Modelling of Land Use/Cover and LST Variations by Using GIS and Remote Sensing: A Case Study of the Northern Pakhtunkhwa Mountainous Region, Pakistan.

Alteration in Land Use/Cover (LULC) considered a major challenge over the recent decades, as it plays an important role in diminishing biodiversity, altering the macro and microclimate. Therefore, the current study was designed to examine the past 30 years (1987−2017) changes in LULC and Land Surface Temperature (LST) and also simulated for next 30 years (2047). The LULC maps were developed based on maximum probability classification while the LST was retrieved from Landsat thermal bands and Radiative Transfer Equation (RTE) method for the respective years. Different approaches were used, such as Weighted Evidence (WE), Cellular Automata (CA) and regression prediction model for the year 2047. Resultantly, the LULC classification showed increasing trend in built-up and bare soil classes (13 km2 and 89 km2), and the decreasing trend in vegetation class (−144 km2) in the study area. In the next 30 years, the built-up and bare soil classes would further rise with same speed (25 km2 and 36.53 km2), and the vegetation class would further decline (−147 km2) until 2047. Similarly for LST, the temperature range for higher classes (27 -< 30 °C) increased by about 140 km2 during 1987−2017, which would further enlarge (409 km2) until 2047. The lower LST range (15 °C to <21 °C) showed a decreasing trend (−54.94 km2) and would further decline to (−20 km2) until 2047 if it remained at the same speed. Prospective findings will be helpful for land use planners, climatologists and other scientists in reducing the increasing LST associated with LULC changes.

Trends and projections of land use land cover and land surface temperature using an integrated weighted evidence-cellular automata (WE-CA) model.

Land use land cover (LULC) change has become a major concern for biodiversity, ecosystem alteration, and modifying the climatic pattern especially land surface temperature (LST). The present study assessed past and predicted future LULC and LST change in the Swabi District of Pakistan. LULC maps were generated from satellite data for years 1987, 2002, and 2017 using supervised classification. Mean LST and its areal change were estimated for different LULC classes from thermal bands of satellite images. LULC and LST were projected for the year 2047 using the integrated weighted evidence-cellular automata (WE-CA) model and a regression equation developed in this study, respectively. LULC change revealed an increase of > 5% in the built-up while a decrease in the agricultural area by ~ 9%. There was an increase of ~ 63% area in the LST class ≥ 27 °C which may create urban heat island (UHI). Simulation results indicated that the built-up area will further be increased by ~ 3% until 2047. Area associated with LST class > 30 °C indicated a further increase of ~ 38% till 2047 with reference to year 2017. Findings of this study suggested proper utilization of LULC in order to mitigate the creation of UHIs associated with urbanization and built-up areas.

Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region.

Rapid urbanization is changing the existing patterns of Land Use Land Cover (LULC) globally which is consequently increasing the Land Surface Temperature (LST) in many regions. Present study was focused on estimating the current and simulating the future LULC and LST trends in the alpine environment of lower Himalayan region of Pakistan. Past patterns of LULC and LST were identified through the Support Vector Machine (SVM) and multi-spectral Landsat satellite images during 1987-2017 data period. The Cellular automata (CA) model and Artificial Neural Network (ANN) were applied to simulate future (years 2032 and 2047) LULC and LST changes, respectively, using their past patterns. CA model was validated for the simulated and the estimated LULC for the year 2017 with an overall Kappa (K) value of 0.77 using validation modules in QGIS and IDRISI software. ANN method was validated by correlating the observed and simulated LST for the year 2017 with correlation coefficient (R) and Mean Square Error (MSE) values of 0.81 and 0.51, respectively. Results indicated a change in the LULC and LST for instance the built-up area was increased by 4.43% while agricultural area and bare soil were reduced by 2.74% and 4.42%, respectively, from 1987 to 2017. The analysis of LST for different LULC classes indicated that built-up area has highest temperature followed by barren, agriculture and vegetation surfaces. Simulation of future LULC and LST showed that the built-up area will be increased by 2.27% (in 2032) and 4.13% (in 2047) which led 42% (in 2032) and 60% (in 2047) of the study area as compared to 26% area (in 2017) to experience LST greater than 27 °C. A strong correlation between built-up area changes and LST was thus found signifying major challenge to urban planners mitigating the consequent of Urban Heat Island (UHI) phenomenon. It is suggested that future urban planning should focus on urban plantation to counter UHI phenomena in the region of lower Himalayas.

Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region.

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 µm-12.51 µm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4-7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of -0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.