Land Surface Temperature Distribution Research Articles

Exploring the Seasonal Comparison of Land Surface Temperature Dominant Factors in the Tibetan Plateau

Abstract. LST (Land Surface Temperature) is a significant parameter that represents the ground energy balance and plays a crucial role in understanding climate change. The LST of the Tibetan Plateau (TP) has a direct influence on the climate and environmental changes of the TP, and it also has a significant impact on global climate and atmospheric circulation. Although there are various factors that drive the spatial and temporal distribution of LST on the TP, the primary driving forces and its seasonal variations of LST are not yet well understood. The research focuses specifically on the TP region, selecting three types of LST data, using geodetector model, to analyze the driving factors affecting the spatial pattern of LST in different seasons. The results indicate that the three factors, Air Temperature (AT), Elevation (Ele), and Permafrost Thermal Stability (PTS), have a significant influence on LST throughout all seasons, whereas other variables demonstrate varying contributions to LST depending on the season. This study contributes to the understanding of the spatial variability of surface thermal conditions and the intricate relationships between their driving factors. It also emphasizes the potential changes in these relationships throughout the year.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Exploring urban land surface temperature using spatial modelling techniques: a case study of Addis Ababa city, Ethiopia

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran’s I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Evaluating the relationship of vegetation dynamics with rainfall and land surface temperature using geospatial techniques in South Wollo zone, Ethiopia

This study was conducted in the South Wollo zone to assess vegetation dynamics and their relationship with rainfall and land surface temperature. To that end, 16-day 250-meter resolution Moderate Imaging Spectro-radiometer (MODIS) Normalized difference vegetation Index (NDVI), 5-kilometer resolution MODIS Land Surface Temperature (LST), 5-kilometer CHIRPS data were used. A Mann-Kendall (MK) trend test was used to determine the trend of each dataset using seasonal and annual time series. In addition, the Pearson correlation coefficient was used to estimate the relationship between NDVI and climatic elements. The results of the study revealed that there was no significant change in the annual and seasonal rainfall and LST during the period 2000 to 2021, except LST in the autumn season (p = 0.01) and NDVI was significant only in the mean annual (p = 0.027) and summer seasons (p = 0.03). The correlation between NDVI and rainfall was positive (r = 0.84, 0.78, 0.72 & 0.79) and obtained for the summer, autumn, winter, and spring season respectively. Similarly, the correlation between NDVI and LST was negative (r= -0.79, -0.75, -0.72, and -0.78) in the summer, autumn, winter, and spring seasons respectively. The outcome of the study indicates precipitation is the most significant factor in the distribution of vegetation and LST

Links between Land Use Change, Land Surface Temperature and Partridge Distribution – An Analysis of Environmental Factors

Links between Land Use Change, Land Surface Temperature and Partridge Distribution – An Analysis of Environmental Factors

Characterisation of Morphological Patterns for Land Surface Temperature Distribution in Urban Environments: An Approach to Identify Priority Areas

The validated influence of urban biophysical structure on environmental processes within urban areas has heightened the emphasis on studies examining morphological patterns to determine precise locations and underlying causes of urban climate conditions. The present study aims to characterise morphological patterns describing the distribution of Land Surface Temperature (LST) based on a prior classification of biophysical variables, including urban density (building intensity and average height), surface characteristics, shortwave solar radiation (broadband albedo), and seasonal variations in vegetation cover (high, medium, and low levels), retrieved from multisource datasets. To describe the distribution of LST, the variables were calculated, classified, and subsequently, analysed individually and collectively concerning winter and summer LST values applied in an urban neighbourhood in Madrid, Spain. The results from the analytical approaches (observation, correlations, and multiple regressions) were compared to define the morphological patterns. The selection of areas resulting from the morphological patterns with the most unfavourable LST values showed agreement of up to 89% in summer and up to 70% for winter, demonstrating the feasibility of the methods applied to identify priority areas for intervention by season. Notably, low and high vegetation levels emerged as pivotal biophysical characteristics influencing LST distribution compared to the other characteristics, emphasising the significance of integrating detailed seasonal vegetation variations in urban analyses.

Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021

Physical and climatic variables such as Tree Canopy coverage, Normalized Difference Vegetation Index (NDVI), Distance to Roads, Distance to the Coast, Impervious Surface, and Precipitation can affect land surface temperature (LST). This paper examines the relationships using linear regression models and explores LST trends in the Miami Statistical Area (MSA) between 2002 and 2021. This study evaluates the effect of dry and wet seasons as well as day and night data on LST. A multiscale investigation is used to examine LST trends at the MSA scale, the individual county level, and at the pixel level to provide a detailed local perspective. The multiscale results are needed to understand spatiotemporal LST distributions to plan mitigation measures such as planting trees or greenery to regulate temperature and reduce the impacts of surface urban heat islands. The results indicate that LST values are rising in the MSA with a positive trend throughout the 20-year study period. The rate of change (RoC) for the wet season is smaller than for the dry season. The pixel-level analysis suggests that the RoC is primarily in rural areas and less apparent in urban areas. New development in rural areas may trigger increased RoC. This RoC relates to LST in the MSA and is different from global or regional RoC using air temperature. Results also suggest that climatic explanatory variables have different impacts during the night than they do in the daytime. For instance, the Tree Canopy variable has a positive coefficient, while during the day, the Tree Canopy variable has a negative relationship with LST. The Distance to the Coast variable changes from day to night as well. The increased granularity achieved with the multiscale analysis provides critical information needed to improve the effectiveness of potential mitigation efforts.

Comparison of multi-factor spatial downscaling models for high-resolution LST estimation in mountainous and hilly open-pit mines

The stripping of soil and vegetation by mining activities in mountainous and hilly areas of southern China has caused drastic surface disturbance and serious ecological and environmental problems. The spatial distribution of Land Surface Temperature (LST) in the mine site can better reflect the ecological disturbances caused by mining and is an important parameter for assessing the ecological environment of the mining sites. The classic Distrad thermal pixel decomposition model was improved by using different regression factors and linear and nonlinear fitting methods in this study, and three multi-factor downscaling models were proposed. Finally, four downscaling models were used to estimate the high spatial resolution LST for three three typical open-pit mining areas and explore the contribution of different surface parameters in the downscaling process from qualitative and quantitative perspectives. The results show that: (1) The MTVET (Extremely Randomized Trees regression model with multitype predictor variables) model exhibits the most accurate prediction accuracy in all three mining areas. The spatial distribution of errors shows that the area share of absolute errors between 0 and 1 K exist in 86.48 %, 81.38 %, and 84.51 % of the areas in Ganzhou rare earth mine, Dexing copper mine and Pingxiang coal mine, respectively. Based on the Root Mean Square Error (RMSE) assessment, the three mines are 0.699 K, 0.790 K, and 0.751 K, respectively, which are all controlled within 1 K; (2) The downscaled results of the MTVET model show rich and clear LST spatial texture features when typical mine sites are selected in each mining areas for detail comparison; (3) It was found that the topographic index has the greatest impact on accuracy enhancement, suggesting that topographic factors have an important influence in the study of LST downscaling in mountainous and hilly areas of southern China.

MEASURING THE INFLUENCE OF A MOTORWAY CONSTRUCTION ON LAND SURFACE TEMPERATURE USING LANDSAT THERMAL DATA: A CASE STUDY IN THE METROPOLITAN CITY OF MILAN

Abstract. Cities have been identified as a landmark for climate change, being among the direct targets of its negative feedbacks. The combined effect of climate change and rapidly growing urbanization is exacerbating the urban heat island phenomenon in cities worldwide. The availability of multiple geo-data sources including satellite remote sensing products is significantly empowering the investigation of its driving factors. This is a crucial step to implement ad hoc mitigation and adaptation strategies. In view of the above, the goal of this study is to measure the effect of a motorway on the Land Surface Temperature (LST) space-time patterns by leveraging Landsat 5 and 8 thermal data of the period from 2006 to 2022. The study area is around the motorway A58 and connected roads in the Metropolitan City of Milan (northern Italy). LST patterns are investigated along the motorway track and in the neighbouring areas before and after the motorway construction, in both the cold and warm seasons. Results show that the motorway significantly affects the LST distribution during summer with a median increase of 2.5 °C along the road track with respect to the surrounding area. The warming effect is also recorded in the road buffers with decreasing LST with increasing distance from the road. On the contrary, no meaningful variation in terms of LST is measured in winter. These experiments provide insightful measures of the effect of a highway on the local climate conditions in an urban area, thus representing crucial pieces of information for driving evidence-based urban planning activities.

Land surface dynamics and meteorological forcings modulate land surface temperature characteristics

This study examines the effect of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics (LVCET) on land surface temperature (LST) distribution over West Africa (WA). We employ fourteen machine-learning models, which preserve nonlinear relationships, to downscale LST and other predictands while preserving the geographical variability of WA. Our results showed that the random forest model performs best in downscaling predictands. This is important for the sub-region since it has limited access to mainframes to power multiplex machine-learning algorithms. In contrast to the northern regions, the southern regions consistently exhibit healthy vegetation. Also, areas with unhealthy vegetation coincide with hot LST clusters. The positive Normalized Difference Vegetation Index (NDVI) trends in the Sahel underscore rainfall recovery and subsequent Sahelian greening. The southwesterly winds cause the upwelling of cold waters, lowering LST in southern WA and highlighting the cooling influence of water bodies on LST. Identifying regions with elevated LST is paramount for prioritizing greening initiatives, and our study underscores the importance of considering LVCET factors in urban planning. Topographic slope-facing angles, heat loads, and diurnal anisotropic heat all contribute to variations in LST, emphasizing the need for a holistic approach when designing resilient and sustainable landscapes.

Thermal pattern of Tatun volcanic system by satellite-observed temperatures and its correlation with earthquake magnitudes

The land surface temperature (LST) of volcanoes detected from satellite sensors reflects the thermal status of heat sources in the subsurface. Volcanic earthquakes occur as magma and volcanic fluids transport to the surface from depth. Thus, both LST and earthquake magnitude are key parameters to the study of active volcanoes. Here we investigate the volcanic status of Tatun Volcanic Group (TVG) based on LST and seismic observations. The Earth-observing satellites onboard thermal sensor derived land surface temperature, and the seismic records retrieved volcanic earthquake magnitude are used to delineate the past and current pattern of volcanic activity plus the future trend of the TVG. The spatiotemporal distribution of LST and volcanic earthquake magnitude in TVG are analyzed. The high-similarity trends of the 4-decade LST time series and 3-decade earthquake magnitude time series are inspected. The retrieved surface thermal pattern shows the non-steady-state nature of the subsurface thermal sources at this volcanic complex. The LST trend exhibits a rather positive correlation with the energy released from volcanic earthquakes and consequently, the presumption on the connection between LSTs and earthquakes is validated.

Reconstruction of Land Surface Temperature Derived from FY-4A AGRI Data Based on Two-Point Machine Learning Method

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R2 of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R2 of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution.

The analysis of fire hotspot distribution in Kalimantan and its relation- ship with ENSO phases

Kalimantan experiences fire hazards almost every year, which threaten the largest tropical forest in South- east Asia. Climatic conditions, such as increasing surface temperature and decreasing rainfall, become important es- pecially when El Nino Southern Oscillation (ENSO) occurs. Studies on fire are commonly conducted based on the climatic condition such as the dry or wet season, but those which focused on analysis of fire occurrences with the specific ENSO phases are still limited. This study aims to identify the spatial and temporal distribution of rainfall, land surface temperature, and soil moisture and analyses the distribution of hotspots in Kalimantan from 2014 to 2020 dur- ing different ENSO phases. The data used are Moderate Resolution Imaging Spectroradiometer (MODIS) for hotspot analysis, Global Precipitation Measurement (GPM) for rainfall analysis, MODIS Land Surface Temperature (LST) for surface temperature analysis and Soil Moisture Active Passive (SMAP) for soil moisture analysis. The methods used were descriptive and spatial analyses based on each ENSO phase, which were then combined to analyse the temporal and spatial distribution of fire, rainfall, LST and soil moisture. The temporal distribution shows a positive relationship between ENSO, rainfall, LST, soil moisture and hotspots with a confidence level of 90% in the dry months of August– October. Fire occurred in most parts of West and Central Kalimantan, associated with low elevation, organic soil types and agricultural peatland. The average trend of increasing hotspots is 17.4% in the El Nino phase and decreasing hot- spots by 84.7% in the La Nina phase during August–October in Kalimantan.

A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis of Land Surface Temperature

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product while remaining immune to the pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to a 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of the ERA5 LST with minimal missing pixels. The pixel-wise average R2 and mean absolute error for the MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions on a 1000 m scale. The accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only the clear-sky LST used as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.

Constructing an urban cooling network based on PLUS model: Implications for future urban planning

Many studies on mitigation of the surface urban heat island (SUHI) are conducted from the perspective of landscape patch allocation, and few studies have investigated the overall impact of network connectivity on SUHI. In this study, an ecological network framework is proposed to identify the key patches and cooling corridors. Based on surface temperature inversion data, morphological spatial pattern analysis (MSPA) and landscape connectivity indexes are used to identify heat island and cold island patches with high ecological value on which to construct the network. The innovative patch-generating land use simulation (PLUS) model is introduced to construct a resistance surface, and Circuit theory (CT) is used to connect cold islands and heat islands in cooling corridors. The results show that from 1999 to 2019, land surface temperature (LST) increased significantly in the city of Haikou, the extreme LST became more and more significant, and the area with the highest temperatures showed a southward trend. The connectivity between cold and heat island patches declined, and the Loop, Bridge and Branch areas in heat islands increased. The latter scenario is conducive to the construction of cooling corridors. The land cover index had a great influence on SUHI, and the resistance surface presented a distribution pattern with high values in the northwest and low values in the southeast, which is consistent with the distribution of LST. Finally, we extracted the resultant cold-heat island cooling network, which was mainly concentrated along the southern edge of the study area, and found that corridor construction faced fewer obstacles in 2019 than in 1999. This study can guide urban planners to reinforce the parts of ecological networks that will mitigate the urban heat island effect.

Study on the influence of urban tree canopy on thermal environment in Luoping County

Urban forest is an integral part of the complex urban ecosystem, and tree canopy plays a key role in improving urban climatic environment. Urban Tree Canopy (UTC) is strongly linked to urban thermal environment and living quality of residents. In this study, Luoping County, a mountainous county in southwest China, was selected as the study area to uncover the inner connections between tree canopy and thermal environment, and provide relevant scientific references for the construction of livable forest cities in similar areas. Through eCongnition Developer, ENVI and ArcGIS software, the distribution of Land Surface Temperature (LST) and land cover types in the study area was extracted, 63 patches with super-large and extra-large tree canopy coverage selected, to explore the regulatory effect of UTC patches on urban thermal environment based on SPSS software. Results showed that the highest LST in the research area was 37.63 ℃, the lowest 24.73 ℃, and the average 30.83 ℃. Among the land cover types, the area of buildings and impervious surfaces was 1615.71 hm2, accounting for 55.76% of the total study area, which was the largest proportion and with widespread distribution; the area of grassland and water body was 57.48 hm2 and 12.35 hm2, respectively, taking up 1.98% and 0.43%, with a smaller proportion. Mean LST: impervious surface > bare land > grassland > tree canopy > water body. By increasing the area and perimeter of the patch covered by tree canopy, the cooling rate of the patch can be increased while the temperature inside the patch can be reduced. The relationship between the area and cooling rate is closer than that between perimeter and cooling rate. The increase of perimeter has a stronger alleviation effect on the internal temperature of the patch, whereas, the increase of area has a weaker effect in this respect.

Application of MODIS land surface temperature data on ENSO-based analysis in Kalimantan

El Nino Southern Oscillation as climate anomaly has been impacted Land Surface Temperature (LST) in Indonesia. Higher temperature during El Nino has been considered to give more risk in fire hazards in Kalimantan, thus, analysis of spatial and temporal distribution of LST became crucial. This study aims to analyze the spatial and temporal distribution of land surface temperature in Kalimantan based on the ENSO phases from 2014 to 2020. MODIS MOD11A1 images and ONI index were used as analysis materials. Image processing was conducted by selecting pixels using the QC band and then processing the LST values using a scale using RStudio software. The result shows the distribution of higher LST values on the southern side of Kalimantan, influenced by low elevation, peat soil, and land use change. The temporal distribution shows a tendency for higher temperatures in the dry months of August to October, with an increase of 4.5 percent during El Nino and a decrease of 0.9 percent during La Nina from 2014 to 2020. This study could contribute to management and mitigation for fire risk in certain area and period as ENSO occurred in Kalimantan.

Identification and Prediction Network Analysis Based on Multivariate Data of Urban Form: A Case Study of Shenzhen, China

The rapid growth of urban populations has resulted in a scarcity of land, thus making sustainable urban development an urgent matter. Although Shenzhen has implemented land policies and optimized its functional layouts, these measures have inadvertently contributed to a shortage of available land for development. The city’s exponential population growth and expansive urban expansion have outpaced the supply of land. This study endeavors to identify urban commercial patterns by employing multiple data sources and applying machine learning and network analysis to predict future commercial areas. The results demonstrated that the identification of commercial points of interest and analysis of land surface temperature distributions made Futian district the primary area for ongoing commercial development, while also revealing a positive correlation between these two datasets. By leveraging network analysis to thoroughly examine this data, Bao’an district was highlighted as the future focal point for Shenzhen’s commercial sector, with 22 core nodes identified in total. Finally, by assessing the network centrality within the spatial networks, and utilizing clustering algorithms to categorize nodes into groups, the economic clustering pattern was determined as the predominant model for Shenzhen’s commercial growth. This research represents a significant contribution to the realm of sustainable urban development and presents a valuable framework for other cities to adopt.

Cooling effects of wetland parks in hot and humid areas based on remote sensing images and local climate zone scheme

Wetland parks, as essential components of urban blue and green landscapes, have long been proven to provide cooling benefits to high-temperature urban spaces. This study aims to clarify the comprehensive cooling effects of wetland parks on the surrounding built-up areas. Ten typical wetland parks in Guangzhou, China, were selected as the research objects, and the local climate zone (LCZ) around these parks was divided considering a radius of 600 m. Land surface temperature (LST) distribution transitions from winter to summer were obtained for a six-month period using a mono-window algorithm retrieved from remote sensing image data from Landsat 8. Cooling factors of wetland park morphological indices and LCZ indicators were calculated using buffer zone analysis and treated as independent variables. Cooling effect indicators, namely cooling distance (PCD), cooling range (PCR) and cooling gradient (PCG), were considered as dependent variables. A quantitative correlation factor analysis was performed to examine the relationship between these variables. The results showed that these factors have varying degrees of influence on the cooling indices. Among them, LCZ characteristic factors, including sky view factor (SVF), permeable surface fraction (PSF), and aspect ratio (AR), have a greater influence on the cooling effects than the wetland park's own characteristics. Based on these results, we attempted to determine quantitative equations illustrating the relationships between the cooling effect indices and the influencing factors to provide theoretical guidance for climate-sensitive urban design near wetland parks.

A study of urban heat island effects using remote sensing and GIS techniques in Kancheepuram, Tamil Nadu, India

Aspects of ecological issues arising from rapid urbanization are investigated in this paper using Land Surface Temperature (LST) in terms of radiation budgets studied by heat conduct balance using a geospatial approach. It uses the data from USGS Landsat ETM+ 4–5 and Landsat-8 OLI imagery acquired on February 6, 1988 and October 14, 2021. The geospatial technique examines the effects of changes in Land Use/Land Cover (LSLC) in terms of NDVI classification on the distribution of surface temperature. The remote sensing approach identifies changes in land use, their influence on surface temperature, and variations in average LST caused by these hotspots. This study examines the distribution of LST in Kancheepuram district, Tamil Nadu, India. The LSTs ranged from 14 °C to 31 °C in 1985, from 25 °C to 39 °C in 2005, and from 31 °C to 47 °C in 2021. The results show a 152% increase in the mean LST in 4 decades. The LST high values were observed in urban and industrial regions with buildings, impervious sidewalks, and sparse vegetation. The intensity at hotspots was highest in urbanized and sparsely vegetated regions. The low Urban Heat Island (UHI) effect was measured in vegetated areas. Industrial areas and parking lots were commonly classified as high-intensity hotspots. The elevated temperatures are attributable to the materials used in the construction at these locations. Further research is needed to better understand the reflectance of thermal radiations by certain materials used in urban regions and how to minimize temperatures in such locations.