Abstract:
Mapping land use land cover (LULC) change by using Remote Sensing data (RS) and
Geographic Information System (GIS) has gain a great concern in current strategies for
managing natural resources. Both natural and anthropogenic activities are modifying the land
cover which needs to be mapped and quantified. The objective of this study was to map the
LULC in Mansehra district during 2000-2016 in relation with population, and finding its
relationship with the climatic variables like temperature and precipitation. Moreover, climate
variables were also incorporated to find their impact on snow cover. This study utilized
satellite Landsat (TM, ETM+ and OLI) data for mapping land cover changes and MODIS
snow data (MODI 0A2) for finding the change in snow cover area. Two different sources of
climate data were used i.e. observed data from Pakistan Meteorological Department (PMD)
and Water and Power Development Authority (WAPDA) and gridded atmospheric
reanalysis data (ERA-Interim) for finding trend of climate variables and their comparison
from these two different sources. Primarily, LULC maps of year 2000, 2005, 2010 and 2016
were prepared using supervised classification approach and the temporal change from 2000-
2016 was quantified by post-classification comparison. LULC change showed significant
increase in built-up (+327.5%), cultivated area (+49.18%) and decrease in forest cover (-
13.4%). Population also showed increase of 16% from 1998 to 2017 which also corresponds
to increase in built-up. Mann-Kendall trend was used to find the trend of climatic variables
and snow. Temperature showed positive (increasing trend) while precipitation showed
negative (decreasing trend) at all stations both annually and seasonally. In comparison with
the PMD stations (low-elevation stations), ERA-Interim underestimated the temperature
values (positive bias) while the data obtained from WAPDA stations (high elevation
stations). it overestimated the average temperature (negative bias). In case of precipitation,
negative bias is shown which clearly indicates that ERA-Interim overestimates the
precipitation values in comparison with the ground observation. Bias occurs between ground
station and ERA-Interim values but overall same pattern of trend line is traced in case of
temperature. However, in case of precipitation, trend pattern somewhat differs for different
years. A strong Correlation was observed between ground-station and ERA-Interim temperature values in comparison with precipitation data (weak correlation) which shows that ERA-lnterim temperature product can be used as a reliable data for capturing temperature trends. Snow cover didn ·t show any significant trend.
