., Clarke, M., Buxton, L., Soto-Berelov, M., Page, S., Mellor, A., & Haywood, A. (2013). Creating a large area landcover dataset for public land monitoring and reporting. In Geospatial Science Research Symposium (pp. 116-132), Melbourne, Australia.
- General Directorate of Forestry. (2021). Türkiye Orman Varlığı (Forest Assets of Türkiye). General Directorate of Forestry, Ministry of Environment and Forest, Ankara, Türkiye.
- Gökburun, İ. (2017). İstanbul’da Nüfusun Gelişimi ve İlçelere Dağılımı (1950–2015). Journal of Anatolian Cultural Research (JANCR), 1(3), 110-130. https://ankad.org/index.php/ankad/article/view/27
- Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric Engineering and Remote Sensing, 60(3), 331-337.
- Günsen, H. B., & Atmiş, E. (2019). Analysis of forest change and deforestation in Türkiye. International Forestry Review, 21(2), 182-194. https://doi.org/10.1505/146554819826606577
- Hame, T. H. (1986). Satellite image aided change detection. Remote Sensing-Aided Forest Inventory, Research Notes, 19, 47-60.
- Hayati, Z., Yeşil, A., Asan, U., Bettinger, P., Cieszewski, C., & Siry, J. P. (2013). Evolution of modern forest management planning in the Republic of Türkiye. Journal of Forestry, 111(4), 239-248. https://doi.org/10.5849/jof.11-103
- Jensen, J. R., & Lulla, K. (1987). Introductory digital image processing: A remote sensing perspective. Geocarto International, 2(1), 65. https://doi.org/10.1080/10106048709354084
- Jin, Y., Liu, X., Chen, Y., & Liang, X. (2018). Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong. International journal of remote sensing, 39(23), 8703-8723.
- Kilic, S., Evrendilek, F., Berberoglu, S., & Demirkesen, A. (2006). Environmental monitoring of land-use and land-cover changes in a Mediterranean region of Türkiye. Environmental Monitoring and Assessment, 114, 157-168. https://doi.org/10.1007/s10661-006-2525-z
- Macleod, R. D., & Congalton, R. G. (1998). A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 64(3), 207-216.
- Maragos, P., & Pessoa, L. (1999). Morphological filtering for image enhancement and detection. In The Image and Video Processing Handbook (pp. 135-156).
- R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org.
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference Proceedings (pp. 234-241). Springer International Publishing.
- Ruiz-Luna, A., & Berlanga-Robles, C. A. (2003). Land use, land cover changes and coastal lagoon surface reduction associated with urban growth in northwest Mexico. Landscape Ecology, 18(2), 159-171. https://doi.org/10.1023/A:1024461215456
- Schlamadinger, B., Bird, N., Johns, T., Ward, M., & Yamagata, Y. (2007). A synopsis of land use, land-use change and forestry (LULUCF) under the Kyoto Protocol and Marrakech Accords. Environmental Science & Policy, 10(4), 271-282. https://doi.org/10.1016/j.envsci.2006.11.002
- Solórzano, J. V., Mas, J. F., Gao, Y., & Gallardo-Cruz, J. A. (2021). Land use land cover classification with U-Net: Advantages of combining Sentinel-1 and Sentinel-2 imagery. Remote Sensing, 13(18), 3600. https://doi.org/10.3390/rs13183600
- Tolunay, D., Karabiyik, B., & Temerit, A. (2011). First results of a nation-wide systematic forest condition survey in Türkiye. iForest-Biogeosciences and Forestry, 4(3), 145. https://doi.org/10.3832/ifor0567-004
- Voelsen, M., Bostelmann, J., Maas, A., Rottensteiner, F., & Heipke, C. (2020). Automatically generated training data for land cover classification with CNNs using Sentinel-2 images. ISPRS Archives, 43(B3), 767-774. https://doi.org/10.15488/10821
- Watson, R., Noble, I. R., Bolin, B., Ravindranath, N. H., Verardo, D. J., & Dokken, D. J. (2000). Land Use, Land-Use Change, and Forestry: A Special Report. Cambridge University Press.
- Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., & Gao, Y. (2019). Automatic land-cover mapping using Landsat time-series data based on Google Earth Engine. Remote Sensing, 11(24), 3023. https://doi.org/10.3390/rs11243023
- Yin, J., Zhong, H., Xu, S., Hu, X., Wang, J., & Wu, J. (2011). Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China. Environmental Monitoring and Assessment, 177, 609-621. https://doi.org/10.1007/s10661-010-1660-8
- Yuan, X., Shi, J., & Gu, L. (2021). A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Systems with Applications, 169, 114417. https://doi.org/10.1016/j.eswa.2020.114417
- Zengin, H., Yeşil, A., Asan, U., Bettinger, P., Cieszewski, C., & Siry, J. P. (2013). Evolution of modern forest management planning in the Republic of Turkey. Journal of Forestry, 111, 239-248.
- Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE geoscience and remote sensing magazine, 5(4), 8-36.
İSTANBUL ORMAN BÖLGE MÜDÜRLÜĞÜ'NDE ORMAN ALANLARININ İZLENMESİNDE YENİ YAKLAŞIM: U-NET DERİN ÖĞRENME YÖNTEMİNİN ENTEGRASYONU
Öz
Bu çalışma, İstanbul Orman Bölge Müdürlüğü (OBM) sınırları içerisinde arazi kullanım ve örtüsündeki değişimlerin kapsamlı bir analizini sunmakta olup, piksel tabanlı sınıflandırma olarak bilinen semantik segmentasyon yöntemi kullanılmıştır. Özellikle orman alanlarındaki dinamiklere odaklanılan çalışmada, 2019–2023 yılları arasındaki beş yıllık dönemi kapsayan Sentinel-2 uydu görüntüleri U-Net mimarisi ile işlenmiştir. Çalışma alanı; çeşitli orman ekosistemlerini, kentsel/yerleşim alanlarını, su kütlelerini, mera alanlarını, sulak alanları ve tarım arazilerini içermektedir. Gelişmiş uzaktan algılama tekniklerinin uygulanmasıyla, orman ve mera alanlarında sırasıyla 15.250 hektar ve 13.226 hektarlık bir azalma belirlenmiş ve nicel olarak ortaya konmuştur. Bu durum, söz konusu bölgede arazi kullanımındaki dönüşümleri etkileyen unsurları ve sonuçlarını ortaya koymaktadır. Buna karşılık, yerleşim ve tarım alanlarında ise beş yıllık süreçte sırasıyla 13.878 hektar ve 15.953 hektar artış tespit edilmiştir. Elde edilen bulgular, orman dinamiklerine ilişkin daha derin bir anlayış kazandırmakta ve İstanbul OBM sınırları içerisindeki ormanlık alanların ekolojik bütünlüğünü ve sosyo-ekonomik değerini korumaya yönelik sürdürülebilir yönetim stratejilerinin geliştirilmesine katkı sağlamaktadır. Ayrıca, her bir arazi örtüsü sınıfı için ortalama F-1 skorunun her yıl yaklaşık %90 düzeyinde olduğu, ormanlık alanlarda ise ortalama F-1 skorunun yaklaşık %92 olarak gerçekleştiği tespit edilmiştir. Bu durum, kullanılan sınıflandırma yönteminin sağlamlığını ve doğruluğunu ortaya koymaktadır.
Anahtar Kelimeler
Orman Amenajmanı Uzaktan Algılama Derin Öğrenme İstanbul U-Net
Kaynakça
- Adepoju, M. O., Millington, A. C., & Tansey, K. T. (2006). Land use/land cover change detection in metropolitan Lagos (Nigeria): 1984–2002. P 1-5 in ASPRS 2006 Annual Conference Reno, Nevada May.
- Atalay, I., Efe, R., & Öztürk, M. (2014). Ecology and classification of forests in Türkiye. Procedia-Social and Behavioral Sciences, 120, 788-805. https://doi.org/10.1016/j.sbspro.2014.02.163
- Atmis, E., Günsen, B. H., Yücedağ, C., & Lise, W. (2012). Status, use and management of urban forests in Türkiye. South-east European Forestry: SEEFOR, 3(2), 69-78. https://doi.org/10.15177/seefor.12-08
- Atmis, E., & Cil, A. (2013). Sustainable forestry in Türkiye. Journal of Sustainable Forestry, 32(4), 354-364. https://doi.org/10.1080/10549811.2013.767210
- Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. Remote Sensing of Environment, 127, 210-222. https://doi.org/10.1016/j.rse.2012.09.011
- Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing, 114, 24-31.
- Bozkurt, S. G., Kuşak, L., & Akkemik, Ü. (2023). Investigation of land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems in Istanbul (Türkiye) metropolitan area between 1990 and 2018. Environmental Monitoring and Assessment, 195(1), 196. https://doi.org/10.1007/s10661-023-10975-7
- Cui, L., & Shi, J. (2012). Urbanization and its environmental effects in Shanghai, China. Urban Climate, 2, 1-15. https://doi.org/10.1016/j.uclim.2012.10.008
- Diakogiannis, F. I., Waldner, F., Caccetta, P., & Wu, C. (2020). ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94-114. https://doi.org/10.1016/j.isprsjprs.2020.01.013
- ESA. (2023, June). Copernicus Open Access Hub. https://scihub.copernicus.eu/dhus/#/home Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote sensing of environment, 80(1), 185-201.
- Farmer, E., Jones, S., Clarke, M., Buxton, L., Soto-Berelov, M., Page, S., Mellor, A., & Haywood, A. (2013). Creating a large area landcover dataset for public land monitoring and reporting. In Geospatial Science Research Symposium (pp. 116-132), Melbourne, Australia.
- General Directorate of Forestry. (2021). Türkiye Orman Varlığı (Forest Assets of Türkiye). General Directorate of Forestry, Ministry of Environment and Forest, Ankara, Türkiye.
- Gökburun, İ. (2017). İstanbul’da Nüfusun Gelişimi ve İlçelere Dağılımı (1950–2015). Journal of Anatolian Cultural Research (JANCR), 1(3), 110-130. https://ankad.org/index.php/ankad/article/view/27
- Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric Engineering and Remote Sensing, 60(3), 331-337.
- Günsen, H. B., & Atmiş, E. (2019). Analysis of forest change and deforestation in Türkiye. International Forestry Review, 21(2), 182-194. https://doi.org/10.1505/146554819826606577
- Hame, T. H. (1986). Satellite image aided change detection. Remote Sensing-Aided Forest Inventory, Research Notes, 19, 47-60.
- Hayati, Z., Yeşil, A., Asan, U., Bettinger, P., Cieszewski, C., & Siry, J. P. (2013). Evolution of modern forest management planning in the Republic of Türkiye. Journal of Forestry, 111(4), 239-248. https://doi.org/10.5849/jof.11-103
- Jensen, J. R., & Lulla, K. (1987). Introductory digital image processing: A remote sensing perspective. Geocarto International, 2(1), 65. https://doi.org/10.1080/10106048709354084
- Jin, Y., Liu, X., Chen, Y., & Liang, X. (2018). Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong. International journal of remote sensing, 39(23), 8703-8723.
- Kilic, S., Evrendilek, F., Berberoglu, S., & Demirkesen, A. (2006). Environmental monitoring of land-use and land-cover changes in a Mediterranean region of Türkiye. Environmental Monitoring and Assessment, 114, 157-168. https://doi.org/10.1007/s10661-006-2525-z
- Macleod, R. D., & Congalton, R. G. (1998). A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 64(3), 207-216.
- Maragos, P., & Pessoa, L. (1999). Morphological filtering for image enhancement and detection. In The Image and Video Processing Handbook (pp. 135-156).
- R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org.
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference Proceedings (pp. 234-241). Springer International Publishing.
- Ruiz-Luna, A., & Berlanga-Robles, C. A. (2003). Land use, land cover changes and coastal lagoon surface reduction associated with urban growth in northwest Mexico. Landscape Ecology, 18(2), 159-171. https://doi.org/10.1023/A:1024461215456
- Schlamadinger, B., Bird, N., Johns, T., Ward, M., & Yamagata, Y. (2007). A synopsis of land use, land-use change and forestry (LULUCF) under the Kyoto Protocol and Marrakech Accords. Environmental Science & Policy, 10(4), 271-282. https://doi.org/10.1016/j.envsci.2006.11.002
- Solórzano, J. V., Mas, J. F., Gao, Y., & Gallardo-Cruz, J. A. (2021). Land use land cover classification with U-Net: Advantages of combining Sentinel-1 and Sentinel-2 imagery. Remote Sensing, 13(18), 3600. https://doi.org/10.3390/rs13183600
- Tolunay, D., Karabiyik, B., & Temerit, A. (2011). First results of a nation-wide systematic forest condition survey in Türkiye. iForest-Biogeosciences and Forestry, 4(3), 145. https://doi.org/10.3832/ifor0567-004
- Voelsen, M., Bostelmann, J., Maas, A., Rottensteiner, F., & Heipke, C. (2020). Automatically generated training data for land cover classification with CNNs using Sentinel-2 images. ISPRS Archives, 43(B3), 767-774. https://doi.org/10.15488/10821
- Watson, R., Noble, I. R., Bolin, B., Ravindranath, N. H., Verardo, D. J., & Dokken, D. J. (2000). Land Use, Land-Use Change, and Forestry: A Special Report. Cambridge University Press.
- Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., & Gao, Y. (2019). Automatic land-cover mapping using Landsat time-series data based on Google Earth Engine. Remote Sensing, 11(24), 3023. https://doi.org/10.3390/rs11243023
- Yin, J., Zhong, H., Xu, S., Hu, X., Wang, J., & Wu, J. (2011). Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China. Environmental Monitoring and Assessment, 177, 609-621. https://doi.org/10.1007/s10661-010-1660-8
- Yuan, X., Shi, J., & Gu, L. (2021). A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Systems with Applications, 169, 114417. https://doi.org/10.1016/j.eswa.2020.114417
- Zengin, H., Yeşil, A., Asan, U., Bettinger, P., Cieszewski, C., & Siry, J. P. (2013). Evolution of modern forest management planning in the Republic of Turkey. Journal of Forestry, 111, 239-248.
- Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE geoscience and remote sensing magazine, 5(4), 8-36.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Modelleme ve Simülasyon, Yapay Zeka (Diğer), Fotogrametri ve Uzaktan Algılama |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Haziran 2025 |
| Gönderilme Tarihi | 29 Mayıs 2025 |
| Kabul Tarihi | 24 Haziran 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 7 Sayı: 1 |