Normalized Difference Built-up Index Research Articles

Spatial distribution and environmental correlations of Culex pipiens pallens (Diptera: Culicidae)in Haidian district, Beijing.

Culex pipiens pallens Coquillett, 1898(Diptera: Culicidae)was the dominant health threat to mosquito species in Beijing, and it is important to unravel the spatial distribution and environmental correlations of Cx. pipiens pallens in Beijing. 3S technology methods and spatial statistics were used to clarify the distribution pattern. Subsequently, linear and spatial regression were performed to detect the environmental factors linked with the density of Cx. pipiens pallens. The same "middle peak" spatial distribution pattern was observed for Cx. pipiens pallens density at the community, subdistrict, and loop area levels in our study area. In addition, there were various correlated environmental factors at the community and subdistrict scales. At the community scale, the summary values of the Modified Normalized Difference Water Index (MNDWI) in 2 km buffer zone (MNDWI_2K) were negatively correlated, and the summary values of Normalized Difference Built-up Index (NDBI) in 800 m buffer zone (NDBI_800) was positively correlated to the Cx. pipiens pallens density. However, the summary values of Normalized Difference Vegetation Index and Nighttime Light Index significantly affected Cx. pipiens pallens density at the subdistrict scale. Our findings provide insight into the spatial distribution pattern of Cx. pipiens pallens density and its associated environmental risk factors at different spatial scales in the Haidian district of Beijing for the first time. The results could be used to predict the Cx. pipiens pallens density as well as the risk of lymphatic filariasis (LF) infection, which would help implement prevention and control measures to prevent future risks of biting and LF transmission in Beijing.

Characterizing Land Surface Temperature (LST) through Remote Sensing Data for Small-Scale Urban Development Projects in the Gulf Cooperation Council (GCC)

Characterizing Land Surface Temperature (LST) through Remote Sensing Data for Small-Scale Urban Development Projects in the Gulf Cooperation Council (GCC)

Examining the relationship of major air pollutants with land surface parameters and its monthly variation in Indian cities using satellite data

The relationship of land surface parameters with the air pollutants is worth exploring. The present study investigates the relation of Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) with six air pollutants such as Aerosol Index (AI), Carbon monoxide (CO), formaldehyde (HCHO), Sulphur Dioxide (SO2), Nitrogen dioxide (NO2) and ozone (O3) from January to December month of year 2022 in four cities situated at varying climatic zones of India. Except for O3, which exhibits lower concentration in winter and greater in summer, the air pollutants concentration showed lower values during monsoon season and higher during summer and post-monsoon seasons. The relationship of LST, NDBI and NDVI with different air pollutants were found to vary throughout the year in the four cities. The magnitude of correlation coefficient (R) was found greater for AI and NO2 as compared to other pollutants depicting greater impact of land surface parameters on concentration of AI and NO2. The surface urban cool island effect in Bikaner showed strong negative relation of LST with NO2 with magnitude of R greater than 0.41. The surface urban heat island formation in Varanasi showed strong positive correlation with the air pollutants such as AI, CO, NO2 and O3 with magnitude of R value greater than 0.61, 0.31, 0.59 and 0.32 for AI, CO, NO2 and O3, respectively. Even though, the correlation of LST with air pollutants varied with seasons and cities, NDVI showed negative correlation with most of the air pollutants in the cities except in Bikaner where vegetation content is negligible. Thus, increasing the amount of vegetation in a city can improve the air quality by lowering the quantity of air pollutants there.

Revealing the Urban Heat Island: Investigating Spatiotemporal Surface Temperature Dynamics, Modeling, and Interactions with Controllable and Non-controllable Factors

The unchecked growth of urban populations has spurred rapid and unsustainable urban expansion, exacerbating environmental degradation both locally and globally. This research offers a comprehensive analysis of the Urban Heat Island (UHI) phenomenon in Bangalore and Hyderabad, India, focusing on its spatial and temporal distribution and its correlation with air pollutants. Conducted over the course of summer and winter seasons from 2001 to 2021, this study advances our understanding of UHI dynamics in rapidly urbanizing regions, shedding light on the complex interplay between urbanization, climate, and air quality. The primary objective of this study is to assess and compare the predictive performance of baseline Random Forest (RF) and XGBoost models for forecasting Land Surface Temperature (LST) in Hyderabad, India, while examining the impact of integrating spatial information into these models. Incorporating an extensive array of input variables, ranging from spectral indices to terrain features, land use/land cover (LULC) data, atmospheric parameters, and spatial data, the analysis reveals significant relationships between LST and various factors. Notably, Aerosol Optical Depth (AOD) and Normalized Difference Built-up Index (NDBI) exhibit strong positive correlations with LST, while Enhanced Vegetation Index (EVI) demonstrates a pronounced negative correlation. Similarly, Modified Normalized Difference Water Index (MNDWI) displays the strongest positive correlation with LST (r = 0.49), whereas EVI exhibits the most notable negative correlation (r = -0.67). Additionally, Modified Built-up Index (MBI) demonstrates a noteworthy negative correlation (r = -0.48) with LST. Atmospheric components such as CO, NO2, and O3 also show significant positive correlations with LST, with NO2 displaying the highest correlation during winter in urban areas. XGBoost emerges as the superior model, achieving high R2 values of 0.84 for Bangalore and 0.88 for Hyderabad. Integration of spatial information consistently enhances the performance of both RF and XGBoost models, resulting in reduced Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. However, predictions over water bodies tend to be underestimated. The study underscores the importance of incorporating spatial data for accurate LST prediction, with implications for environmental monitoring and management. Despite the advancements made, further research is needed to elucidate the specific mechanisms through which spatial information improves model performance, suggesting avenues for future investigation and refinement of modeling methodologies. At the intersection of rapid urban growth and environmental conservation, these findings are poised to inform policymakers, urban planners, and researchers, guiding them towards innovative and sustainable urban development strategies aimed at mitigating the adverse impacts of rapid urban expansion while promoting ecosystem well-being. The study contributes to spatial modeling strategies and refines LST prediction methodologies, offering valuable insights for addressing the challenges posed by urbanization and climate change in rapidly urbanizing regions.

Monitoring of land-use change in 2 periods by using Remote Sensing Technology and Spectral indices

This research aims to monitor land-use change in 2 periods, i.e., 2019 and 2023, by using Remote Sensing Technology and band indices. Research methodology consisted of: 1) classifying land-use based on data obtained from Sentinel-2 satellite into 4 types including: agriculture, forest, urban, and water; 2) analyzing Normalized Difference Vegetation Index (NDVI), Normalized Difference Built - up Index (NDBI), and Normalized Difference Water Index (NDWI) as well as analyzing relationship between land-use and NDVI, NDBI, and NDWI; and 3) comparing land-use change of 2019 and 2023. The results revealed that land-use change in Mueang Maha Sarakham District, Maha Sarakham Province, in 2 periods of time was as follows: reduced areas were agriculture and forest, and increased areas were urban and water.

Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study in Tam Ky City, Quang Nam Province, Vietnam

Changes in land use/land cover (LULC) play a critical role in effective natural resource management, monitoring, and development, particularly within the realm of urban planning. In the examination of Tam Ky city, Quang Nam province, Vietnam, spanning from 2000 to 2020, remote sensing and Geographic Information System (GIS) techniques were employed. The Landsat satellite data (Landsat 7 ETM+ for 2000, Landsat 5 TM for 2010, and Landsat 8 OLI for 2022) underwent analysis using the supervised classification method in ArcGIS 10.8 software to identify and categorize six primary LULC classes: water bodies, agriculture, settlements, vegetation, construction, and bare soil/rocks. The reliability of the classification was evaluated through k values, revealing high accuracy with values of 0.951, 0.953, and 0.950 for the years 2000, 2010, and 2020, respectively. Notable shifts in LULC were observed during the period from 2000 to 2020. The areas covered by vegetation and settlements expanded by 53 and 1300 ha, respectively, while water bodies, agriculture, construction, and bare soil/rocks experienced reductions of 466, 48, 413, and 425 ha, respectively. To facilitate a rapid assessment, the study also incorporated the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The trends identified in this study are consistently aligned with the results of the supervised classification. The identified changes in LULC pose a substantial environmental threat, and the study's outcomes serve as a valuable asset for future land use planning and management in the area. The method's high accuracy enhances the dependability of the results, making them crucial for well-informed decision-making and sustainable development initiatives.

SPATIOTEMPORAL ANALYSIS OF DENGUE CASES IN CEBU CITY FROM YEAR 2015 TO 2022

Abstract. Long-term climate changes, including increased temperature, shift in precipitation, wind patterns, and other climate factors, can disrupt the balance of nature and have significant implications in the transmission of dengue fever. This study investigated the spatial and temporal dynamics of dengue cases in Cebu City, a key metropolitan area in the Philippines characterized by a significant rate of urbanization in recent years. Climate Engine (CE), a cloud-based computing and visualization tool, was utilized in this study for database sources of Landsat 8 pre-processed satellite images. Time-series dataset of land surface temperatures (LST) and varying environmental indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) were examined to investigate the effects of increased urban surface temperatures, expanding urban structures, and diminishing vegetation in Cebu City on dengue cases from 2015 to 2022. The spatial distribution of dengue cases was analyzed through GeoDA to identify hotspots within the city. The annual dengue cases in Cebu City exhibit a temporal trend with a peak in 2016 (4637 cases) and a lowest point in 2021 (399 cases), the year when the pandemic struck. Most dengue cases were recorded between June and December, exhibiting a strong seasonal pattern, and primarily concentrated within the wet season. Barangay Guadalupe topped the number of cases (1781) followed by Barangay Lahug (1219 cases), and Barangay Labangon (1128 cases) from 2015 to 2022. These three residential barangays are in proximity to each other, indicating a potential localized clustering of dengue cases in neighboring areas. The equation derived from the linear regression model serves as a predictive tool for estimating dengue cases in Cebu City and is expressed as Dengue cases = −28.436 + 2.137 (LST) − 13.943 (NDVI) + 8.565 (NDWI) − 10.217 (NDBI). The findings of this study will have practical implications for urban planning and the development of local policies aimed at mitigating the rise in dengue cases.

GIS-BASED REGRESSION ANALYSIS OF AIR QUALITY AND LAND SURFACE TEMPERATURE IN METRO MANILA AIRSHEDS

Abstract. This study examines the spatial relationship of land surface temperature (LST) derived from MODIS, elevation, and air quality parameters (CO, NO2, and SO2) derived from Sentinel 5P in the Metro Manila airsheds from January 2019 to March 2023. Using Ordinary Least Squares (OLS) and Generalized Linear Regression (GLR), mean LST and heat index from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) ground stations exhibit a strong positive correlation, allowing the use of LST for further analysis. Across different combinations between LST, elevation, and air quality parameters, a weak to low negative correlation was seen between DEM to LST, CO, and NO2. In addition, weak to low positive correlation was seen between LST to CO and NO2. Almost no correlation was found between DEM and SO2, and LST and SO2. These results may be unreliable due to overfitting, non-stationarity, bias, or misspecification as implied by their statistical parameters. To enhance the reliability, it is recommended to investigate additional air quality parameters such as Normalized Difference Built-Up Index (NDBI) as high LST, CO, and NO2 have shown clustering in urban areas of Metro Manila. Moreover, it suggests exploring other regression modeling and methodologies, such as training and test sets, to identify the best-fit model. In conclusion, this study provides an exploratory foundation for future research and comparative assessment on using different methods for modeling these variables. This comprehensive approach enhances understanding of the complex interplay between temperature, elevation, and air quality, aiding the development of informed urban climate adaptation strategies.

A comprehensive investigation on the effect of UHI in Baripada city using geo‐spatial technique and MLTHP model

AbstractAccording to the Times of India, Baripada city recorded the highest temperature in the world on April 14, 2023, at 43.5°C. It is located in the south‐west direction of the Similipal Biosphere Reserve. Observing the third highest temperature in the world, we are interested in finding out the reasons affecting climate change. This paper investigates the reasons for the temperature change in Baripada city and its surroundings by considering factors like Normalized Difference Vegetation Index (NDVI), Normalized Multi‐band Drought Index (NMDI), Normalized Difference Water Index (NDWI), Normalized Difference Built‐up Index (NDBI), forest fires, rainfall patterns, wind patterns, and registration of motor vehicles. For this study, data has been collected from USGS (United States Geological Survey), NASA POWER, Forest Survey of India, ArcGIS Online, etc. The study of NDVI shows a significant relationship with the LST. Similarly, NMDI shows an abnormal change in the drought index from 0.092 in 1990 to 0.276 in 2022, and NDWI shows a continuous reduction of the water index of −0.382 in 1990 and −0.176 in 2010, followed by −0.212 in 2022. NDBI values show that the urban build‐up index is increased to an extent due to rapid urban settlements removing natural vegetation. The overall forest fire count found an abnormal rise in the Baripada region during 2011 and 2022 to 143 times. Focusing on all the above factors, an experimental study is finally carried out to confirm the rise in temperature using time‐series analysis and a multi‐layer locally tuned hidden layer perceptron (MLTHP) model to ensure the Times of India's report.

Analytical study of land surface temperature for evaluation of UHI and UHS in the city of Chandigarh India

The Land Surface Temperature (LST) plays a crucial role in the ecological well-being of cities worldwide, intricately tied to the presence of vegetation. This investigation aims to assess the relationship between seasonal LST fluctuations and the Normalized Difference Vegetation Index (NDVI), as well as the correlation between LST and the Normalized Difference Built-up Index (NDBI), utilizing Pearson's correlation coefficient technique with extensive Landsat-8 data spanning 2016 to 2020. Additionally, the study evaluates the ecological and thermal qualities of the city using the Urban Thermal Field Variance Index (UTFVI). The ecological evaluation based on UTFVI for 2020 indicates that 52.03% of the area falls under the excellent category and 31.82% under the worse category. Notably, there is a significant increase in summer mean LST from 32.42°C in 2016 to 38.27°C in 2020. Identified Urban Heat Islands (UHIs), indicative of the warmest areas within city limits due to human activities, show an increasing trend in the summer UHI temperature from 36.92°C to38.06°C between 2016 and 2020, respectively. The study reveals a robust inverse correlation between LST and NDVI, as well as a direct relationship between LST and NDBI across the entire city. The differentiation between UHI and non-UHI areas is achieved using the LST range. Furthermore, LST maps pinpoint UHI hotspots in Chandigarh, highlighting areas unsuitable for human habitation due to extreme heat. Validation of LST values employs Moderate Resolution Imaging Spectroradiometer (MODIS) data as a baseline, where MOD11A1 data's thermal infrared band offers a spatial resolution of 1 km, contrasting with Landsat 8 (Band-10) 100 m resolution. Remarkably, a highly positive correlation is observed between approximated LST values derived from Landsat 8 and MOD11A1 data, without necessitating upscaling or downscaling.

Quantitative Soil Quality Assessment by Using Visual Soil-Field Assessment Tools (VS-FAST)

Unplanned land use reflects the changes from agricultural to urbanization and decreasing of green spaces in cities and agricultural lands surrounding cities. The Soil Adjusted Vegetation Index (SAVI), Dry Bare - Soil Index (DBSI) and Normalized Difference Built-up Index (NDBI) indices were derived to evaluate the rate of the land use/cover changes due to uncontrolled urban sprawl for the years 2001 and 2020. Field indicators were also used to assess soil properties’ degradation by using Land Degradation Assessment in Drylands (LADA) under the FAO Global Environment Facility (GEF) project. A set of 30 soil samples were randomly collected from two locations: unplanned built-up and vegetated sites in Babil Governorate, Iraq. The results showed that soil texture and salinity were the most varied indicators. The poorer areas (unplanned urban areas) were low and assessed as poor quality, while the vegetated site was assessed as moderate quality with high total score values. The findings from spectral indices show a significant rise in the built-up land use/cover category, expanding from 10.367 square kilometres in 2001 to 50.883 square kilometres in 2020. In contrast, the areas of bare-soils and vegetated decreased from 29.29 and 35.474 square kilometres in 2001 to 12.511 and 19.601 square kilometres in 2020, respectively. Various analytical techniques proved instrumental in distinguishing the different land use/cover categories in this research.

Influence of vegetation index to the rainfall intensity in Pasuruan Area, East Java Province, Indonesia

An increase in population increases the rate of urbanization. This results in changes in land cover from vegetation to artificial material. As a result, much of the land surface reflects the sun's energy. Consequently, this increases the surface temperature of the land. An increase in land surface temperature (LST) will increase the intensity of rainfall. Therefore, the present study aimed to investigate the relationship between the increase in LST and rainfall intensity. Changes in land cover can be detected by normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) parameters. Landsat satellite imagery was used to detect NDVI, NDBI, and LST. Image processing was done for imageries scanned in 1995, 2015, 2017, and 2021. Two areas in the East Java Province of Indonesia, namely Malang City and Pasuruan Area, were selected. The daily rainfall intensity data were collected from related rainfall stations in the same year. The Mononobe method was applied to analyze hourly and minute rainfall intensity. IDF curves were drawn from the analyzed results. The relationship between both parameters was analyzed by comparing the LST and hourly rainfall intensity from the IDF curve. The studied results showed that the maximum temperature increase from 1995 to 2021 for the Malang City and Pasuruan Area was 2.60 C and 7.60 C, respectively. For rain, the maximum rainfall intensity increased by 58 mm for Malang City and 18 mm for the Pasuruan Area. LST and rainfall intensity change trends of the two areas had a positive coefficient of regression. The findings can be used to predict the rainfall intensity and floods based on the LST data.  

Groundwater sustainability in the face of urban expansion: A case study on Kolkata's ongoing challenge

This study investigates the impact of urbanization on groundwater sustainability in the megacity of Kolkata, India, amidst the global trend of increasing urban populations. Utilizing a site-specific approach and multidisciplinary framework, the research analyses three decades (1990–2020) of urban development, correlating the expansion of built-up areas with changes in groundwater levels. Remote sensing and Geographic Information System (GIS) techniques, specifically the Normalized Difference Built-up Index (NDBI), are employed to assess built-up area dynamics. Groundwater data from governmental sources, combined with spatial analysis using Geographically Weighted Regression (GWR), reveal a complex relationship. Descriptive analysis indicates a significant rise in average built-up areas over the decades, with a notable increase in 2020. Correlation coefficients shift from negative in 1990 to strongly positive in 2020, signifying a growing dependence of groundwater levels on built-up areas. Spatial autocorrelation showing a strong clustered pattern in case of groundwater scenario over the years. Geographical analysis highlights varying impacts across different wards, especially in the southern part of Kolkata. Time series analysis forecasts a continued increase in built-up areas, potentially leading to a decline in groundwater levels. The study emphasizes the urgent need for site-specific interventions to ensure the sustainability of groundwater resources in Kolkata, as urbanization continues unabated. The methodology and findings offer valuable insights for policymakers and city planners to address the challenges posed by urban development and safeguard groundwater resources in the pursuit of a sustainable future.

Intelligent assessment of building damage of 2023 Turkey-Syria Earthquake by multiple remote sensing approaches

A catastrophic Mw7.8 earthquake hit southeast Turkey and northwest Syria on February 6th, 2023, leading to more than 44 k deaths and 160 k building collapses. The interpretation of earthquake-triggered building damage is usually subjective, labor intensive, and limited by accessibility to the sites and the availability of instant, high-resolution images. Here we propose a multi-class damage detection (MCDD) model enlightened by artificial intelligence to synergize four variables, i.e., amplitude dispersion index (ADI) and damage proxy (DP) map derived from Synthetic Aperture Radar (SAR) images, the change of the normalized difference built-up index (NDBI) derived from optical remote sensing images, as well as peak ground acceleration (PGA). This approach allows us to characterize damage on a large, tectonic scale and a small, individual-building scale. The integration of multiple variables in classifying damage levels into no damage, slight damage, and serious damage (including partial or complete collapses) excels the traditional practice of solely use of DP by 11.25% in performance. Our proposed approach can quantitatively and automatically sort out different building damage levels from publicly available satellite observations, which helps prioritize the rescue mission in response to emergent disasters.

Urban engineering insights: Spatiotemporal analysis of land surface temperature and land use in urban landscape

In the field of urban environment engineering, understanding the relationship between land surface temperature (LST) and land use land cover (LULC) is essential in rapidly growing and climatically unstable urban landscapes such as Chengdu. It helps alleviate the magnitude and intensity of Urban Heat Islands (UHIs). Toward this aim, summer and winter Landsat images were acquired in four years from 1992 to 2021 and were used to extract LULC classes, LST and three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) and analyze their spatiotemporal associations. Results showed that built-up areas expanded approximately six times (820.82 Km2, 584.96%) from 1992 to 2021. Meanwhile, their mean LST increased in both seasons, by 9.94 °C in summer and by 0.95 °C in winter. The LST-NDBI correlation was significant and positive in both the summer and winter of the studied years (0.437< r <0.874, p=0.00) while a very high variability was observed in the LST-NDVI (-0.835< r <0.255, p=0.00) and LST-MNDWI (-0.632< r <0.628, p=0.00) correlation coefficients. According to the results, NDBI can be used as a good intra- and inter-annual predictor of LST in Chengdu, especially in the context of its fast-paced physical expansion and increasing intensity of UHI.

Monitoring the Expansion of Unplanned Urbanization and its Impact on Climate Change based on Google Earth Engine Service, a Case Study of Baghdad / Iraq

     The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, and NDBI) were determined to examine the effects of land cover changes. In addition, the land surface temperature was calculated to assess urbanization expansion's impact on Baghdad's climate warming. The results showed a drastic decrease in vegetative cover and green land, on the other hand, a significant expansion in urbanized areas. Hence, from 2004 to 2021, the urbanized areas and open land rose by 37% and 3%, respectively, while the vegetative cover decreased by 41%. The maximum land surface temperature has risen 4° C, and the minimum land surface temperature has risen 2.5°C.

Structure and Technology of Structuring Marine Areas Using Remote Sensing Data in Semi-Arid Conditions

Correctly distinguishing urbanized marine areas from bare ground is becoming increasingly important in the context of urbanization and environmental management. This study explores the feasibility of using spectral indices to distinguish urbanized marine areas from bare ground with similar spectral signatures. The Landsat-8 data were analyzed and different spectral indices were calculated and tested for their effectiveness in identifying urban areas. The results show that the Normalized Difference Built-up Index (NDBI) and the Vegetation Blending Unit (VBU) have promising potential for distinguishing urban areas from bare ground. The identification of category boundaries based on the distribution of minimum and maximum values of different spectral indices allows a clear delineation of urbanized areas. This study highlights the usefulness of spectral indices in extracting urbanized marine areas from remote sensing data and has practical implications for urban planners, decision makers, and stakeholders involved in urban planning, land use management, and environmental protection. However, caution is needed to avoid misclassification, and careful selection of appropriate indices is crucial to achieve correct classification results.  

Determination of land surface temperature and urban heat island effects with remote sensing capabilities: the case of Kayseri, Türkiye

Kayseri, a densely urbanized province in Türkiye, grapples with pressing challenges of air pollution and limited green spaces, accentuating the need for strategic urban planning. This study, utilizing Landsat 8 and Landsat 9 satellite imagery, investigates the evolution of land surface temperatures (LST) and urban heat island (UHI) effects in key districts—Kocasinan, Melikgazi, Talas, and Hacılar—between 2013 and 2022. This research has been complemented with an analysis of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI), exploring correlations among the LST, UHI, NDVI, and NDBI changes. The findings indicate that a significant portion (65% and 88%) of the study area remained unchanged with respect to the NDVI and NDBI differences. This research’s findings reveal that a substantial portion (65% and 88%) of the study area exhibited consistency in the NDVI and NDBI. Noteworthy increases in the NDVI were observed in 20% of the region, while only 4% exhibited higher NDBI. Strikingly, the UHI displayed strong negative correlations with the NDVI and robust positive correlations with the NDBI. The LST changes demonstrated a reduced temperature range, from 21 to 51 °C in 2013, to 18 to 40 °C in 2022. Localized environmental factors, notably at the National Garden site, showcased the most significant temperature variations. Notably, the UHI exhibited strong negative correlations with the NDVI and strong positive correlations with the NDBI. The study’s results emphasize the interplay among the NDBI, LST, and UHI and an inverse relationship with the NDVI and NDBI, LST, and UHI. These findings hold implications for urban planning and policymaking, particularly in the context of resilient and sustainable land use planning and the UHI mitigation. This research underscores the intricate interplay among the NDBI, LST, and UHI, highlighting an inverse relationship with the NDVI. These findings hold crucial implications for resilient and sustainable urban planning, particularly in mitigating the UHI effects. Despite limited vacant spaces in Kayseri, geospatial techniques for identifying potential green spaces can facilitate swift UHI mitigation measures. Acknowledging Kayseri’s complex dynamics, future research should delve into the UHI responses to urban morphology and design, extending this methodology to analyze the UHI effects in other Turkish cities. This research contributes to a broader understanding of UHI dynamics and sustainable urban planning practices, offering valuable insights for policymakers, urban planners, and researchers alike.

Tracking the Temporal Changes in Land Surface Temperature, Vegetation, and Built-up Patterns in Rizal Province, Philippines using Landsat Imagery

The Rizal Province was subjected to a series of natural and human-induced disturbances throughout the years. Currently, the area is undergoing urbanization which in turn results in shifts in the extent of impervious surfaces that can intensify heat-related health concerns, increase energy consumption for cooling, and alter local weather patterns. This study uses remote sensing images from to quantify the various environmental considerations that remain undocumented and unmapped for areas caused by changes in land use and land cover from Landsat Collection 1- Level 1 (Landsat 4-5 ™ C1- Level 1 &amp; Landsat 8 OLI/ TIRS C1 Level 1) and calculated three parameters namely, (i) Land surface temperature (LST), (ii) Normalized Difference Vegetation Index (NDVI), and (iii) the Normalized Difference Built-up Index (NDBI). The results showed the following: (i) an increase in the vegetation cover from 1993-2020 showed a decrease in LST from 29.34°C to 24.03°C, (ii) the relationship between LST and NDBI is directly proportional, whereas an inversely proportional relationship can be observed between LST and NDVI, and (iii) there is a fluctuating LST due to the changes in the land cover of the study site for almost three decades. This implicates the extensive shift in the ambient temperature of Rizal which further emphasizes the effects of the modification in certain land use land cover classifications, especially in vegetation cover and urban development. This highlights how human-induced and natural factors significantly contribute to the release of heat and ambient temperature, thus, accentuating the need for sustainable urban planning.

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

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