Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan

Abstract

The aim of this research was to assess the land use/land cover (LULC) changes and its impact on land surface temperature (LST) using remote-sensing (RS) technique in the district Khanewal, Punjab, Pakistan. Data were pre-processed using ERDAS imagine 15 and Arc GIS 10.4 software for layer stacking, mosaicking, and sub-setting of Landsat images. After pre-processing, the supervised classification scheme was applied for the years 1980, 2000, and 2020, which explains the maximum likelihood algorithm to identify LULC changes observed in the study area. "Built-up area" in 1980 occupied 1.75% but in 2020, the build-up area increased (5.27%) compared to 2020. Vegetation area was decreased by 4.12% from 1980 to 2020 in district Khanewal. It was observed that there has been a rapid change in vegetation area to build-up area. The LST values were increased by 0.50°C from 1980 to 2020 due to the increasing build-up area from East to West direction in district Khanewal. Maximum and minimum normalized difference vegetation index (NDVI) values were observed 0.72 and −0.2 for 1980 and 2020. The regression line produced a definitive explanation, showing a strong negative correlation with NDVI and LST. The outcomes of this study indicated that a dramatic transformation took place in district Khanewal regarding a decrease in greenness due to a rapid increase in population density, urban growth, and other infrastructural developments. Thus, these results will be used in regional and urban planning and will be used for managing agriculture in the coming years of rapid environmental changes.

Thirty Years of Land Use/Land Cover Changes and Their Impact on Urban Climate: A Study of Kano Metropolis, Nigeria

“…The areas around the city's central core with built-up areas and urban facilities have higher LST due to the impervious surfaces that expose such areas to greater solar radiation. The findings align with recent studies which have opined that the modification of land use due to various socio-economic factors has influenced local climatic condition in urban areas [6,73,78]. Additional studies are therefore needed in order to forecast and mitigate the environmental consequences of such changes.…”

“…The study further witnessed an alteration of NDVI in 2020 with minimum and maximum values of −0.14 and +0.30, respectively. Previous studies indicate that areas with higher NDVI values signify forest and vegetated lands having agricultural farms, while lower NDVI values represent built-up areas and other land uses such as barren land and water bodies [61,73]. The NDVI maps showed a substantial decrease in vegetation cover, with lower NDVI values from 1991 to 2020.…”

Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh

“…To achieve the optimal information from multispectral bands, we used multispectral Landsat imagery which contains more than three bands (for instance, Landsat imagery has 8 spectral bands: blue, green, red, NIR, SWIR1, thermal and SWIR2) Bhuiyan et al 2020a, b;Witharana et al 2019). Moreover, recent research showed that satellite-based remotely sensed Landsat images were successfully applied to generate LULC and LST maps to evaluate the land cover changes replacing natural vegetated surfaces with man-made infrastructures (Ibrahim 2017;Igun and Williams 2018;Hussain and Karuppannan 2021). Therefore, we conducted our study based on seven multi-spectral images acquired by the Landsat satellite sensor.…”

Using space–time scan statistic for studying the effects of COVID-19 in Punjab, Pakistan: a guideline for policy measures in regional agriculture

“…The STSS has been used to detect emerging clusters of shigellosis, measles, thyroid cancer and syndromic observation (Hohl et al 2020). Geographic information systems (GIS) has been applied to the field of agriculture in various domains including land management, regional agricultural resource information and planning (Hussain 2018;Majeed et al 2021), management of grain distribution and decisionmaking related to food production, research in the areas of agricultural production potential and crop yield estimation (Hussain et al 2020b;Khan et al 2020d;Rezaei et al 2020;Hussain and Karuppannan 2021). In the fight against COVID-19, GIS has played a significant role in different aspects, for example tracking of confirmed cases of COVID-19, rapid visualization of COVID data and combination of multi-source big data (Sun et al 2020;Zhou et al 2020;Zhang et al 2020).…”