{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:16:32Z","timestamp":1760242592063,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T00:00:00Z","timestamp":1510617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["2017384"],"award-info":[{"award-number":["2017384"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671425"],"award-info":[{"award-number":["61671425"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Resources and Environmental Informational System"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery.<\/jats:p>","DOI":"10.3390\/rs9111148","type":"journal-article","created":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T10:58:32Z","timestamp":1510657112000},"page":"1148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016"],"prefix":"10.3390","volume":"9","author":[{"given":"Lingfei","family":"Shi","sequence":"first","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"The University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-4897","authenticated-orcid":false,"given":"Feng","family":"Ling","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Yong","family":"Ge","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6464-3054","authenticated-orcid":false,"given":"Giles","family":"Foody","sequence":"additional","affiliation":[{"name":"School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8285-8446","authenticated-orcid":false,"given":"Xiaodong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Lihui","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Yihang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Yun","family":"Du","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/01944369608975688","article-title":"Impervious surface coverage: The emergence of a key environmental indicator","volume":"62","author":"Arnold","year":"1996","journal-title":"J. Am. Plan. Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/136588100240886","article-title":"Modelling sustainable urban development by the integration of constrained cellular automata and gis","volume":"14","author":"Li","year":"2000","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1080\/13658816.2014.997237","article-title":"Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model","volume":"29","author":"Li","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2909","DOI":"10.1073\/pnas.1322280111","article-title":"Urban adaptation can roll back warming of emerging megapolitan regions","volume":"111","author":"Georgescu","year":"2014","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"ref_5","first-page":"1","article-title":"The energetic basis of urban heat island","volume":"108","author":"Oke","year":"1982","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20672","DOI":"10.1073\/pnas.0705527105","article-title":"The evolution of urban sprawl: Evidence of spatial heterogeneity and increasing land fragmentation","volume":"104","author":"Irwin","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0198-9715(99)00040-X","article-title":"Simulating runoff behavior in an urbanizing watershed","volume":"24","author":"Brun","year":"2000","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Schneider, A., and Mertes, C. (2014). Expansion and growth in Chinese cities, 1978\u20132010. Environ. Res. Lett., 9.","DOI":"10.1088\/1748-9326\/9\/2\/024008"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1080\/01431160802555838","article-title":"Synergistic use of optical and insar data for urban impervious surface mapping: A case study in Hong Kong","volume":"30","author":"Jiang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"161","DOI":"10.2747\/1548-1603.46.2.161","article-title":"Quantifying sub-pixel urban impervious surface through fusion of optical and insar imagery","volume":"46","author":"Yang","year":"2013","journal-title":"Gisci. Remote Sens."},{"key":"ref_12","first-page":"67","article-title":"Mapping impervious surfaces from lidar","volume":"4","author":"Denney","year":"2014","journal-title":"Lidar Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating impervious surface distribution by spectral mixture analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4807","DOI":"10.1080\/01431160802665926","article-title":"Estimating impervious surfaces using linear spectral mixture analysis with multitemporal aster images","volume":"30","author":"Weng","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2013.02.005","article-title":"A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution","volume":"133","author":"Deng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"557","DOI":"10.14358\/PERS.76.5.557","article-title":"Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (ndisi)","volume":"76","author":"Xu","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2012.09.009","article-title":"Bci: A biophysical composition index for remote sensing of urban environments","volume":"127","author":"Deng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1080\/2150704X.2013.798710","article-title":"Mndisi: A multi-source composition index for impervious surface area estimation at the individual city scale","volume":"4","author":"Liu","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"230","DOI":"10.5589\/m02-098","article-title":"An approach for mapping large-area impervious surfaces: Synergistic use of landsat-7 etm+ and high spatial resolution imagery","volume":"29","author":"Yang","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2005.04.017","article-title":"Assessments of urban growth in the tampa bay watershed using remote sensing data","volume":"97","author":"Xian","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"473","DOI":"10.14358\/PERS.74.4.473","article-title":"Quantifying multi-temporal urban development characteristics in las vegas from landsat and aster data","volume":"74","author":"Xian","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1080\/01431161003698393","article-title":"Impervious surface mapping with quickbird imagery","volume":"32","author":"Lu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Oliveira, R.V., Henion, J., and Wickremsinhe, E. (2013). A fully-automated approach for on-line dried blood spot extraction and bioanalysis by 2d-lc coupled with high-resolution qtof mass spectrometry. Anal. Chem., 86.","DOI":"10.1021\/ac403672u"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1080\/01431161.2012.703343","article-title":"Rule-based impervious surface mapping using high spatial resolution imagery","volume":"34","author":"Xu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bauer, M.E., Loffelholz, B.C., Wilson, B., and Loeffelholz, B.C. (2007). Estimating and mapping impervious surface area by regression analysis of landsat imagery. Remote Sens. Impervious Surf., 3\u201319.","DOI":"10.1201\/9781420043754.pt1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.isprsjprs.2010.10.010","article-title":"Detection of impervious surface change with multitemporal landsat images in an urban-rural frontier","volume":"66","author":"Lu","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.rse.2014.11.009","article-title":"Extending the vegetation\u2013impervious\u2013soil model using simulated enmap data and machine learning","volume":"158","author":"Okujeni","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1109\/TGRS.2008.917601","article-title":"Medium spatial resolution satellite imagery for estimating and mapping urban impervious surfaces using lsma and ann","volume":"46","author":"Weng","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7609","DOI":"10.1080\/01431161.2012.700424","article-title":"Mapping impervious surface expansion using medium-resolution satellite image time series: A case study in the yangtze river delta, China","volume":"33","author":"Gao","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2012.10.010","article-title":"Long-term land cover dynamics by multi-temporal classification across the landsat-5 record","volume":"128","author":"Sexton","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2014.09.014","article-title":"Corrigendum to \u201curban growth of the Washington, D.C.\u2013Baltimore, md metropolitan region from 1984 to 2010 by annual, landsat-based estimates of impervious cover\u201d [remote sensing of environment 129 (2013) 42\u201353]","volume":"155","author":"Sexton","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2016.01.003","article-title":"Annual dynamics of impervious surface in the pearl river delta, China, from 1988 to 2013, using time series landsat imagery","volume":"113","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.12.027","article-title":"Characterizing the magnitude, timing and duration of urban growth from time series of landsat-based estimates of impervious cover","volume":"175","author":"Song","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2015.06.007","article-title":"A 30-year (1984\u20132013) record of annual urban dynamics of beijing city derived from landsat data","volume":"166","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.rse.2017.05.011","article-title":"Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps","volume":"196","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","unstructured":"Ge, Y., Li, S., Duan, R., Bai, H., and Cao, F. (2008, January 25\u201327). Multi-level measurements for uncertainty in classified remotely sensed imagery. Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Shanghai, China."},{"key":"ref_37","first-page":"413","article-title":"Exploring uncertainty in remotely sensed data with parallel coordinate plots","volume":"11","author":"Ge","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2015.03.004","article-title":"Analysis of uncertainty in multi-temporal object-based classification","volume":"105","author":"Conrad","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5489","DOI":"10.1080\/01431160903130929","article-title":"A method to incorporate uncertainty in the classification of remote sensing images","volume":"30","author":"Fonte","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Schmidt, G., Jenkerson, C., Masek, J., Vermote, E., and Gao, F. (2013). Landsat Ecosystem Disturbance Adaptive Processing System Algorithm Description. 2331\u20131258.","DOI":"10.3133\/ofr20131057"},{"key":"ref_41","unstructured":"Qi, H.N., Yang, J.G., Zhong, Y.W., and Deng, C. (2004, January 26\u201329). Multi-class svm based Remote Sens. image classification and its semi-supervised improvement scheme. Proceedings of the International Conference on Machine Learning and Cybernetics, Shanghai, China."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1080\/01431169508954549","article-title":"Exploring a v-i-s (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities","volume":"16","author":"RIDD","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A thresholding selection method from gray-level histogram","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Atkinson, P.M., and Foody, G.M. (2002). Uncertainty in Remote Sensing and GIS: Fundamentals, John Wiley & Sons, Inc.","DOI":"10.1002\/0470035269.ch1"},{"key":"ref_46","unstructured":"Foody, G.M. (2006). Deriving thematic uncertainty measures in remote sensing using classification outputs. Clin. Chem., 1460\u20131468."},{"key":"ref_47","first-page":"1335","article-title":"Derivation and applications of probabilistic measures of class membership from the maximum likelihood classification","volume":"58","author":"Foody","year":"1992","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bogaert, P., Waldner, F., and Defourny, P. (2016). An information-based criterion to measure pixel-level thematic uncertainty in land cover classifications. Stoch. Environ. Res. Risk Assess., 1\u201316.","DOI":"10.1007\/s00477-016-1310-y"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/01431160802290568","article-title":"Estimating per-pixel thematic uncertainty in remote sensing classifications","volume":"30","author":"Brown","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","first-page":"173","article-title":"Random forests as a tool for estimating uncertainty at pixel-level in sar image classification","volume":"19","author":"Loosvelt","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1046\/j.1466-822X.2003.00015.x","article-title":"A tribute to claude shannon (1916\u20132001) and a plea for more rigorous use of species richness, species diversity and the \u2018shannon-wiener\u2019 index","volume":"12","author":"Spellerberg","year":"2003","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1111\/j.2517-6161.1986.tb01412.x","article-title":"On the statistical analysis of dirty pictures","volume":"48","author":"Besag","year":"1986","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5243","DOI":"10.1080\/01431160903131000","article-title":"Sampling designs for accuracy assessment of land cover","volume":"30","author":"Stehman","year":"2009","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1148\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:49:25Z","timestamp":1760208565000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1148"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,14]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["rs9111148"],"URL":"https:\/\/doi.org\/10.3390\/rs9111148","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2017,11,14]]}}}