Assessing variations in land cover-land use and surface temperature dynamics for Dehradun, India, using multi-time and multi-sensor landsat data

被引:12
作者
Mishra, Kavach [1 ]
Garg, Rahul Dev [1 ]
机构
[1] Indian Inst Technol Roorkee, Civil Engn Dept, Geomatics Engn Grp, Roorkee, Uttaranchal, India
关键词
Urban heat island; Time series analysis; Change detection studies; Landsat; URBAN HEAT-ISLAND; AREA; INDEX; CITY; SATELLITE; POPULATION; TM; IMPACT; CITIES; ETM+;
D O I
10.1007/s10661-023-10945-z
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Rapid urbanisation and industrialisation coupled with overpopulation have altered land cover/land use (LCLU) and surface temperature (ST) patterns in Dehradun. Monitoring these changes through satellite-based remote sensing is required to ensure the sustained development of this ecologically fragile region. Here, LU and ST dynamics of the Dehradun municipal area have been estimated using Landsat-5 datasets for 1991, 1998, and 2008 and Landsat-8 dataset for 2018. LU maps have been extracted using a Gaussian Maximum Likelihood classifier with an overall accuracy of over 88% and Kappa coefficients above 0.85. Results reveal that the urban region expanded by 80.6% in the 27 years while dense vegetation and dry river bed classes have declined sharply. Sparse vegetation has risen by 3 km(2), whereas bare ground has decreased by about 4.3 km(2). Mean ST has increased above 9 degrees C from 1991 to 2018 in every season. A seasonal influence is evident on the mean ST per LU class's trend, which rose between 8 degrees C and 12 degrees C for every LU class, indicating significant warming across each LU class. ST probably has non-linear relationships with its causal factors represented by spectral indices, elevation, and population density. Urban heat island (UHI) formation is thus evinced, promulgating the administration's urgent action to save the environment and redrawing policies for ambitious projects like smart cities.
引用
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页数:31
相关论文
共 76 条
[11]   BCI: A biophysical composition index for remote sensing of urban environments [J].
Deng, Chengbin ;
Wu, Changshan .
REMOTE SENSING OF ENVIRONMENT, 2012, 127 :247-259
[12]   Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City [J].
Ding, Haiyong ;
Shi, Wenzhong .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (15) :5503-5517
[13]  
Duda R.O., 2012, PATTERN CLASSIFICATI, V2nd
[14]  
Dushi M, 2022, Scientific Review Engineering and Environmental Sciences (SREES), V31, P47, DOI [10.22630/srees.2324, 10.22630/srees.2324, DOI 10.22630/SREES.2324]
[15]  
Earth Resources Observation and Science (EROS) Center, 2017, USGS, DOI 10.5066/F7JM284N
[16]  
Forest Survey of India, 2019, IND STAT FOR REP 201
[17]  
Freire S., 2016, AGILE 2016
[18]  
Gallo K.P., 1998, Geocarto International, V13, P35, DOI [10.1080/10106049809354662, DOI 10.1080/10106049809354662]
[19]  
Hulley G, 2017, NASA EOSDIS LAND PRO, DOI [DOI 10.5067/MODIS/MYD21A1D.006, 10.5067/MODIS/MYD21A1D.006]
[20]   Remote sensing of the urban heat island effect across biomes in the continental USA [J].
Imhoff, Marc L. ;
Zhang, Ping ;
Wolfe, Robert E. ;
Bounoua, Lahouari .
REMOTE SENSING OF ENVIRONMENT, 2010, 114 (03) :504-513