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The study is aimed at Regression analysis in both cases showed high relationships between LST, NDVI and NDBI. LST relationships with NDVI showed a strong negative correlation having an R 2 value of 0.7638 highlighting the extraordinary role of vegetation towards reducing the SUHI effect while LST relationships with NDBI showed a strong positive cor...
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... Two spectral indices, NDVI and NDBI, were calculated to analyze their connection with LST. (Florim et al., 2021). Table 3 gives details of these spectral indices. ...
Rapid urbanization in Lahore has dramatically transformed land use and land cover (LULC), significantly impacting the city's thermal environment and intensifying climate change and sustainable development challenges. This study aims to examine the changes in the urban landscape of Lahore and their impact on the Urban thermal environment between 1990 and 2020. The previous studies conducted on Lahore lack the application of Geospatial artificial intelligence (GeoAI) to quantify land use and land cover, which is successfully covered in this study. This study analyzes how urban sprawl has driven LULC shifts and assesses their direct impact on Land Surface Temperature (LST) using Geographic Information System (GIS) and remote sensing techniques. Landsat imagery, processed using Google Earth Engine (GEE), was employed for LULC classification and LST calculation, ensuring high accuracy through multi-level change detection and a thorough accuracy assessment. Pearson's correlation was also calculated in this study to assess the impact of decreased green cover on LST. The findings highlight a substantial decrease in green cover, from 1,292.8 km² in 1990 to 754 km² in 2020, alongside a marked increase in built-up areas, expanding from 262 km² to over 550 km². Additionally, barren land showed significant growth, while water bodies diminished. The spatiotemporal analysis of LST indicates a considerable rise in high-temperature zones, specifically the industrial zones, with areas exceeding 40 °C expanding from 2 km² to 1,075 km² over the study period. A strong positive correlation between increased urbanization and rising LST, particularly in areas within a 10 to 40 km radius of the Central Business District (CBD), is evident. The overall accuracy of LULC classification surpassed 94%, with the kappa coefficient above 92%, ensuring the robustness of the results. Future research should focus on evaluating the long-term socioeconomic impacts of urban sprawl and LST increment while developing heat mitigation strategies. Recommendations include adopting sustainable urban planning practices prioritizing green infrastructure, energy-efficient building designs, and policies promoting environmental preservation. This study offers valuable insights for policymakers and underscores the urgency of balancing urban growth with strategies that mitigate thermal stress, combat climate change, and foster sustainable development in Lahore.
... In 1988, the NDBI value ranged from -0.57 to +0.74 (Fig. 5d); in 2004, NDBI values ranged from -0.83 to +0.65 (Fig. 5e); and in 2023, the NDBI values ranged from -0.47 to +0.65 (Fig. 5f) The red areas in Figures 5d,e and 5f show minimal vegetation cover, such as built-up and barren land. Fig. 5. NDVI and NDBI maps for Binh Duong province in 1988, 2004 A linear regression analysis was conducted to demonstrate the relationship between two indices (NDVI and NDBI) (Florim et al., 2021). The changes in NDBI values related to land use were assessed by evaluating the variations in land use intensity within the LULC units through regression analysis (R 2 ) (Majeed et al., 2021). ...
Understanding changes in land use and land cover (LULC) is crucial for effective land management, environmental planning, and decision-making. It helps identify areas of environmental concern, assess the impacts of human activities on ecosystems, and develop strategies for conservation efforts and sustainable land use. In this study, remote sensing and geographic information systems (GIS) were used to monitor LULC changes in Binh Duong province, Vietnam from 1988 to 2023. The supervised classification method in ArcGIS 10.8 software was applied to Landsat satellite data (Landsat 5-TM for 1988 and 2004, and Landsat 9-OLI/TIRS for 2023) to detect and classify five main LULC types: arable land, barren land, built-up areas, forests and waterbodies. The classification accuracy was evaluated using kappa coefficients, which were 0.877, 0.894 and 0.908 for 1988, 2004, and 2023, respectively. During the period of 1988–2023, the forest, barren land, and waterbodies class areas decreased by 560.55 km2, 200.04 km2, and 19.68 km2, respectively. Meanwhile, the arable land and built-up areas classes increased by 343.80 km2 and 436.47 km2, respectively. Furthermore, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were used to quickly assess changes in LULC, and their trends were found to be consistent with the supervised classification results. These changes in LULC pose significant threats to the environment and the findings of this study can serve as valuable resources for future land management and planning in the region.
... As shown in Table 3, NDVI showed a negative correlation with LST. On the contrary, in Table 4, NDBI showed a positive correlation with LST (Enhanced Building and Bare Ground Index; EBBI has the same characteristics of reflecting built-up areas of the city as NDBI) [14]. Rashid et al. found that R 2 was higher in the regression model of NDBI and LST, which implies that the degree of correlation between NDBI and LST is higher [15]. ...
... Reduced vegetation is an important reason for the UHI effect, leading to higher local LST [10]. Florim et al. found that urban built-up areas and bare surfaces are the main cause of SUHI, which is closely related to LST [14]. Halder et al. also proposed an LST-based UHI assessment by comparing builtup areas (in yellow colour) with vegetation-covered areas (in green colour) to plan urban land use [10]. ...
... Vegetation in cities can redistribute heat energy and influence latent heat, thus reducing solar radiation. In addition, low CO2 concentration in areas with dense vegetation cover improves regional environmental atmospheric cleanliness and comfort for urban residents [14]. Evola et al. found that UHI can be mitigated and resident comfort and urban livability can be improved through the use of programs such as green roofs and cool pavements, but the efficiency and cost need to be considered [19]. ...
Rapid urbanization has led to an increase in the urban heat island (UHI) effect. The UHI effect leads to localized high temperatures, reduced air quality, increased risk of heat stress and other diseases among residents. At the same time, it also reduces socialization among residents. Remote sensing technology, with its advantages of quantification, automation and real-time, can be used to analyze the UHI effect and further assess the livability of cities. In this paper, based on remote sensing image processing methods, the indexes of land surface temperature (LST), normalized vegetation index (NDVI), normalized difference build-up index (NDBI), and urban heat island intensity (UHII) were selected to analyze the scope of influence of UHI and urban livability. The important index of urban livability is human comfort, while UHI affects human comfort by influencing temperature and humidity. This paper concludes that the UHI effect is mainly reflected in the significant decrease in NDVI value and the significant increase in NDBI value. Meanwhile, there is a linear regression relationship between UHI and urban livability. In addition, UHI increases urban energy consumption and decreases environmental quality, destroying livability. This paper has proposed green city programs such as green roofs and cool sidewalks to improve the urban spatial structure. However, such programs still have the limitations of lower efficiency and higher cost. Therefore, in the future, UHI will be mitigated at the source by directly reducing solar radiation.
... RS data proves valuable for conducting LULC inventory and mapping. Landsat sensors like Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced TM Plus (ETM+), and Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) provide a range of satellite data that plays a crucial role in detecting changes in Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and LULC for LULC planners [Zheng et al., 2021;Florim et al., 2021;Dash et al., 2023]. Change detection involves quantitatively analyzing the previous effects of an occurrence using RS information, thereby assisting in identifying changes related to LULC properties with reference to various satellite datasets. ...
... By utilizing satellite imagery, we can estimate the NDVI and NDBI, which provide valuable information for monitoring vegetation health and urbanization processes [Florim et al., 2021]. The NDVI serves as a vegetation index, utilizing the near-infrared (NIR) and red (RED) bands of satellite images to distinguish vegetation [Florim et al., 2021]. ...
... By utilizing satellite imagery, we can estimate the NDVI and NDBI, which provide valuable information for monitoring vegetation health and urbanization processes [Florim et al., 2021]. The NDVI serves as a vegetation index, utilizing the near-infrared (NIR) and red (RED) bands of satellite images to distinguish vegetation [Florim et al., 2021]. As vegetation cover ex-pands, the NDVI value increases, while it decreases with diminishing vegetation cover. ...
Почва – это важный природный ресурс для любой страны. Изучение изменений в использовании и покрытии земли (LULC) играет важную роль в различных областях, таких как управление природными ресурсами, мониторинг, земельное планирование и решение проблем глобальных изменений. В этом исследовании использовались географические информационные системы (ГИС) и технологии дистанционного зондирования (ДЗ) для мониторинга изменений LULC в провинции Ханам, Вьетнам с 1992 по 2022 год. Метод классификации с обучением в программном обеспечении ArcGIS 10.8 применялся к данным спутника Landsat (Landsat 5-TM для 1992 и 2003 годов, и Landsat 8-OLI/TIRS для 2022 года) для выявления и классификации пяти основных типов LULC: сельскохозяйственные угодья, бесплодные угодья, застроенные территории, лес и водоемы. Точность классификации оценивалась с использованием коэффициентов каппа, которые составили 0,886, 0,905 и 0,933 для 1992, 2003 и 2022 годов, соответственно. За период с 1992 по 2022 годы площади классов сельскохозяйственных угодий, леса и водоемов уменьшились на 102,85 км2, 48,57 км2 и 5,25 км2, соответственно. В то время как площади классов застроенных территорий и бесплодных угодий увеличились на 150,08 км2 и 6,59 км2, соответственно. Рост населения, урбанизация, политика городского планирования и переход от аграрной к индустриальной экономике способствовали расширению застроенных территорий и сокращению сельскохозяйственных земель, лесов и водоемов в провинции Ханам. Кроме того, мы использовали индекс нормализованной разницы вегетационного покрытия (NDVI) и индекс нормализованной разницы застроенных территорий (NDBI) для быстрой оценки изменений в LULC и обнаружили, что их тенденции соответствуют результатам, полученным с помощью надзорной классификации. Эти изменения в LULC представляют серьезные риски для окружающей среды, и результаты этого исследования могут предоставить ценные исследовательские данные для предстоящих инициатив по управлению и планированию земельных ресурсов в этом регионе.
... Moreover, in the analysis of UHI the NDVI is employed to quantify the vitality and concentration of vegetation. The NDVI can be employed to examine the impact of UHI phenomenon, as vegetation plays a role in alleviating its effects (Florim et al., 2021). Reduced NDVI values in urban settings might signify a scarcity of vegetation, resulting in increased intensity of the UHI effect. ...
The increasing need for land has resulted in a higher rate of land conversion and urbanization, leading to a rise in urban density and the occurrence of an Urban Heat Island (UHI) effect. The application of remote sensing and GIS can serve as a substitute for data collection in monitoring the UHI phenomena. This work utilizes Landsat 8 OLI satellite image data, namely band 10, to analyze Land Surface Temperature (LST). Bands 5 and 4 are employed to assess the distribution of Normalized Difference Vegetation Index (NDVI) in Bekasi Regency during the years 2014 and 2020. The relationship between NDVI and LST is highly correlated as they can effectively forecast the influence of areas with sparse vegetation on temperature. The guided classification approach, employing the maximum likelihood algorithm and kappa validation, is utilized to evaluate alterations in land use. The kappa accuracy test yielded a score of 0.90% for 2014 and 0.99% for 2020. The research conducted between 2014 and 2020 revealed changes in land distribution. Specifically, the built-up land area increased by 99.92 Km2, empty land expanded by 280.82 Km2, bodies of water covered an additional 46.13 Km2, and vegetation expanded by 293.91 Km^2. According to the UHI research, it is evident that there has been a rise in surface temperature in Bekasi Regency from 2014 to 2020. In 2014, the minimum temperature reached 30 °C, and the maximum temperature reached 51 °C. In 2020, the minimum temperature was recorded at 34 °C, while the maximum temperature reached 52 °C.
... In this study, we considered the LST, LSE, UTFVI, & UHI as dependent or response variables, while NDVI, Pv, NDBI, and EBBI are independent or explanatory variables. Marzban et al. (2018) and Isufi et al. (2021) have also considered LST and NDVI as a response and independent variables, respectively to assess their relationships during the day and night-time. The overall approached used in this study is presented in Fig. 2. ...
The detection of Urban Thermal Field Variance Index and Ecological condition was generally conducted based on thermal remote sensing. This study aims to model the Land Surface Temperature (LST), Urban Heat Island (UHI), Urban Heat Hotspot (UHS), Normalized Difference Built-Up Index (NDBI), Enhanced Built-Up and Bareness Index (EBBI), Ecological Evaluation Index (EEI), Urban Thermal Field Variance Index (UTFVI) of Mekelle City, Tigray-Northern Ethiopia. The Landsat 8 Operational Land Imager (OLI) and Thermal InfraRed Sensor (TIRS-1) bands were used. The results indicated that the mean LST was highest (38.69 °C) in the Semen sub-city. A total area of 33.4 Km 2 (30.56%) was classified as UHI dominantly located in the Semen sub-city. Moreover, about 38 Km 2 (34.61%) were characterized by the worst EEI. The UHS zones covered a total area of 3.03 Km 2 (2.8%) and were mainly concentrated in the Semen sub-city. In addition, the statistical relationship between LST and NDBI is negative and moderately strong (r =-0.51, p < 0.001) due to the fact that most of the built-up area in the city is mixed with vegetation cover that cools the environment. We also found an R 2 of 0.997 (p < 0.001) in all UTFVI indices. The multiple regression model result indicated that Pv, NDBI, and EBBI control LST negatively, whereas LST, UTFVI, EEI and NDVI influence LST positively.
... Equation (1) transfers a digital number into spectral reflectance for Landsat 8 thermal bands (band 10). The thermal band of Landsat 8 imagery, band 10 (10.6 μm -11.19 μm), is used to calculate the land surface temperature [36]. ...
Urban heat island (UHI) frequency and emergence are strongly associated with variations in land use/land cover (LU/LC) and land surface temperature (LST). This study investigates the impact of LU/LC class changes on LST based on the distribution of UHI spot maps in Mansoura city, Egypt, using Landsat satellite images from 1991 to 2021. Based on these estimated LU/LC and LST maps, machine learning algorithms, cellular automata, and artificial neural network approaches were used to predict future changes in LU/LC and LST for 2031. The influence of UHI may be quantified using the urban thermal field variance index (UTFVI). The geographic information system (GIS) add-in UHI calculator ArcGIS tool was created because we considered the spatial consequences of employing remote sensing data. This tool includes all the methods and procedures for calculating LST and UHI. The analysis revealed a positive correlation between LST and normalized difference built-up index and a negative correlation between LST and normalized difference vegetation index. The forecasted results for 2031 also show that the built-up area will grow roughly 20%, with a considerable drop in vegetation by 18%. If the city's fast urbanization continues, more than 40% of Mansoura will have land surface temperatures above 45°C by 2031. Avoiding dense built-up areas and growing vegetation spaces remain efficient means of minimizing the influence of UTFVI in urban construction practice. Therefore, this research will help to achieve sustainable development by providing essential insights into the complicated interplay between diverse aspects of urban settings and promoting city competency.
... The UHI phenomenon has three types -depending on how the temperature is measured (Fabrizi, Bonafoni & Biondi, 2010;Sherafati, Saradjian & Rabbani, 2018;Berila & Isufi, 2021b;Isufi, Berila & Bulliqi, 2021): a) Canopy layer heat island (CLHI) -this layer lies approximately at the average height of buildings and is determined by measuring the air temperature at a height of 2 m above the ground. The CLHI has usually been measured using sensors mounted on fixed meteorological stations (Nichol, Fung, Lam & Wong, 2009;Clay et al., 2016;Berila & Isufi, 2021b;Isufi et al., 2021). ...
... The UHI phenomenon has three types -depending on how the temperature is measured (Fabrizi, Bonafoni & Biondi, 2010;Sherafati, Saradjian & Rabbani, 2018;Berila & Isufi, 2021b;Isufi, Berila & Bulliqi, 2021): a) Canopy layer heat island (CLHI) -this layer lies approximately at the average height of buildings and is determined by measuring the air temperature at a height of 2 m above the ground. The CLHI has usually been measured using sensors mounted on fixed meteorological stations (Nichol, Fung, Lam & Wong, 2009;Clay et al., 2016;Berila & Isufi, 2021b;Isufi et al., 2021). b) Boundary layer heat island (BLHI) -lies above the CLHI layer and can reach a thickness of up to 1 km. ...
... b) Boundary layer heat island (BLHI) -lies above the CLHI layer and can reach a thickness of up to 1 km. It is measured using special platforms, such as radiosondes and aircraft (Berila & Isufi, 2021b;Isufi et al., 2021). c) Surface urban heat island (SUHI) -difference in radiant temperature between urban and non-urban surfaces. ...
The whole study was conducted for the Municipality of Prizren and aims to to determine the effect that the population density has on land surface temperature (LST). All this was achieved through the connection of land surface temperature (LST) and population density. The free Landsat 8 satellite image downloaded from the United States Geological Survey website was used and then processed using GIS and remote sensing techniques. To understand the relationship between population density and LST, we performed a regression analysis. This analysis showed a strong positive relationship with a value of r = 0.8206, emphasizing the important role that the population has in creating empowering areas that generate surface urban heat island (SUHI) effect. The results of the study clearly showed that in the northern, central, and western parts there are pixels with high LST values. This presentation corresponds with the population density, which means that it is precisely the actions of the population that help generate, display, and strengthen the harmful effect of the SUHI. The map with areas of high LST pixels are of great importance to the policymakers and urban planners of Prizren so that they can orient themselves in these areas and take all actions necessary to minimize this harmful effect which is worrying citizens. If it continues with unplanned development, the peripheral parts of Prizren are seriously endangered by the damage of the spaces which offer protection (green spaces) from the SUHI phenomenon.
Urban Heat Island (UHI) is a phenomenon specific to urban areas where higher air temperatures manifest in the city area in relation to its surrounding rural landscape. Currently, UHI is one of the most dangerous environmental conditions for cities as well as their residents. It is expected that the intensity of UHI will increase with climate change. This work presents an analysis of the UHI phenomenon for the City of Zagreb, Croatia in the summertime period 2013–2022. In order to explore UHI, Land Surface Temperature (LST) was calculated using Landsat 8 (OLI TIRS sensor) satellite imagery. After the delineation of UHI, calculated temperatures were put in relation to NDVI (Normalised Difference Vegetation Index) and NDBI (Normalised Difference Built-Up Index) indices for the study area. Results show the similarity of mean temperatures over the observed period. However, the influence of external variables on UHI’s spatial expression was observed. Forest-covered areas and other green parts of the city’s infrastructure express the lowest temperatures, while built-up sites are the hottest points in cities. Results confirm the importance of urban green infrastructure for resilient cities and present the results of a long-term UHI observation in a Southeast European city.
This contribution deals with the use of data obtained from Landsat 8 satellite imaging to identify surface temperature variability in the example of the city of Bratislava, with an emphasis on identifying hotspots outside the built-up area, for example, on agricultural land—locations which are part of the European Network of Protected Areas. Surface temperature variability is presented in two time periods, on the daytime image taken on 26 July 2021 and on the nighttime image from 28 June 2021. Surface temperature is projected in a profile cut of the area. It vertically illustrates the temperatures of individual types of surfaces. Surfaces are classified by Urban Atlas classes. Areas reflecting the spatial distribution of the residential development in the city of Bratislava have been identified by satellite images in the studied area, and they represent a phenomenon of the urban heat island. Such areas were also identified outside the built-up area, in agricultural areas. The results of our research show that it is important to deal with UHI outside the built-up areas of cities and to orient the attention the territory planning and also to the proposal of measures for the management of these areas. Especially if these areas also include territories of the European system of protected areas, as it is in the case of Bratislava city (e.g., SPA029 Sysľovské polia). The results of reducing the impacts of climate change in cities concern not only the residents. In spatial planning, it is also necessary to address the management of non-built-up areas—localities with a quasi-natural character (e.g., areas with diverse vegetation cover). In order to recognize UHI within residential areas, it is essential to identify areas with significant differences between daytime and nighttime surface temperatures. Large differences between night and daytime surface temperatures can be seen in areas outside the built-up area in Bratislava on arable land where the difference is up to 8.0 °C (in the continuous housing class where the proportion of impermeable surfaces is higher than 80% with a temperature difference of 7.6 °C). Identification of overheated surfaces in the territory makes an important basis for modification of the landscape management and management of nature protection areas. It is important to propose measures related to the reduction in the negative impacts of climate change on the landscape and biodiversity.
