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Land Use Land Cover (LULC) and Land Surface Temperature (LST) Changes and its Relationship with Human Modification in Islamabad Capital Territory, Pakistan
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Abstract
Anthropogenic activities are changing the current Land use Land Cover (LULC) and Land Surface Temperature (LST) patterns worldwide. The current study uses Landsat satellite images (Landsat 5 TM and Landsat 8 OLI) during the years 1988, 2002, and 2016 in an alpine environment of Islamabad Capital Territory, Pakistan, to assess the past patterns of LULC variation using Maximum Likelihood Classification (MLC) method. The LST was derived from thermal bands (6, 10 and 11) of Landsat series data. The Human Modification Index (HMI) relationship with LULC and LST was also assessed using Google Earth Engine (GEE) data. The built-up area expanded by + 9.94%, while agricultural and bare soil dropped by -3.81% and − 3.94%, respectively. The results showed a considerable shift in the LULC and LST with a -1.99% loss in vegetation. The built-up region has the greatest temperature, followed by barren, agricultural, and vegetation classes, according to the LST study for various land cover classes. Similarly, the results of the HMI in different LST classes indicated that high LST classes have high human modification compared to lower LST classes. The statistical analysis between HMI and LST showed a significant association (R-value = 0.61). The results could be used for sustainable urban management and biodiversity conservation.
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Title: Land Use Land Cover (LULC) and Land Surface Temperature (LST) Changes and its Relationship with Human Modification in Islamabad Capital Territory, Pakistan
Description:
Abstract
Anthropogenic activities are changing the current Land use Land Cover (LULC) and Land Surface Temperature (LST) patterns worldwide.
The current study uses Landsat satellite images (Landsat 5 TM and Landsat 8 OLI) during the years 1988, 2002, and 2016 in an alpine environment of Islamabad Capital Territory, Pakistan, to assess the past patterns of LULC variation using Maximum Likelihood Classification (MLC) method.
The LST was derived from thermal bands (6, 10 and 11) of Landsat series data.
The Human Modification Index (HMI) relationship with LULC and LST was also assessed using Google Earth Engine (GEE) data.
The built-up area expanded by + 9.
94%, while agricultural and bare soil dropped by -3.
81% and − 3.
94%, respectively.
The results showed a considerable shift in the LULC and LST with a -1.
99% loss in vegetation.
The built-up region has the greatest temperature, followed by barren, agricultural, and vegetation classes, according to the LST study for various land cover classes.
Similarly, the results of the HMI in different LST classes indicated that high LST classes have high human modification compared to lower LST classes.
The statistical analysis between HMI and LST showed a significant association (R-value = 0.
61).
The results could be used for sustainable urban management and biodiversity conservation.
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