Monitoring of Land Cover Changes between 1990 and 2014 Over Cyprus Using Multi-Temporal Landsat Imagery
Abstract
Determining changes in forest resources and land cover/land use is crucial for sustainable forest planning. This study aims to determine changes in land cover classes, including forest areas, agricultural areas, settlement areas, other non-forest areas, and water bodies in the study area located in Cyprus between 1990 and 2014. The study utilized digital management plans, a high-resolution base map, and Landsat satellite images for the relevant years. Necessary preprocessing steps were applied to prepare the satellite data for classification. Initially, unsupervised classification was conducted on the images to determine the number of distinguishable sub-information classes. Subsequently, supervised classification was performed using the maximum likelihood algorithm with the provision of training areas. The sub classes generated based on the supervised classification were consolidated into five main classes. After the classification process, the accuracy of the classification for each image was determined. Accordingly, the overall classification accuracy of the map from the 1990 Landsat 5 TM satellite image was 92%, with 0.90 Kappa Statistics. The overall classification accuracy of the map from 2014 Landsat 8 OLI satellite image was 89.20%, with 0.87 Kappa statistics. Then, land cover change analysis was conducted to compare a twenty-four-year period within the study area.
Keywords
Remote sensing, Change analysis, Post-classification comparison, Image classification, Forestry
Supporting Institution
Scientific Research Projects Coordination Unit of Istanbul University - Cerrahpasa
Project Number
2565
Thanks
I would like to express my gratitude to the Scientific Research Projects Unit of Istanbul University – Cerrahpaşa for providing financial support for my work (Project number: 2565). Additionally, I extend my thanks to the Directorate of the Forestry Department of the Turkish Republic of Northern Cyprus for their contributions to the development of forest management plans.
References
- Andualem, T.G., Peters, S., Hewa, G.A., Boland, J., Myers, B.R. 2023. Spatiotemporal trends of urban-induced land use and land cover change and implications on catchment surface imperviousness. Applied Water Science, 13(12): 223. https://doi.org/ 10.1007/s13201-023-02029-7
- Berberoglu, S., Akin, A. 2009. Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. International Journal of Applied Earth Observation and Geoinformation, 11(1): 46–53. https://doi.org/ https://doi.org/10.1016/j.jag.2008.06.002
- Bhattacharjee, S., Islam, M.T., Kabir, M.E., Kabir, M.M. 2021. Land-Use and Land-Cover Change Detection in a North-Eastern Wetland Ecosystem of Bangladesh Using Remote Sensing and GIS Techniques. Earth Systems and Environment, 5(2): 319–340. https:// doi.org/10.1007/s41748-021-00228-3
- Brown, D.G., Johnson, K. M., Loveland, T.R., Theobald, D.M. 2005. Rural land-use trends in the conterminous united states, 1950–2000. Ecological Applications, 15(6): 1851–1863.
- CCRS. 2009. Fundamentals of Remote Sensing. Canada Center for Remote Sensing. 258 p.
- Chungtai, A.H., Abbasi, H., Karaş, İ.R. 2021. A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications Society and Environment, 22(2): 100482.
- Çoban, H.O. 2006. Uydu Verileri ile Orman Alanlarındaki Zamansal Değişimlerin Belirlenmesi [Doctoral Thesis]. Istanbul Üniversity. Istanbul.
- Das, S., Angadi, D.P. 2022. Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: a micro-level study. GeoJournal, 87(3): 2101–2123. https://doi.org/10.1007/s10708-020-10359-1
- Deering, D.W., Rouse, J.W., Haas, R.H., Schell, J.A. 1975. Measuring “forage production” of grazing units from Landsat MSS data. 10th International Symposium on Remote Sensing of Environment, 1169–1178.
- ESRI. 2024. World Imagery. Retrieved July 10, 2024, from https://www.arcgis.com/home/item.html?id= 10df2279f9684e4a9f6a7f08febac2a9%3FWT.mc_id%3DEmailCampaign15361
