Urban tree planting should consider local characteristics: assessing spatial heterogeneity in canopy cooling effects on land surface temperature using Bayesian spatially varying coefficient models

Land surface temperature is different from air temperature and is caused by various things, one of which is due to changes in vegetation density. Central Lombok Regency is one of the locations that has experienced massive and well-known development in Indonesia. Based on these circumstances, this research was conducted with several objectives, namely: to determine changes in vegetation density, as well as changes in land surface temperature in Central Lombok Regency in 2018 and 2023, and prove the relationship between vegetation density and changes in land surface temperature. The research method used in this research is descriptive quantitative because it involves numerical data, to find the relationship of vegetation density to changes in land surface temperature. In addition, this research also uses the NDVI method to determine the level of vegetation density, and LST to determine the land surface temperature there. The results showed changes in vegetation density and changes in land surface temperature in Central Lombok Regency. The average change in vegetation density and land surface temperature occurred in the Central region of Central Lombok Regency which is the economic center area. Then there is a strong relationship between vegetation density and land surface temperature changes.

Urban heat island (UHI) attenuation is an essential aspect for maintaining environmental sustainability at a local, regional, and global scale. Although impervious surfaces (IS) and green spaces have been confirmed to have a dominant effect on the spatial differentiation of the urban land surface temperature (LST), comprehensive temporal and quantitative analysis of their combined effects on LST and surface urban heat island intensity (SUHII) changes is still partly lacking. This study took the plain area of Beijing, China as an example. Here, rapid urbanization and a large-scale afforestation project have caused distinct IS and vegetation cover changes within a small range of years. Based on 8 scenes of Landsat 5 TM/7ETM/8OLI images (30 m × 30 m spatial resolution), 920 scenes of EOS-Aqua-MODIS LST images (1 km × 1 km spatial resolution), and other data/information collected by different approaches, this study characterized the interrelationship of the impervious surface area (ISA) dynamic, forest cover increase, and LST and SUHII changes in Beijing’s plain area during 2009–2018. An innovative controlled regression analysis and scenario prediction method was used to identify the contribution of ISA change and afforestation to SUHII changes. The results showed that percent ISA and forest cover increased by 6.6 and 10.0, respectively, during 2009–2018. SUHIIs had significant rising tendencies during the decade, according to the time division of warm season days (summer days included) and cold season nights (winter nights included). LST changes during warm season days responded positively to a regionalized ISA increase and negatively to a regionalized forest cover increase. However, during cold season nights, LST changes responded negatively to a slight regionalized ISA increase, but positively to an extensive regionalized ISA increase, and LST variations responded negatively to a regionalized forest cover increase. The effect of vegetation cooling was weaker than ISA warming on warm season days, but the effect of vegetation cooling was similar to that of ISA during cold season nights. When it was assumed that LST variations were only caused by the combined effects of ISA changes and the planting project, it was found that 82.9% of the SUHII rise on warm season days (and 73.6% on summer days) was induced by the planting project, while 80.6% of the SUHII increase during cold season nights (and 78.9% during winter nights) was caused by ISA change. The study presents novel insights on UHI alleviation concerning IS and green space planning, e.g., the importance of the joint planning of IS and green spaces, season-oriented UHI mitigation, and considering the thresholds of regional IS expansion in relation to LST changes.

The study aims to analyze land use changes in the area to determine whether these changes impact environmental quality. The conversion of vegetated land into residential areas has led to an increase in surface temperature. Surface temperature refers to the temperature of objects on the Earth's surface. Based on image analysis from 2000 to 2023, the researchers classified the land into five categories: vegetated land, built-up land, mining land, open land, and water bodies. The results indicate significant changes in both land use and land surface temperature (LST) across different land use categories in Awang Bangkal Barat Village, with some areas experiencing an increase while others showed a decrease. The classification of land use and surface temperature was derived from satellite data processing using Landsat 7 for the years 2000 to 2010 and Landsat 8 for the period from 2015 to 2023. Land cover classification was conducted through on-screen digitization, and surface temperature values were extracted from the thermal bands of Landsat imagery. The study found that built-up areas and mining lands showed higher LST values, indicating the effect of urbanization and mining activities on increasing surface temperatures. Conversely, vegetated areas generally exhibited lower LST, emphasizing the importance of maintaining vegetation to regulate local climate. This research highlights the need for sustainable land management practices to mitigate the negative environmental impacts associated with land use changes. By providing a comprehensive analysis of land use patterns and their correlation with LST, the findings offer valuable insights for policymakers in planning urban development while considering environmental sustainability. The integration of long-term satellite data allows for better monitoring and understanding of the dynamics between land use and surface temperature changes over time.

The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

A large-scale afforestation project has been carried out since 1999 in the Loess Plateau of China. However, vegetation-induced changes in land surface temperature (LST) through the changing land surface energy balance have not been well documented. Using satellite measurements, this study quantified the contribution of vegetation restoration to the changes in summer LST and analyzed the effects of different vegetation restoration patterns on LST during both daytime and nighttime. The results show that the average daytime LST decreased by 4.3°C in the vegetation restoration area while the average nighttime LST increased by 1.4°C. The contributions of the vegetation restoration project to the changes in daytime LST and nighttime LST are 58% and 60%, respectively, which are far greater than the impact of climate change. The vegetation restoration pattern of cropland (CR) converting into artificial forest (AF) has a cooling effect during daytime and a warming effect at nighttime, while the conversion of CR to grassland has an opposite effect compared with the conversion of CR to AF. Our results indicate that increasing evapotranspiration caused by the vegetation restoration on the Loess Plateau is the controlling factor of daytime LST change, while the nighttime LST change is affected by soil humidity and air humidity.

Environmental studies that include studies related to climate change, change in biodiversity, and hydrology consider land surface temperature (LST) and near-surface air temperature as significant variables to contribute. In this context, we investigated the time series assessment of the relationship between LST and elevation over the two decades (1995–2017) using remotely sensed (Landsat 5, Landsat 7, Landsat 8, MODIS) and ground weather stations’ datasets. The study area that is Gilgit-Baltistan, northern province of Pakistan in Hindukush and Himalaya Region (HKH), was selected for quantitative analysis in this study as it has high significance in terms of climate change due to presence of very high and large number of mountains and difficult terrain heavily covered with snow and glaciers. To assess the LST, a linear regression model was developed to quantify the significance of key factors affecting it. Our research results show that there exists a strong negative linear relationship (with correlation coefficient of 0.61 on average) for all datasets (remotely sensed and in situ) between LST and elevation and it is consistent over the study period (1995–2017) for both seasons of winter and summer. Further, there is a difference of change in LST for different seasons (average winter and summer difference is 3.0 °C) and for different datasets (Landsat, MODIS, and weather stations’ dataset). There is a greater change in LST for change of elevation in 1000 m vertically as compared to change horizontally. Our results indicate that the change trend in LST from 1995 to 2007 is downward with minor variation, however, this trend changes into upward trend from 2007 to 2017. The comparison of results of remotely sensed and weather stations’ dataset shows that there is a consistency of change in temperature and LST due to change in elevation. However, there is a less difference in summers (0.76 °C) and large difference in winters, i.e., 3.94 °C.

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

Assessment of land surface temperature and land cover variability during winter: A spatio-temporal analysis of Pabna municipality in Bangladesh

Linkages between urban growth and land surface temperature variations in the Seoul metropolitan area: A spatial first-order difference approach

This study shows how remote sensing and Geographic Information System (GIS) can extract land surface temperature (LST) from the Landsat 5, 7, and 8 datasets. In this research, LST over Kharun's lower catchment, located in Chhattisgarh, India, has been estimated. LST data from 2000, 2006, 2011, 2016, and 2021 were analyzed to see how the LULC pattern changed and how that changed LST. In 2000, the average temperature of the study region was 27.73 °C, whereas in 2021, it reached 33.47 °C. When the average temperature values for each class were determined, it was discovered that forest and adjacent waterbodies had the lowest values, with about 24.15 °C in 2000 and 27.65 °C in 2021, whereas urban regions had more variation in values, ranging from 30.15 °C in 2000 to 38.95 °C in 2021. There could be an increase in LST over time because cities are replacing the green cover. For example, there was a notable increase of 5.74 °C in the mean LST over the research area. The findings revealed that places with extensive urban sprawl had LST between 26 and 45°, which was greater than other natural land cover types, such as vegetation and waterbodies, which was between 24 and 35°. These findings support the suggested method's effectiveness for retrieving LST from the Landsat 5, 7, and 8 thermal bands when combined with integrated GIS approaches. So, the goal of this study is to look at Land Use Change (LUC) and changes in LST using Landsat data and figure out how they are related to LST, the Normalized Difference Vegetation Index (NDVI), and the Normalized Built-up Index (NDBI), which are used as major components.

ABSTRACTThe Yarlung Zangbo River Basin (YZRB), situated within the Qinghai‐Tibetan Plateau, has experienced significant alterations due to global warming and vegetation greening. This region serves as a critical indicator of the interplay between vegetation growth and climatic fluctuations, as evidenced by substantial changes in spatiotemporal land surface temperature (LST) over recent decades. In this research, we assessed the components of the water and energy cycles from 1980 to 2015 utilising the variable infiltration capacity (VIC) model to generate a continuous daily LST data over a 35‐year period. Subsequently, we analysed the variations in LST and identified the influence of environmental factors on temperature changes. Notably, while greening was observed, LST exhibited an upward trend. By differentiating the effects of climatic and anthropogenic factors on LST, we found that climate was the predominant influence, accounting for a contribution rate of 70.36% from 1980 to 1995. In contrast, human activities became the primary driver of LST changes, contributing 55% after 1995. Grasslands with moderate coverage demonstrated potential cooling effects. Among the various environmental factors examined, albedo exhibited a negative and delayed impact on LST, while temperature, precipitation and evapotranspiration were positively correlated with LST, displaying relatively synchronous variations. Additionally, soil moisture and the normalised difference vegetation index (NDVI) were identified as leading contributors to positive changes in LST. This study enhances the understanding of the mechanisms influencing LST and provides essential insights for socio‐economic development in areas with sensitive ecosystem.

The urban heat island effect has posed negative impacts on urban areas with increased cooling energy demand followed by an altered thermal environment. While unusually high temperature in urban areas has been often attributed to complex urban settings, the function of urban forests has been considered as an effective heat mitigation strategy. To investigate the cooling effect of urban forests and their influence range, this study examined the spatiotemporal changes in land surface temperature (LST) of urban forests and surrounding areas by using Landsat imageries. LST, the size of the urban forest, its vegetation cover, and Normalized Difference Vegetation Index (NDVI) were investigated for 34 urban forests and their surrounding areas at a series of buffer areas in Seoul, South Korea. The mean LST of urban forests was lower than that of the overall city, and the threshold distance from urban forests for cooling effect was estimated to be roughly up to 300 m. The group of large-sized urban forests showed significantly lower mean LST than that of small-sized urban forests. The group of urban forests with higher NDVI showed lower mean LST than that of urban forests with lower mean NDVI in a consistent manner. A negative linear relationship was found between the LST and size of urban forest (r = −0.36 to −0.58), size of vegetation cover (r = −0.39 to −0.61), and NDVI (r = −0.42 to −0.93). Temporal changes in NDVI were examined separately on a specific site, Seoul Forest, that has experienced urban forest dynamics. LST of the site decreased as NDVI improved by a land-use change from a barren racetrack to a city park. It was considered that NDVI could be a reliable factor for estimating the cooling effect of urban forest compared to the size of the urban forest and/or vegetation cover.

The land surface temperature (LST) changes in North America are very abnormal recently, but few studies have systematically researched these anomalies from several aspects, especially the influencing forces. After reconstructing higher quality MODIS monthly LST data (0.05° * 0.05°) in 2002–2018, we analyzed the LST changes especially anomalous changes and their driving forces in North America. Here we show that North America warmed at the rate of 0.02 °C/y. The LST changes in three regions, including frigid region in the northwestern (0.12 °C/y), the west coast from 20°N–40°N (0.07 °C/y), and the tropics south of 20°N (0.04 °C/y), were extremely abnormal. The El Nino and La Nina were the main drivers for the periodical highest and lowest LST, respectively. The North Atlantic Oscillation was closed related to the opposite change of LST in the northeastern North America and the southeastern United States, and the warming trend of the Florida peninsula in winter was closely related to enhancement of the North Atlantic Oscillation index. The Pacific Decadal Oscillation index showed a positive correlation with the LST in most Alaska. Vegetation and atmospheric water vapor also had a profound influence on the LST changes, but it had obvious difference in latitude.

Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan.

It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003–2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er–ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003–2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, p < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, p < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa.