Abstract:
More than half of the world population lives in Cities, therefore, urbanization has made a
significant contribution to the global warming. In rapidly rising Mega-Cities results in a
major change in land use and land cover (LULC) and substantial impact in land surface
temperature (LST). Abrupt rise of LST has also adversely impacted some of the urban
phenomenon. The aim was to analyze the pattern of LULC and LST change in Mirpur
and its surrounding area for the last 30 years using Landsat Satellite images and remote
sensing indices and develop relationship between LULC types and LST and analyze their
impact on local warming. Later prediction of LULC and LST change for next 20 years
was carried out using this analyzed data. Landsat-4 & 5 (TM) and Landsat-8 (OLI)
images were used to track the relation between the LULC changes and LST from 1989 to
2019 at an interval of five years. The LULC and LST maps were simulated for the year
2039 by Cellular Automata based Artificial Neural Network (CA-ANN) algorithm. Two
environmental indices such as Normalized Difference Vegetation Index (NDVI) and
Normalized Difference Built-up Index (NDBI) were analyzed to show their
interrelationship with LST. Findings of relationship among LST and LULC types signify
that built up area increases LST by replacing natural vegetation with non-evaporating
surfaces. Increasing trend of average surface temperature has been found and it has been
rising gradually for last 30 years. For the year 2019 it was found that approximately 86%
area was converted to built up area so far and 89% area had LST more than 28°C. The
simulation shows that if the present trend continues, 72% of Mirpur area is likely to
experience temperature close to 32°C in the year 2039. Further, LST showed a strong and
positive correlation with NDBI and negative correlation with NDVI. The overall accuracy
for LULC was over 90% where the result of the Kappa coefficient was 0.83 that was
more than 0.75. The study may help urban planners and environmental engineers to
understand and recommend effective policy steps and plans for LULC adjustments to
reduce its consequences.
