Monitoring the changes in impervious surface ratio and urban heat island intensity between 1987 and 2011 in Szeged, Hungary

被引:21
作者
Henits, Laszlo [1 ]
Mucsi, Laszlo [1 ]
Liska, Csilla Mariann [1 ]
机构
[1] Univ Szeged, Dept Phys Geog & Geoinformat, Szeged, Hungary
关键词
Impervioussurface ratio; Landsat timeseries data; Urban heat island; Spectral mixture analysis; Land cover change; Census data; SPECTRAL MIXTURE ANALYSIS; QUALITY-OF-LIFE; LAND-USE; CENSUS-DATA; SOIL MODEL; VEGETATION; URBANIZATION; INDIANAPOLIS; AREA; INTEGRATION;
D O I
10.1007/s10661-017-5779-8
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Landsat time series data make it possible to continuously map and examine urban land cover changes and effects on urban environments. The objectives of this study are (1) to map and analyse an impervious surface and its changes within a census district and (2) tomonitor the effects of increasing impervious surface ratios on population and environment. We used satellite images from 1987, 2003 and 2011 tomap the impervious surface ratio in the census district of Szeged, Hungary through normalized spectral mixture analysis. Significant increases were detected from 1987 to 2011 in industrial areas (5.7-9.1%) and inner residential areas (2.5-4.8%), whereas decreases were observed in the city centre and housing estates due to vegetation growth. Urban heat island (UHI) values were derived from the impervious surface fraction map to analyse the impact of urban land cover changes. In 2011, the average value in the industrial area was 1.76 degrees C, whereas that in the inner residential area was 1.35-1.69 degrees C. In the city centre zones and housing estates, values ranging from 1.4 to 1.5 degrees C and from 1.29 to 1.5 degrees C, respectively, were observed. Our study reveals that long-term land cover changes can be derived at the district level from Landsat images and that their effects can be identified and analysed, providing important information for city planners and policy makers.
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页数:13
相关论文
共 54 条
[1]  
ADAMS JB, 1986, J GEOPHYS RES-SOLID, V91, P8098, DOI 10.1029/JB091iB08p08098
[2]  
[Anonymous], 2014, DEMOGR RES
[3]  
Bauer ME, 2008, T&F SER REMOTE SENS, P3
[4]  
Blazovich L., 2005, 1943 SZEGED ROVID TO
[5]   NONLINEAR SPECTRAL MIXING MODELS FOR VEGETATIVE AND SOIL SURFACES [J].
BOREL, CC ;
GERSTL, SAW .
REMOTE SENSING OF ENVIRONMENT, 1994, 47 (03) :403-416
[6]   Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges [J].
Chander, G ;
Markham, B .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41 (11) :2674-2677
[7]  
Chavez PS, 1996, PHOTOGRAMM ENG REM S, V62, P1025
[8]   Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes [J].
Chen, Xiao-Ling ;
Zhao, Hong-Mei ;
Li, Ping-Xiang ;
Yin, Zhi-Yong .
REMOTE SENSING OF ENVIRONMENT, 2006, 104 (02) :133-146
[9]   Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface cover [J].
Chormanski, Jaroslaw ;
Van de Voorde, Tim ;
De Roeck, Tim ;
Batelaan, Okke ;
Canters, Frank .
SENSORS, 2008, 8 (02) :910-932
[10]   Synergy in remote sensing - what's in a pixel? [J].
Cracknell, AP .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1998, 19 (11) :2025-2047