{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:18:51Z","timestamp":1764937131963,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"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>Urban expansion results in landscape pattern changes and associated changes in land surface temperature (LST) intensity. Spatial patterns of urban LST are affected by urban landscape pattern changes and seasonal variations. Instead of using LST change data, this study analysed the variation of LST aggregation which was evaluated by hotspot analysis to measure the spatial dependence for each LST pixel, indicating the relative magnitudes of the LST values in the neighbourhood of the LST pixel and the area proportion of the hotspot area to gain new insights into the thermal effects of increasing impervious surface area (ISA) caused by urbanization in Fuzhou, China. The spatio-temporal relationship between urban landscape patterns, hotspot locations reflecting urban land cover change in space and the thermal environment were analysed in different sectors. The linear spectral unmixing method of fully constrained least squares (FCLS) was used to unmix the bi-temporal Landsat TM\/OLI imagery to derive subpixel ISA and the accuracy of the percent ISA was assessed. Then, a minimum change threshold was chosen to remove random noise, and the change of ISA between 2000 and 2016 was analysed. The urban area was divided into three circular consecutive urban zones in the cardinal directions from the city centre and each circular zone was further divided into eight segments; thus, a total of 24 spatial sectors were derived. The LST aggregation was analysed in different directions and urban segments and hotspot density was further calculated based on area proportion of hotspot areas in each sector. Finally, variations of mean normalized LST (NLST), area proportion of ISA, area proportion of ISA with high LST, and area proportion of hotspot area were quantified for all sectors for 2000 and 2016. The four levels of hotspot density were classified for all urban sectors by proportional ranges of 0%\u201325%, 25%\u201350%, 50%\u201375% and 75%\u2013100% for low-, medium-, sub-high, and high density, and the spatial dynamics of hotspot density between the two dates showed that urbanization mainly dominated in sectors south\u2013southeast 2 (SSE2), south\u2013southwest 2 (SSW2), west\u2013southwest 2 (WSW2), west\u2013northwest 2 (WNW2), north\u2013southwest 2 (NSW2), south\u2013southeast 3 (SSE3) and south\u2013southwest 3 (SSW3). This paper suggests a methodology for characterizing the urban thermal environment and a scientific basis for sustainable urban development.<\/jats:p>","DOI":"10.3390\/rs11232810","type":"journal-article","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T11:07:00Z","timestamp":1574852820000},"page":"2810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Directional and Zonal Analysis of Urban Thermal Environmental Change in Fuzhou as an Indicator of Urban Landscape Transformation"],"prefix":"10.3390","volume":"11","author":[{"given":"Youshui","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Geography, Fujian Normal University, Fuzhou 350007, China"},{"name":"State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou 350007, China"},{"name":"Key Laboratory of Spatial Data Mining &amp; Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Xiaoqin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining &amp; Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9053-4684","authenticated-orcid":false,"given":"Heiko","family":"Balzter","sequence":"additional","affiliation":[{"name":"Centre for Landscape and Climate Research, School of Geography, Geology and Environment, University of Leicester, Leicester LE1 7RH, UK"},{"name":"National Centre for Earth Observation, University of Leicester, Leicester LE1 7RH, UK"}]},{"given":"Bingwen","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining &amp; Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Jingyuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Geography, Fujian Normal University, Fuzhou 350007, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,27]]},"reference":[{"key":"ref_1","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. 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