Land Surface Temperature Response Research Articles

The contrasting trend of global urbanization-induced impacts on day and night land surface temperature from a time-series perspective

Understanding the warming effect of the impervious surface area (ISA) quantitively is crucial to alleviate the urban heat island effect. However, the quantitative analyses of urbanization-induced land surface temperature (LST) change globally remain to be determined during the global warming hiatus. Here, we first characterized the response of LST to ISA (i.e., δLST) in 369 global cities using MODIS LST product (1 km) and the global artificial impervious area dataset (30 m). We then investigated the spatiotemporal changes of δLST and its associated driving factors, including enhanced vegetation index (1 km), albedo (ALB, 500 m), population (100 m), air temperature (AT, 0.1°), and precipitation (0.1°). The analysis was conducted using linear regression models from 2000 to 2010 at the pixel level. The results illustrate that (1) the daytime δLST in urban areas shows a negative trend with –0.2971 °C/% and –0.0870 °C/% in arid and temperate regions, and the nighttime δLSTs were 0.0938 °C/% and 0.0048 °C/%, respectively. In tropical regions, the δLST remains positive throughout the day, while it exhibits an opposite trend in snow regions. (2) The magnitude of nighttime δLST is stronger compared to daytime globally, which is more significant in arid regions with the largest ΔδLST values (i.e., diurnal variations) in urban (0.3909 °C/%) and rural (0.3262 °C/%) areas. (3) In temperate regions, the response of ALB to ISA (δALB, negative effect) and the response of AT to ISA (δAT, positive effect) are dominant factors in regulating δLST during daytime and nighttime, respectively However, in arid regions, the daytime and nighttime δLSTs are negatively correlated by urban population and vegetation, respectively. These findings provide a quantitative understanding on long-term LST variations and driving factors behind at the pixel level, suggesting that urban greening and increasing surface albedo are effective strategies to mitigate the urbanization-induced surface warming.

Cooling Effect of Green Spaces on Urban Heat Island in a Chinese Megacity: Increasing Coverage versus Optimizing Spatial Distribution.

Enhancing the cooling effectiveness of green spaces (GSs) is crucial for improving urban thermal environments in the context of global warming. Increasing GS coverage and optimizing its spatial distribution individually proved to be effective urban cooling measures. However, their comparative cooling effectiveness and potential interaction remain unclear. Here, using the moving window approach and random forest algorithm, we established a robust model (R2 = 0.89 ± 0.01) to explore the relationship between GS and land surface temperature (LST) in the Chinese megacity of Guangzhou. Subsequently, the response of LST to varying GS coverage and its spatial distribution was simulated, both individually and in combination. The results indicate that GS with higher coverage and more equitable spatial distribution is conducive to urban heat mitigation. Increasing GS coverage was found to lower the city's average LST by up to 4.73 °C, while optimizing GS spatial distribution led to a decrease of 1.06 °C. Meanwhile, a synergistic cooling effect was observed when combining both measures, resulting in additional cooling benefits (0.034-0.341 °C). These findings provide valuable insights into the cooling potential of GS and crucial guidance for urban green planning aimed at heat mitigation in cities.

Response of Land Surface Temperature to Heatwave-Induced Bio-Geophysical Changes in Tropical Forests on Hainan Island from 2010 to 2022

Land surface temperature plays an important role in the water cycle and surface energy balance. Using data collected by a vorticity covariance tower from 2010 to 2022, the relative threshold method and TRM method were employed to study the land–atmosphere exchange of water and the heat flux of rubber forest ecosystems under heatwave and non-heatwave conditions. The results show that the latent heat flux, sensible heat flux, and incoming and outgoing radiation increase from non-heatwave to heatwave conditions. In addition, the multi-year average LST was 6.7 °C higher under HW conditions than under non-HW conditions at the 99% confidence level. Further attribution analysis demonstrates that heatwave-induced land surface temperature change is mainly governed by atmospheric factors rather than by land surface factors. Specifically, radiative forcing shows the largest positive contribution, which is partly offset by the negative contributions of air temperature and relative humidity. In particular, the contributions of radiative forcing, air temperature, relative humidity, and atmospheric pressure to LST were 14.70 K, −4.76 K, −5.86 K, and −0.04 K, respectively. Moreover, surface resistance contributed to LST by 2.42 K, aerodynamic resistance by −0.23 K, and soil heat flux by −0.91 K.

Land surface temperature responses to land use dynamics in urban areas of Doha, Qatar

Rapid urbanization primarily converts naturally vegetated areas and pervious surfaces into impervious built-up areas, significantly transforming microclimates and ecological dynamics. The impervious surfaces, marked by their higher thermal conductivity, disrupt surface energy balance and accumulate solar heat, subsequently elevating the land surface temperatures (LSTs). This study investigates the impact of land use and land cover changes on summer and winter LSTs in Doha and Al Dayeen municipalities of Qatar, spanning from the years 2000 to 2023, using remote sensing techniques and Geographic Information Systems (GIS). The analysis of land use and land cover changes reveals a remarkable 343.16 % increase in the built-up area from 2000 to 2023, at the expense of previously existing desert lands and water bodies. While Qatar's desert land has high land surface temperature, substituting such areas with built-up exhibits a notable rise in temperatures. Additionally, land reclamation also results in elevated LSTs. The LST data derived from remote sensing sources demonstrates an upward trend for summer and a contrasting trend for winter. Specifically, the mean summer LST increases by 7.64 °C (0.34 °C annually), and the mean winter LST decreases by 4.87 °C (0.22 °C annually). Notably, built-up areas and desert lands consistently recorded the highest mean LST in both seasons in all observed years. A strong correlation was observed between summer and winter LST with land use and land cover patterns using Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up index (NDBI) and Normalized Difference Barrenness Index (NDBal). The results imply the negative influence of climate change and the urgent need for urban planning mitigation measures to counteract the adverse effects of increasing LSTs, particularly in summer months, to ensure the human well-being and resilience of the urban environments.

The cooling and warming effects of potential forest transition on local land surface temperature in Northeast China

Forest cover change directly affects the surface energy balance by altering the radiative and non-radiative properties of the surface. These competing processes may yield considerable biophysical impacts on local and regional climate that depend on background climate and specific forest type. Here we compared the differences in land surface temperature (LST) between temperate forests and adjacent croplands/grasslands to quantify the potential biophysical effects of transitions from croplands/grasslands to forests in Northeast China, based on multiple satellite observation products from 2001 to 2016. Forests were found to show a slight lower daytime LST and strong higher nighttime LST than adjacent croplands or grasslands. As a result, the daily LST of forests was 0.15 ± 0.06 ℃ and 0.32 ± 0.04 ℃ higher than that of croplands and grasslands, respectively. On the seasonal scale, the warming effect of cropland/grassland-to-forest transition during non-growing season outweighed the cooling effect during growing season, and dominated the annual positive LST response. Moreover, the cooling effect of cropland-to-forest transition during growing season was stronger in the early growth stage than late growth stage (−1.65 ± 0.9 ℃ in May to June, −0.39 ± 0.5 ℃ in July to September). The net cooling effect of warm temperate broadleaved forests was found to shift toward net warming effect of cold temperate coniferous forests with increasing latitude. The daytime cooling effect of forests was mainly driven by increased non-radiative evapotranspiration. Overall, the radiative process related to the solar energy dominated the net warming effect of forest transitions over Northeast China. Estimating the climate feedback potential of forests could be useful to provide theoretical references for the adaptation and mitigation strategies for climate change.

Impervious Surface Area Patterns and Their Response to Land Surface Temperature Mechanism in Urban–Rural Regions of Qingdao, China

The expansion of impervious surface area (ISA) in megacities of China often leads to land surface temperature (LST) aggregation effects, which affect living environments by impacting thermal comfort levels, thus becoming an issue of public concern. However, from an urban–rural synchronous comparison perspective, the study of LST responses to ISA changes is still lacking in the central coastal megalopolises of China. To solve this issue, a collaborative methodology of artificial digitization—fully constrained least squares mixed pixel decomposition—split-window algorithm—PCACA model was established for Qingdao using land use dataset and remote sensing images. The conclusions are below. Long time series of land use monitoring indicated that the expansion ratios of urban and rural areas were 131.29% and 43.42% in the past 50 years (i.e., from 1970 to 2020). Within urban and rural areas, a synchronous ISA increase was observed, with ratios of +9.14% (140.55 km2) and +7.94% (28.04 km2), respectively. Higher ratios and area changes were found in the urban regions, and a similar ISA change pattern in both urban and rural regions was captured by the ISA horizontal epitaxial expansion and vertical density enhancement. Further, the horizontal gradient effect displayed that the mean LSTs were 28.75 °C, 29.77 °C and 31.91 °C in the urban areas and 28.73 °C, 29.66 °C and 31.65 °C in the rural areas in low-, medium-, and high-density ISAs. The vertical density effect showed that the LST change was 1.02 °C and 2.14 °C in the urban areas but 0.93 °C and 1.99 °C in the rural areas during the ISA-density transition from low- to medium- and from medium- to high-density, respectively. Potential surface thermal indicators were assessed, and the urban regions displayed higher sensible heat flux (280.13 W/m2) compared to the rural regions (i.e., 274.76 W/m2). The mechanism effect of the ISA changes on LST in the urban and rural regions was revealed. These findings form a new comparative perspective of the urban–rural synchronous change in the central coastal megalopolis of China and can provide a practical reference for relevant studies.

Feedback and contribution of vegetation, air temperature and precipitation to land surface temperature in the Yangtze River Basin considering statistical analysis

ABSTRACT Land surface temperature (LST), especially day-night LST difference , is a key variable for the stability of terrestrial ecosystems, affected by vegetation and climate change. Quantifying the contribution and feedback of vegetation and climate to LST changes is critical to developing mitigation strategies. Based on LST, Normalized vegetation index (NDVI), land use (LU), air temperature (AT) and precipitation (Pre) from 2003 to 2021, partial correlation was used to analyze the response of LST to vegetation and climate. The feedback and contribution of both to LST were further quantified by using spatial linear relationships and partial derivatives analysis. The results showed that both interannual LST and responded negatively to vegetation, and vegetation had a negative feedback effect in areas with significantly altered. Vegetation was also a major contributor to the decline of With the advantage of positive partial correlation area of 94.99%, AT became the main driving factor and contributor to change trend. Pre contributed negatively to both and , with contributions of −0.004 °C/y and −0.022 °C/y, respectively. AT played a decisive role in LST warming of YRB, which was partially mitigated by vegetation and Pre. The present research contributed to the detection of LST changes and improved understanding of the driving mechanism.

Four-decade response of land surface temperature to urban expansion in Beijing

Urban expansion with increased impervious surface area (ISA) can affect the thermal regimes by altering the water and energy balance. Previous studies have indicated that ISA is the primary surrogate of rising land surface temperature (LST). However, urban-induced LST changes are not yet well quantified because of the temporal limitations of the LST datasets and the uncertainties from climate variabilities. In this study, we integrated multiple satellite-based products (vegetation coverage, albedo and radiation forcing) into a physically-based land surface model i.e., Variable Infiltration Capacity (VIC), to detect multi-temporal scale response of LST to the increasing ISA in Beijing. Heat transfer-related parameters in the model were updated along with urbanization processes. The integrated modeling presented high performance for simulating LST, as evaluated with ground-based observations and the MODerate-resolution Imaging Spectroradiometer (MODIS) LST product. We found that the average LST over Beijing increased from 10.3 °C in 1980–1990 to 11.2 °C in 2010–2020, with nearly 33% contributed by the urban growth. The urban-induced thermal effect was particularly strong in summer daytime and winter nighttime. The frequency of heat days (the day with the maximum LST over 40 °C) in a year for Beijing appeared to linearly correlate with the impervious surface fraction (ISF) when ISF > 25%. For the urbanizing area, moreover, the four-decade LST estimates indicated that the annual overheating duration was obviously extended with a rate of about 5 days per decade. We therefore conclude that the urban expansion in Beijing not only amplified heat stress but also altered heat phenology, which have implications for urban planning and the construction of heat mitigation facilities.

Temporal and Spatial Variation of Land Surface Temperature in Recent 20 Years and Analysis of the Effect of Land Use in Jiangxi Province, China

Under the background of global warming, it is of great significance to study the temporal and spatial evolution of land surface temperature (LST) on long-time scale and the impacts of land use in the fields of urban thermal environment and regional climate change. Based on MODIS LST long time series remote sensing data, the temporal and spatial evolution characteristics of pixel-wise LST in Jiangxi Province, the middle inland province of China from 2000 to 2020 were analyzed by using Theil-Sen + Mann-Kendall, coefficient of variation and Hurst index, and the response of LST to land use was identified by combining the contribution and diversity index. The results showed as follows: (1) LST was generally distributed as "high in Middle-East-West-South and low in North-northwest-southeast direction". LST showed an overall downward trend, indicating a weakening of the warming trend. The dynamic trend of LST was characterized by more descending than ascending tendency. The dynamic stability showed a coexistence of high and low fluctuation tendency, with a higher proportion of medium and low fluctuation areas having obvious spatial differences. The overall dynamic sustainability was characterized by uncertainty of future change trend. (2) The LST were strongly affected by land use in the past 20 years. Firstly, the areas of high LST were mostly located in construction land and unused land, while the areas of low LST were mostly in water area and forest land. However, forest land and water area of high temperature were gradually turned to construction land later on. Secondly, the land use structure and pattern had an strong effects on LST. With the increase of the area proportion of different land use, the LST showed significant differences. The more complex the spatial pattern of land use, the more obvious its impact on LST. The research results will provide some reference for the regions with the same characteristics as Jiangxi Province to deal with LST under the background of global climate change.

Changing coordination between urban area with high temperature and multiple landscapes in Wuhan City, China

Changes in urban landscapes are a direct inducement for changes in the urban thermal environment. Previous studies have focused on a single landscape in the urban thermal environment, but there is a lack of the research on the coupling effects of multiple landscapes. In this study, partial correlation analysis and spatial coupling model were used to explore the changing coordination between urban areas with high temperature and multiple landscapes (gray, green, and blue landscapes) in Wuhan City and their effects on land surface temperature (LST). The results showed that 6.22%, 0.52% and 25.91% of the blocks at the sub-district level behaved the dominant strong coupling relationship between the change trends of blue, green, as well as gray landscapes and urban thermal environment, respectively. For gray-green dominant strong coupling area, the change from blue landscape in 1988 to gray landscape in 2018 resulted in an average LST increase of 4.07 °C, which was 0.1 °C less than that in a single gray strong coupling area. It can be concluded that compared with single endothermic or exothermic landscape dominant strong coupling area, the multiple landscapes combining dominant strong coupling area will experience distinctly enhanced or weakened LST response to the same landscape change, respectively.

Biophysical effects of paddy rice expansion on land surface temperature in Northeastern Asia

The pending extensive rice expansion in northeastern Asia, especially in northeast China, affects regional climate by altering both biogeochemical and biophysical processes. While the biogeochemical effects (e.g., CO2, CH4) of rice expansion have attracted plenty of attention, its biophysical effects have not been well documented, especially its influences on diurnal and seasonal land surface temperature (LST). In this study, we used a pair-wise comparison approach to examine biophysical effects of paddy rice expansion at different temporal scales (diurnal and seasonal) in northeast China, based on satellite-derived biophysical proxies and a high-resolution crop map in 2017. We found that the daily mean LST of rice paddies was 0.5 °C lower than that of corn and soybean fields during the growing season (from May to September), as a result of daytime cooling (-1.8 and -2.0 °C) and nighttime warming (0.8 and 1.1 °C), which subsequently led to a narrower diurnal LST range (-2.6 and -3.0 °C) than in upland crops (i.e. corn and soybean). The cooling effects were stronger in the early period of the growing season (May and June) than in the late season (July to September). Using a temperature response model, we found that the nonradiative processes (i.e., evapotranspiration and sensible heat) dominated the LST response in paddy rice, while the radiative process (i.e., albedo) played a secondary role. The daytime cooling and nighttime warming implies that we need to consider the unsymmetrical diurnal LST dynamics when evaluating the short-term effects of paddy rice expansion. Stronger cooling effects in the early growing season has to be accounted for when modeling its biophysical impacts at seasonal scale. This study explained the local climate effects of rice expansion through the biophysical mechanism with both radiative and nonradiative controls on the surface energy balance, which can contribute to improved modeling of biophysical effects of land use change.

Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China

LST (Land surface temperature) is an important indicator for monitoring dynamic changes in the earth’s resources and environment. However, the complexity of obtaining long-term, continuous LST data hinders the development of research on LST responses to meteorological factors or LUCC in areas where data is lacking. The objective of this research was to use the VIC-3L (Variable Infiltration Capacity) based on multi-source remote sensing data to simulate and explore spatio-temporal changes in the LST, to analyze the relationship between the LST and meteorological elements by using cross-wavelet transform (XWT) and wavelet coherence (WTC), the relationship between the LST and LUCC by using three-phase remote sensing images of LUCC. The following results were obtained. The annual average LST of the study area is increasing at a rate of 0.027 °C per year. The annual average LST level is relatively high in the central and eastern regions. The average temperature has an important influence on LST, which is mainly reflected in the period scale of 1~4a in 1963–1972, 1980–1996, and 2004–2010. The sharp decline in open shrubs may have exacerbated the increase in LST in the study area. This study provides a scientific reference for studying LST in arid areas.

Satellite observed cooling effects from re-vegetation on the Mongolian Plateau

During the past decades, a series of new policies and ecological projects have been implemented to mitigate land degradation on the Mongolian Plateau. However, climatic effects from re-vegetation still remain largely unknown. In this paper, we investigate local land surface temperature response to re-vegetation changes by comparing between locations with forest or grassland gains and their nearby unchanged land units based on satellite observations. Our results demonstrate that reforestation in humid regions and grassland cover gains in arid regions result in annual net cooling effect, but temperature response to reforestation shows asymmetric diurnal (daytime cooling but nighttime warming) and seasonal (summer cooling but winter warming during daytime) cycle. Local cooling effect of transition land cover is enhanced with continuous restoration of vegetation. The underlying process is mainly controlled by biophysical effects from surface albedo and evapotranspiration. Increased albedo associated with snow cover in winter significantly contributes to the cooling effect of grassland, and evapotranspiration along with increase in precipitation amplifies interannual temperature differences especially in summer. This study reminds that rational land use policy should be formulated carefully to realize potential climatic benefits from re-vegetation projects.

Sensitivities and Responses of Land Surface Temperature to Deforestation-Induced Biophysical Changes in Two Global Earth System Models

AbstractWhile the significance of quantifying the biophysical effects of deforestation is rarely disputed, the sensitivities of land surface temperature (LST) to deforestation-induced changes in different biophysical factors (e.g., albedo, aerodynamic resistance, and surface resistance) and the relative importance of those biophysical changes remain elusive. Based on the subgrid-scale outputs from two global Earth system models (ESMs, i.e., the Geophysical Fluid Dynamics Laboratory Earth System Model and the Community Earth System Model) and an improved attribution framework, the sensitivities and responses of LST to deforestation are examined. Both models show that changes in aerodynamic resistance are the most important factor responsible for LST changes, with other factors such as albedo and surface resistance playing secondary but important roles. However, the magnitude of the contributions from different biophysical factors to LST changes is quite different for the two ESMs. We find that the differences between the two models in terms of the sensitivities are smaller than those of the corresponding biophysical changes, indicating that the dissimilarity between the two models in terms of LST responses to deforestation is more related to the magnitude of biophysical changes. It is the first time that the attribution of subgrid surface temperature variability is comprehensively compared based on simulations with two commonly used global ESMs. This study yields new insights into the similarity and dissimilarity in terms of how the biophysical processes are represented in different ESMs and further improves our understanding of how deforestation impacts on the local surface climate.

Attribution of the land surface temperature response to land-use conversions from bare land

Abstract Since the early 1980s, large-scale revegetation has been implemented in Northwest China and has been found to be useful in desertification prevention. However, it remains unclear, how land cover changes affect local meteorological conditions, such as land surface temperature. In this study, the land surface temperature response to land-use changes was comparatively evaluated using micrometeorological observations from a cropland site, a mixed forest site, a shrubland site, and adjacent bare land sites in the Heihe River Basin, Northwest China. The surface temperature changes (ΔTs) are decomposed into contributions from changes in radiative forcing, heat resistance, evapotranspiration, soil heat flux and air temperature based on the intrinsic biophysical mechanism (IBM) and the two-resistance mechanism (TRM). The results indicate that 1) the IBM attribution method is more applicable than the TRM method in these arid ecosystems; 2) the influence of different types of vegetation cover on the surface temperature exhibits temporal variance on the diurnal and seasonal time scales; 3) the dominant biophysical components in the daytime of the growing season are evaporative cooling in the cropland paired sites and heat resistance change in the mixed forest paired sites, but these two components are both at a moderate level in the shrubland paired sites. This study provides a useful reference for local climate impact assessments when implementing environmental protection projects in similarly arid areas.

Wetland Monitoring and Land Surface Temperature Response in Panjin City Based on Tiangong-2 Data

In the research area Panjin city, visible and near-infrared remote sensing images by Tiangong-2 wide-band imaging spectrometer was used to extracte wetland. According to the established remote sensing interpretation signs, the wetland types of Panjin city in 2018 are extracted by using the method of partition classification. The thermal infrared imaging spectrometer of Tiangong-2 was used to invert the land surface temperature based on the split-window algorithm. This paper comprehensively analyzed the distribution of wetland cover types of Panjin city in 2018 and their impact on land surface temperature. The results show that in 2018, Panjin wetland covers an area of 3,184.89 km2 and the major type of wetland is artificial wetland. The area is reduced successively according to paddy field, reed swamp, mudflat wetland, suaeda salsa swamp, aquaculture farm, reservoir/pond and river wetland. The overall accuracy of partition classification method is 89.97%, kappa coefficient is 0.8761. The applicability of Tiangong-2 wide-band imaging spectrometer in wetland monitoring is verified. The split-window algorithm can be used to effectively invert the land surface temperature of Tiangong-2 data, and the land surface temperature results obtained by inversion are in good consistency with those obtained by MODIS inversion, the correlation coefficient reached 0.79. Through counting, the land surface temperature of different wetland types is different, among which the land surface temperature of mudflat wetland is the highest and that of reservoir is the lowest.

Impacts of spatial heterogeneity patterns on long-term trends of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature time series

Land surface temperature (LST) is a crucial parameter for global climate change studies. LST changes are also directly associated with the large-scale changes in land cover. Previous studies carried out a comparative analysis of satellite-derived LST response between periods before and after homogenous land cover changes. We present an alternative approach that quantifies long-term LST variability in response to various land use/land cover change (LULCC) patterns over Phuket Island, Thailand, from 2003 to 2017. First, four Moderate Resolution Imaging Spectroradiometer (MODIS) overpass times of LST time series were adjusted for seasonal effects using a cubic spline function to preserve the number of original data and enable estimates of LST dynamics and trends using the generalized least squared models. Second, LULCC patterns were classified according to land cover type conversion and spatial pattern transformations between the years 2000 and 2016. Spatial homogeneity and heterogeneity were quantified by the coverage percentage for each land use and land cover (LULC) type within a given location. Finally, the influence of LULCC patterns on the long-term spatiotemporal behavior of LST was assessed using the generalized estimating equation model. Results showed that different land cover transitions influence the dynamics of daytime LST but not the nighttime LST. The proportion of different land cover types within an LST pixel and transition amounts contributed to the quantity of increasing surface temperature, especially over impervious surface types. Diverse LULCC patterns with considerations of spatial heterogeneity improved our insight about a relatively strong effect of combined LULC types on LST responses. The climatic effect through the gradual conversion of heterogeneous land cover is necessary to be considered in climate research studies.

Thermal response to patch characteristics and configurations of industrial and mining land in a Chinese mining city

Abstract Industrial and mining land is an important heat source in mining cities, however this heat has not been separated from that of built-up land in most studies of the thermal environment. This study measures the response of land surface temperature (LST) to patch characteristics and the configurations of four sub-classified industrial and mining land parcels in Wu’an, a mining city in China. We use Landsat-8 and ZY03 satellite imagery to obtain the LST and land use type. A heating rate index (Tm) is defined as the increase in LST per 1 ha increase in patch area. Multivariate regression analyses is used to analyse the relationship between thermal indicators and landscape metrics, both at the patch and village scales. Our results indicate that, at the patch scale, the mean LST (LSTm) can be described well by nonlinear binary functions of the area and landscape shape index (LSI), with different forms for each type of industrial and mining land patch. When the LSI does not vary, LSTm increases with increasing area, within area thresholds (0–50 ha for smelter or processing lands, and 5–30 ha for mining lands), while for a constant area, the LSTm decreased with increasing LSI. Below the critical LSI (~2.2), more complex patch shape had lower Tm values. Above the critical LSI, Tm do not increase or decrease as LSI increased. At the village scale, patch configuration of industrial and mining land has an effect on (LSTv). The landscape percentage and aggregation index are both directly correlated with the LST of villages that contained industrial and mining land. Our findings show that the degree of LSTv response to a land patch configuration metrics change is, in order, iron and smelter land > coal processing land > coal mining land > iron ore mining land. This study provides an improved understanding of the thermal environmental response to mining activities, and can serve as a reference for future industrial layouts and land use planning in mining cities.

The Response of Land Surface Temperature to the Changing Land-Use Land-Cover in a Mountainous Landscape under the Influence of Urbanization: Gilgit City as a case study in the Hindu Kush Himalayan Region of Pakistan

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes.

The Response of Land Surface Temperature to the Changing Land-Use Land-Cover in a Mountainous Landscape under the Influence of Urbanization: Gilgit City as a case study in the Hindu Kush Himalayan Region of Pakistan

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes.