Land use & land cover change detection in rapid growth urbanized area have been studied by many researchers and there are many works on this topic. Commonly, settlement sprawl in area depends on many factors such as eco-nomic prosperity and population growth. Iraq is one of the countries which witnessed rapid development in the settlement area. Remote sensing and geographic information system (GIS) are analytical software technologies to evaluate this familiar worldwide phenomenon. This study illustrates settlement development in Sulaimaniyah Governorate from 2001 to 2017 using Landsat satellite imageries of different periods. All images had been classified using remote sensing software in order to proceed powerful mapping of land use classification. Maximum likelihood method is used in the accurately extracted solution information from geospatial imagery. Landsat images from the study area were categorized into four different classes. These are: forest, vegetation, soil, and settlement. Change detection analysis results illustrate that in the face of an explosive demographic shift in the settlement area where the record + 8.99 percent which is equivalent to 51.80 Km 2 over a 16-year period and settlement area increasing from 3.87 percent in 2001 to 12.86 percent in 2017. Accuracy assessment model was used to evaluate (LULC) classified images. Accuracy results show an overall accuracy of 78.83% to 90.09% from 2001 to 2017 respectively while convincing results of Kappa coefficient given between substantial and almost perfect agreements. This study will help decision-makers in urban plan for future city development.
KeywordsSettlement ExpansionGeographic Information System (GIS)Land Use Land Cover (LULC)Land Use ClassificationSatellite ImagesAccuracy Assessment and Change Detection
Comber, A.J. (2008) Land Use or Land Cover? Journal of Land Use Science, 3, 199-201. https://doi.org/10.1080/17474230802465140
Tilahun, A. (2015) Accuracy Assessment of Land Use Land Cover Classification Using Google Earth. American Journal of Environmental Protection, 4, 193-198. https://doi.org/10.11648/j.ajep.20150404.14
Clark, B.J.F. and Pellikka, P.K.E. (2009) Landscape Analysis Using Multiscale Segmentation and Object Orientated Classification. In: Roder and Hill, Eds., Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment, Taylor & Francis Group, London, 323-342.
Rathcke, B. and Lacey, E.P. (1985) Phenological Patterns of Terrestrial Plants. Annual Review of Ecology and Systematics, 16, 179-214. https://doi.org/10.1146/annurev.es.16.110185.001143
Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P. and Macomber, S.A. (2001) Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment, 75, 230-244. https://doi.org/10.1016/S0034-4257(00)00169-3
Wu, C., et al. (2017) Land Surface Phenology Derived from Normalized Difference Vegetation Index (NDVI) at Global FLUXNET Sites. Agricultural and Forest Meteorology, 233, 171-182. https://doi.org/10.1016/j.agrformet.2016.11.193
Alexakis, D.D., Agapiou, A., Hadjimitsis, D.G. and Retalis, A. (2012) Optimizing Statistical Classification Accuracy of Satellite Remotely Sensed Imagery for Supporting Fast Flood Hydrological Analysis. Acta Geophysica, 60, 959-984. https://doi.org/10.2478/s11600-012-0025-9
Kim, D.H., Narashiman, R., Sexton, J.O., Huang, C. and Towns-hend, J.R. (2011) Methodology to Select Phenologically Suitable Landsat Scenes for Forest Change Detection. International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC, 24-29 July 2011, 2613-2616. https://doi.org/10.1109/IGARSS.2011.6049738
McIver, D.K. and Friedl, M.A. (2002) Using Prior Probabilities in Decision-Tree Classification of Remotely Sensed Data. Remote Sensing of Environment, 81, 253-261. https://doi.org/10.1016/S0034-4257(02)00003-2
Hassen, E.E. and Assen, M. (2018) Land Use/Cover Dynamics and Its Drivers in Gelda Catchment, Lake Tana Watershed, Ethiopia. Environmental Systems Research, 6, 4. https://doi.org/10.1186/s40068-017-0081-x
Rosenfield, G.H. and Fitzpatrick-Lins, K. (1986) A Coefficient of Agreement as a Measure of Thematic Classification Accuracy. Photogrammetric Engineering and Remote Sensing, 52, 223-227.
Foody, G.M. (2002) Status of Land Cover Classification Accuracy Assessment. Remote Sensing of Environment, 80, 185-201. https://doi.org/10.1016/S0034-4257(01)00295-4
Weil, G., Lensky, I.M., Resheff, Y.S. and Levin, N. (2017) Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection. Remote Sensing, 9, 1130.
Mosammam, H.M., Nia, J.T., Khani, H., Teymouri, A. and Kazemi, M. (2017) Monitoring Land Use Change and Measuring Urban Sprawl Based on Its Spatial Forms: The Case of Qom City. The Egyptian Journal of Remote Sensing and Space Science, 20, 103-116.
Barsi, J.A., Lee, K., Kvaran, G., Markham, B.L. and Pedelty, J.A. (2014) The Spectral Response of the Landsat-8 Operational Land Imager. Remote Sensing, 6, 10232-10251. https://doi.org/10.3390/rs61010232
Morisette, J.T., et al. (2009) Tracking the Rhythm of the Seasons in the Face of Global Change: Phenological Research in the 21st Century. Frontiers in Ecology and the Environment, 7, 253-260. https://doi.org/10.1890/070217
Tilahun, A. (2015) Accuracy Assessment of Land Use Land Cover Classification Using Google Earth. American Journal of Environmental Protection, 4, 193. https://doi.org/10.11648/j.ajep.20150404.14
Congalton, R.G. and Green, K. (2009) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd Edition, CRC Press, Boca Raton.
Zhu, X. and Liu, D. (2014) Accurate Mapping of Forest Types Using Dense Seasonal Landsat Time-Series. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 1-11. https://doi.org/10.1016/j.isprsjprs.2014.06.012
Gashaw, T., Tulu, T., Argaw, M. and Worqlul, A.W. (2017) Evaluation and Prediction of Land Use/Land Cover Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia. Environmental Systems Research, 6, 17. https://doi.org/10.1186/s40068-017-0094-5
Congalton, R.G. (2005) Thematic and Positional Accuracy Assessment of Digital Remotely Sensed Data. Proceedings of the 7th Annual Forest Inventory and Analysis Symposium, Portland, 3-6 October 2005, 149-154.
Gashaw, T., Bantider, A. and Mahari, A. (2014) Evaluations of Land Use/Land Cover Changes and Land Degradation in Dera District, Ethiopia: GIS and Remote Sensing Based Analysis. International Journal of Scientific Research in Environmental Sciences, 2, 199-208.
Mosammam, H.M., Nia, J.T., Khani, H., Teymouri, A. and Kazemi, M. (2017) Monitoring Land Use Change and Measuring Urban Sprawl Based on Its Spatial Forms: The Case of Qom City. The Egyptian Journal of Remote Sensing and Space Sciences, 20, 103-116. https://doi.org/10.1016/j.ejrs.2016.08.002
Fleiss, J.L. (1971) Measuring Nominal Scale Agreement among Many Raters. Psychological Bulletin, 76, 378-382. https://doi.org/10.1037/h0031619
Landis, J.R. and Koch, G.G. (1977) The Measurement of Observer Agreement for Categorical Data. Biometrics, 33, 159. https://doi.org/10.2307/2529310
Foody, G.M. (2002) Status of Land Cover Classification Accuracy Assessment. Remote Sensing of Environment, 80, 185-201. https://doi.org/10.1016/S0034-4257(01)00295-4
Missions, L. (2013) Landsat 8 Data Product Information. 7-9.
Clark, B.J.F. and Pellikka, P.K.E. (2009) Landscape Analysis Using Multiscale Segmentation and Object-Orientated Classification. In: Roder, A. and Hill, J., Eds., Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment, CRC Press, Boca Raton, 323-342.
Planning, M.O.F., Cooperation, D. and Health, M.O.F. (2008) Comprehensive Food Security and Vulnerability Analysis in Iraq.
Seto, K.C. and Christensen, P. (2013) Remote Sensing Science to Inform Urban Climate Change Mitigation Strategies. Urban Climate, 3, 1-6. https://doi.org/10.1016/j.uclim.2013.03.001
Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P. and Macomber, S.A. (2001) Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment, 75, 230-244. https://doi.org/10.1016/S0034-4257(00)00169-3
Schulz, J.J., Cayuela, L., Echeverria, C., Salas, J. and Rey Benayas, J.M. (2010) Monitoring Land Cover Change of the Dryland Forest Landscape of Central Chile (1975-2008). Applied Geography, 30, 436-447. https://doi.org/10.1016/j.apgeog.2009.12.003
Rosenfield, G.H. and Fitzpatrick-Lins, K. (1986) A Coefficient of Agreement as a Measure of Thematic Classification Accuracy. Photogrammetric Engineering and Remote Sensing, 52, 223-227.
Barsi, J.A., Lee, K., Kvaran, G., Markham, B.L. and Pedelty, J.A. (2014) The Spectral Response of the Land-sat-8 Operational Land Imager. Remote Sensing, 6, 10232-10251. https://doi.org/10.3390/rs61010232
McIver, D.K. and Friedl, M.A. (2002) Using Prior Probabilities in Decision-Tree Classification of Remotely Sensed Data. Remote Sensing of Environment, 81, 253-261. https://doi.org/10.1016/S0034-4257(02)00003-2
Pontius, R.G. and Millones, M. (2011) Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment. International Journal of Remote Sensing, 32, 4407-4429. https://doi.org/10.1080/01431161.2011.552923
Alexakis, D.D., Agapiou, A., Hadjimitsis, D.G. and Retalis, A. (2012) Optimizing Statistical Classification Accuracy of Satellite Remotely Sensed Imagery for Supporting Fast Flood Hydrological Analysis. Acta Geophysica, 60, 959-984. https://doi.org/10.2478/s11600-012-0025-9
Rientjes, T.H.M., Perera, B.U.J., Haile, A.T., Reggiani, P. and Muthuwatta, L.P. (2011) Regionalisation for Lake Level Simulation—The Case of Lake Tana in the Upper Blue Nile, Ethiopia. Hydrology and Earth System Sciences, 15, 1167-1183. https://doi.org/10.5194/hess-15-1167-2011