{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:41:59Z","timestamp":1773898919947,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2014,11,14]],"date-time":"2014-11-14T00:00:00Z","timestamp":1415923200000},"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>There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and  non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods.<\/jats:p>","DOI":"10.3390\/rs61111372","type":"journal-article","created":{"date-parts":[[2014,11,17]],"date-time":"2014-11-17T03:15:21Z","timestamp":1416194121000},"page":"11372-11390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA"],"prefix":"10.3390","volume":"6","author":[{"given":"Xiaoxiao","family":"Li","sequence":"first","affiliation":[{"name":"Julie Ann Wrigley Global Institute of Sustainability, Arizona State University, Tempe, AZ 85287, USA"}]},{"given":"Guofan","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USA"}]}],"member":"1968","published-online":{"date-parts":[[2014,11,14]]},"reference":[{"key":"ref_1","unstructured":"van der Meer, F.D., and de Jong, S.M. (2006). Imaging Spectrometry, Springer."},{"key":"ref_2","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_3","first-page":"321","article-title":"Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography","volume":"33","author":"Li","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.gloenvcha.2012.12.009","article-title":"Land system architecture: Using land systems to adapt and mitigate global environmental change","volume":"23","author":"Turner","year":"2013","journal-title":"Glob. Environ. Change"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1007\/s10980-013-9894-9","article-title":"Landscape sustainability science: Ecosystem services and human well-being in changing landscapes","volume":"28","author":"Wu","year":"2013","journal-title":"Landsc. Ecol."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-based crop identification using multiple vegetation indices, textural features and crop phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.rse.2005.11.002","article-title":"Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery","volume":"100","author":"Ellis","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.3390\/s8031613","article-title":"Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data","volume":"8","author":"Zhou","year":"2008","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4645","DOI":"10.1080\/01431160500444731","article-title":"Object-oriented classification for urban land cover mapping with ASTER imagery","volume":"28","author":"Chen","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","first-page":"973","article-title":"Earth observation for urban planning and management: State of the art and recommendations for application of earth observation in urban planning","volume":"73","author":"Nichol","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1080\/01431160412331291198","article-title":"Urban vegetation monitoring in Hong Kong using high resolution multispectral images","volume":"26","author":"Nichol","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1890\/1540-9295(2007)5[80:SHIUER]2.0.CO;2","article-title":"Spatial heterogeneity in urban ecosystems: Reconceptualizing land cover and a framework for classification","volume":"5","author":"Cadenasso","year":"2007","journal-title":"Front. Ecol. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.3390\/rs3081743","article-title":"Collective sensing: Integrating geospatial technologies to understand urban systems\u2014An overview","volume":"3","author":"Blaschke","year":"2011","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"381","DOI":"10.14358\/PERS.69.4.381","article-title":"Irrigated vegetation assessment for urban environments","volume":"69","author":"Stow","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1111\/j.1461-0248.2005.00792.x","article-title":"Predicting species distribution: Offering more than simple habitat models","volume":"8","author":"Guisan","year":"2005","journal-title":"Ecol. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1111\/j.1744-7909.2008.00712.x","article-title":"Vegetation mapping of the Mond Protected Area of Bushehr Province (south-west Iran)","volume":"51","author":"Mehrabian","year":"2009","journal-title":"J. Integr. Plant Biol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"463","DOI":"10.14358\/PERS.74.4.463","article-title":"Per-pixel classification of high spatial resolution satellite imagery for urban land-cover mapping","volume":"74","author":"Hester","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.jenvman.2006.12.010","article-title":"Classifying environmentally significant urban land uses with satellite imagery","volume":"86","author":"Park","year":"2008","journal-title":"J. Environ. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1111\/j.1538-4632.2006.00691.x","article-title":"Urban textural analysis from remote sensor data: Lacunarity measurements based on the differential box counting method","volume":"38","author":"Myint","year":"2006","journal-title":"Geogr. Anal."},{"key":"ref_21","first-page":"68","article-title":"A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States","volume":"10","author":"Weng","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.isprsjprs.2007.08.007","article-title":"Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site","volume":"63","author":"Mallinis","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1016\/j.rse.2009.04.007","article-title":"Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study","volume":"113","author":"Zhou","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","unstructured":"Cremers, A., and Greve, K. (2000). Environmental Information for Planning, Politics and the Public, Metropolis Verlag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1080\/01431161.2012.714508","article-title":"Object-based urban vegetation mapping with high-resolution aerial photography as a single data source","volume":"34","author":"Li","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","unstructured":"Strobl, J., Blaschke, T., and Griesebner, G. (2000). Angewandte Geographische Informations-Verarbeitung XII, Wichmann Verlag."},{"key":"ref_29","first-page":"12","article-title":"What\u2019s wrong with pixels? Some recent developments interfacing remote sensing and GIS","volume":"6","author":"Blaschke","year":"2001","journal-title":"GeoBIT\/GIS"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/S0304-3800(03)00139-X","article-title":"A multi-scale segmentation\/object relationship modelling methodology for landscape analysis","volume":"168","author":"Burnett","year":"2003","journal-title":"Ecol. Model."},{"key":"ref_31","unstructured":"Blaschke, T., Burnett, C., and Pekkarinen, A. (2004). Remote Sensing Image Analysis: Including the Spatial Domain, Springer."},{"key":"ref_32","first-page":"339","article-title":"An automated object-based approach for the multiscale image segmentation of forest scenes","volume":"7","author":"Hay","year":"2005","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1016\/j.rse.2010.02.018","article-title":"Updating the 2001 National Land Cover Database impervious surface products to 2006 using Landsat imagery change detection methods","volume":"114","author":"Xian","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"National Agricultural Statistics Database (2010). National Agricultural Statistics Service-Official Site, Available online:http:\/\/www.nass.usda.gov\/."},{"key":"ref_35","unstructured":"Liu, Z., Wang, J., and Liu, W. (2005, January 25\u201329). Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS \u201905, Seoul, Korea."},{"key":"ref_36","unstructured":"Indiana Spatial Data Portal Indiana Geological Survey-Official Site. Available online:http:\/\/ igs.indiana.edu\/."},{"key":"ref_37","unstructured":"Census Gazetteer Data for United States counties (2010). United States Census Bureau, Available online:http:\/\/www.census.gov\/tiger\/tms\/gazetteer\/county2k.txt."},{"key":"ref_38","unstructured":"Tovari, D., and Vogtle, T. (2004, January 3\u20136). Object classification in laser scanning data. Proceedings of the ISPRS Working Group VIII\/2, Laser-Scanners for Forest and Landscape Assessment, Freiburg, Germany."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7129","DOI":"10.1080\/01431160802238419","article-title":"Land use studies in drylands: An evaluation of object-oriented classification of very high resolution panchromatic imagery","volume":"29","author":"Elmqvist","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","unstructured":"(2009). Definiens eCognition Developer 8 Reference Book, Definiens AG."},{"key":"ref_42","unstructured":"Feitosa, R.Q., Costa, G.A., Cazes, T.B., and Feij\u00f3, B. (2006, January 4\u20135). A genetic approach for the automatic adaptation of segmentation parameters. Proceedings of the First International Conference on Object-Based Image Analysis, Salzburg, Austria."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/BF01223345","article-title":"Comparison of transform coding techniques for arbitrarily-shaped image segments","volume":"1","author":"Chang","year":"1994","journal-title":"Multimed. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/11\/11372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:09:23Z","timestamp":1760216963000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/11\/11372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11,14]]},"references-count":43,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2014,11]]}},"alternative-id":["rs61111372"],"URL":"https:\/\/doi.org\/10.3390\/rs61111372","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,11,14]]}}}