| Land Use and Land Cover(LULC)alteration can be attributed to both natural and manmade factors.Earthquake and erosion are examples of natural causes,whereas overpopulation,urbanisation,deforestation,and subsequent climate change are examples of human-made causes.In terms of LULC,the planet has seen tremendous global change in recent decades.Rapid urban growth is displacing natural regions with built-up areas,which raises the Land Surface Temperature(LST).The substantial inflow of migrants from the region is modifying the LULC and LST patterns in Northern Pakistan,which is also having an impact on urban areas.This study’s main objective was to assess the alteration in LULC and LST for three decades(1987-2017)and projected(2047)periods in Pakistan’s Northern Pakhtunkhwa.To identify prior trends in LULC and LST,historical remote sensing record from the different sensors of Landsat such as Landsat 5,7 and 8 were collected and processed using Arc GIS 10.5,QGIS 2.18,and ENVI5.3 software.With the use of Ground Control Points(GCP)gathered in the field during a survey,Landsat data was verified.Maximum Likelihood Catagorization(MLC)and Support Vector Machine(SVM)methods were used to identify LULC changes in certified Landsat data for the periods 1987-2017.For the data period,LST was calculated in Arc Map using bands of thermal for Landsat images.Using the models of Cellular Automata and Artificial Neural Network(CA-ANN)with linear regression methods,future trends of LULC and LST were simulated.Models were used for past trends in the LULC,LST,and LULC indices as input to forecast upcoming changes.The accuracy of LULC simulation was assessed by comparing classified and simulated image of 2017.The simulated image of 2017 was generated from the classified images of 1987 and 2002 using the QGIS-MULSCE tool.The validation assessed using Kappa co-efficient and percentage accuracy.The LULC model validations(percent)accuracy value was higher than 70%.This study also investigated the relationship of driving variables with LULC and LST using two statistical techniques: Pearson correlation(r)and geographically weighted regression(GWR).For this purpose,population data from Pakistan Bureau of Statistics(PBS)were correlated with LULC and LST for the years1987,2002,and 2017.The analyses were accomplished using various steps.First,the relationship between migration data and LULC changes were examined using Pearson correlation analysis in SPSS software,and then LST were calculated for various LULC classes using Zonal analysis in Arc Map 10.5 software.The GWR model was used to assess the overall population density effect on LST.The relationship between urban footprint and changes in LST and LULC was also examined using statistical analysis.Study area(analyse areas 1 and 2 in Northern Pakhtunkhwa province)was split into two points to study general trends before focusing analysis on two crucial regions(research region 1 Terbella and research region 2 district Shangla).The builtup area expanded by(+1.2%)respectively,over the study period 1987-2017 for study areas 1 Terbella according to the data for LULC changes.During the study period overall periodic changes from 1987-2017 indicated that,the amount of vegetation was decline by(13.92%)in the study area 1 Terbella.For the period of 1987-2002,built-up and bare soil increased by(+0.98%)and(+20.98%)respectively,while vegetation(-15.33%),agriculture(-8.65%)and water bodies showed decreasing trend.For the period 2002-2017,built-up(+0.13%),vegetation(+1.41%)and agriculture(+11.35%)showed increasing trend,while bare soil(-12.33%)showed decreasing trend in the study area 1 Terbella.Similarly for study area 2 Shangla,overall changes occur in built-up(+2.22%)and vegetation(+11.08%)class showed increasing trend while bare soil(-8.47%)agriculture(-4.55%)and water body(-0.28%)indicated decreasing trend during(1987-2017).For the period 1987-2002,built-up(+1.101%)and vegetation showed(+4.839%)increasing trend while bare soil(-3.029%),agriculture(-2.718%),and water body(-0.198%)showed decreasing trend.For the period 2002-2017,builtup(+1.127%)and vegetation showed(+6.25%)increasing trend while bare soil(-5.444%),agriculture(-1.839%),and water body(-0.09%)showed decreasing trend in the research region 2 Shangla.Similarly,LST estimation for various LULC classes revealed that for both study locations,built-up area had the upper most mean LST,followed by bare soil,farmland,vegetation,and water body.The area in range of LST classes with lower than 24°C indicated decreasing trend,while the area of higher classes in range of 24°C or above showed increasing pattern which might result in the formation of Urban Heat Islands(UHIs).According to the results of the future LULC simulation for both the study areas,1 and 2 over the data periods,the built-up region built-up area might be enlarge 2.44% and 2.74% respectively in comparison to the base year 2017.According to the LST simulation,the study areas 1 and 2 will be 5.6%and 20.12% higher for LST class in the anticipated year 2047,respectively.Most of the lower LST classes in study areas 1 and 2 were transformed into higher LST classes.The results for LULC classes and migration data indicated that classes of LULC changes were significantally related to the population increase for both study areas(1,2),as indicated by the Pearson(r)values.The highest Pearson values were found for built-up areas,which was 0.59 and 0.6,respectively for study area 1 and 2.For the study area 1 all other classes showed positive correlation with migration except of vegetation with the Pearson(r)value of-0.34 while in the study area 2 bare soil,agriculture and water body showed negative correlation with migration.The built-up and vegetation showed positive correlation with migration in the study area 2.The LULC changes have also strongly correlated with LST in study area 1 and 2.The Zonal analyses between LST and LULC that built-up has the highest mean LST,followed by bare soil,agriculture,vegetation and water body.The GWR model results indicated that LST has positive correlation(R square values)with population in both study areas(1 and 2).This research would be extremely helpful in establishing a thorough framework for comprehending the connections and interactions between LULC and LST as well as in LULC management and planning.Government authorities and planners might utilize the study’s findings to build a strategy for UHIs mitigation and for land use planners and urban managers.The urban forecasts,increase in vegetation cover,green roofing,cool pavements,and smart growth all should be part of the UHIs mitigation strategy in the research area urban centres. |
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