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Studies have shown that the greenspace ratio has a cooling effect on the land surface temperature (LST) in different regions. Sample plots were obtained from 32 different regions in Istanbul. For 1985, 2000, 2013, and 2021, LSTs were determined using Landsat satellite images, and their relationships with GI, NDVI, and NDBI were examined. In the 1985–2021 period, the rate of greenspace in the sample plots decreased from 23 to 6.9%. The lowest LSTs were obtained in 1985, whereas the highest LSTs were found in 2013. Generally, low LSTs values were obtained in areas with a high greenspace ratio. The GI index decreased from 0.23 to 0.07 from 1985–2021. There was an increase of 10.07°C in average LST values in the 1985–2021 period. Negative correlations were found between NDVI and GI indices and LST, and positive correlations were found between NDBI and LST. In 1985, the lowest LST value was obtained in the 31st region with a greenspace ratio of 54%, while in other periods it was 27th region, some of which was covered with forests and the percentage of greenspace ratio decreased from 21 to 11%. The areas with the highest LST values were generally obtained in areas with dense population and urbanization and with a greenspace rate of 1% or less. The DUNCAN test was used to separate regions in terms of LST values. [ABSTRACT FROM AUTHOR]

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future. [ABSTRACT FROM AUTHOR]

The identification and systematic monitoring of water spread regions using remote sensing techniques necessitates precise temporal resolution datasets. Landsat data are one of the most popular data sources that provides satellite derived images of the Earth's surface for examining the satellite-based indices of a specific region. In the present research study, Normalized Difference Water Index is employed to identify the surface area of 11 system and non-system lakes in a part of Coimbatore region over the time span of 32 years (1990–2022). Based on the analysis, the water spread region in 1990 is seen to be 13.01 ha which gradually increased to 435.17 ha in 2022. This clearly indicates that there is substantial difference in the rainfall statistics over the years. Computations for cross-validation are done using other indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI). A 500 m buffer is created and calculated for vegetation and built-up indices like Chinna kulam 1 and 2, Kumaraswamy Lake, Valankulam, Singanallur and Sengulam lakes which show the decreasing trend of NDVI. Simultaneously, NDBI increased in 2000, which shows that measures were taken to tackle the encroachment of lakes in the city and the concerted efforts of the Governmental authorities and Non-governmental organizations (NGOs) were fruitful. A decreasing trend is noted in NDBI during 2011 and 2020. The microclimatic conditions and advection current of waterbodies along with boating facilities and parks in some lakes offer city dwellers a tranquil recreational area with respite from vehicular pollution and heat generation from the concrete jungle. This study will be helpful in facilitating policy decisions for future planning and sustainable development of water bodies. It will also be beneficial in devising rehabilitation measures for irrigation tanks and will aid in bringing about agrarian stability of tanks in urban locales. [ABSTRACT FROM AUTHOR]

An urban heat island emerged due to micro urban temperature variations are also referred to as urban heat islands or urban hot spots. The high-rise buildings along the roads form "Urban Canyons" that inhibit reflected radiation from the built-up surface. Urban heat island develops over the cities due to man-made activity and the landscape. An understanding of the urban heat island and its formation is not only helpful in understanding urban thermal characteristics but also helps in understanding human comfort. A geospatial technique has the ability to acquire updated and cost-effective data over large regions. For urban climatology studies, remote sensing and geographic information systems are an important source of information and an effective methodology. Since 1971, the city of Krishnanagar and its vicinity have been witnessing rapid urban growth. Due to its dense population, urban climate and rapid urban expansion, they cause environmental degradation. Appraisal and Impact of urbanization on micro-climate in the Krishnanagar city complex based on satellite derived parameters. For the years 1995, 2007 and 2018, several satellite image analysis approaches such as NDVI, NDWI and NDVI were computed. Significant differences in land surface temperature were observed between 1995 and 2007, as compared to 2007 and 2018. [ABSTRACT FROM AUTHOR]

The interactions between the normalised difference vegetation index (NDVI), the normalised difference built-up index (NDBI), and land surface temperature (LST) are complex. The assessment of land use/land cover (LULC) changes in the North-western region of Algeria between 1995 and 2021 confirms the direct influence of these factors on surface thermal processes. The use of new information technologies, particularly remote sensing coupled with GIS, favourably contributes to the processing of a large volume of data and to the use of specific methods aimed at confirming and/or disproving the hypotheses put forward. The application of LULC classification methods clearly highlights the magnitude of transformations, predominantly in favour of intensified urbanisation over the past two decades. Indeed, agricultural lands have experienced a reduction of 17.45%, while urbanised areas have nearly doubled. This phenomenon can, in part, be attributed to the mass migration of populations from inland areas to the coast, not only due to climate change: secondary for political problems between 1990 and 2001. Similarly, barren lands have increased by 10.45%. These changes have real implications for ecosystems (mainly loss of biodiversity) and the climate (pollution, GHG emissions, and rising ambient temperatures). The estimation of average LST from multiple satellite scenes reveals an increasing trend, rising from 36.6 °C in 1995 to 40.35 °C in 2021. The direct relationship between LST and NDVI and between LST and NDBI confirms the close association between land use change and increasing surface temperatures. The Pearson coefficient between LST and NDVI showed a negative correlation, ranging between -0.52 and -0.47, while it was positively correlated between LST and NDBI, with values around 0.66. The emergence of hotspots in the region, confirmed by the results of analysis employing the Getis-Ord G* method, is marked by clearly increasing spatial envelopes. This phenomenon is associated with a distinct reduction in vegetation cover density, coupled with an increased vulnerability to drought conditions. These initial results argue in favour of preserving green and blue networks and, more largely, ecosystems. [ABSTRACT FROM AUTHOR]

The interactions between the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-Up Index (NDBI), and Land Surface Temperature (LST) are complex. The assessment of Land Use/Land Cover (LULC) changes in the North-western region of Algeria between 1995 and 2021 confirms the direct influence of these factors on surface thermal processes (LST). The use of new information technologies, particularly remote sensing coupled with GIS, favourably contributes to the processing of a large volume of data and to the use of specific methods aimed at confirming and/or disproving the hypotheses put forward. The application of LULC classification methods clearly highlights the magnitude of transformations, predominantly in favor of intensified urbanization over the past two decades. Indeed, agricultural lands have experienced a reduction of 17.45%, while urbanized areas have nearly doubled. This phenomenon can, in part, be attributed to the mass migration of populations from inland areas to the coast due to climate change not only: secondary for political problems between 1990 and 2001. Similarly, barren lands have increased by 10.45%. These changes have real implications for ecosystems (mainly loss of biodiversity) and the climate (pollution, GHG emissions, and rising ambient temperatures), often leading to the creation of in certain places. The estimation of average LST from multiple satellite scenes reveals an increasing trend, rising from 36.61°C in 1995 to 40.35°C in 2021. The direct relationship between LST and NDVI and between LST and NDBI confirms the close association between land use change and increasing surface temperatures. The Pearson coefficient between LST and NDVI showed a negative correlation, ranging between -0.52 and -0.47, while it was positively correlated between LST and NDBI, with values around 0.66. The emergence of hotspots in the region, confirmed by analysis results employing the Getis-Ord G* method, is marked by clearly increasing spatial envelopes. This phenomenon is associated with a distinct reduction in vegetation cover density, coupled with an increased vulnerability to drought conditions. These initial results argue in favor of preserving green and blue networks and, more largely, ecosystems.

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices. [ABSTRACT FROM AUTHOR]

Remote sensing plays a vital role in analyzing urban changes. In this regard, various datasets collected from satellites today serve as a foundation for decision-makers and urban planners. This study compares the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) as indicators for the creation of surface heat islands. Using Landsat 8 OLI/TIRS C2 L2 images, spatial correlations between land surface temperature (LST) were examined for August 2013, 2019 and 2023. Urban heat islands (UHI) are a contemporary phenomenon and increasingly common in large urban areas compared to surrounding, less populated areas. With the advancement in remote sensing, it is possible to adequately determine the spatial differentiation and prevalence of urban heat islands (UHI). The study is based on Landsat 8 satellite image sets for the Sarajevo basin in August 2013, 2019 and 2023, which were used to analyze LST, NDVI, and NDBI indices. This work indicates a relationship between LST and NDVI but varies depending on the analyzed year. Normalized Difference Built-up Index (NDBI) serves as a suitable indicator for surface UHI effects and can be used as an indicator to assess its spatial distribution within a larger urban environment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The growing importance of climate change underlines the need to comprehend Urban Heat Islands (UHI), particularly those influenced by urban morphology. As progress has been made in understanding the macroscale relationship between urban morphology and UHIs, the microscale effects are often overlooked. This study, conducted in the city of Erzurum in Turkey, delves into the complex relationship between urban morphology and UHI intensity in different housing areas with distinct microclimates, focusing particularly on street networks, building systems, and land use. Pearson correlation analysis was performed to investigate the relationships between morphological indicators and UHIs in different housing areas. Key findings include that (1) noticeable UHI effects were observed, especially in dense areas with high-rise buildings. (2) UHIs reveal a strong correlation with both 2D and 3D urban morphological indicators. A moderate-to-high Sky View Factor (SVF) tends to reduce UHIs, while an extremely high SVF aggravates UHIs. (3) Enhancing street network integration emerges as a more effective strategy for mitigating UHI effects in mid-rise buildings compared to other morphological factors. The Normalised Difference Built-Up Index (NDBI) and Normalised Difference Vegetation Index (NDVI) may not reliably indicate UHIs in housing areas with a predominantly rural character. Consequently, this article recommends that urban morphology optimisation for UHI mitigation should prioritise spatial and indicator specificity in urban design and spatial planning for cities. Future research endeavours should investigate the influence of morphological indicators on UHI dynamics in different seasons, including various remote sensing indicators related to morphological structure, to enrich our understanding of daily UHI fluctuations within urban morphology research. [ABSTRACT FROM AUTHOR]

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Converting green areas and agricultural land into built-up areas is one of the most significant effects of urbanization in Iraqi cities. Greenery spaces are a fundamental requirement for any city because they promote a healthy lifestyle and preserve urban areas' aesthetic and ecological beauty. The current study examines urbanization's effect on Baghdad city vegetation and land surface temperature. The Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST) over Baghdad were used to determine the relationship among urban areas, vegetation areas, water bodies, and land temperature. The Baghdadvector-data from the General Survey Authority was used along with Landsat Thematic Mapper for 2004 and 2008 and Landsat Operational Land Imager for 2013, 2017, and 2021. In order to understand the correlation between urban areas, water bodies, and green areas with LST, a correlation was carried out using ArcGIS software, and a scatter diagram was made to evaluate the relationship among the elements. The results showed that the temperature increased on Baghdad's land surface between 2004 and 2021. Moreover, built-up areas increased from 17% in 2004 to 53.2% in 2021; in contrast, the green areas drastically declined by 39.7%. [ABSTRACT FROM AUTHOR]

The rapid increase in the urbanisation process and other developmental activities across the globe have increased the land surface temperature of built-up areas which is considered as an emerging urban environmental problem. The rapid unplanned urban sprawl has influenced the land use and land cover of the urban area leading to the development of the phenomenon of Urban Heat Island. The present study highlights how land use and land cover changes have impacted the land surface temperature and urban heat islands phenomenon in the greater Imphal city of Manipur in India. The study was carried out with multi-spectral and multi-temporal satellite imageries of 1988, 2000, 2011, and 2021, respectively. The extracted information from the rectified imageries highlights a significant increase in the land surface temperature in the built-up area of the city. The findings illustrate that the maximum and minimum LST of the Imphal urban area has significantly increased from 28.77 to 31.25 ℃ and 10.44 to 11.47 ℃, respectively, for the month of February from 1988 to 2021. The increase in land surface temperature is directly attributed to the increased built-up area (24.06% to 44.85%) and reduction in the urban forest cover (28.17% to 16.65%). Cumulatively, there is a 2.44 ℃ and 1.03 ℃ rise in maximum and minimum LST over three decades. The variability in the LST shows positive correlations with the NDBI and negative correlations with NDVI. The study witnessed nearly about 0.74 ℃ (maximum) and 0.31 ℃ (minimum) decadal changes in the overall LST in the greater Imphal area. [ABSTRACT FROM AUTHOR]

Los humedales costeros adyacentes a zonas urbanas, como los Pantanos de Villa en la ciudad de Lima, Perú, están sujetos a cambios constantes en el uso del suelo debido a la interacción antropogénica. Esto ha llevado a la reducción de áreas de vegetación densa, dando paso a zonas edificadas. Por esta razón, el presente artículo aborda la problemática de los cambios físicos experimentados en las zonas urbanas adyacentes al humedal, como las unidades de ordenamiento ambiental, en este caso la Zona de Protección de Afloramiento y Escorrentía Superficial (ZPAES y la Zona de Protección Paisajística de Borde (ZPB), así como el Refugio de Vida Silvestre Pantanos de Villa (RVSPV), como consecuencia de la rápida expansión urbana. Por ello, el objetivo de esta investigación es analizar la evolución espacial y temporal del entorno de los Pantanos de Villa y la urbanización dentro de las Unidades de Ordenamiento Ambiental ZPB, ZPAES y el RVSPV. Para ello, se utilizó la plataforma de Google Earth Engine (GEE) y se procesaron imágenes satelitales de los sensores Landsat 5 TM y Sentinel 2 MSI. Estas imágenes se trabajaron en la interfaz de GEE y se analizaron utilizando índices espectrales, NDVI y NDBI, mediante códigos de álgebra de mapas y el enmascaramiento de píxeles. Para la representación cartográfica, se empleó un Sistema de Información Geográfica (SIG). Los resultados muestran un aumento del 291.12% en la superficie edificada y una reducción del 26% en la superficie vegetal en los últimos 35 años, para las Unidades de Ordenamiento Ambiental ZPB y ZPAES, y el RVSPV. La reducción identificada se relaciona directamente con la expansión no planificada de la superficie urbana y las actividades antropogénicas.

Satellite imagery-based spectral indices are essential for monitoring natural resource changes and urban environments. Assessing these indices is vital for natural resource management and environmental sustainability. This study adopted geospatial techniques and satellite imagery (Landsat 5 TM and Landsat 8 OLI/TIRS) to analyze changes in key spectral indices, i.e. Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), and Normalized Difference Water Index (NDWI) over the past three decades (1991–2022) in the low land region of Far Western Nepal. The study examined the temporal trends and intricate relationships between these indices during this time frame. The substantial changes in NDVI, NDBI, and NDWI within the study area have been quantified from 1991 to 2022. The findings revealed significantly elevated NDBI values in 1991, 2001, and 2013 compared to 2022, while NDWI and NDVI values were consistently lower in 1991,2001, and 2013 compared to 2022. Notably, a negative correlation was observed between NDVI and both NDBI and NDWI, contrasting with the positive correlation found between NDBI and NDWI. The study underscores the potential of combining these spectral indices to evaluate vegetated areas, built-up areas, and water bodies, providing valuable insights for effective land management, urban planning, environmental monitoring, and sustainable water resource management. HIGHLIGHTS Used geospatial techniques and Landsat imagery to explore variations in spectral indices.; Examined NDVI, NDBI, and NDWI trends and their interrelationships.; Significant changes in NDVI, NDBI, and NDWI from 1991 to 2022.; Observed negative correlation between NDVI, NDBI, and NDWI; and a positive correlation between NDBI and NDWI.; Highlighted the potential of indices for mapping land features and recommended considering various factors in future research.;

Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change.

This study focuses on examining the historical trends of the surface urban heat island (SUHI) in relation to land use/cover (LULC) changes in Jeddah City from 2013 to 2022 using Landsat data. To achieve this, various parameters were analyzed, including LULC changes, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and LST. During this timeframe, Jeddah City underwent a comprehensive development initiative aimed at demolishing informal and illegal settlements and improving the overall quality of life, aligning with the Kingdom's 2030 vision. This provides an interesting opportunity to investigate the effect of these urban transformations on the variation, presence, and intensity of SUHIs. Based on the results, a significant positive correlation between the density of built-up and LST was found, while a negative relationship was noticed between NDVI and LST for all time points. Furthermore, a remarkable observation in 2022 was the complete absence of the SUHI phenomenon following the removal of old informal settlements from the city. The outcomes of this study highlight the impact of urban development and hold the potential to uncover environmental imbalances and contribute to the development of strategies to mitigate the negative effects of LULC changes, thereby restraining the rise in LST. [ABSTRACT FROM AUTHOR]

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

Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change. [ABSTRACT FROM AUTHOR]

Much of the urbanization that occurs in Africa creates the potential for technological development and economic growth but is also a breeding ground for environmental and health problems. This study was undertaken to evaluate the urban-induced land use/land cover (LULC) change and its contribution to the land surface temperature (LST) and urban heat fluxes from 2001 to 2021. More specifically, the study analyzed different scenarios of LULC change and retrieved the LST to evaluate the trends of the urban heat flux (UHI) in response to the urban-induced LULC change. The analysis of LULC change from 2001 to 2021 indicated that built-up and bare land showed the highest rate of increase at the expense of declining open spaces, agricultural land, and vegetation areas. The built-up areas in Nekemte and Jimma City increased by 929.25 ha (172.75%) and 2285.64 ha (226.93%) over the investigated period, respectively. The highest changes in LULC are seen in built-up areas followed by agricultural land, while the smallest changes are shown by water body followed by bare land. Built-up areas showed the highest net gain, while agricultural land experienced the greatest loss. In areas where the vegetation cover is low, low LST was depicted, and high LST was shown in areas where built-up areas were concentrated in both cities. Due to the LULC changes, the average LST increased by 1.9 °C and 2.2 °C in Nekemte and Jimma City, respectively, over the last 21 years. The urbanization-induced LULC change does not only cause changes in the hydrological process but also changes in the thermal variations and urban heat stress of the two urban centers. The result indicates that the increases in vegetation and green areas are significant in improving the heat stress and thermal characteristics of urban areas. Overall, to achieve sustainable urban development, the integration of land use with urban planning policies could be critical to the resilience of local environment and urban ecosystem. [ABSTRACT FROM AUTHOR]

هدف از این تحقیق بررسی روابط بین LST و LULC در منطقه گردنه حیران می باشد. شاخص های LULC متشکل از شاخص های تفاوت نرمال شده پوشش گیاهی (NDVI) ، تفاوت نرمال شده ساخت و ساز (NDBI) و تفاوت نرمال شده و اصلاح شده آب (MNDWI) می باشد. مساحت منطقه مورد مطالعه 95/156 کیلومتر مربع بوده که از این میزان در سال 1401، حدود 7/122 کیلومتر مربع اختصاص به پهنه جنگلی داشته و تنها 2/33 کیلومتر مربع اختصاص به زمین کشاورزی دارد. مقادیر شاخص MNDWI در غنی ترین منطقه در سال 1397 از مساحتی معادل 27/12 کیلومتر مربع برخوردار بوده و با کاهش شدید در سال 1401 مواجه شده و به 68/1 کیلومتر مربع رسیده است. پهنه های ساخت و ساز شده (NDBI) تا سال 1397 با افزایش مواجه بوده و تا سال 1401 با کاهش قابل توجهی روبرو گردید. حداکثر دمای سطح زمین (LST) از 42/35 درجه سانتیگراد در سال 1392 به 04/39 درجه سانتیگراد در سال 1401 رسیده است. پهنه برخوردار از دمای 20 تا 25 درجه سانتیگراد از 9/67 کیلومتر مربع به 124 کیلومتر مربع رسیده است. در نهایت، روابط همبستگی پیرسون نشان داد که شاخص NDVI و MNDWI با شاخص LST از همبستگی منفی برخوردار بوده و بین شاخص LST با شاخص NDBI همبستگی مثبت برقرار است. بیشترین همبستگی مثبت به میزان 77/0 بین LST و NDBI مربوط به بهار 1397 بوده و بیشترین همبستگی منفی به میزان 71/0- متعلق به شاخص MNDWI و LST بوده که در پاییز 1397 به ثبت رسیده است.

Increased urbanization and growing needs in urban areas resulted in causing extensive changes to the land use land cover (LULC) of urban areas. It was observed from many previous studies that the LULC changes in urban areas result in short and long-term repercussions, including increased land surface temperature (LST) and urban heat island. However, the established relationship between the LULC changes and LST needs a deeper understanding. This study examines and explores the relationship between the LULC changes in Bengaluru, India, and the corresponding impact on the LST. LULC classification done using Landsat images revealed that over 50% of vegetation had been lost between 2003 and 2021, while the built-up area had nearly doubled during the same period. Subsequently, the city experienced an increase in the minimum temperature from 16 °C in 2003 to 21 °C in 2021, whereas the mean LST had increased from 26 °C in 2003 to 29 °C in 2021. Spatial aggregation of the LST is examined using Moran's I spatial autocorrelation, and the results suggested moderately clustered LST values in the study area. Spatial indicators like normalized differentiated vegetative index (NDVI) and normalized differentiated built index (NDBI) are calculated and compared against the LST using scatter plots. The relationship is identified using simple linear regression (SLR) and geographically weighted regression (GWR), considering the non-spatial and spatial attributes inherently present within the data. Results from GWR suggest a more defined relationship between LULC and LST, NDVI, and NDBI than SLR. While NDVI exhibited a negative relationship with LST, NDBI displayed a positive relationship with LST. The results from this study highlight the need for planned growth of urban areas with sustained growth in vegetated areas through urban planning and design strategies, which can help contain the increase in LST in future scenarios. [ABSTRACT FROM AUTHOR]

The main objective of this study is to show which of the LST-NDVI and LST-NDBI relationships can determine the most accurate index that can be used as an indicator of the effects of urban heat islands in the municipality of Guelma, using Landsat data. 8 OLI/TIRS and the geographic information system. The application of the calculation formulas made it possible to extract the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built up Index (NDBI) of the municipality of Guelma for the four seasons of 2019. This calculation led to the determination of the relationship between all three indicators. The results obtained show a strong correlation between the LST and the NDBI for the four seasons of the year. They suggest that the NDBI is an accurate indicator of the heat island effect in Guelma. This indicator can serve as a tool for future urban planning by those in charge of this department. However, there is currently and urgent need to strengthen strategies for reducing the effects of urban heat islands in order to preserve the quality of urban life of the inhabitants and by setting up emergency programs. [ABSTRACT FROM AUTHOR]

ABSTRACTThe study focuses on evaluating the relationship between land surface temperature (LST) with four land use/land cover (LULC) indices (MNDWI, NDBaI, NDBI, and NDVI) in Hyderabad City of India using four Landsat 8 data from the winter season of 2020–21. Pearson’s linear correlation coefficient method is applied in determining the correlation analysis. The results represent a stable status of the indices in the winter season as the range of the mean is significantly low (0.04 for MNDWI, 0.04 for NDBaI, 0.02 for NDBI, and 0.05 for NDVI). All the LULC indices are very stable with each other. Moreover, these indices also build a stable relationship with LST. The indices respond differently to the change of LST. LST builds a neutral relationship with NDVI (average r = −0.07), a moderate negative relationship with MNDWI (average r = −0.57), and a moderate positive relationship with NDBaI (average r = 0.48) and NDBI (average r = 0.55). The dry winter season affects the vegetation life and generates a neutral relationship between LST and NDVI. Built-up and bare land surfaces enhance the LST while water surface reduces the LST. The study is suitable for a stable land use planning system.

The study focused on investigating the seasonal and spatiotemporal relationship between the relationships of LST with four spectral indices (MNDWI, NDBaI, NDBI, and NDVI) in and around Manipur City of India using eight cloud-free Landsat data from the summer and winter seasons for 1991, 2001, 2011, and 2021. These spectral indices respond differently to the change of LST in an urban landscape. Pearson’s linear correlation coefficient was the basis of the correlation analysis. The study finds that LST builds a moderate negative relationship with NDVI (R = -0.42) and MNDWI (R = -0.42), a moderate positive relationship with NDBaI (R=0.48), and NDBI (R = 0.61). The relationship is more stable in the winter season (CV = 7.31, 7.04, 10.45, and 28.71 for MNDWI, NDBaI, NDBI, and NDVI, respectively) than in summer (CV = 44.46, 36.09, 23.67, and 29.71 for MNDWI, NDBaI, NDBI, and NDVI, respectively). The strength of the relationship is gradually increasing in the winter season while there is no such effect noticed on the trend in the summer season. The LST-NDBI relationship is the most consistent (CV = 18.19), while the LST-NDVI relationship is the most variable (CV = 30.37).

In recent years, climate change has caused an increase in the air temperature and consequently the temperature of the earth's surface. The temperature of the earth's surface has had various effects on urban life and has caused the formation of thermal islands in cities. Thermal islands are one of the important criteria in regional planning. The city of Shiraz is considered to be a large population center in the southwest of Iran on the one hand and one of the important tourism centers of Iran on the other hand, so the purpose of this research is to monitor the spatial form of thermal islands in this city. The satellite images of Shiraz city were first collected and extracted in the hot periods of the year from 1985-to 2020 using the data of Landsat’s 4 and 5 (TM), 7 (ETM+), and 8 (OLI/TIRS). After required preprocessing, the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI), and the urban heat dispersion index were determined by the Urban Thermal Field Variance Index (UTFVI). The regions with UHI, the variation of the temperature in the city, and the relationship between the LST and surface vegetation were evaluated based on the results of image processing to determine the UHIs of Shiraz. The temporal variations of the temperature pattern of Shiraz city indicated that about 12.76 km2 was added to the area of the fourth temperature class from 1985 to 2020. Results of NDVI evaluation in the studied period showed a decline in the vegetation from 22.28 km2 in 1985 to 17.54 km2 in 2020 due to alteration of the land use which can explain the formation and increase of the UHIs in the mentioned regions. At the beginning of the investigated period (1985), the temperature of the earth's surface was 17°Celsius and reached 21°Celsius at the end of the period (2020). The monitoring of the spatial form of the surface temperature of the earth shows that heat islands are moving from the outskirts of the city towards the city center. UTFVI also revealed that the awful regions (very hot temperatures) are mainly concentrated in the west and north-west to the south west of Shiraz (including some parts of districts 9 and 10), south east of district 7, and north of district 1.

Rapid urbanization and land use transformation coupled with climate change impact are witnessed for development urban heat island worldwide and make it major environmental concerns for human health. National Capital Region (NCR) is one of the fast sprawling National Capital city of India. The aim of the present study is to explore the spatial changes in major biophysical parameters of city over last 20 years from 2000 to 2020 using satellite data. The spatial variation in the land surface temperature and its relation with Land use, NDVI and Built-up area were evaluated and discussed in the present work. The major changes were observed in minimum and maximum temperature of the city, in 2000 minimum temperature was 23.20 °C and maximum 34.85 °C, which increased to 26.31 and 39.92 °C in 2010; and 31.70 to 44.74 °C in 2020 . Normalized differential vegetation index (NDVI) decrease from 82 to 62%, Non-Vegetated area has reduced to 62% from 82% due to fast urbanization and land transformation in last two decades. Barren and fallow land also reduces about 27% in last two decades and built-up area also shown increasing trend . The spatial changes in biophysical parameter of the city clearly indicated that NDVI, NDBI and Land use/land cover charges are the main driving forces for development of urban heat island in the city and many health issues related to heat waves are already reported in the city along with many other negative impacts on natural landscape and resources. The observations from present work will be very useful scientific information to understand the causes of elevated land surface temperature and mitigation measures. Results could be used by policy makers to develop the suitable model for long term sustainability of the cities.

High levels of rainfall, tidal flooding, land subsidence, intensified urban development, scarce barren land and a shortage of green open spaces (GOS) are contributing factors to the persistent flooding in Jakarta. Therefore, this study was conducted to map the GOS, built-up, and barren land in the city in order to calculate the biopore infiltration hole (LRB) potential for water infiltration as part of Jakarta's flood mitigation efforts using the Landsat 8 operational land imager (OLI). The Landsat data acquired on September 11, 2019, with path/row 122/064 were processed using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method for the radiometric correction, and geometric correction with a root mean square error (RMSE) of 7.57 meters. Moreover, the normalized difference vegetation index (NDVI) was applied to classify the GOS, the normalized difference built-up index (NDBI) for the built-up areas, and the normalized difference barren land index (NDBaI) for barren land areas which were further confirmed using NDBI to distinguish them from the built-up areas. It is also important to note that the LRB potential was calculated by adding the GOS and barren land, dividing the result by the ideal land area multiplied by the ideal number of holes. The results showed that the GOS, built-up area, and barren land were 8.34%, 85.29%, and 2.48%, respectively. Furthermore, the LRB potential through the optimization of GOS and barren land was found to be 70.06 km2 and produced 16,816,248 LRB (18.27% of total needed). The realization of this value is expected to reduce the potential inundation in Jakarta by 15.6%. [ABSTRACT FROM AUTHOR]

Increasing land surface temperature (LST) is one of the major urban climatology problems arising in urban development. In this paper, the impact of vegetation and built-up areas on the LST and impact of LST on human health are assessed using the Landsat thermal data in Bartin, Turkey. The results show that there is a constant change in the share of vegetation and built-up areas due to rapid urbanization in Bartin. Strong positive correlation has been found between NDBI and LST while strong negative correlation has been found between NDVI and LST, suggesting their strong impacts on land surface temperatures. Similarly, a strong positive correlation has been observed between LST, sleep deprivation, and heat stress. This study provides precise information on effects of urbanization and man-made activities, which cause major changes in micro-climate and human health in the city. This study can assist decision-makers or planners to plan future developments sustainably. [ABSTRACT FROM AUTHOR]

Natural and man-made changes in the environment cause surface heating or cooling in the metropolitan areas in comparison with the rural areas. The urban area and surrounding rural areas have differences in energy balance due to variations in surface characteristics. These differences in temperature are the main reason for the formation of Urban Heat Islands or Urban Cool Islands. This research explores the spatio-temporal variations in surface temperatures during the daytime in Bhopal city—a case of the fast-developing metropolitan city having a composite climate. To explore the surface cool island phenomena, this research built a temporal and spatial difference in Local Climate Zones (LCZ) with Land Surface Temperature (LST) and Spatial indices. Remotely sensed Satellite data—Landsat-5 and Landsat-8, from 1990 to 1995 and 2015 to 2020, were acquired to investigate the LST and generate the LCZs. Both pre-monsoon and post-monsoon period data were analysed. LCZ was classified on the basis of built-up area, vegetation, and agricultural areas of Bhopal. The LCZ classes were analysed with LST, and LST was further correlated with vegetation and moisture indices using Pearson's correlation. Different indices are investigated in order to determine the major causes of observed LST patterns in different LCZ. A linear negative correlation was found between NDVI and LST up to − 0.4 at 0.01 significance. The statistical association between NDBI and LST has shown a positive correlation across the seasons, indicating that impervious surfaces result in high surface temperatures. The LST of water surfaces, i.e. LCZ G is 3 °C to 5 °C lower than that of other LCZ classes. The moisture Index (NDMI) and LST have a negative correlation of −0.55 that was greater and more consistent. The result from 1990 to 2020 shows that the LST of LCZ in rural areas of Bhopal is almost 2.5 °C to 3 °C higher than the urban area LCZs. The above spatio-temporal analysis of LST thus confirms the formation of Surface Urban Cool Island in the Bhopal urban area during day time and has a comparatively low surface temperature as compared to its surroundings. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze the trend of change in land use land cover (LULC) and land surface temperature (LST) in Mirpur and its surrounding area over the last 30 years using Landsat satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, 5-year intervals of Landsat 4–5 TM and Landsat 8 OLI images were utilized to track the relationship between LULC changes and LST. The modeled LST was validated with MODIS-derived LST within the study area. Cellular automata-based artificial neural network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were analyzed to determine their link with LST. The relation between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature was increasing steadily for the last 30 years. For the year 2019, it was determined that roughly 86% of total land area has been converted to built-up area and that 89% of land area had an LST greater than 28 °C. According to the study, if the current trend continues, 72% of the Mirpur area is predicted to see temperatures near 32 °C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC. [ABSTRACT FROM AUTHOR]

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In recent years, climate change has caused an increase in the air temperature and consequently the temperature of the earth's surface. The temperature of the earth's surface has had various effects on urban life and has caused the formation of thermal islands in cities. Thermal islands are one of the important criteria in regional planning. The city of Shiraz is considered to be a large population center in the southwest of Iran on the one hand and one of the important tourism centers of Iran on the other hand, so the purpose of this research is to monitor the spatial form of thermal islands in this city. The satellite images of Shiraz city were first collected and extracted in the hot periods of the year from 1985-to 2020 using the data of Landsat's 4 and 5 (TM), 7 (ETM+), and 8 (OLI/TIRS). After required preprocessing, the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI), and the urban heat dispersion index were determined by the Urban Thermal Field Variance Index (UTFVI). The regions with UHI, the variation of the temperature in the city, and the relationship between the LST and surface vegetation were evaluated based on the results of image processing to determine the UHIs of Shiraz. The temporal variations of the temperature pattern of Shiraz city indicated that about 12.76 km2 was added to the area of the fourth temperature class from 1985 to 2020. Results of NDVI evaluation in the studied period showed a decline in the vegetation from 22.28 km2 in 1985 to 17.54 km2 in 2020 due to alteration of the land use which can explain the formation and increase of the UHIs in the mentioned regions. At the beginning of the investigated period (1985), the temperature of the earth's surface was 17°Celsius and reached 21°Celsius at the end of the period (2020). The monitoring of the spatial form of the surface temperature of the earth shows that heat islands are moving from the outskirts of the city towards the city center. UTFVI also revealed that the awful regions (very hot temperatures) are mainly concentrated in the west and north-west to the south west of Shiraz (including some parts of districts 9 and 10), south east of district 7, and north of district 1. [ABSTRACT FROM AUTHOR]

Land is a vital component of nature around the world and is essential for humans, terrestrial plants, and animals. However, urbanization is growing worldwide and the groundwater quality in urban areas is declining due to rapid development, industrialization, and pollution. Hence, it is important to determine the hydrogeochemistry and changes concerning land use and land cover (LULC). This study was conducted to investigate the hydrogeochemical mechanism of Sargodha, one of Pakistan's fastest-growing cities, during 2015 and 2021 under two different LULC indices named normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). Forty-eight groundwater samples were collected in 2015 and 2021 to assess groundwater quality for domestic use and irrigation. The Piper diagram revealed that (Ca2++Mg2+–HCO3−+CO32−) and (Na++K+–SO42−+Cl−) were the main components of hydrogeochemistry in both years. The Gibbs plot and silicate weathering of groundwater samples indicated that rock dominance and silicate weathering played a major role in aquifers. Additionally, the results showed that 4% of the groundwater in 2015 and 21% of the groundwater samples in 2021 were unfit for human consumption. However, 37% and 40% of samples were considered not fit for irrigation in 2015 and 2021, respectively. The LULC, NDVI, and NDBI clearly revealed higher urban areas in 2021 compared to 2015. The relationship between groundwater parameters and land use land cover indices (NDVI and NDBI) explained that none had a major relationship. The non-carcinogenic risk showed health quotient HQ < 1, indicating no severe health risk due to nitrate (NO3−) exposure in the city. The current study suggests conducting future investigations considering a larger scale to recommend efficient management strategies, urbanization planning, and ensuring safe irrigation and drinking water to prevent groundwater pollution. [ABSTRACT FROM AUTHOR]

Researching on urban heat island (UHI) is a hot topic among urban designers due to its adverse impacts. This paper focuses on studying spatial and temporal dynamicity of surface UHI in the Colombo district based on correlations between land surface temperatures (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) using Landsat data from 1988 to 2019. Image processing and statistical analysis were done using QGIS Desktop 3.16.0 and RStudio softwares respectively. The mean of LSTs were continuously increasing from 1988 to 2019. The highest LSTs were observed at the Colombo harbour area in both 1997 and 2007. After initiation of the port city project in 2015, these values have been increased rapidly around the Colombo port city area. The expansion of UHI area was 71.55% between 1988 to 2019, and they were distributed from the western coastal belt to the east along with the central part of the district. The urban hot spots (UHS) were compacted at harbour and port city area. Additionally, new hot spots have been generated since 2017 adjacent to “Seethagama”. These small pockets are too hot and not very conducive for human settlements. Parking lots, compacted built-up areas, and ongoing industrial construction areas influence the formation of UHS. Considering this critical situation, it is highly recommended that to move mitigation strategies like urban greening methods, cooling pavements and cooling roofs, etc. These results could be used towards a well-designed urban planning system to maintain the ecological balance within the study area.