Built-up Index Research Articles

Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques

Abstract. With the increase in built-up areas and rising urban populations, Land Surface Temperature (LST) has significantly increased, leading to the proliferation of Urban Hot Spots (UHS) in urban environments. To mitigate UHS proactively, researchers have conducted studies using various models to predict LST. However, current predictions are primarily based on data samples from isolated stations, making them unfeasible for continuous LST prediction on larger scales, such as regional levels. Therefore, this research aims to use Singapore as a case study to predict UHS on a regional scale using machine learning based on essential variables. Specifically, this research proposes training a Convolutional Neural Network (CNN) model using identified independent variables, including elevation, Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), population, and Land Use and Land Cover (LULC), along with the target variable, UHS. After training, the model achieves high test accuracy and is fed projected data, subsequently producing projected UHS locations. The findings indicate that new UHS primarily appear in southwestern areas, Marina Bay, and the northwest regions of the country. This research predicts that 2.24 percent of the site could be classified as UHS by 2025, compared to the current percentage of 0.95 percent. Based on these projections, the research proposes preventative measures to proactively mitigate UHS. This research fills the gap by constructing a prediction model that can predict UHS locations on a regional scale.

Evaluating urban heat island to achieve sustainable development goals: A case study of Tiruchirappalli city, India

Tiruchirappalli City the fourth largest municipal corporation in Tamilnadu, India is witnessing accelerated urban growth. The city has exacerbated the microclimate indicating a significant increase in land surface temperature. The study aims to establish the differentiation of land surface temperature of city between 2001, 2011, and 2021 of summer months using Landsat images to examine the effects of urban heat island. The results shows that the temperature ranged from 31.23°C to 37.45°C between 2011 and 2021. The assessed land surface temperature (LST) is correlated and the result shows a positive correlation between the LST-normalised difference built-up index (NDBI) and a negative correlation between the LST-normalised difference vegetation index (NDVI) and a weak correlation between LST-BSI and LST-MNDWI of summer months examined. Further, the urban thermal field variance index (UTFVI) of the city is analysed. The thermal condition of the city during April has significantly changed from 5.14 % of area in 2001 to 12.19 % of area in 2021 (UTFVI >0.020, Strongest) of worst ecological valuation index. The selected city is experiencing major thermal conditions (UTFVI 0.010–0.015) of ecological evaluation index from bad to worst. This study will help mitigate the urban heat island effect and promote sustainable development for rapid urbanization.

An analytical study on urban indices and land surface temperature

Any urban landscape needs to investigate the rising trend of land surface temperature (LST) with its surface materials. The present study analyzes the relationship of LST with three urban indices namely normalized difference built-up index (NDBI), urban index (UI), and built-up index (BUI) (by Pearson correlation coefficient method) using nine Landsat 8 OLI and TIRS data of May from 2013 to 2021 in a tropical Indian city, Raipur. Results show that the mean LST of the city was above 40 oC in 2013 but it is controlled in successive years by executing some eco-friendly activities. All the indices build a moderate to strong positive correlation with LST. NDBI is the least deviating index and it generates the best correlation. As surface materials are directly responsible for the rise of LST, suitable ecological planning is necessary for long-term urban thermal sustainability.

Efficiency of Built-up Land Use on the Socio-Economic Conditions of Cities: Case Study of Bandar Lampung City

Dynamic city development has an impact on land consumption levels, as an effort to increase city capacity, so this is not a sustainable option both socially and economically. As one of the provincial capitals on the island of Sumatra and the city closest to the island of Java, the city of Bandar Lampung certainly experiences the same demands regarding the use of built-up land. In identifying these conditions, this article uses a decoupling analysis method of built-up land area, population and local revenue. This research uses NDBI (Normalized Difference Built-Up Index) to describe built-up land, population and LGR (Local Government Revenue) to describe the social and economic conditions of Bandar Lampung City. The results of the NDBI analysis from 2014 to 2022 show that built-up land in Bandar Lampung City has reached 61.67%. In the same period, the city's population increased by an average of 6.01% and Bandar Lampung City's LGR increased by an average of 13.55% in 2022 amounting to IDR 645 billion. In the decoupling analysis of built-up land to population, Bandar Lampung City has an expansive negative decoupling category, so there are indications that the growth of built-up land is not adjusting to the number of residents to be accommodated. In the decoupling analysis of built-up land against PAD, the decoupling index falls into the Expansive coupling category, which means that built-up land and PAD have increased, but the growth of built-up land is higher than the PAD of Bandar Lampung City. In both conditions, it shows that population growth and local income of Bandar Lampung City is still below the growth of built-up land, so it is indicated that the growth of built-up land in Bandar Lampung City is less efficient.

Dynamics of urban development patterns on thermal distributions and their implications on water spread areas of Vellore, Tamil Nadu, India

Recent satellite maps have reported that India is experiencing extreme heat waves, surpassing even Middle Eastern countries. This study addresses a critical gap in understanding how land use land cover (LULC) changes impact land surface temperature (LST), urban heat intensity (UHI), and water spread area (WSA) in rapidly growing cities such as Vellore and Katpadi over three decades (1997–2024). We used Landsat thermal bands and the support vector machine (SVM) algorithm to investigate LULC and LST patterns, examining the effects of urbanization and water body reduction on local climate dynamics. The LULC results showed an increase in built-up lands from 5.89 to 25.89%, while zooming water areas shrank from 3.15 to 1.02%. LST showed a significant increasing trend, with temperatures for water bodies and vegetation ranging from 17.4°C to 26°C, and for barren and built-up areas from 28°C to 42.6°C. The results of the multivariate analysis revealed a positive correlation between LST and the Normalized Difference Built-up Index (NDBI) and negative correlations between LST and the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Modified Normalized Difference Water Index (MNDWI). Moreover, spatial and time series analyses of WSAs indicated a significant increase in LST. Furthermore, a strong negative correlation was found between WSA and LST, with a 10% decrease in WSA potentially increasing LST by 0.12°C to 0.55°C in surrounding regions. This study offers important contributions to improving land use policy and water resource management in urban areas, while addressing environmental concerns related to rising temperatures. The findings underscore the urgency of mitigating heat impacts and managing water resources in rapidly expanding cities. Our results provide valuable insights for policymakers and practitioners aiming to develop more sustainable, resilient, and livable urban environments.

Impact of land cover changes on land surface temperature variations in Adama City, Ethiopia

ABSTRACT Land surface temperature (LST) is becoming a serious environmental issue, since it is an essential controller of city climate. Currently, addressing this challenge in urban planning has gained worldwide importance. The study aims to analyze the effect of land cover dynamics on LST using geographic information system and remote sensing techniques. In this study, Landsat 7 ETM+ (2002) and Landsat 8 TIRS (2022) data were used. The LST was retrieved from Landsat datasets. The correlation analyses were conducted on LST, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The findings suggested that there was a negative correlation between LST and the NDVI, while a positive correlation existed with the NDBI. Moreover, NDBI was found to be a better predictor of LST than NDVI. Additionally, it was revealed that the proportion of dense and sparse vegetation cover was reduced from 24.14 km2 (17.47%) in 2002 to 18.17 km2 (13.15%) in 2022. Similarly, the average LST increased from 28.25 to 31.78°C. This indicated that the rapid growth of urban development was the primary factor behind the rise in LST. Therefore, it was deemed crucial to create a smart urban land use plan to mitigate the impacts of microclimate change.

Assessing the Impacts of Migration on Land Degradation in the Savannah Region of Nigeria

Migration-induced land degradation is a challenging environmental issue in Sub-Saharan Africa. The need for expansion due to urban development has raised the question of effective sustainable measures. Understanding migration and land degradation links is paramount for sustainable urban development and resource use. This is particularly true in Nigeria, where elevated migration levels frequently result in accelerated land degradation due to urban expansion. Given the need to understand the impact of migration on land degradation in the Savannah Region of Nigeria (SRN), this study introduces a novel approach by integrating remote sensing data (NDVI, NDBI) with local community perceptions (mixed-methods approach) to assess the impact of migration on land degradation in four migration destination communities located in two local government areas (LGAs) (Sabon Gari East and Sabon Gari West of Fagge LGA; Zuba and Tungamaje of Gwagwalada LGA). We conducted focus group discussions and a semi-structured survey with 360 household heads to obtain a comprehensive view of perceptions. Our findings revealed that 41.1% and 29.5% of the respondents agreed and strongly agreed that migration significantly contributes to land degradation. We analysed the spatiotemporal patterns of the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Built-Up Index (NDBI) acquired from Landsat 8 datasets for 2014 to 2023. While increasing NDBI values were observed in all communities, a slight decrease in NDVI was noted in Sabon Gari East and Tungamaje. Our analyses highlighted activities leading to land degradation such as land pressure due to built-up expansion at Sabon Gari East, Sabon Gari West, and Tungamaje, and deforestation at Zuba. Based on the varying challenges of migration-induced land degradation, we recommend adequate community participation in suggesting targeted interventions and policies to foster various adaptive capacities and sustainable environments within SRN communities and Sub-Saharan Africa.

Delineating the climate change impacts on urban environment along with heat stress in the Indian tropical city

India experiences significant levels of temperature variance as a result of urbanization, deforestation, and industrial growth, all of which contribute to a progressive increase in heat island effects. Significant landform change has occurred in Bhubaneswar City, Odisha, in recent decades as a result of major plant loss and temperature unpredictability. Thus, during the summer and winter months, an examination of the long-term heat island impacts is essential for Bhubaneswar. The evolution of landforms and heat island impacts in Bhubaneswar are evaluated using Landsat images from 1991 to 2021 datasets and correlation analysis of heat islands and geospatial variables. Between 1991 and 2021 with a 10-year gap, the landforms studied showed an increase in built-up land (38.009 km2), a decrease in vegetation (13.875 km2), bare ground (16.306 km2), and water bodies (7.828 km2). The annual temperature variance in Bhubaneswar was 0.124 °C (summer), and 0.293 °C (winter). Surface temperatures varied by about 3.71 °C (summer) and 8.80 °C (winter). During the study years, heat islands increased from 2.89 to 4.27 (summer) and 2.30 to 3.87 (winter). In Bhubaneswar, there was an increase in ecological variation of 0.092 (summer), and 0.071 (winter). There is a statistically significant negative correlation between the R2 values and the temperature and the built-up indices. Using this dataset, administrative and development authorities can choose which measures to take to counteract the effects of variations in thermal variation. Finding out how man-made land use structures, industrial zones, and other areas contribute to the built-up area a direct consequence of heat islands was the aim of this study.

Remote Sensing Monitoring and Multidimensional Impact Factor Analysis of Urban Heat Island Effect in Zhengzhou City

In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents.

Evaluating the Correlation Between Impacting Factors and Land Surface Temperature via Spatial Regression Model and Random Forest

Identifying accurate correlations between land surface temperature (LST) and its contributing factors is crucial for mitigating LST. Current studies, however, lack precise methods to determine key impacting factors for correlation analyses. Additionally, existing dominant analysis methods, which are spatial regression models and random forest (RF), have strengths and weaknesses, with insufficient comparative studies to determine their suitability for LST analysis. This study, informed by comprehensive literature reviews, identifies key factors affecting LST. Using Singapore as the study site, both multiscale geographically weighted regression (MGWR; spatial regression) and RF models were employed to analyze these factors, considering spatial variance and nonlinear correlations. The results demonstrate high accuracy, with R 2 values of 89 percent for MGWR and 81 percent for RF. The analyses reveal that, in terms of impact magnitude, normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) significantly affect LST. Regarding the direction of the correlations, NDBI is positively correlated with LST in both models, whereas green view index (GVI) consistently shows negative correlations with LST. Other variables show variable correlations depending on location, reflecting context-dependent impacts. Based on these results, to mitigate LST, NDBI should be decreased by converting builtup areas to green spaces where feasible. Increasing GVI is also recommended to increase the proportion of green space from a street view, thereby lowering LST.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

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.

Spatio-temporal analysis of Karachi metropolitan as an urban heat island

Karachi occupies over 79 % of the geographically rugged and arid region. Urbanization has been increasing rapidly which is unplanned and uncontrolled. It is therefore a concrete jungle of buildings and infrastructures formed in Karachi. The built environment traps heat from the environment in its surroundings and thus heat accumulates. In existing research contagious metropolitan region of Karachi has been monitored. Landsat 8 and 9 images for May have been used. Remote sensing indices i.e., NDBI, NDVI, LST, and UHI were used to analyze for 2013, 2018, and 2023. Land cover change has also been measured through ArcGIS Pro. Observation-Based Environmental Surveying and Mapping (OBESM) were done in 2023 followed by weather data collected for 2013 and 2018. Interpolation (IDW method) has been performed in ArcGIS 10.8. The built-up index increased from 0.2, 0.3, and 0.39 in 2013, 2018, and 2023 respectively. NDVI ranges approx. 0.52 to −0.2 in years 2023 and 2013 respectively while for the year 2018 NDVI ranges from 0.42 to −0.1 approx. 0.52 is a good value for NDVI. The highest surface temperature range is recorded in 2018 i.e. 43.8–48.3 °C. The average temporal change between 2013, 2018, and 2023 for LST is 3.6 °C. A 3.9 °C rise in temperature has been recorded within the Karachi metropolis. The exact situation fits the UHI effect as through the analysis it has been assessed that the temperatures of Karachi metropolis reach up to 43.4 °C. Comparing the 2023 results to 2013 more hot spots of high temperatures emerged thus depicting LULCC affected the variations in weather activity. LULC classification estimated built-up land increased by 6.24 % from 2013 to 2023. TOA (2023) ranges from 8.51 to 11.5, BT (2023) ranges from 18 − 39.84 and, PVI (2023) ranges from 0.1 to 0.9. The existing study is novel and deals with NDBI, NDVI, LST, UHI, and LULCC through SC, TOA, BT, and PVI. This research will be signified in identifying the hotspots of urban heat islands and implementing the mitigation techniques.

Correlating Cases of Soil-Transmitted Helminths and Remote Sensing Covariates: A Case Study Utilizing 2015, 2017, and 2019 Metadata of Camarines Sur, Philippines.

Soil-transmitted helminth (STH) is a group of helminths that are considered to be neglected tropical diseases (NTDs) and, globally, affect more than 1.5 to 2.6 million people yearly. Depending on the species, they can be acquired by ingesting embryonated eggs from contaminated matter or by skin penetration. Most species of STH are found in the tropics, such as the Philippines. Despite the Mass Drug Administration (MDA), the cases of STH infection continue to rise in the country. Surveys from the Research Institute of Tropical Medicine (RITM) indicate that a high prevalence of STH (Ascaris lumbricoides, Trichuris trichiura, and Necator americanus) was primarily observed in the provinces of the country, such as in Camarines Sur. To correlate remote sensing covariates such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) - to STH-infected cases of the 37 municipalities of Camarines Sur. The available public health record of STH cases from 2015 to 2019 were calculated using the Quantum Geographic Information System (QGIS)and correlated using Pearson Correlation Coefficient. The results showed that infection was higher in children than adults, and A. lumbricoides caused 60% of infection. No correlation of indices with infection cases during 2015 and 2017 was observed; however, 2019 showed a moderate strength (p = 0.037) in correlation. This indicates that infection relied not mainly on vegetation and urbanization but on additional environmental factors and non-environmental variables.

Analyzing the spatio-temporal pattern of urban growth and its influence on urban heat islands in the Sekondi-Takoradi metropolis, Ghana

The rapid urbanization in Sekondi-Takoradi, Ghana has significantly transformed the land cover, resulting in the proliferation of impervious surfaces and a decline in vegetation. However, the influence of this urban growth on the development of urban heat islands (UHIs) in the metropolis remains understudied. This study aimed to fill this research gap by employing Landsat images to explore the influence of urban growth on urban heat islands in the metropolis from 1991 to 2023. The supervised random forest technique was utilized to map the land cover changes. Furthermore, the computed normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), land surface temperature (LST), and urban thermal field variance index (UTFVI) were used to analyze the influence of urban expansion on UHIs. The findings revealed a 63.07 km2 increase in built-up areas and a 60.99 km2 decrease in vegetation cover during the study period. This dramatic land use change led to a 3.1°C rise in mean LST and a 19.38 km2 expansion of areas affected by the UHI effect. The UTFVI analysis further indicated a 33.63 km2 increase in the worst ecological zone due to the temperature rise. Statistical analysis between LST, NDVI, and NDBI revealed significant variability in explaining the intensity of LST and UHI in the metropolis over the study period. The study equips city authorities and planners with the fundamental knowledge needed to prepare a sustainable development plan that alleviates adverse effects of urban growth and elevated temperature-related issues. Also, the findings contribute to the global efforts in promoting more livable and climate-resilient urban environments.

Assessment of Landscape Change and its Relationships with Surface Temperature and Geo-spatial Indices during 1992 - 2022 at Haldia Industrial Region, West Bengal, India

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.

Comparing the seasonal relationship of land surface temperature with vegetation indices and other land surface indices

ABSTRACT Land surface temperature (LST) is largely influenced by various biophysical properties of land surface materials, especially in urban areas where multiple spectral bands directly impact LST. The present study compares the seasonal relationship of LST with six vegetation indices, one built-up index, one water index and one soil index in Imphal City, India, using Landsat 8 satellite images from the summer and winter of 2021. The study found that these nine indices exhibit different responses to changes in LST in urban landscapes. Pearson’s correlation coefficient, scatter plot graphs, box plot graphs, and cross-section drawings are used to present the relationships between LST and the indices. The study discovered that all six vegetation indices show a moderate negative relationship with LST in both summer and winter. Additionally, the water index demonstrates a weak negative relation in summer and a moderate negative relation in winter. The built-up index shows a neutral relation in summer and a weak positive relation in winter, while the soil index forms a weak positive relation in summer and a moderate positive relation in winter. Further analysis presents that higher values of vegetation indices correspond to lower LST, while built-up and bare surfaces generate high LST.

Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression

Purpose This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices. Design/methodology/approach The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation. Findings The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker. Originality/value This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

Variability in land surface temperature concerning escalating urban development using thermal data of Landsat sensor: A case study of Lower Kharun Catchment, Chhattisgarh, India

Over the past few years, there has been a revitalized emphasis on comprehending the shifts in land cover and their implications for a range of environmental factors. This investigation seeks to analyze how changes in land surface temperatures (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and alterations in land cover intersect within the lower Kharun catchment area. The primary dataset utilized in this study is for 2001 and 2021 Landsat 7 and 8, part of the Landsat program managed by the United States Geological Survey (USGS), offer essential Earth observation data using their multispectral and thermal sensors which are designed to detect thermal radiation emitted from the Earth's surface. When these bands are properly processed, they enable accurate temperature measurements. Visual interpretation was conducted on these images, categorizing them into five specific classes of land cover these were vegetation, open land, settlement, waterbodies, and cultivation. Following this, spectral indices like NDVI and NDBI were calculated, and LST was derived using a single-channel algorithm. Subsequently, correlation analysis was utilized to explore the interconnectedness or mutual relationship among the spatial distribution of these parameters. Over the period from 2001 to 2021, the most significant changes in land use were observed in the settlement area and cultivation, which increased by 6.92 and 6.23 sq. km, respectively. Conversely, open land, vegetation, and waterbodies experienced decreases of 7.13, 5.56, and 0.46 sq. km, respectively. The patterns in which LST, NDBI, and NDVI are distributed, exhibited corresponding variations following changes in land cover. The observed alterations in LST, NDBI, and NDVI are believed to be primarily influenced by the expansion of built-up areas. A noticeable association suggests that as built-up areas increase, both NDBI and LST values typically rise.Furthermore, a correlation observed between LST with NDVI was negative, suggesting an inverse relationship between these parameters. On the other hand, the correlation of LST with NDBI observed was positive, indicating that these parameters exhibit a direct relationship. Overall, these findings seem to be complex and highlight the interactions between changing land cover and environmental parameters, underscoring the importance of understanding these relationships for effective land management and environmental monitoring.

Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030)

Modeling the impacts of Land Use/Land Cover changes (LULCC) on Land Surface Temperature (LST) is crucial in understanding and managing urban heat islands, climate change, energy consumption, human health, and ecosystem dynamics. This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. A set of maps for land cover, LST, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and topography was integrated into the Cellular Automata-Artificial Neural Network (CA-ANN) and the Long-Short-Term Model (LSTM) models to predict the LULC and LST for 2030. The results showed an expansion of urban areas in GAM from 54.13 km2 (6.6%) in 1980 to 374.1 km2 (45.3%) in 2023. However, agricultural areas decreased from 152.13 km2 (18.5%) in 1980 to 140.38 km2 (17%) in 2023, while barren lands decreased from 54.44 km2 (6.6%) in 1980 to 34.71 km2 (4.22%) in 2023. Forested areas declined from 4.58 km2 (0.56%) in 1980 to 4.35 km2 (0.53%) in 2023. Rangelands/ sparsely vegetated areas declined from 557 km2 (67.7%) in 1980 to 270.71 km2 (32.9%) in 2023. The results of modeling LST showed an increase in average LST for all land cover types, with the most significant increases evident within urban areas and Rangelands/Sparsely vegetated areas. The slightest increase in LST was within forested areas as the average LST increased from 28.42 °C in 1980 to 34.16 °C in 2023. The forecasts for the future showed a continuous increase in LST values in all land cover types. These findings highlight the impact of land surface dynamics and their impact on increasing land surface temperature, which urges the adoption of more sustainable planning policies for more livable and thermally comfortable cities.

Utilizing multitemporal indices and spectral bands of Sentinel-2 to enhance land use and land cover classification with random forest and support vector machine

Multitemporal imagery offers a critical advantage by capturing seasonal variations, which are essential for differentiating between land use and land cover (LULC) types. While these types may appear similar when examined at one specific time, they exhibit distinct phenological patterns across different seasons. This temporal depth is crucial for enhancing model accuracy, particularly in heterogeneous landscapes where LULC transitions are frequent and complex. This paper made use of spectral bands and indices of Sentinel-2 from April to September 2020 for LULC classification using two advanced machine learning models: Random forest (RF) and support vector machine (SVM). The spectral indices include the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and modified normalized water index (MNDWI). The dataset was divided into four temporal feature sets: April-May, June-July, August-September, and the entire period from April-September. For each two-month period, the median values of the spectral bands and indices were used. Both models were evaluated based on overall accuracy, F1-score, Kappa coefficient, precision, and recall. Results indicate that incorporating multitemporal features enhanced the performance of the chosen models, with overall accuracy increasing from 82.4% to 94.03% for RF and from 75.4% to 93.54% for SVM. Additionally, the RF algorithm demonstrated higher accuracy than the SVM model across various time periods, with notable improvements in other performance metrics. These improvements also underscore the ability of the models to leverage the rich multitemporal data provided by Sentinel-2 for accurate LULC classification. This study highlights the importance of incorporating the dynamics of features in remote sensing applications to enhance the precision and reliability of LULC classification.