{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T16:29:05Z","timestamp":1783355345178,"version":"3.54.6"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,10,31]],"date-time":"2017-10-31T00:00:00Z","timestamp":1509408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471366"],"award-info":[{"award-number":["41471366"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification (FBA-DTC) method for mapping LULC based on spectral and topographic information. Landsat 5 TM and Land 8 OLI images were employed, and the technique was applied to the coastal city of Xiamen, China. The method integrates multi-spectral features such as SAVI (soil adjustment vegetation index), NDWI (normalized water index), MNDBaI (modified normalized difference barren index), BI (brightness index), and WI (wetness index), with topographic features including DEM and slope. In addition, the new approach distinguishes between fallow land and cropland, and separates high-rise buildings from beaches and water bodies. Several of the FBA-DTC parameters (or rules) from 1997 to 2015 remained constant (i.e., DEM and slope), whereas others changed slightly. WI was negatively related to percent area of beach land, and BI was negatively related to percent area of arable land. The FBA-DTC method had an average user\u2019s accuracy (UA) of 91.36% for built-up land, an average overall accuracy (OA) of 92.13%, and a Kappa coefficient (KC) of 0.90 for the period from 2003 to 2015, representing respective increases of 15.87%, 10.17%, and 0.13, compared with values calculated using maximum likelihood classification (MLC). Over the past 12 years, built-up land increased from 23.67% to 43.17% owing to occupation of coastal reclamation, arable land, and forest land. The FBA-DTC method presented here is a valuable technique for evaluating urban growth and changes in LULC classification for coastal cities.<\/jats:p>","DOI":"10.3390\/ijgi6110331","type":"journal-article","created":{"date-parts":[[2017,10,31]],"date-time":"2017-10-31T12:48:31Z","timestamp":1509454111000},"page":"331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China"],"prefix":"10.3390","volume":"6","author":[{"given":"Lizhong","family":"Hua","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, 600 Ligong Road, Xiamen 361024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, 600 Ligong Road, Xiamen 361024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Yin","sequence":"additional","affiliation":[{"name":"The Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 9 Dengzhuang South Road, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7975-2472","authenticated-orcid":false,"given":"Lina","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"084592","DOI":"10.1117\/1.JRS.8.084592","article-title":"Monitoring bidecadal development of urban agglomeration with remote sensing images in the Jing-Jin-Tang area, China","volume":"8","author":"Lu","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1126\/science.1150195","article-title":"Global Change and the Ecology of Cities","volume":"319","author":"Grimm","year":"2008","journal-title":"Science"},{"key":"ref_3","first-page":"3998","article-title":"Urban land cover classification with airborne hyperspectral data: What features to use?","volume":"7","author":"Tong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hu, T., Yang, J., Li, X., and Gong, P. (2016). Mapping Urban Land Use by Using Landsat Images and Open Social Data. Remote Sens., 8.","DOI":"10.3390\/rs8020151"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10593","DOI":"10.3390\/rs61110593","article-title":"Examining Land Use and Land Cover Spatiotemporal Change and Driving Forces in Beijing from 1978 to 2010","volume":"6","author":"Tian","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1080\/17538947.2012.671378","article-title":"Evaluation of diverse classification approaches for land use\/cover mapping in a Mediterranean region utilizing Hyperion data","volume":"7","author":"Elatawneh","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.rse.2009.10.009","article-title":"Wetland monitoring using classification trees and SPOT-5 seasonal time series","volume":"114","author":"Davranche","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.3390\/s7112860","article-title":"Object-based classification of Ikonos imagery for mapping large-scale vegetation communities in urban areas","volume":"7","author":"Mathieu","year":"2007","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.14358\/PERS.76.10.1159","article-title":"Land cover classification in a complex urban-rural landscape with quickbird imagery","volume":"76","author":"Lu","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"073573","DOI":"10.1117\/1.JRS.7.073573","article-title":"Improved land cover mapping using high resolution multiangle 8-band WorldView-2 satellite remote sensing data","volume":"7","author":"Jawak","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TGRS.2008.922034","article-title":"Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles","volume":"46","author":"Fauvel","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"084597","DOI":"10.1117\/1.JRS.8.084597","article-title":"Spatiotemporal analysis of urban environment based on the vegetation\u2013impervious surface\u2013soil model","volume":"8","author":"Guo","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/rs1030243","article-title":"An automated artificial neural network system for land use\/land cover classification from Landsat TM imagery","volume":"1","author":"Yuan","year":"2009","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.compenvurbsys.2016.02.002","article-title":"Characterizing urban landscapes using fuzzy sets","volume":"57","author":"Gopal","year":"2016","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1080\/10106049.2012.668950","article-title":"Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use\/cover mapping in a Mediterranean region","volume":"28","author":"Petropoulos","year":"2013","journal-title":"Geocarto Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.1080\/01431160310001618428","article-title":"A hybrid approach to urban land use\/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images","volume":"25","author":"Lo","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.1080\/01431160801905497","article-title":"Expert system classification of urban land use\/cover for Delhi, India","volume":"29","author":"Wentz","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An assessment of the effectiveness of decision tree methods for land cover classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"073457","DOI":"10.1117\/1.JRS.7.073457","article-title":"Classification of land-cover types in muddy tidal flat wetlands using remote sensing data","volume":"7","author":"Wang","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5577","DOI":"10.1016\/j.eswa.2010.10.078","article-title":"Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data","volume":"38","author":"Punia","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1672\/0277-5212(2006)26[465:MWARAU]2.0.CO;2","article-title":"Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models","volume":"26","author":"Baker","year":"2006","journal-title":"Wetlands"},{"key":"ref_22","first-page":"S27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/j.rse.2007.08.025","article-title":"Integrating Landsat TM and SRTM\u2013DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments","volume":"112","author":"Sesnie","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.rse.2008.10.005","article-title":"Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications","volume":"113","author":"Tooke","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_25","first-page":"186","article-title":"Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach","volume":"10","author":"Kandrika","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2011.11.001","article-title":"A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data","volume":"118","author":"Qi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2006.09.005","article-title":"Subpixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil","volume":"106","author":"Powell","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.ocecoaman.2012.06.014","article-title":"Urban spatial expansion and its impacts on island ecosystem services and landscape pattern: A case study of the island city of Xiamen, Southeast China","volume":"81","author":"Lin","year":"2013","journal-title":"Ocean Coast. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.cities.2012.09.001","article-title":"City profile: Xiamen","volume":"31","author":"Tang","year":"2013","journal-title":"Cities"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/13504509.2010.487410","article-title":"Spatio-temporal dynamic analysis of island-city landscape: A case study of Xiamen Island, China","volume":"17","author":"Hua","year":"2010","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3899","DOI":"10.3390\/su6063899","article-title":"Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China","volume":"6","author":"Hua","year":"2014","journal-title":"Sustainability"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.habitatint.2010.09.001","article-title":"The development and redevelopment of urban villages in Shenzhen","volume":"35","author":"Hao","year":"2011","journal-title":"Habitat Int."},{"key":"ref_35","first-page":"1025","article-title":"Image-based atmospheric corrections\u2014Revisited and revised","volume":"62","author":"Chavez","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.rse.2005.09.006","article-title":"Image-based atmospheric correction of QuickBird imagery of Minnesota cropland","volume":"99","author":"Wu","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1080\/01431160110109642","article-title":"Assessment of atmospheric correction methods applicable to Amazon basin LBA research","volume":"23","author":"Lu","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","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_39","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_40","unstructured":"Zhao, H.M., and Chen, X.L. (2005, January 25\u201329). Use of normalized difference bareness index in quickly mapping bare areas from TM\/ETM+. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea."},{"key":"ref_41","unstructured":"Kauth, R.J., and Thomas, G.S. (July, January 29). The tasseled cap\u2014A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, IN, USA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/0034-4257(85)90102-6","article-title":"A TM tasseled cap equivalent transformation for reflectance factor data","volume":"17","author":"Crist","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/2150704X.2014.915434","article-title":"Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance","volume":"5","author":"Baig","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_44","first-page":"114","article-title":"A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing","volume":"11","author":"Biradar","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6026","DOI":"10.3390\/rs5116026","article-title":"Exploring the use of Google Earth imagery and object-based methods in land use\/cover mapping","volume":"5","author":"Hu","year":"2013","journal-title":"Remote Sens."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/11\/331\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:59Z","timestamp":1760208539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/11\/331"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,31]]},"references-count":45,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["ijgi6110331"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6110331","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,31]]}}}