Accuracy Assessment of Different Land Use/Land Cover Maps: A Case Study of TR21 Thrace Region
DOI:
https://doi.org/10.24925/turjaf.v11i3.505-512.5879Keywords:
TR21 Trakya Bölgesi, Arazi kullanım/arazi örtüsü, Kappa istatistiği, Uzaktan algılama, Coğrafi Bilgi SistemleriAbstract
The compatibility of land use/land cover maps with ground-truth datasets is very important in accurate and effective land planning. In this study, the accuracy assessment of current global and regional land use/land cover maps has been evaluated for TR21 Thrace Region. 400 reference points are allocated in a stratified random for each land use/land cover class. Using by the Kappa test for Copernicus, ESA, Corine level 1 and level 2 maps, the land use/land cover accuracy was determined as 0.63, 0.83, 0.80, and 0.71 respectively. It has been concluded that the classified maps represent very well the cropland and forest areas that cover a lot of space in the region, however, the accuracy rate is low in shrublands and grasslands. When all maps are evaluated together, it will be very useful to use ESA data at a regional scale and Corine level 2 data at a local scale in spatial planning
References
Anderson JR, Hardy EE, Roach JT, Witmer RE. 1976. A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper 964. US Geological Survey Government Printing Office: Washington, DC, USA. https://doi.org/10.3133/pp964.
Anonim 2013. TR21 Trakya Bölgesi Mevcut Durum Analizi. https://www.trakyaka.org.tr/upload/Node/33085/xfiles/Trakya_2013_Mevcut_Durum_Analizi.pdf. [Erişim tarihi: 15 Ekim 2022]
Belward AS, Skøien JO. 2015. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS Journal of Photogrammetry and Remote Sensing, 103: 115–128. https://doi.org/10.1016/j.isprsjprs.2014.03.009
Buchhorn M, Lesiv M, Tsendbazar NE, Herold M, Bertels L, Smets B. 2020. Copernicus Global Land Cover Layers—Collection 2. Remote Sensing, 12(6): 1044. https://doi.org/10.3390/rs12061044
Büttner G. 2014. CORINE Land Cover and Land Cover Change Products. In: Manakos I. Braun M. (editors). Land Use and Land Cover Mapping in Europe. Berlin/Heidelberg, Germany: Springer. pp. 55–74.
Cohen J. 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(3): 37–46. http://dx.doi.org/10.1177/001316446002000104.
Congalton RG. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37: 35-46.
Congalton RG, Green K. 1998. Assessing the accuracy of remotely sensed data: principles and practices. Lewis Publishers, New York.
Di Gregorio A, Jansen LJM. 2005. Land Cover Classification System (LCCS): Classification Concepts and User Manual. Available from: http://www.fao.org/docrep/003/x0596e/ x0596e00.HTM [Accessed 28 October 2022]
European Commission 1994. CORINE landcover. Available from: http://www.eea.europa.eu/publications/COR0-land cover. [Accessed 10 November 2022]
Goldewijk KK, Ramankutty N. 2004. Land Cover Change over the Last Three Centuries Due to Human Activities: The Availability of New Global Data Sets. GeoJournal, 61: 335-344. https://doi.org/10.1007/s10708-004-5050-z
Hord RM, Brooner W. 1976. Land use map accuracy criteria. Photogrammetric Engineering and Remote Sensing, 42(5): 671-677.
Kasperson JX, Kasperson RE, Turner BL II. 1995. Regions at Risk: Comparisons of Threatened Environments. Tokyo: United Nations University. ISBN: 92-808-0848-6.
Landis JR, Koch GG. 1977. The Measurement of ObserverAgreement for Categorical Data. Biometrics, 33: 159-174. https://doi.org/10.2307/2529310
Maingi JK, Marsh SE, Kepner WG, Edmonds CM. 2002. An Accuracy Assessment of 1992 Landsat-MSS Derived Land Cover for the Upper San Pedro Watershed (U.S./Mexico). U.S. Environmental Protection Agency, Washington DC. (EPA/600/R-02/040)
Olofsson P, Foody GM, Herold M, Stehman SV, Woodcock CE, Wulder MA. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148: 42-57. https://doi.org/10.1016/j.rse. 2014.02.015
Rosenfield GH. 1982. Sample design for estimating change in land use and land cover. Photogrammetric Engineering and Remote Sensing, 48(5): 793-801. Available from: https://www.asprs.org/wp-content/uploads/pers/1982journal/ may/1982_may_793-801.pdf [Accessed 15 November 2022]
Sertel E, Algan IY, Alp G, Musaoğlu N, Kaya Ş. 2017. Yüksek Çözünürlüklü Uydu Verileri Kullanılarak 1:25.000 Ölçekli Ulusal Arazi Örtüsü/Kullanımı Sınıflandırma Sisteminin Geliştirilmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17: 232-241. Retrieved from https://fenbildergi.aku.edu.tr/wpcontent/uploads/2017/11/2739_Elifsertel-232-241.pdf
TÜİK, 2022. Türkiye İstatistik Kurumu 2021 yılı Nüfus ve Demografi İstatistikleri. https://data.tuik.gov.tr/Bulten/ Index?p=Adrese-Dayal%C4%B1-N%C3%BCfus Kay%C4%B1t-Sistemi-Sonu%C3%A7lar%C4%B1-2021-45500&dil=1. [Erişim tarihi: 20 Şubat 2023]
Tyson P, Steffen W, Mitra AP, Fu C, Lebel L. 2001. The Earth System: Regional-Global Linkages. Regional Environmental Change, 2(3): 128–140.
Verburg PH, Veldkamp A. 2004. Projecting Land Use Transitions at Forest Fringes in the Philippines at Two Spatial Scales. Landscape Ecology, 19(1): 77–98. https://doi.org/10.1023/B:LAND.0000018370.57457.58
Zanaga D, Van De Kerchove R, Daems D, De Keersmaecker W, Brockmann C, Kirches G, Wevers J, Cartus,O, Santoro M, Fritz S, Lesiv M, Herold M, Tsendbazar NE, Xu P, Ramoino F, Arino O. 2022. ESA WorldCover 10 m 2021 v200. https://doi.org/10.5281/zenodo.7254221
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
