Land Surface Temperature Maps Research Articles

Crustal thickness variations beneath Egypt through gravity inversion and forward modeling: linking surface thermal anomalies and Moho topography

This study aims to quantify the topography of the Moho boundary, the lower crust and uppermost mantle contact of Egypt, in order to estimate the crustal thickness variation and its link to the distribution of thermal anomalies under Egypt. This is accomplished by modeling satellite gravity, supported by the passive seismic constraints throughout Egypt. However, when estimating the thickness of the crust in Egypt using just seismic data, substantial uncertainty and deviation are produced due to the sparsely dispersed stations. Integrating seismic and gravity data minimizes uncertainty and improves estimate accuracy. The investigation is broken down into four stages, the first involving utilizing the Sentinel-3B satellite to create land surface temperature maps. The subsequent steps consist of gravity and seismic data adjustments, inversion and forward modeling. We used seismically restricted nonlinear inversion to look at Goco06s satellite gravity data to model the Moho’s topographic surface. The data gathered from deep seismic refraction and receiver functions adjusted the analyzed data. The inversion process relies on the adapted Bott's approach and Tikhonov regularization, using the assumption of the sphericity of the Earth planet. Reference values for depth of Moho and density contrast were set at 35 km and 500 kg/m3, respectively. The average statistical difference for Moho depth between gravity-based model and seismic data is − 0.10 km. Through forward gravity modeling, five gravity profiles were chosen and interpreted in 2.5D models. The results indicated that the Moho depth in the south varies from 35 to 39 km and decreases in the north and the Mediterranean. In upper Egypt, the highest Moho depth is 39 km. The depth varies beneath the Sinai Peninsula as it is about 35 km in its south, reaches 30 km in the northern portion, and ranges along the Red Sea’s Rift Margin from 29 to 32 km. Moreover, the final model shows the relation between Moho coincides with the surface temperature anomalies approved by satellite images and hot springs. The model reveals a correlation between Moho discontinuity and surface temperature anomalies, revealing the highest geothermal potential in a rectangular area in central Egypt, between latitudes 25°N and 30°N, based on satellite imagery and hot springs distribution.

A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning

Downscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage.

IoT sensors as a tool for assessing spatiotemporal risk to extreme heat

The safety of urban populations sensitive to extreme heat is under increasing threat. Few studies examine the potential benefits of deploying IoT environmental sensors in the urban context and their integration with large-scale human activity data. This paper examines the deployment of IoT sensors in high-resolution extreme heat risk assessment in the case of Seoul, South Korea. This study conducted spatiotemporal analysis on heat exposure with IoT sensors, compared it with an existing land surface temperature map for validation, combined it with human activity data for risk assessment, and finally discussed the benefits of IoTs in detecting abnormal weather events. The results show that extreme heat risks and characteristics vary by age group, and socio-demographic nature overlaps with contextual factors concerning climate risk. This paper discussed possible policy implications to better deal with recurring climate hazards using IoT sensors.

A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations

Land surface temperature (LST) is a crucial parameter in the circulation of water, exchange of land-atmosphere energy, and turbulence. Currently, most LST products rely heavily on thermal infrared remote sensing, which is susceptible to cloud and rain interference, leading to inferior temporal continuity. Microwave remote sensing has the advantage of being available “all-weather” due to strong penetration capability, which provides the possibility to simulate time-continuous LST data. In addition, the continuous increase in high-density station observations (>10,000 stations) provides reliable measured data for the remote sensing monitoring of LST in China. This study aims to adopt the “Earth big data” generated from high-density station observation and microwave remote sensing data to monitor LST based on deep learning (U-Net family) for the first time. Given the significant spatial and temporal variability of LST and its sensitivity to various factors according to radiation transmission equations, this study incorporated climatic, anthropogenic, geographical, and vegetation datasets to facilitate a multi-source data fusion approach for LST estimation. The results showed that the U-Net++ model with modified skip connections better minimized the semantic discrepancy between the feature maps of the encoder and decoder subnetworks for 0.1° daily LST mapping across China than the U-Net and U2-Net deep learning models. The accuracy of the LST simulation exhibited favorable outcomes in the spatial and temporal dimensions. The station density met the requirements of monitoring air-ground integration monitoring in China. Additionally, the temporal change in the simulation accuracy fluctuated in a W-shape owing to the limited simulation capability of deep learning in extreme scenarios. Anthropogenic factors had the largest influence on LST changes in China, followed by climate, geography, and vegetation. This study highlighted the application of deep learning in remote sensing monitoring against the background of “big data” and provided a scientific foundation for the response of climate change to human activities, ecological environmental protection, and sustainable social and economic development.

Assessment of Spatio-Temporal changes of Forest Cover using Remote Sensing techniques in Pavagadh Region, Gujarat State

For effective forest management, it is essential to consider forest patterns and periodic changes in forest cover. Several spectral vegetation indices derived from multi-temporal Remote Sensing data are useful to track the changes over time. The major objective of this study was to monitor the changes in forest cover during the past three decades in the Pavagadh area of Panchmahal district, Gujarat State, India. Various indices like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature, and Urban Thermal Field Variance Index (UTFVI) with the Ecological Evaluation Index were analyzed for assessment of Spatio-temporal changes in the forest cover. Multi-temporal Landsat-TM and OLI sensor data for the years 1991, 2001, 2011, and 2021 were utilized covering the study area. The results indicated that the total forest cover area has gradually increased from 1991 to 2021 and the total forest area has doubled during this period of 30 years. The comparative study of NDVI and Land Surface Temperature map brings out a significant fact that in the areas where moderate and dense forest cover is present, the Land Surface Temperature was lower as compared to areas with poor vegetation cover. This indicates that there is an inverse relationship between forest cover distribution and Land Surface Temperature. However, the Ecological evaluation index shows that the forest vegetation quality is gradually improving to normal conditions with the excellent category and UTFVI value (< 0) in reference to the year 1991.

Identification of Geothermal System in Cisolok, West Java based on the Correlation of Gravity Method, ADMT (Active Directory Magnetotelluric), and Drill Log Data

Indonesia has 40% geothermal potential of the world’s geothermal potential. One proof of its potential is in Cisolok, West Java. The method used in this study is the gravity method from 2013 GGMPlus satellite with First Horizontal Derivative (FHD) analysis which is correlated with ADMT and drill log data. Based on overlaid between Land Surface Temperature (LST) map and residual anomaly map, Cisolok has a temperature of around 23.8 – 26.5 °C. These correlations describe the normal fault structure in the south that controls the Cisolok geothermal system. The results of the 3D inversion modeling show alternating layers of tuff, tuff breccia and silt as a cap rock with a density contrast of 0 – 0.2 gr/cc at a depth of 0 – 325 m, thick limestone layers with a density contrast of -0.1 – (-0.05) gr/cc at a depth of 325 – 700 m, and a layer of granodiorite rock as an intrusion body with a density contrast of -0.2 – (-0.15) gr/cc at a depth of 700 – 900 m. Based on the results obtained, it can be proven that the gravity method and the ADMT method have a fairly good correlation in geothermal exploration activities.

Digital image processing (DIP) applied to the analysis of the use and coverage of São Sebastião, federal district, Brazil

The article explores the application of Digital Image Processing (DIP) and Remote Sensing (RS) techniques in analyzing land use and land cover changes in the Administrative Region of São Sebastião, Federal District, Brazil. DIP plays a pivotal role in automated data analysis and interpretation, particularly in the context of RS, where it efficiently processes Earth's surface data collected by sensors on satellites, aircraft, and Unmanned Aerial Vehicles (UAVs). The study covers the period from 2000 to 2020, utilizing data from satellites such as Landsat to provide a continuous view of the region. The spatial and temporal analyses incorporate considerations of hypsometry, slope, and surface temperature, aiming to understand the dynamics of land use and its impact on the microclimate. The methodology involves a comprehensive literature review, acquisition of spatial data from government agencies, and the use of computational tools such as ArcGIS and ENVI. Classification of Landsat images employs false-color compositions and Maximum Likelihood Classification to identify urban areas, water bodies, vegetation, shrubland, and exposed soil. The analysis also includes generating land surface temperature maps. Results indicate significant urban expansion, particularly in the Southeast direction, with a concentration of urban activities. The study highlights a continuous growth trend over the years, emphasizing the importance of understanding and monitoring these transformations for sustainable planning. The article contributes to the field by providing valuable insights into the dynamics of urban growth, offering a clear spatial distribution of changes over time. The detailed methodology ensures the accuracy and reliability of the analysis, supporting informed decision-making in the context of environmental exploration and regional development planning.

Evaluation of Urban Sprawl and Land Surface Temperature along with Vegetation and Built-up Index for Nagpur City, Maharashtra

Introduction: Land use and Land cover (LULC) are now major worldwide issues. The need for land is growing due to urbanisation and industrialisation, thus to meet this need, forest and vegetation land are transformed to open land that is either utilised for colonisation of urban areas or industrial usage. Patents are done on the calculation of LST. Introduction: Land use and Land cover (LULC) are now major worldwide issues. The need for land is growing due to urbanisation and industrialisation, thus to meet this need, forest and vegetation land are transformed to open land that is either utilised for colonisation of urban areas or industrial usage. Patents are done on the calculation of LST. background: The LULC classification is performed based on four classes Bare land, Built-up, Vegetation and Waterbodies. Method: The study aims to provide a detailed analysis of land and temperature change with variation in Normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI) for the study area using a geospatial technique. The LULC classification is performed based on four classes which are Bare land, Built-up, Vegetation, and Waterbodies from the year 2000 to 2020. The classified data is further used to extract the Land Surface Temperature (LST) data from the thermal band to generate LST maps. The NDVI and NDBI maps are also generated using the land sat imageries. From the above-mentionedanalysis, it is found that Nagpur city temperature has risen by 3.67 °C in two decades. Whereas, LULC results show that bare land and vegetation decreased by 11.88% and 14.93% respectively, while an increase is seen for built-up and water bodies by 25.62% and 0.19% respectively. objective: The classified data is further used to extract the LST data from thermal band to generate LST maps. Result: Regression analysis between temperature and NDVI, NDBI shows that temperature and NDVI have a negation relation and NDBI has a positive relation with temperature (Pearson’s r: between -0.89 to -0.81and between 0.90 to 0.81respectively) for both the years. The increased temperature is a result of urbanization in the study area. The study reveals that for assessment of LULC and LST with the incorporation of GIS and Remote sensing can be effective and swift. method: The NDVI and NDBI maps are also generated using the land sat imageries. From the analysis of above mentioned it is found that Nagpur city temperature is raised by 3.67 °C in two decades. Conclusion: This study recommends that policymakers develop policies that should minimize the transition of different classes and check the outcome of industries and the temperature of the surroundings. conclusion: The increase temperature is a result of urbanization for study area. The study reveals that for assessment of LULC and LST with the incorporation of GIS and Remote sensing can be effective and swift. other: .

Pendugaan Kadar Air di Lahan Tanaman Kopi Malang Selatan dengan Penginderaan Jauh

Soil water availability is the basis for plant growth, so its availability is the main key for coffee plants growing in South Malang. Fluctuations in groundwater content, especially in the dynamics of climate change, must be managed so that plant water needs are always met, while remote sensing is able to provide aspects of estimating the condition of soil water content in coffee plantations in South Malang. This research was conducted from July 2019 to October 2019. The water content in coffee plantations can be estimated by combining the vegetation index value and soil surface temperature because coffee plants have shade. The method used in this research is to make a tentative map obtained from the overlay between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) maps, the data is then regressed with the field results. The selection of research locations is based on the area of the coffee plantation using the smallest pixel, which is in an area of 3 km square meters. Each location of the research location was carried out four times for sampling the soil moisture content. The results showed that the correlation value between water content and the estimation results had a correlation of 0.814, with a regression value of 0.663 and a probability value of 0.9226.

Urban expansion and urban heat island effects on Bangkok metropolitan area in the context of eastern economic corridor

The unprecedented and fast urban expansion process can violate the livability of cities by forming Urban Heat Island (UHI) and exposing the public to heat related health risk due to vegetation area declination and urban area inclination. The study is focused on the changes of Land Use Land Cover (LULC), Land Surface Temperature (LST), UHI, Urban Density (UD) and Urbanization Rate (UR) of Bangkok Metropolitan Area (BMA) along with the development of Eastern Economic Corridor (EEC) Project. We used Landsat-7, Landsat-8, and Sentinel-2 data from 2015 to 2022 to perform the analysis. Different normalized indices are computed to achieve the high accuracy of LULC result maps. UHI maps are computed by LST maps and UD is computed by dividing the urban area by the total area of the resulted LULC Maps. UD of each study year is used to generate Ring Map to present the temporal distribution of urban growth. Analysis results stated that 8.4% of increased urban areas can create 3.15 °C increased in LST within an 8-years period. For the study periods (2015, 2017, 2021, and 2022), the computed average LST in Bangkok Province were 27.49 °C, 30.99 °C, 30.91 °C, and 31.22 °C respectively. Analysis results of UD and UR stated that the urban areas in the BMA shifted from 1053.91 to 1177.7213 km2, corresponding to the urban proportion in land cover structure of 67.1% and 75.5% in 2015 and 2022, in turn. Therefore, the study suggested policy makers and city planners to apply urban resilience and sustainable urban development to reduce the risks and provide effective solutions.

Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model

The analysis of the near-surface air temperature is vital for many applications such as urban heat islands and climate change studies. In particular, extreme weather events are typically localized and so should the corresponding adaptation measures. However, climate scientists are often confronted with the difficulty of providing reliable predictions at high spatial resolutions in the order of 1 km. We propose to train a convolutional neural network model to emulate the hourly high-resolution near-surface air temperature field simulated by the Weather Research and Forecasting (WRF) software over a period of 18 months. The model is driven by current and past lags of coarse SEVIRI land surface temperature fields. This nowcasting application implements a downscaling of the spatial resolution of the input by about a factor of four, while establishing a correlation between current and past land surface temperature maps and the current near-surface air temperature field. The U-Net variant that is proposed in this study uses regularization to prevent over-fitting and implements a novel space-time approach, where multiple time steps are fed into the model through 3D convolution layers. Besides LST, the model also uses urban density as additional static input to be able to predict the temperature more accurately in urban areas and to improve the generalizability of the trained model. The performance of our U-Net model is assessed via comparison to an MLR benchmark (ridge regularization). The model is shown to be superior on all performance metrics. It achieves a mean absolute error of 1.36 °C versus 1.49 °C for benchmark (a 9% relative improvement) and a root mean square error of 1.77 °C versus 1.91 °C for benchmark (a 7% relative improvement). The absolute error of the model is less than 2 °C for 77% of the prediction points versus 72% for the benchmark (a 7% relative improvement). The improvement over the benchmark is even more significant during extreme hot periods. We demonstrate the generalizability of the approach by testing the trained model on unseen spatial domains.

Circuit VRC: A circuit theory-based ventilation corridor model for mitigating the urban heat islands

This paper reports on Circuit VRC, a ventilation corridor model that integrates circuit theory and ventilation resistance coefficient (VRC) to address urban heat island mitigation. Taking Wuhan's main urban area as an example, the Circuit VRC model was employed to identify the paths and barriers of the urban ventilation corridors and simulate the airflow at a macro scale. Combined with land surface temperature (LST) maps, the accuracy of the Circuit VRC model was evaluated. This paper further proposes an optimization strategy for ventilation corridors based on this model. The results indicated that (1) The proposed Circuit VRC model exhibited high reliability. Under the prevailing wind direction, the mitigation effect of Circuit VRC-based ventilation corridor areas on urban heat islands is on average 46.3% higher than that of non-corridor areas. (2) The airflow values in Circuit VRC-based ventilation corridors showed a non-linear correlation with LST. As the airflow value increases from 0 to 0.5, the LST reduces significantly. (3) The constructed ventilation corridors can be greatly optimized by using the floor area ratio (FAR)-controlled method in the Circuit VRC model. The maximum increase in airflow value in the ventilation corridor caused by a 0.1 FAR decrease within the barriers is 17.8%. The model proposed in this study provides crucial technical support for mitigating urban heat islands.

Konya'da kentsel ısı adası ve karbon monoksit değişiminin Google Earth Engine kullanılarak incelenmesi

The increasing population has been causing changes in and around urban areas. As a result of this situation, it is observed that the amount of heat and air pollution in cities is higher than in rural areas. Urban heat islands (UHI) are a factor that affects people's quality of life. Therefore, monitoring temperature changes and taking regional measures is necessary. 
 In this study, the Landsat 8 satellite was used to determine the UHI, and the Sentinel-5P satellite was used to determine carbon monoxide (CO) pollution between 2019-2021. The central districts of Konya have been selected as the study area. To determine UHI between the seasons, a total of 12 land surface temperature (LST) maps were generated. In addition, 36 CO maps of twelve months were created for the same region using the Sentinel-5P satellite. When the maps created were evaluated, it was concluded that there was a significant result between temperature and CO. It was found that the areas with higher ST also had higher levels of CO.

Delineating reservoir area using geologic remote sensing analysis, Jizan (Saudi Arabia): Implications for geothermal exploration

Jizan Province is considered one of the potential geothermal areas in Southwest Saudi Arabia. However, the prospective geothermal reservoir is still not delineated and the geothermal energy reserve is not estimated properly. Therefore, this study aims to delineate the geothermal prospect area using remote sensing-based analysis. Permeable areas, the high anomaly of Land Surface Temperature (LST), and the existence of granitic rock were the parameters used in the remote sensing analysis. Digital Elevation Models (DEM) SRTM and Landsat 8 images were used as the data source. Automatically- and manually-extracted structural lineaments supported by fractures/faults interpretation were integrated to generate a final map of the permeable areas defined in the study area (with three main permeable zones; Zones 1, 2 and 3). The average value of five LST maps and the Natural Different Building Index (NDBI) were used to investigate the high anomaly of LST. Final fuzzy logic integration of the defined permeable area, high LST anomaly, and the existence of granitic rock allowed a unique opportunity for the delineation of the prospective geothermal reservoir area. Remote sensing results were validated using the field-based studies performed in the same area. Three zones are suggested as prospective geothermal potential areas (Zones A, B, and C), where Zone A is the most favourable due to its estimated largest area and is interpreted to belong to the same geothermal system as Zone C. This study improves the understanding of the prospective geothermal reservoir area in Jizan based on the delineation of high permeable zones, and high anomaly LST. The spatial distribution of radiogenic elements in the rock units is also considered an indicator of the heat source of the geothermal system.

Machine Learning in Urban Tree Canopy Mapping: A Columbia, SC Case Study for Urban Heat Island Analysis

As the world’s urban population increases to the predicted 70% of the total population, urban infrastructure and built-up land will continue to grow as well. This growth will continue to have an impact on the urban heat island effect in all of the world’s cities. The urban tree canopy has been found to be one of the few factors that can lessen the effects of the urban heat island effect. This study seeks to accomplish two objectives: first, we examine the use of a commonly used machine learning classifier (e.g., Support Vector Machine) for identifying the urban tree canopy using no-cost high resolution NAIP imagery. Second, we seek to use Land Surface Temperature (LST) maps derived from no-cost Landsat thermal imagery to identify correlations between canopy loss and temperature hot spot increases over a 14-year period in Columbia, SC, USA. We found the SVM imagery classifier was highly accurate in classifying both the 2005 imagery (94.3% OA) and the 2019 imagery (94.25% OA) into canopy and other classes. We found the color infrared image available in the 2019 NAIP imagery better for identifying canopy than the true color images available in 2005 (97.8% vs. 90.2%). Visual analysis based on the canopy maps and LST maps showed temperatures rose near areas where tree canopy was lost, and urban development continued. Future studies will seek to improve classification methods by including other classes, other ancillary data sets (e.g., LiDAR), new classification methods (e.g., deep learning), and analytical methods for change detection analysis.

Search and assessment of urban heat islands on digital satellite images

Formulation of the problem. The development of urbanization processes resulted in Urban Heat Islands (UHI) - ur-ban areas of high temperatures due to the dominance of artificial surfaces that absorb and retain heat. UHIs adversely affect the environmen-tal conditions of cities and people's health. Satellite images in the thermal infrared range of the electromagnetic spectrum allow identifying areas of the high thermal back-ground of urbanized territories for management decisions support aimed at improving urban landscapes. The aim of the work is to develop a methodology for temperature anomalies analy-sis and urban heat islands detection on digital space images from Landsat 8 satellite in the thermal infrared range of the electromagnetic spectrum. Methodology of UHI detection. The methodology proposed in the paper includes a sequence of stages of collecting, and preprocessing satellite data, generating land sur-face temperature maps, identifying temperature anomalies, comprehensive analysis of high-temperature zones based on different-time data, and identifying areas of high temperatures stable over time. Land Surface Temperature estimation. Land Surface Temperature (LST) is esti-mated from the band 10 data of space images from the Landsat 8 satellite. The initial data are subjected to atmospheric correction, after which the spectral radiation in the band is converted to brightness temperature, and then to LST (Celsius degrees). UHI mapping. On each survey date, UHIs are identified as temperature anomalies - LST map pixels with values higher than the LST mean plus standard deviation within the study area. Time series analysis of satellite images. UHI mapping is based on a time series of Landsat 8 images acquired during the warm season (when ambient temperatures are above zero) to eliminate the effect of snow and ice on the observation results. According to the time series of LST maps of different years, stable temperature anomalies are dis-tinguished - UHIs presented on all images of the time series. Conclusions. Time-stable UHIs of the urbanized territory are mapped and ana-lyzed using Landsat 8 LST data time series. The areas of the high thermal background are compared with the classes of the earth's surface. It is concluded that increasing ur-ban density contributes to the accumulation of heat and increases the ambient tem-perature. The maps of LST distribution, vegetation indices, and UHI generated in the work can be used in urban planning tasks together with traditional sources of spatial information to obtain new information on improving the city's resistance to heat waves. UHI observation data are necessary for zoning areas with different land use types, for example, increasing the amount of open space and greenery, and improving the thermal comfort of citizens.

Impact of Urbanization on Groundwater and Surface Temperature Changes: A Case Study of Lahore City

The over-exploitation of groundwater resources is a significant concern due to the potential risks associated with the depletion of this valuable freshwater source. Future planning must consider changes in groundwater availability and urban expansion which are critical for understanding urban growth patterns. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the Land surface temperature (LST) analysis has been performed to find the spatial spread of urbanization and its impact on surface temperature. The Gravity Recovery and Climate Experiment (GRACE) data for groundwater storage monitoring and Landsat data for land cover and LST mapping have been used. The GRACE-based Groundwater Storage (GWS) anomaly has been correlated with Tropical Rainfall Measuring Mission (TRMM)-based precipitation data. The GWS is further cross validated with the groundwater monitoring stations in the study area and the correlation of 0.7 is found. The time series analysis of GWS and the land cover maps with a decadal interval from 1990 to 2020 has been developed to find the impact of groundwater change due to urbanization. The results demonstrate a rapid increase in groundwater depletion and urbanization rates over the past decade. The LST spatial pattern is increasing similarly with the study area’s urban expansion, indicating the temperature rise due to urbanization. The study highlights the limitation of effective policies to regulate groundwater extraction in urban areas and the importance of proper planning to ensure the long-term sustainability of freshwater resources.

Urban Green Space Analysis and its Effect on the Surface Urban Heat Island Phenomenon in Denpasar City, Bali

The Urbanization process in Indonesia’s big cities causes adverse environmental impacts such as climate change and land cover change. Urban climate change causes the warming of urban areas compared to rural areas; it is called Urban Heat Island phenomenon. Loss of vegetation due to urban development is one of several causes that contribute to urban heat islands. This study examines the availability of green spaces and their effects on the surface urban heat island in Denpasar city. This study used the spatial approach for Urban Green space mapping with digitizing methods. Landsat 8's thermal band is used for land surface temperature mapping and to conduct a spatial pattern analysis of the SUHI phenomena. The Global Moran’s Index and Local Indicator of Spatial Association (LISA) were used to determine the correlation between urban green space and SUHI. The study result shows that Denpasar City's urban green space area covers 28.22 km2. That's equal to 22.1% of the Denpasar City Administrative area. Denpasar Selatan district has the largest urban green space cover, with 14.19 km2 covered, or 50.27% of all the green space in Denpasar City. The majority of Denpasar is affected by UHI occurrences, except the northern region of North Denpasar and the southern region of South Denpasar. The maximum UHI level reaches 4-5°C, located on the east side of South Denpasar, especially in the Sanur coastal area. According to the spatial pattern study, the association between urban green space and SUHI only exists on the north side of Denpasar. The correlation between low-SUHI intensity clusters and high cover of green space is shown in the same area. However, the association between High-UHI intensity and low green space cover has not significantly happened. It indicated that other factors besides green space could affect the land surface temperature.

Insights into the Effect of Urban Morphology and Land Cover on Land Surface and Air Temperatures in the Metropolitan City of Milan (Italy) Using Satellite Imagery and In Situ Measurements

With a concentration of people, activities, and infrastructures, urban areas are particularly vulnerable to the negative effects of climate change. Among others, the intensification of the Urban Heat Island (UHI) effect is leading to an increased impact on citizen health and the urban ecosystem. In this context, this study aims to investigate the effect of urban morphology and land cover composition—which are established by exploiting the Local Climate Zone (LCZ) classification system—on two urban climate indicators, i.e., Land Surface Temperature (LST) and air temperature. The study area is the Metropolitan City of Milan (northern Italy). LCZ and LST maps are derived by leveraging satellite imagery and building height datasets. Both authoritative and crowdsourced in situ measurements are used for the analysis of air temperature. Several experiments are run to investigate the mutual relation between LCZ, LST, and air temperature by measuring LST and air temperature patterns in different LCZs and periods. Besides a strong temporal correlation between LST and air temperature, results point out vegetation and natural areas as major mitigating factors of both variables. On the other hand, higher buildings turn out to increase local air temperature while buffering LST values. A way lower influence of building density is measured, with compact building areas experiencing slightly higher air temperature yet no significant differences in terms of LST. These outcomes provide valuable tools to urban planners and stakeholders for implementing evidence-based UHI mitigation strategies.

Detection of the Land Surface Temperature Changes in Ma’an Governorate using Remote Sensing Data during the Period (1990-2018)

This paper investigates multi-temporal land surface temperature (LST) for large ungauged areas of Ma’an Governorate, Jordan, based on changes in normalized difference vegetation index (NDVI) using remotely sensed data. Five mosaic images were taken for both the 1990 and 2018 years by Landsat 5 (TM) and Landsat 8 (OLI) (path/row, 174/36- 174/37). These were used as the basic data source, where most of Ma’an Governorate has no meteorological stations. The five-mosaic images for both Landsat 5 and Landsat 8-OLI were taken in September. LST and NDVI maps have been generated to determine the changes in LTS during the monitoring period. The results showed that the minimum value of LST increased by 4°C, and the mean surface temperature increased nearly by 2°C between 1990 and 2018. The average LST has been rising at a rate of 0.071°C/y.