{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T11:49:46Z","timestamp":1784116186137,"version":"3.55.0"},"reference-count":70,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T00:00:00Z","timestamp":1524441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41331175"],"award-info":[{"award-number":["41331175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51378399"],"award-info":[{"award-number":["51378399"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land Surface Temperature (LST) is a critical component to understand the impact of urbanization on the urban thermal environment. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization. This paper presents a workflow to extract the spatio-temporal pattern of LST through time series clustering by focusing on the LST of Wuhan, China, from 2002 to 2017 with a 3-year time interval with 8-day MODerate-resolution Imaging Spectroradiometer (MODIS) satellite image products. The Latent pattern of LST (LLST) generated by non-parametric Multi-Task Gaussian Process Modeling (MTGP) and the Multi-Scale Shape Index (MSSI) which characterizes the morphology of LLST are coupled for pattern recognition. Specifically, spatio-temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of   k   -means and the Back-Propagation neural networks (BP-Net). The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. For spatio-temporal pattern recognition, LLSTs and MSSIs of the 6 years are regarded as geo-referenced time series. Multiple algorithms including traditional   k   -means with Euclidean Distance (ED), shape-based   k   -means with the constrained Dynamic Time Warping (   c   DTW) distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA) centroid computation method (   k   -   c   DBA) and   k   -shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal   k   -   c   DBA as the optimal time series clustering algorithm for our study. The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST patterns. The homogeneous geographical clusters correspond to the zoning custom of urban planning and design, and thus, may efficiently bridge the urban and environmental systems in terms of research scope and scale. The proposed workflow can be utilized for other cities and potentially used for comparison among different cities.<\/jats:p>","DOI":"10.3390\/rs10040654","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T04:44:48Z","timestamp":1524545088000},"page":"654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology"],"prefix":"10.3390","volume":"10","author":[{"given":"Huimin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Urban Design, Wuhan University, 8 Donghu South Road, Wuhan 430072, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5619-6610","authenticated-orcid":false,"given":"Qingming","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Urban Design, Wuhan University, 8 Donghu South Road, Wuhan 430072, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3836-0146","authenticated-orcid":false,"given":"Chen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Urban Design, Wuhan University, 8 Donghu South Road, Wuhan 430072, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiong","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 Enschede, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.isprsjprs.2009.03.007","article-title":"Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends","volume":"64","author":"Weng","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.rse.2013.09.002","article-title":"Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of landsat tir data","volume":"140","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.12.040","article-title":"A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery","volume":"175","author":"Fu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1175\/BAMS-D-11-00019.1","article-title":"Local climate zones for urban temperature studies","volume":"93","author":"Stewart","year":"2012","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2003.11.005","article-title":"Estimation of land surface temperature\u2013vegetation abundance relationship for urban heat island studies","volume":"89","author":"Weng","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2005.11.016","article-title":"Remote sensing image-based analysis of the relationship between urban heat island and land use\/cover changes","volume":"104","author":"Chen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.landurbplan.2013.11.014","article-title":"The relationships between landscape compositions and land surface temperature: Quantifying their resolution sensitivity with spatial regression models","volume":"123","author":"Song","year":"2014","journal-title":"Landsc. Urban Plan."},{"key":"ref_8","first-page":"55","article-title":"Characterizing the spatial dynamics of land surface temperature\u2013impervious surface fraction relationship","volume":"45","author":"Wang","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1080\/13658816.2010.508043","article-title":"Space, time and visual analytics","volume":"24","author":"Andrienko","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.scitotenv.2014.11.006","article-title":"Impact of urbanization and land-use\/land-cover change on diurnal temperature range: A case study of tropical urban airshed of india using remote sensing data","volume":"506\u2013507","author":"Mohan","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2014.08.009","article-title":"Modeling diurnal land temperature cycles over Los Angeles using downscaled goes imagery","volume":"97","author":"Weng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1109\/LGRS.2015.2455019","article-title":"Temporal dynamics of land surface temperature from landsat TIR time series images","volume":"12","author":"Fu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sismanidis, P., Keramitsoglou, I., and Kiranoudis, C.T. (2017, January 6\u20138). Identifying and characterizing the diurnal evolution of urban land surface temperature patterns. Proceedings of the Urban Remote Sensing Event (JURSE 2017), Dubai, UAE.","DOI":"10.1109\/JURSE.2017.7924598"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Haashemi, S., Weng, Q., Darvishi, A., and Alavipanah, S.K. (2016). Seasonal variations of the surface urban heat island in a semi-arid city. Remote Sens., 8.","DOI":"10.3390\/rs8040352"},{"key":"ref_15","first-page":"1","article-title":"Mapping the Spatiotemporal Dynamics of Europe\u2019s Land Surface Temperatures","volume":"99","author":"Sismanidis","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.1016\/j.rse.2009.07.021","article-title":"Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use\/cover in the Tabriz urban area, Iran","volume":"113","author":"Amiri","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.3390\/rs4072033","article-title":"The impacts of rapid urbanization on the thermal environment: A remote sensing study of guangzhou, south china","volume":"4","author":"Xiong","year":"2012","journal-title":"Remote Sens."},{"key":"ref_18","unstructured":"Wang, S., Ma, Q., Ding, H., and Liang, H. (2016). Detection of urban expansion and land surface temperature change using multi-temporal landsat images. Resour. Conserv. Recycl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1126\/science.268.5207.59","article-title":"The seasons, global temperature, and precession","volume":"268","author":"Thomson","year":"1995","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2017.01.001","article-title":"Characterizing the relationship between land use land cover change and land surface temperature","volume":"124","author":"Tran","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kisilevich, S., Mansmann, F., Nanni, M., and Rinzivillo, S. (2009). Spatio-temporal clustering. Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/978-0-387-09823-4_44"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1016\/j.patcog.2005.01.025","article-title":"Clustering of time series data\u2014A survey","volume":"38","author":"Liao","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Paparrizos, J., and Gravano, L. (June, January 31). k-Shape: Efficient and Accurate Clustering of Time Series. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD 2015), Melbourne, Australia.","DOI":"10.1145\/2723372.2737793"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.is.2015.04.007","article-title":"Time-series clustering\u2014A decade review","volume":"53","author":"Aghabozorgi","year":"2015","journal-title":"Inf. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data Clustering: 50 Years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.engappai.2014.12.015","article-title":"Fuzzy clustering of time series data using dynamic time warping distance","volume":"39","author":"Izakian","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_27","first-page":"103","article-title":"State: Pace discrimination and clustering of atmospheric time series data based on kullback information measures","volume":"19","author":"Thomas","year":"2007","journal-title":"Environmetrics"},{"key":"ref_28","first-page":"1","article-title":"A dynamic fuzzy cluster algorithm for time series","volume":"2013","author":"Ji","year":"2013","journal-title":"Abstr. Appl. Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"605","DOI":"10.5194\/npg-21-605-2014","article-title":"Trend analysis using non-stationary time series clustering based on the finite element method","volume":"21","author":"Sayemuzzaman","year":"2014","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hallac, D., Vare, S., Boyd, S., and Leskovec, J. (2017, January 13\u201317). Toeplitz inverse covariance-based clustering of multivariate time series data. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098060"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhan, Q., and Guo, H. (2016). The Morphology, Dynamics and Potential Hotspots of Land Surface Temperature at a Local Scale in Urban Areas. Remote Sens., 8.","DOI":"10.3390\/rs8010018"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.ecolind.2012.01.001","article-title":"Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators\u2014An application for the city of leipzig (Germany)","volume":"18","author":"Schwarz","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1080\/02693798708927820","article-title":"A spatial analytical perspective on geographical information systems","volume":"1","author":"Goodchild","year":"1987","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"615","DOI":"10.2307\/3235878","article-title":"Integrating GIS and remote sensing for vegetation analysis and modeling: Methodological issues","volume":"5","author":"Goodchild","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1080\/01431160110115023","article-title":"A remote sensing study of the urban heat island of houston, Texas","volume":"23","author":"Streutker","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/S0034-4257(03)00007-5","article-title":"Satellite-measured growth of the urban heat island of houston, Texas","volume":"85","author":"Streutker","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"34","article-title":"Assessment with satellite data of the Urban Heat Island effects in Asian mega cities","volume":"8","author":"Hung","year":"2006","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3080","DOI":"10.1016\/j.rse.2011.06.014","article-title":"Identification and analysis of urban surface temperature patterns in Greater Athens, Greece, using MODIS imagery","volume":"115","author":"Keramitsoglou","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2008.05.002","article-title":"Urban heat island monitoring and analysis using a non-parametric model: A case study of Indianapolis","volume":"64","author":"Rajasekar","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.rse.2011.12.019","article-title":"A daily merged modis aqua\u2013terra land surface temperature data set for the conterminous United States","volume":"119","author":"Crosson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4061","DOI":"10.1029\/1999JD901088","article-title":"Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 2. Cloudy-pixel treatment","volume":"105","author":"Jin","year":"2000","journal-title":"J. Geophys. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2015.11.005","article-title":"Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of wuhan in china","volume":"172","author":"Shen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_43","unstructured":"Wan, Z. (2007). Collection-5 Modis Land Surface Temperature Products Users\u2019 Guide. Institute for Computational Earth System Science (ICESS), University of California, Santa Barbara."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A generalized split-window algorithm for retrieving land-surface temperature from space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2006.06.026","article-title":"New refinements and validation of the MODIS land-surface temperature\/emissivity products","volume":"112","author":"Wan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New refinements and validation of the collection-6 modis land-surface temperature\/emissivity product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"Bonilla, E.V., Chai, K.M., and Williams, C. (2008). Multi-task gaussian process prediction. Advances in Neural Information Processing Systems, Donnelley & Sons."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"32141","DOI":"10.1029\/1998JD200032","article-title":"Discriminating clear sky from clouds with MODIS","volume":"103","author":"Ackerman","year":"1998","journal-title":"J. Geophys. Res."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bonde, U., Badrinarayanan, V., and Cipolla, R. (2013). Multi Scale Shape Index for 3D Object Recognition, Springer.","DOI":"10.1007\/978-3-642-38267-3_26"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/0262-8856(92)90076-F","article-title":"Surface shape and curvature scales","volume":"10","author":"Koenderink","year":"1992","journal-title":"Image Vis. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1023\/A:1008045108935","article-title":"Feature detection with automatic scale selection","volume":"30","author":"Lindeberg","year":"1998","journal-title":"Int. J. Comput. Vis."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0165-0114(01)00063-X","article-title":"Simultaneous grouping of parts and machines with an integrated fuzzy clustering method","volume":"126","author":"Josien","year":"2003","journal-title":"Fuzzy Sets Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.jenvman.2016.11.059","article-title":"Attenuating the surface Urban Heat Island within the Local Thermal Zones through land surface modification","volume":"187","author":"Wang","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust. Speech Signal"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.patcog.2010.09.013","article-title":"A global averaging method for dynamic time warping, with applications to clustering","volume":"44","author":"Petitjean","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1023\/A:1024988512476","article-title":"On the need for time series data mining benchmarks: A survey and empirical demonstration","volume":"7","author":"Keogh","year":"2003","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_57","unstructured":"Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., and Keogh, E. (2008, January 24\u201330). Querying and mining of time series data: Experimental comparison of representations and distance measures. Proceedings of the 34th International Conference on Very Large Databases (VLDB), Auckland, New Zealand."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10618-012-0250-5","article-title":"Experimental comparison of representation methods and distance measures for time series data","volume":"26","author":"Wang","year":"2013","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Faloutsos, C., Ranganathan, M., and Manolopoulos, Y. (1994, January 24\u201327). Fast subsequence matching in time-series databases. Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data (SIGMOD 1994), Minneapolis, Minnesota.","DOI":"10.1145\/191839.191925"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ulanova, L., Begum, N., and Keogh, E. (May, January 30). Scalable Clustering of Time Series with U-Shapelets. Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, British Columbia, BC, Canada.","DOI":"10.1137\/1.9781611974010.101"},{"key":"ref_61","unstructured":"Vlachos, M., Lin, J., Keogh, E., and Gunopulos, D. (2003, January 1\u20133). A Wavelet-Based Anytime Algorithm for K-Means Clustering of Time Series. Proceedings of the Workshop on Clustering High Dimensionality Data and Its Applications, San Francisco, CA, USA."},{"key":"ref_62","first-page":"1999","article-title":"A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China","volume":"22","author":"Weng","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"8935","DOI":"10.1073\/pnas.1606037114","article-title":"Sustainability in an urbanizing planet","volume":"114","author":"Seto","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_64","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_65","unstructured":"Stewart, I., and Oke, T. (2009, January 11\u201315). Newly developed \u201cthermal climate zones\u201d for defining and measuring urban heat island magnitude in the canopy layer. Proceedings of the Eighth Symposium on Urban Environment, Phoenix, AZ, USA."},{"key":"ref_66","first-page":"1","article-title":"The energetic basis of the urban heat island","volume":"108","author":"Oke","year":"1982","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Fischer, M.M., and Nijkamp, P. (2014). Handbook of Regional Science, Springer.","DOI":"10.1007\/978-3-642-23430-9"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1016\/j.eswa.2007.12.013","article-title":"Temperature prediction and taifex forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series","volume":"36","author":"Wang","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2121","DOI":"10.1016\/j.eswa.2014.09.036","article-title":"A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering","volume":"42","author":"Askari","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ghofrani, M., Carson, D., and Ghayekhloo, M. (2016, January 18\u201320). Hybrid clustering-time series-bayesian neural network short-term load forecasting method. Proceedings of the North American Power Symposium (NAPS 2016), Denver, CO, USA.","DOI":"10.1109\/NAPS.2016.7747865"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:01:47Z","timestamp":1760194907000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,23]]},"references-count":70,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040654"],"URL":"https:\/\/doi.org\/10.3390\/rs10040654","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,23]]}}}