{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T03:40:59Z","timestamp":1771472459505,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,10]],"date-time":"2019-02-10T00:00:00Z","timestamp":1549756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover\/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification.<\/jats:p>","DOI":"10.3390\/rs11030345","type":"journal-article","created":{"date-parts":[[2019,2,12]],"date-time":"2019-02-12T03:18:20Z","timestamp":1549941500000},"page":"345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":109,"title":["Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Paria","family":"Ettehadi Osgouei","sequence":"first","affiliation":[{"name":"Institute of Science and Technology, Graduate School of Science, Engineering and Technology, ITU Ayazaga Campus, Istanbul Technical University, Sariyer 34469, Istanbul, Turkey"}]},{"given":"Sinasi","family":"Kaya","sequence":"additional","affiliation":[{"name":"Geomatics Engineering Department, Civil Engineering Faculty, ITU Ayazaga Campus, Istanbul Technical University, Sariyer 34469, Istanbul, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-494X","authenticated-orcid":false,"given":"Elif","family":"Sertel","sequence":"additional","affiliation":[{"name":"Geomatics Engineering Department, Civil Engineering Faculty, ITU Ayazaga Campus, Istanbul Technical University, Sariyer 34469, Istanbul, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5693-3614","authenticated-orcid":false,"given":"Ugur","family":"Alganci","sequence":"additional","affiliation":[{"name":"Geomatics Engineering Department, Civil Engineering Faculty, ITU Ayazaga Campus, Istanbul Technical University, Sariyer 34469, Istanbul, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2005.08.006","article-title":"Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing","volume":"98","author":"Yuan","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1080\/014311600750037507","article-title":"Dynamics of urban growth in the Washington DC metropolitan area, 1973\u20131996, from Landsat observations","volume":"21","author":"Masek","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1002\/joc.2036","article-title":"Impacts of land cover data quality on regional climate simulations","volume":"30","author":"Sertel","year":"2010","journal-title":"Int. J. Clim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1175\/2009BAMS2769.1","article-title":"Impacts of land use landcover change on climate and future research priorities","volume":"91","author":"Mahmood","year":"2010","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1002\/joc.3736","article-title":"Land cover changes and their biogeophysical effects on climate","volume":"34","author":"Mahmood","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/nclimate2196","article-title":"Land management and land-cover change have impacts of similar magnitude on surface temperature","volume":"4","author":"Luyssaert","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s00704-017-2253-z","article-title":"Impact of land cover data on the simulation of urban heat island for Berlin using WRF coupled with bulk approach of Noah-LSM","volume":"134","author":"Li","year":"2018","journal-title":"Theor. Appl. Climatol."},{"key":"ref_8","first-page":"380","article-title":"The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review","volume":"52","author":"Aburas","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"63","DOI":"10.30897\/ijegeo.303545","article-title":"High resolution mapping of urban areas using SPOT-5 images and ancillary data","volume":"2","author":"Sertel","year":"2015","journal-title":"Int. J. Environ. Geoinform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/S0959-3780(03)00056-6","article-title":"Historical footprints in contemporary land use systems: Forest cover changes in savannah woodlands in the Sudano-Sahelian zone","volume":"13","author":"Wardell","year":"2003","journal-title":"Glob. Environ. Chang."},{"key":"ref_11","first-page":"77","article-title":"Monitoring land use\/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India","volume":"18","author":"Rawat","year":"2015","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.apgeog.2010.10.012","article-title":"Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data","volume":"31","author":"Ismail","year":"2011","journal-title":"Appl. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1016\/j.asr.2012.06.032","article-title":"Selection of classification techniques for land use\/land cover change investigation","volume":"50","author":"Srivastava","year":"2012","journal-title":"Adv. Space Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/TGRS.2008.2011983","article-title":"A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples","volume":"47","author":"Bruzzone","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TGRS.2008.916480","article-title":"Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas","volume":"46","author":"Dalponte","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2012.04.001","article-title":"Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points","volume":"70","author":"Shao","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MGRS.2016.2641240","article-title":"Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques","volume":"5","author":"Maulik","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1080\/10106049.2014.894586","article-title":"Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China","volume":"29","author":"Jia","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1080\/01431160410001720748","article-title":"Land-cover binary change detection methods for use in the moist tropical region of the Amazon: A comparative study","volume":"26","author":"Lu","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TGRS.2003.815238","article-title":"Spectral resolution requirements for mapping urban areas","volume":"41","author":"Herold","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"964","DOI":"10.3390\/rs6020964","article-title":"Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhao, C., Yang, H., Song, X., Chen, J., and Li, Z. (2017). Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis. Remote Sens., 9.","DOI":"10.3390\/rs9090939"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhang, R., Wang, S., and Wang, F. (2018). Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy. Sensors, 18.","DOI":"10.3390\/s18072013"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"Deng","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.rse.2010.01.006","article-title":"The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis","volume":"114","author":"Ozdogan","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1109\/LGRS.2017.2751559","article-title":"Locality Adaptive Discriminant Analysis for Spectral\u2013Spatial Classification of Hyperspectral Images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","unstructured":"Huang, H., Liu, J., and Pan, Y. (September, January 25). Semi-Supervised Marginal Fisher Analysis for Hyperspectral Image Classification. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (XXII ISPRS Congress), Melbourne, Australia."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/01431161.2010.481681","article-title":"Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach","volume":"1","author":"He","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/S0034-4257(02)00037-8","article-title":"Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach","volume":"82","author":"Ceccato","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.compenvurbsys.2005.01.005","article-title":"Use of satellite-derived landscape imperviousness index to characterize urban spatial growth","volume":"29","author":"Yang","year":"2005","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.14358\/PERS.73.12.1381","article-title":"Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique","volume":"73","author":"Xu","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.3390\/rs4102957","article-title":"Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area","volume":"4","author":"Adnyana","year":"2012","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, C., Zhong, C., Su, A., Xiong, C., Wang, J., and Liu, J. (2017). Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index. Remote Sens., 9.","DOI":"10.3390\/rs9030249"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"(2018, April 30). Turkish Statistical Institute (TurkStat), Available online: www.turkstat.gov.tr\/."},{"key":"ref_42","unstructured":"Goksel, C., Kaya, S., and Musaoglu, N. (,  2001). Using satellite data for land use change detection: A case study for Terkos water basin. Proceedings of the 21st EARSeL Symposium, Rotterdam, The Netherlands."},{"key":"ref_43","first-page":"18","article-title":"Monitoring urban growth on the European side of the Istanbul metropolitan area: A case study","volume":"8","author":"Kaya","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1089\/ees.2005.0040","article-title":"Multitemporal Analysis of Rapid Urban Growth in Istanbul Using Remotely Sensed Data","volume":"24","author":"Kaya","year":"2007","journal-title":"Environ. Eng. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7213","DOI":"10.3390\/s8117213","article-title":"Analysis of Land Use Change and Urbanization in the Kucukcekmece Water Basin (Istanbul, Turkey) with Temporal Satellite Data using Remote Sensing and GIS","volume":"8","author":"Coskun","year":"2008","journal-title":"Sensors"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.jenvman.2017.06.008","article-title":"Change detection using Landsat images and an analysis of the linkages between the change and property tax values in the Istanbul Province of Turkey","volume":"200","author":"Canaz","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_47","unstructured":"ESA (2018, April 30). ESA Sentinel. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/home."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the archive: How free data has enabled the science and monitoring promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(00)00169-3","article-title":"Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?","volume":"75","author":"Song","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zheng, H., Du, P., Chen, J., Xia, J., Li, E., Xu, Z., Li, X., and Yokoya, N. (2017). Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features. Remote Sens., 9.","DOI":"10.3390\/rs9121274"},{"key":"ref_52","first-page":"106","article-title":"Downscaling in remote sensing","volume":"22","author":"Atkinson","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1080\/01431169408954146","article-title":"Investigation of image resampling effects upon the textural information content of a high spatial resolution remotely sensed image","volume":"15","author":"Roy","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","unstructured":"Sentinel 2 User Handbook (2018, April 30). ESA Standard Document. Available online: https:\/\/earth.esa.int\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_55","unstructured":"Rouse, J., Haas, R., Schell, J., and Deering, D. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, Washington, DC, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.06.011","article-title":"A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species","volume":"124","author":"Pu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, K., Liu, L., Myint, S.W., Wang, S., Liu, H., and He, Z. (2017). Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms. Remote Sens., 9.","DOI":"10.3390\/rs9101060"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/01431160010006962","article-title":"Mapping of several soil properties using DAIS-7915 hyperspectral scanner data\u2014A case study over clayey soils in Israel","volume":"23","author":"Patkin","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s12524-015-0460-6","article-title":"A New Spectral Index for Extraction of Built-Up Area Using Landsat-8 Data","volume":"43","author":"Bouzekri","year":"2015","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Jieli, C., Manchun, L., Yongxue, L., Chenglei, S., and Wei, H. (2010, January 18\u201320). Extract residential areas automatically by New Built-up Index. Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China.","DOI":"10.1109\/GEOINFORMATICS.2010.5567823"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"6361","DOI":"10.1080\/01431161.2012.687842","article-title":"Efficient segmentation of urban areas by the VIBI","volume":"33","author":"Stathakis","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.1080\/01431160802039957","article-title":"A new index for delineating built-up land features in satellite imagery","volume":"29","author":"Xu","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","first-page":"321","article-title":"Relation between Social and Environmental Conditions in Colombo Sri Lanka and the Urban Index Estimated by Satellite Remote Sensing Data","volume":"31","author":"Kawamura","year":"1996","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_69","unstructured":"Roy, P., Miyatake, S., and Rikimaru, A. (2018, July 22). Biophysical Spectral Response Modeling Approach for Forest Density Stratification. Available online: http:\/\/www.gisdelopment.net\/aars\/acrs\/1997\/tTM5\/tTM5008a.shtml."},{"key":"ref_70","first-page":"87","article-title":"Using Thematic Mapper Data to Identify Contrasting Soil Plains and Tillage Practices","volume":"63","author":"Ward","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"416","DOI":"10.3390\/rs2020416","article-title":"Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover","volume":"2","author":"Daughtry","year":"2010","journal-title":"Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.iswcr.2016.04.002","article-title":"Evaluating spectral indices for determining conservation and conventional tillage systems in a vetch-wheat rotation","volume":"4","author":"Eskandari","year":"2016","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_73","first-page":"975","article-title":"Probability estimates for multi-class classification by pairwise coupling","volume":"5","author":"Wu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_74","unstructured":"ENVI Documentation Center (2018, September 11). Support Vector Machine. Available online: https:\/\/www.harrisgeospatial.com\/docs\/SupportVectorMachine.html."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s10661-017-5818-5","article-title":"Analysis of land cover\/use changes using Landsat 5 TM data and indices","volume":"189","author":"Kaya","year":"2017","journal-title":"Environ. Monit. Assess."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:30:54Z","timestamp":1760185854000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,10]]},"references-count":76,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030345"],"URL":"https:\/\/doi.org\/10.3390\/rs11030345","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,10]]}}}