Normalized Difference Built-up Index Research Articles

Divergent impact of urban 2D/3D morphology on thermal environment along urban gradients

Optimizing urban morphology is an effective way to alleviate the thermal environment, and it has always been the focus of urban planning. Although previous scholars have explored the impact of urban 2D/3D morphology on Land Surface Temperature (LST), they have rarely revealed differences along urban gradients, thus ignoring the nonlinear relationship between urban morphology and LST. In this study, we use multi-source data such as night-time lights, Landsat 8, land cover, and buildings, and employ dynamic threshold, single-window algorithm, and boosted regression trees methods to reveal the gradient and marginal effects of urban 2D/3D morphology on LST. Results showed that the proportion of built-up land (PCL), digital elevation model (DEM) and normalized difference built-up index (NDBI) have important contribution to LST. Moreover, relative influence of PCL, NDBI and building density (BD) on LST varies significantly along urban gradients, which are +25.87%, +14.85% and − 10.88%, respectively. Interestingly, we found that as the value of each variable increased, its relationship to LST changed. For example, when PCL < ∼49%, it is negatively correlated with LST, so lowering PCL can alleviate thermal environment. Our findings inform the sound practice of urban planning by accounting for marginal effects of urban morphology on thermal environment.

Spatio-temporal analysis of built-up area and land surface temperature in Surakarta using Landsat imageries

The need for built-up areas continues to increase, along with the increasing population in the city of Surakarta and its surrounding. This condition affects the land surface temperature which then leads to the change in climatic conditions. The availability of land for settlement and surface temperature will affect the comfort level of living in a city. For this reason, this study aims to map the distribution of built-up area and the surface temperature of Surakarta city and discusses the relationship between these two aspects spatially and temporally. The data used are Landsat imageries recorded in 2000, 2013, and 2019. The built-up area was identified using Normalized Difference Built-Up Index (NDBI), while the temperature data was obtained through thermal band processing using Land Surface Temperature (LST) method. The results showed that during the period of the study, the built-up area and the surface temperature in Surakarta and its surroundings increased, especially in the eastern and southern parts of Surakarta. The results also showed that there is a positive correlation between the built-up index and its surface temperature.

Land surface temperature and human thermal comfort responses to land use dynamics in Chittagong city of Bangladesh

Intense urbanization alters the microclimate and ecology of cities by converting naturally vegetated and permeable surfaces into impervious built-up surfaces. These artificial impermeable surfaces re-balance the surface energy budget by storing solar heat due to their higher thermal conductivity, and consequently, increase the Land Surface Temperature (LST). The higher LST affects the city dwellers' Human Thermal Comfort (HTC). To address these issues, unlike most prior research, we assess not only the influence of Land Use and Land Cover (LULC) alterations on summer LST but also on winter LST in Chittagong City of Bangladesh between 1993 and 2020 by using Remote Sensing (RS) and Geographic Information Systems (GIS). Additionally, the study evaluates the impact of LULC changes on the HTC during summer as LST substantially affects HTC in summer. The LULC analysis shows an increase in built-up area by 204% from bare lands, vegetated lands, lowlands, and water bodies between 1993 and 2020. In contrast, bare lands were converted from naturally vegetated surfaces, followed by lowlands and water bodies because of anthropogenic activities. The LSTs of Chittagong city, derived from remote sensing data, show a strong upward trend, with summer (winter) ranges of 20.62–34.07 °C (7.50–27.52 °C), 22.82–37.62 °C (14.92–29.32 °C), and 22.32–43.52 °C (17.08–31.83 °C) for 1993, 2007, and 2020, respectively. Between 1993 and 2020, the spatial mean winter and summer LSTs increased by 4.04 °C and 6.45 °C, respectively, or 0.15 °C and 0.24 °C per year. Chittagong had the highest mean LST in built-up areas for all the years. In addition, the study area's HTC gradually shifted to intense heat stress. The summer LST strongly correlated with normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and normalized difference water index (NDWI) while winter LST exhibited poor correlation with these indices.

Extraction of Urban Quality of Life Indicators Using Remote Sensing and Machine Learning: The Case of Al Ain City, United Arab Emirates (UAE)

Urban quality of life (UQoL) study is very important for many applications such as services distribution, urban planning, and socioeconomic analysis. The objective of this study is to create an urban quality of life index map for Al Ain city in the United Arab Emirates (UAE). The research aligns with the United Nations Sustainable Development Goals number ten (reduce inequalities) and eleven (sustainable cities and communities). In this study, remote sensing images and GIS vector datasets were used to extract biophysical and infrastructure facility indicators. The biophysical indicators are normalized difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), soil adjusted vegetation index (SAVI), enhanced normalized difference impervious surfaces index (ENDISI), normalized difference built-up index (NDBI), land surface temperature (LST), slope, and land use land cover (LULC). In addition, infrastructure facility indicators such as distances to main roads, parks, schools, and hospitals were obtained. Additional infrastructure facility variables namely built-up to green area and build-up to bare soil area ratio were extracted from the LULC map. Machine learning was used to classify satellite images and generate LULC map. Random Forest (RF) was found as the best machine learning classifier for this study. The overall classification and Kappa hat accuracy was 95.3 and 0.92, respectively. Both biophysical and infrastructure facility indicators were integrated using principal component analysis (PCA). The PCA analysis identified four components that explain 75% of the variance among the indicators. The four factors were interpreted as the effect of LULC, infrastructure facility, ecological, and slope. Finally, the components were assigned weights based on the percentage of variance they explained and developed the UQoL map. Overall, the result showed that greenness has a greater effect on the spatial pattern of UQoL in Al Ain city. The study could be of a value to policy makers in urban planning and socioeconomic departments.

Use of evidence NDVI, NDBI, NDWI, MSAVI to reveal the uses of agricultural land in karma district using modern technologies

The study of agricultural land uses in karma district relied on a range of ideas using modern geographical techniques to build a model of simulation of spatial distribution of land uses, which used sensor data after key sources in detections uses of land cover through reliance on satellite data (Land Sat-8) and sensor (OLI) For 2020, to achieve the study's objective, normalized Difference Vegetation Index (NDVI), Normalized Difference Built Up Index (NDBI), Normalized Difference Up Index (NDBI), Normalized Difference Water Index (NDWI) and Ms AVI ) Modified Soil Adjusted Vegetation Index.

The effects of climate on land use/cover: a case study in Turkey by using remote sensing data.

Economic and industrial development results in worldwide population concentration in cities, leading to increases in impervious surfaces. Thus, the surface temperatures increase and cities are exposed to the urban heat island effect. This study analyzed the changes in the urban heat island effect in the 30years (from 1990 to 2021) in the central district of Bartin. In this sense, there were two primary goals. Firstly, land use/land cover change, land surface temperature (LST), normalized difference built-up index (NDBI), and normalized difference vegetation index (NDVI) were analyzed by using remote sensing methods between 1990 and 2021. Secondly, a linear regression analysis was conducted to determine the factors associated with LST, NDVI, and NDBI. The study results revealed increases in urban surfaces and the average land surface temperature values in the past 30years and showed a decline in the vegetation. Regression analysis results indicated a strong negative relationship between LST and NDVI and a strong positive relationship between LST and NDBI. It was also found a robust negative relationship between NDBI and NDVI. In light of the findings, it was stated that the amount of open and green areas should be increased in order to prevent the negative effects of the urban heat island in the central district of Bartin. For this purpose, it has been proposed to encourage green roof systems throughout the city, to create city parks and to create a green belt system. In addition, as a result of the study, the importance of preventing forest destruction caused by over settlement in the Mountains, which is one of the rare habitats of the world with different plant species, was emphasized. In this sense, legal sanctions should be employed to protect those areas and prevent construction.

Quantifying Spatiotemporal Heterogeneities in PM2.5-Related Health and Associated Determinants Using Geospatial Big Data: A Case Study in Beijing

Air pollution has brought about serious challenges to public health. With the limitations of available data, previous studies overlooked spatiotemporal heterogeneities in PM2.5-related health (PM2.5-RH) and multiple associated factors at the subdistrict scale. In this research, social media Weibo data was employed to extract PM2.5-RH based on the Bidirectional Encoder Representations from Transformers (BERT) model, in Beijing, China. Then, the relationship between PM2.5-RH and eight associated factors was qualified based on multi-source geospatial big data using Geographically Weighted Regression (GWR) models. The results indicate that the PM2.5-RH in the study area showed a spatial pattern of agglomeration to the city center and seasonal variation in the spatially non-stationary effects. The impacts of varied factors on PM2.5-RH were also spatiotemporally heterogeneous. Specifically, nighttime light (NTL), population density (PD) and the normalized difference built-up index (NDBI) had outstanding effects on PM2.5-RH in the four seasons, but with spatial disparities. The impact of the normalized difference vegetation index (NDVI) on PM2.5-RH was significant in summer, especially in the central urban areas, while in winter, the contribution of the air quality index (AQI) was increased. This research further demonstrates the feasibility of using social media data to indicate the effect of air pollution on public health and provides new insights into the seasonal impacts of associated driving factors on the health effects of air pollution.

Time-series metrics applied to land use and land cover mapping with focus on landslide detection

Landslides are a recurring phenomenon in Brazil and have caused many socioeconomic losses and casualties. To monitor them, land use and land cover (LULC) and landslide inventory maps are essential to identifying high susceptibility areas. In this sense, the main aim of this study is to produce LULC classification focused on landslide detection via semi-automatic methods, using data mining techniques with remote sensing time-series imagery. For that, different indices, such as the normalized difference vegetation index, the normalized difference built-up index (NDBI), and the soil adjusted vegetation index were extracted from Sentinel-2 imagery. Basic, polar, and fractal metrics were extracted from the time series. From the Shuttle Radar Topography Mission digital elevation model, six geomorphometric features were extracted. Then, classification was performed with random forest with four different approaches: mono-temporal, bi-temporal, metrical, and all. In every approach, the NDBI index or metric derived from it presented the highest importance, and the slope was ranked among the six first predictors. The all approach showed the highest overall accuracy (OA) (88.96%), followed by metrical (87.90%), bi-temporal (82.59%), and mono-temporal (74.95%). Briefly, the metrical approach presented the most beneficial result, presenting high OA and low levels of commission and omission errors.

Use of cellular automata-based artificial neural networks for detection and prediction of land use changes in North-Western Dhaka City.

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 30years 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 30years. 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.

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

Rapid urbanization has induced land use and land cover change (LULC) that increases land surface temperature (LST). Analyzing seasonal variations of LULC and LST is a precondition for mitigating heat island effects and promoting a sustainable living environment. The objective of this study is to explore the association between the seasonal LST dynamics and LULC indices for the Dhaka district of Bangladesh. The LULC indices are comprised of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBAI), and Modified Normalized Difference Water Index (MNDWI). The results show that the LULC effect on LST in Dhaka is significant, with an increase in summer season LST from 34.58 °C to 37.66 °C and in winter season LST from 24.710C to 26.24 °C. Predictably, the highest and lowest LST values were observed in the built-up and vegetation-covered areas, respectively. Secondly, the correlation values indicate a significant inverse correlation (R2 > 0.50) between NDVI and LST, as well as MNDWI and LST. On the contrary, positive correlations were observed between NDBI and LST, and between NDBAI and LST for both the summer and winter seasons. Finally, subsequent vegetation decline (-69.34%) and increasing built-up area (+11.30%) between 2000 and 2020 in Dhaka district were found to be the most significant factors for the increasing trend and spatial heterogeneity of LST in Dhaka. The methodological approach of this study offers a low-cost efficient technique for monitoring LST hotspots, which can guide land use planners and urban managers for spatial intervention to ensure a livable environment.

Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE)

The urban surface temperature is a complex integrated natural-human geographic phenomena; with the development of geostatistical methods and the application of multisource data, its research has gradually shifted from a single perspective to a study that integrates multiple factors such as nature and humanity. However, based on the context of the integration of natural and human factors and mutual constraints of each factor, the research on the mechanism of influence on urban habitat thermal environment needs to be further deepened. Therefore, this paper explores the spatial and temporal heterogeneity of urban surface temperature in Zhengzhou City during the summer of 2013–2020 from the perspective of multi-source data fusion, and uses the Geodetector model to quantitatively reveal the main influencing factors of urban surface temperature and the impact of superimposed factors on the compound effect of surface temperature. The results show that: (1) the urban thermal environment in the central of Zhengzhou city (region within the first ring) is obvious, and it is mainly concentrated in commercial and densely populated areas. (2) According to trend analysis, the northwest-southeast direction of the city continues to increase in temperature from 2013–2020, coupled with the direction of urban development. (3) Among the factors affecting urban surface temperature, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), tasseled cap wetness (TCW), and human elements are particularly typical. NDVI and TCW are strongly negatively correlated with the urban thermal environment, while NDBI and human elements are strongly positively correlated. (4) Mitigation of the urban thermal environment can start with the interaction mechanism of positive and negative factors. This study provides new ideas for the mechanism analysis of spatial and temporal evolution patterns of the urban thermal environment under multifactorial constraints, and provides suggestions and decisions for promoting green and sustainable urban development.

Impact of Climate and Land-Use Change on Groundwater Resources, Study of Faisalabad District, Pakistan

Groundwater depletion has become a major concern all over the world. Recently, the rapid population growth and need for water and food have placed a massive strain on land and water resources. In this study, groundwater depletion resulting from land-use and climate change was investigated in the Faisalabad district, Pakistan, from 2000 to 2015. A Pearson correlation analysis between climatic parameters and land-use indices with groundwater was conducted to explore the major influencing factors. Interpolation maps of groundwater were generated using the inverse distance weighting interpolation (IDW) method. The Normalized Difference Built-up Index (NDBI) of five-year intervals demonstrated a strong increasing trend, whereas the Normalized Difference Vegetation Index (NDVI) presented a declining trend. The results also indicated a significant declining trend in groundwater levels in the region, with the annual average groundwater level decreasing at a rate of approximately 0.11 m/year. Climatic parameters (i.e., precipitation and temperature) further reveal an insignificant increasing trend estimated using the Mann–Kendall test and Sens’s slope. Overall, spatial analysis results showed a statistically significant positive trend in the groundwater level of the Faisalabad district, where the NDBI ratio is high and the NDVI is low, owing to the extensive extraction of groundwater for domestic and industrial use. These findings may be useful for a better understanding of groundwater depletion in densely populated areas and could also aid in devising safety procedures for sustainable groundwater management.

Assessment of land use/land cover changes and its effect on land surface temperature using remote sensing techniques in Southern Punjab, Pakistan.

Land surface temperature (LST) is defined as a phenomenon which shows that microclimate of an urban system gets heated much faster than its surrounding rural climates. The expansion of buildings has a noteworthy influence on land use/land cover (LULC) due to conversion of vegetation land into commercial and residential areas and their associated infrastructure by which LST is accelerated. The objective of the research was to study the impact of changes in LULC on LST of Southern Punjab (Pakistan) through remote sensing (RS) data. Landsat images of 30-year duration (1987, 1997, 2007 and 2017) were employed for identifying vegetation indices and LST in the study region. These images also helped to work out normalized difference water index (NDWI) and normalized difference built-up index (NDBI) maps. There was an increase from 29620 (3.63 %) to 88038 ha (10.8 %) in built-up area over the 30 years. LST values were found in the range 12-42 °C, 11-44 °C, 11-45 °C and 11-47 °C in the years 1987, 1997, 2007 and 2017, respectively. Regression coefficients (R2) 0.81, 0.78, 0.84 and 0.76 were observed between NDVI and LST in the corresponding years respectively. Our study showed that NDVI and NDWI were negatively correlated with less LST; however, NDBI showed positive correlation with high LST. Our study gives critical information of LULC and LST and will be a helpful tool for policy makers for developing effective policies in managing land resources.

Impact of landuse change and urbanization on urban heat island effect in Narayanganj city, Bangladesh: A remote sensing-based estimation

Urban development always had a significant influence on Land Surface Temperature (LST). The impacts of urbanization on the trend of temperature rise and the degradation of the ecological environment over a period of 8 years before and after the establishment of Narayanganj City Corporation have been studied in this paper. LST has been calculated by utilizing land-use change map and computation of vegetation coverage using the Normalized Difference Vegetation Index (NDVI), assessment of building coverage using the Normalized Difference Built-up Index (NDBI), and using the Urban Thermal Field Variance Index (UTFVI) for the evaluation of ecological index. The result indicates that the LST has been tremendously affected by the built-up areas, especially where the heavy industries had been established. The mean LST between 2011 and 2019 went upward by 1.86°C. A negative correlation has been found between the LST and NDVI, which suggests that the green coverage debilitates the effect on urban temperature. On the other hand, the positive correlation found between the LST and NDBI shows that the built-up or paved area intensifies the LST and gives rise to Urban Heat Island (UHI). The areas around vegetation and waterbody showed comparatively lower temperatures while the temperature of the built-up area exhibited an increasing trend. The study reveals that proper heat action plans and toolkits are necessary for urban heat management and ensuring sustainable city development.

Exploring Built-Up Indices and Machine Learning Regressions for Multi-Temporal Building Density Monitoring Based on Landsat Series.

Uncontrolled built-up area expansion and building densification could bring some detrimental problems in social and economic aspects such as social inequality, urban heat islands, and disturbance in urban environments. This study monitored multi-decadal building density (1991–2019) in the Yogyakarta urban area, Indonesia consisting of two stages, i.e., built-up area classification and building density estimation, therefore, both built-up expansion and the densification were quantified. Multi sensors of the Landsat series including Landsat 5, 7, and 8 were utilized with some prior corrections to harmonize the reflectance values. A support vector machine (SVM) classifier was used to distinguish between built-up and non built-up areas. Regression algorithms, i.e., linear regression (LR), support vector regression (SVR), and random forest regression (RFR) were explored to obtain the best model to estimate building density using the inputs of built-up indices: Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), and NIR-based built-up index based on the red (VrNIR-BI) and green band (VgNIR-BI). The best models were revealed by SVR with the inputs of UI-NDBI-IBI and LR with a single predictor of UI, for Landsat 8 (2013–2019) and Landsat 5/7 (1991–2009), respectively, using separate training samples. We found that machine learning regressions (SVM and RF) could perform best when the sample size is abundant, whereas LR could predict better for a limited sample size if a linear positive relationship was identified between the predictor(s) and building density. We conclude that expansion in the study area occurred first, followed by rapid building development in the subsequent years leading to an increase in building density.

Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia

Impervious surface extraction with high accuracy is important for monitoring urban expansion to sustainably manage the land resources and save the environment. In this context, use of spectral built-up indices has been extensively explored. This study examines the performance of Built-up Area Extraction Index (BAEI), Band Ratio for Built-up Area (BRBA), Modified Built-up Index (MBI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Normalized Difference Built-up Index (NDBI), and Urban Index (UI) in the classification and change detection of impervious surfaces using sentinel-2A MSI imagery. All these maps were classified into built-up and non-built-up areas, and evaluated based on histogram overlap and spectral discrimination index (SDI). Simultaneously, support vector machine (SVM) algorithm was employed to classify the imageries into land-use and land-cover (LULC) classes, viz., bare land, built-up, forest, vegetation, and water bodies. The findings indicate that NBAI, NBI, and NDBI have the highest SDI values of 1.24, 1.23 and 1.54; 1.06, 1.08, and 1.23; 1.16, 1.1 and 1.26 for 2016, 2018, and 2020, respectively. However, other indices show unsatisfactory results. The LULC change between 2016 and 2020 showed that the built-up area, bare land, and water bodies have increased by 9,084.5 ha, 813 ha, and 2 ha, respectively whereas vegetation and forest areas declined by 9,279.3 ha and 620.2 ha, respectively. The classification overall accuracy was 81%, 86%, and 82% for 2016, 2018, and 2020 images, respectively thereby affirming that spectral built-up indices in urban environments can deduce impervious surface extraction quickly and accurately.

Spatiotemporal Analysis of Urban Heat Islands in Relation to Urban Development, in the Vicinity of the Atacama Desert

Near the Atacama Desert, Tacna city in Peru is among the largest arid cities with constant urban development, thus understanding of the urban surface thermal pattern is needed. We propose a comprehensive study of the urban heat island phenomenon, with the objective of (1) determining the spatial and temporal variations of the urban heat islands (UHIs), in the period 1985 to 2020; (2) analyzing the relationship between the UHI and influencing factors such as vegetation, urban area, and population, using indices calculated with satellite images. The Google Earth Engine repository was used to evaluate the corrected images from the years 1985 to 2020. The coincidence between the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) was good, negative between NDVI and the land surface temperature (LST), attributable to dense vegetation, and negative and very high (−0.81) between NDBI and NDVI, as massive urbanization leads to the reduction in the vegetal surface. The NDBI has a high impact on the LST; a coefficient of connections is recorded as 0.46. Tacna is a very arid region, and an increase in the time of the LST occurred with the increase in industrialization and urbanization. The land use/cover change (LUCC) evidences change in the climate in the city of Tacna; temperatures of 24.2 °C to 44.2 °C are observed in the built-up areas. In vegetated areas, the temperature remains below 24 °C, which is associated with a high rate of potential evapotranspiration. Thus, this study shows that variations in urban form and growth have produced the development of intraurban surface thermal patterns.

Urbanization implications on hydro-meteorological parameters of Saroor Nagar Watershed of Telangana

Rapid urbanization in India is creating significant changes in the land use patterns of the country, which has had a considerable impact on the rainfall patterns, groundwater availability, and land surface temperature over the years. The primary objective of this research was to examine the implications of spatiotemporal variations of urban growth on the hydrometeorological parameters in the Saroor Nagar Watershed of Telangana, India between 2008 and 2020. The study is also aimed at identifying the correlation between Land Surface Temperature (LST) and spectral indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and SWIRED Index (SWIRED). The results revealed that the study area has witnessed remarkable growth in the built-up area, and a systematic increase in Land Surface Temperature. The built-up area has increased by 23%, rainfall by 40%, the surface temperature by 0.55°C per annum, and groundwater levels have decreased significantly. LST has a positive correlation with NDBI, SWIRED, and a negative correlation with NDVI. Our study would help land management specialists, planners, and local government officials in determining places where expansion must be curtailed to avoid additional environmental deterioration, thus aiding in the development of systematic urban planning approaches.

Study on the influencing factors and the spatiotemporal heterogeneity of Urban Heat Island effect in Nanchang City of China

ABSTRACT Although some researchers have explored spatiotemporal characteristics of the land surface temperature (LST) and analyzed how the influencing factors impact the LST, the comprehensive influencing factors and specific spatial relationships that explore the Urban Heat Island (UHI) effect from long time scale have rarely received widespread attention. Taking Nanchang as an example, this paper first uses Landsat data to retrieve the LST in years 2000, 2010, and 2019. Then, the Geographically Weighted Regression (GWR) model is utilized to investigate the various influencing factors. The results showed as follows: (1) The UHI effect was intensified in the past 20 years in Nanchang City. From 2000 to 2019, the “heat island” increased from 63.06% to 64.07%, while the “green island” decreased from 36.94% to 35.93%. (2) The spatiotemporal anisotropy of the UHI effect was significant. (3) The GWR model could better reveal the spatial heterogeneity of the factors affecting the UHI effects. The relationship between Soil Brightness Index (NDSI), Normalized Difference Built-up Index (NDBI), and Night-lighting (NL) and the relative UHI effect intensity had significant spatial non-stationarity. This study can provide some references from reasonable urban planning to alleviate the urban heat island.

Assessing spatiotemporal dynamics of land surface temperature and satellite-derived indices for new town development and suburbanization planning

Pervasive development across the country has resulted in unprecedented urbanization with a phenomenal change to the urban landscape. This rapid expansion of cities has generated characteristic local urban climates with an increased land surface temperature (LST) and the intensities of urban heat islands (UHI). The study on LST and UHI can be performed to investigate the spatiotemporal variation in dynamics of land surface temperature and satellite-derived indices for enhanced urban planning and development. The study undertakes a comprehensive assessment for the city of Bangalore to understand its impact on environmental quality. The corresponding satellite-derived indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) are used to express the Built-up area (BUA) to analyse the dynamics of LST on UHI profiles. The results of the study reveal that the mean LST has a positive correlation for impervious surfaces and a negative correlation with the green surfaces, which is common for all Indian cities and it results in degradation of environmental eminence. The results shows that the area has undergone a drastic transformation and has resulted in a rise in overall temperature and UHI intensity due to an increase of urbanized areas by 3% between 2007 and 2020. The urban policy and planning for new town development is being seen from various aspects such as design and architecture, geography, and sociology, with much of the literature concentrating on the substantive challenges of the development process. However the account of its technical challenges arising from the influential actors and factors are lacking in the planning process of smart cities. This works address focuses on the critical assessment of the spatiotemporal dynamics of land surface temperature and satellite-derived indices as key factors and actors involved in development decisions of New town development and suburbanization planning. The discoveries of the study can be endorsed to monitor the impacts of UHI intensity for future studies and can be used for sustainable urban planning of cities.