Normalized Difference Building Index Research Articles

Spatial-temporal analysis of atmospheric environment in urban areas using remote sensing and neural networks

Rapid urbanization has given rise to escalating land surface temperatures, climate change, and the emergence of surface urban heat islands (SUHIs) and urban hot spots (UHSs), posing significant environmental challenges. This study, situated in the dynamic urban landscape of southern Iran, leverages Landsat satellite imagery to scrutinize the repercussions of temperature escalation on the environment. Our approach harnesses a novel Urban Thermal Field Variance Index (UTFVI) in conjunction with thermal and spectral indices to gain insights into these challenges. We employ a multifaceted methodology that integrates linear regression, cellular automata (CA)-Markov chains, and advanced neural network techniques to predict land surface temperature (LST) values and associated indicators. Over the span of 2000–2019, our findings reveal a 5% augmentation in urban heat islands (UHIs), signifying an alarming temperature increase. A striking 46% of the region, as uncovered by UTFVI, falls into the most severe categories of ecological discomfort. Our analysis underscores the robust correlations between LST and critical indices, notably the Normalized Difference Built Index (NDBI) (0.96), Normalized Difference Vegetation Index (NDVI) (-0.71), UTFVI (0.98), and SUHI (0.82). Notably, our original contributions lie in the application of Artificial Neural Networks (ANNs), wherein the Multilayer Perceptron (MLP) method excels in predicting UTFVI (R2=0.96) and NDBI (R2=0.96), while the Radial Basis Function (RBF) method demonstrates remarkable accuracy in forecasting the SUHI index (R2=0.96). These achievements signify a groundbreaking advancement in comprehending the intricate dynamics of urban environmental conditions. The repercussions of increased urbanization, the proliferation of barren land, and dwindling vegetation in 2019 manifest in a marked decline in ecological quality, with a concomitant surge in temperatures within the study area. These findings underscore the pressing need for informed urban planning and sustainable practices to mitigate the detrimental effects of urban heat islands and their impact on local climates.

Multitemporal Analysis of Seagrass Dynamics on Derawan Island (2003–2021) Using Remote Sensing Techniques

The shallow waters around Derawan Island are renowned for their beauty, attracting a significant number of tourists. Since the 2008 National Sports Week (PON) in East Kalimantan, the construction of inns and jetties has enhanced both accommodation and accessibility on the island. However, this development has also impacted the seagrass beds in the surrounding shallow waters. This study examines the changes in the area and density of seagrass beds from 2003 (prior to the PON activities) through to 2011 (a few years post-PON) and in 2021 (the most recent conditions), assessing the effects of lodging and jetty construction on these beds. Data were collected via field surveys using the photo transect method, and the benthic habitat map was created using Landsat 8 OLI Imagery, applying the Lyzenga water column correction algorithm and unsupervised classification method. The Normalized Difference Building Index (NDBI) algorithm and land digitization were utilized to track the development of the inns and jetties, revealing a rapid, widespread increase in construction throughout the island's southern region (R-square = 0.59). The study findings indicate a significant degradation of seagrass meadows between 2003 and 2021, particularly near populated areas on the southern coast, resulting in decreased density levels.

Assessment of the Spatiotemporal Variability of Urban Heat Island Intensity in Colombo Metropolitan Area


 
 
 During the last few decades, the Colombo Metropolitan Area (CMA) has experienced rapid population growth and unplanned urbanization, resulting in the replacement of natural landcover with more impervious surfaces. The primary approach of current study was to determine the gradients of air temperature and assess the spatiotemporal variation of atmospheric urban heat island (AUHI) intensity in the CMA considering a range of rural sites extending in different radial directions from the urban center. A total of sixteen rural observation sites that extend from an urban center in four directions were selected within the CMA, with four sites 3.5 km apart from each other in any direction. The air temperatures at each site were measured using HOBO UX-100-003 temperature data loggers for an 8-day period from 21st to 28th, of February 2022. The normalized difference vegetation index (NDVI) and normalized difference building index (NDBI) were used to determine the influence of land use/land cover on air temperatures. The results revealed that air temperature had a strong positive correlation with NDBI. Thus, the current study suggests that the impervious surface/s is a significant predictor of air temperature over the study area. The higher air temperatures were observed at the urban center in relation to the selected suburban and rural observation sites. Hence, the current study confirms that AUHI exists within the selected study area over the CMA. It was found that AUHI tends to be present throughout the day in the CMA. However, it becomes strong between 12.00 A.M. and 6.00 A.M and mostly weakens in the late afternoon hours. Furthermore, this study found that the timing and magnitudes of AUHI vary from direction to direction across the study area depending on the site-specific characteristics of the rural observation sites. The current study emphasizes that site-specific land use/land cover characteristics can have a significant impact on the interpretation of UHI study results even within a single metropolitan area. Therefore, taking only one rural site might not be effective in determining the UHI intensity and a range of sites need to be considered (to select from) for in-depth analysis of UHI in any given area. The findings of the current study will provide firsthand knowledge for urban planners to identify future trends in UHI in the CMA and will promote further UHI research in this area.
 Keywords: Rural observation site, Atmospheric urban heat island, Normalized Difference Vegetation Index
 
 

Cost-Effective Groundwater Potential Mapping by Integrating Multiple Remote Sensing Data and the Index–Overlay Method

The Choushui River groundwater basin (CRGB) in Yunlin County, Taiwan, is a significant groundwater source for the western part of the region. However, increasing groundwater demand and human activities have triggered a potential crisis due to overexploitation. Therefore, groundwater potential zone (GWPZ) maps are crucial for mapping groundwater resources and water resource management. This study employs the normalized index–overlay method and fuzzy extended analytical hierarchy process (FE-AHP) to map GWPZs cost-effectively. The methodology objectively incorporates weightings from various thematic layers by normalizing and correlating parameters with observed groundwater availability (GA). Site-specific observations, including aquifer thickness, depth to the groundwater level, and porosity, inform GA calculations. Seven comprehensive layers derived from remote sensing (RS) data are processed to obtain weightings and ratings for the groundwater potential index (GWPI) in the CRGB. Selected parameters are categorized into hydrological processes, human interventions, geological, and surface profiles. Hydrological processes include precipitation, modified normalized difference water index (MNDWI), and drainage density. Human interventions consist of the enhanced vegetation index (EVI) and normalized difference building index (NDBI). Surface profiles encompass the terrain ruggedness index (TRI) and slope, enhancing the study’s multi-criteria approach. The observed GA validates the GWPZ accuracy, classifying zones into five categories. According to the GWPI of FE-AHP, about 59.56% of the CRGB area can be categorized as “moderate” to “very good” potential groundwater recharge zones. Pearson’s correlation coefficient between GWPI and GA, based on FE-AHP, outperforms the conventional AHP. This RS-based approach efficiently evaluates GA in aquifers with limited wells, highlighting crucial zones in CRGB’s proximal-fan and southeastern mid-fan for informed groundwater management strategies.

Assessing the Impact of Spatiotemporal Land Cover Changes on the Urban Heat Islands in Developing Cities with Landsat Data: A Case Study in Zhanjiang

Land cover changes (LCCs) due to urbanization cause urban heat islands (UHIs), significantly affecting land surface temperature (LST) through spatiotemporal changes in compositions, parameters, and patterns. Land cover and LST have been studied in various cities; however, indicative research into heterogeneous LCC’s impact on LST in less-developed cities remains incomplete. This study analyzed new Landsat images of Zhanjiang, taken from 2004 to 2022, to determine the impact of three LCC indicators (compositions, parameters, and patterns) on LSTs. The urban thermal field variance index (UTFVI) was used to describe the distribution and variation in LST. We also quantified the cooling or warming benefits of various LCCs. The results indicate that the average temperature in the land urban heat island (SUHI) area rose to 30.6 °C. The average temperature of the SUHI was 3.32 °C higher than that of the non-SUHI area, showing the characteristic of shifting to counties and multi-core development. The LST increases by 0.37–0.67 °C with an increase of 0.1 in the normalized difference building index (NDBI), which is greater than the cooling benefit of the normalized difference of vegetation index (NDVI). The impact of landscape pattern indices on impervious surfaces and water is higher than that on vegetation and cropland, with a rising influence on impervious surfaces and a decreasing impact on water. The predominant cooling patches are vegetation and water, while large areas of impervious surface and cropland aggravate UHIs for industrial and agricultural activities. These findings are intended to guide future urban layouts and planning in less-developed cities, with thermal climate mitigation as a guiding principle.

Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification

Google Earth Engine (GEE) is presently the most innovative international open-source platform for the advanced-level analysis of geospatial big data. In this study, we used three machine learning algorithms to apply this cloud platform for Land Use Land Cover (LULC) research in the Mardan, Pakistan. The machine learning algorithm in GEE is the most advanced technique to generate reliable and informative LULC maps from various satellite data to present reliable results. The primary goal of the present study is to compare the performance of various machine learning models (i.e., classification and regression trees [CART], support vector machine [SVM], and random forest [RF]) in GEE for the reliable four classes LULC maps using the Sentinel-2 imageries of 2022. In the current study, three satellite indices like the Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, and Normalized Difference Built Index were applied to detect the features (i.e., vegetation, built, barren land, and water bodies in the study area). The performance of all three models was evaluated by validation and accuracy assessments. The Kappa coefficients of CART, SVM, and RF for Sentinel-2 images were 94%, 95%, and 97%, while the average overall accuracy is 96.25%, 97%, and 98.68%, respectively. The present study illustates that in this classification and comparison, RF performed better than SVM and CART. The current research study revealed that GEE has speedily processed the satellite imageries to develop the four classes of reliable LULC maps of the study area with the best accuracy results and deliver excellent support for further analysis.

Identification of Critical Urban Clusters for Placating Urban Heat Island Effects over Fast-Growing Tropical City Regions: Estimating the Contribution of Different City Sizes in Escalating UHI Intensity

The incessant rise of artificial surfaces has increased the temperatures of cities, distressing urban health and sustainability. Fast-growing tropical cities particularly call for an understanding of this phenomenon, known as the urban heat island (UHI). The present study was conducted to detect UHI dynamics over the National Capital Region of India. Stretching over more than 32 000 km2, this region consists of urban centers of varying sizes. Landsat thermal bands were processed to extract temperature patterns between 1999 and 2019. Urban climate change was prominent, as a 2349-km2 expansion in UHI area was spotted. Urban clusters of different sizes were demarcated by applying the k-nearest neighbor algorithm on the normalized difference building index maps. This empirical analysis helped to form a logarithmic relation between city size and UHI intensity. Observed results set a framework to assess the thermal environment of numerous urban centers from any tropical country. UHI intensity values for various city sizes were computed, as they were crucial to decide the outdoor comfort zones based on the base temperature conditions of other cities. Further, the critical zones in each urban cluster were identified using the vegetation index, and scopes of landscaping were suggested based on the observed building morphologies of different local climate zones.

A geospatial analysis of cardiometabolic diseases and their risk factors considering environmental features in a midsized city in Argentina.

New approaches to the study of cardiometabolic disease (CMD) distribution include analysis of built environment (BE), with spatial tools as suitable instruments. We aimed to characterize the spatial dissemination of CMD and the associated risk factors considering the BE for people attending the Non-Invasive Cardiology Service of Hospital Nacional de Clinicas in Córdoba City, Argentina during the period 2015-2020. We carried out an observational, descriptive, cross-sectional study performing non-probabilistic convenience sampling. The final sample included 345 people of both sexes older than 35 years. The CMD data were collected from medical records and validated techniques and BE information was extracted from Landsat-8 satellite products. A geographic information system (GIS) was constructed to assess the distribution of CMD and its risk factors in the area. Out of the people sampled, 41% showed the full metabolic syndrome and 22.6% only type-2 diabetes mellitus (DM2), a cluster of which was evidenced in north-western Córdoba. The risk of DM2 showed an association with high values of the normalized difference vegetation index (NDVI) (OR= 0.81; 95% CI: - 0.30 to 1.66; p=0.05) and low normalized difference built index (NDBI) values that reduced the probability of occurrence of DM2 (OR= -1.39; 95% CI: -2.62 to -0.17; p=0.03). Considering that the results were found to be linked to the environmental indexes, the study of BE should include investigation of physical space as a fundamental part of the context in which people develop medically within society. The novel collection of satellite-generated information on BE proved efficient.

Evaluation and Analysis of the Effectiveness of the Main Mitigation Measures against Surface Urban Heat Islands in Different Local Climate Zones through Remote Sensing

The significant transformation of land use as a consequence of current population growth, together with global warming (atmospheric emissions and extreme weather events), is generating increases in ambient temperatures. This circumstance is affecting people’s quality of life, especially those considered more vulnerable or with fewer economic resources. Currently, 30% of the world’s population suffers climatic conditions of extreme heat, and forecasts indicate that in the next 20 years, this number will reach 74%. The present study analyzes the effectiveness of the main mitigation strategies for the surface urban heat island (SUHI) effect between the years 2002 and 2022 in the different local climate zones of the city of Granada (Spain). Using Landsat 5 and 8 images, the evolution experienced by the land surface temperature and the surface urban heat island was determined and connected to the following variables: normalized difference vegetation index, vegetal proportion, normalized difference building index, and albedo. Our results indicate that compact and industrial areas have higher temperatures and lower vegetation and albedo in contrast to open areas, which have lower temperatures and higher vegetation and albedo. The mitigation measures analyzed presented similar efficiencies, but a greater minimization of the SUHI was reported when vegetation was increased in open areas as opposed to in closed areas, where the increase in albedo was more effective. Our study will allow the implementation of more efficient measures based on the types of LCZs in cities.

Driving Mechanism of Differentiation in Urban Thermal Environment during Rapid Urbanization

To achieve sustainable urban development, it is essential to gain insight into the spatial and temporal differentiation characteristics and the driving mechanisms of the urban thermal environment (UTE). As urbanization continues to accelerate, human activity and landscape configuration and composition interact to complicate the UTE. However, the differences in UTE-driven mechanisms at different stages of urbanization remain unclear. In this study, the UTE of Shenyang was measured quantitatively by using the land surface temperature (LST). The spatial and temporal differentiation characteristics were chronologically studied using the standard deviation ellipse (SDE) and hotspot analysis (Getis–Ord Gi*). Then, the relationship between human activities, landscape composition and landscape configuration and LST was explored in a hierarchical manner by applying the geographical detector. The results show that the UTE in Shenyang continues to deteriorate with rapid urbanization, with significant spatial and temporal differentiation characteristics. The class-level landscape configuration is more important than that at the landscape level when studying UTE-driven mechanisms. At the class level, the increased area and abundance of cropland can effectively reduce LST, while those of impervious surfaces can increase LST. At the landscape level, LST is mainly influenced by landscape composition and human activities. Due to rapid urbanization, the nonlinear relationship between most drivers and LST shifts to near-linear. In the later stage of urbanization, more attention needs to be paid to the effect of the interaction of drivers on LST. At the class level, the interaction between landscape configuration indices for impervious surfaces, cropland and water significantly influenced LST. At the landscape level, the interactions among the normalized difference building index (NDBI) and other selected factors are significant. The findings of this study can contribute to the development of urban planning strategies to optimize the UTE for different stages of urbanization.

Differences in urban heat island and its driving factors between central and new urban areas of Wuhan, China.

Urban heat island (UHI) is one of the important effects of urbanization on built environment. Land surface temperature data was taken from moderate-resolution imaging spectroradiometer (MODIS) to investigate the long-term spatiotemporal patterns of UHI in Wuhan during 2001~2018 and, the UHI intensity changes of built-up land in 13 administrative regions in Wuhan were analyzed. Furthermore, 34 spatial error models and 34 ordinary least squares models were established and compared. Spatial error models showed good fitting effect, which were used to determine the influence of normalized difference vegetation index (NDVI), normalized difference building index (NDBI), and social-economic factors (population and nighttime light) on UHI intensity in central urban area and new urban area. The explanatory power changes of these four indicators during 2001~2018 were explored as well. The average UHI intensity in 2014~2018 has increased by about 0.45 °C compared to that in 2001~2005. NDBI is the most dominant factor contributing to the increase in temperature. The impact of NDVI on UHI intensity changes from negative to positive, and the impact of NDBI on UHI intensity in central urban area is weakened during 2001-2018. Social-economic factors have a greater impact on new urban area than on central urban area. These findings show the effects and the explanatory power changes of driving factors during 18 years, which can provide a better understanding of the formation and development of UHI and support for the future urban planning of Wuhan.

Spatiotemporal analysis of the surface urban heat island (SUHI), air pollution and disease pattern: an applied study on the city of Granada (Spain)

There is worldwide concern about how climate change —which involves rising temperatures— may increase the risk of contracting and developing diseases, reducing the quality of life. This study provides new research that takes into account parameters such as land surface temperature (LST), surface urban heat island (SUHI), urban hotspot (UHS), air pollution (SO2, NO2, CO, O3 and aerosols), the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI) and the proportion of vegetation (PV) that allows evaluating environmental quality and establishes mitigation measures in future urban developments that could improve the quality of life of a given population. With the help of Sentinel 3 and 5P satellite images, we studied these variables in the context of Granada (Spain) during the year 2021 to assess how they may affect the risk of developing diseases (stomach, colorectal, lung, prostate and bladder cancer, dementia, cerebrovascular disease, liver disease and suicide). The results, corroborated by the statistical analysis using the Data Panel technique, indicate that the variables LST, SUHI and daytime UHS, NO2, SO2 and NDBI have important positive correlations above 99% (p value: 0.000) with an excess risk of developing these diseases. Hence, the importance of this study for the formulation of healthy policies in cities and future research that minimizes the excess risk of diseases.

A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression

Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. However, existing downscaling methods are unable to sufficiently address both spatial heterogeneity and complex nonlinearity, especially in high-resolution scenes (<120 m), and accordingly limit the representation of regional details and accuracy of temperature inversion. In this study, by integrating normalized difference vegetation index (NDVI), normalized difference building index (NDBI), digital elevation model (DEM), and slope data, a high-resolution surface temperature downscaling method based on geographically neural network weighted regression (GNNWR) was developed to effectively handle the problem of surface temperature downscaling. The results show that the proposed GNNWR model achieved superior downscaling accuracy (maximum R2 of 0.974 and minimum RMSE of 0.896 °C) compared to widely used methods in four test areas with large differences in topography, landforms, and seasons. We also achieved the best extracted and most detailed spatial textures. Our findings suggest that GNNWR is a practical method for surface temperature downscaling considering its high accuracy and model performance.

Assessing Moisture Content and Its Mitigating Effect in an Urban Area Using the Land Surface Temperature–Vegetation Index Triangle Method

Nowadays, climate change and heat extremes are becoming highly challenging problems in many cities across the globe. One of the solutions to overcome this problem is the use of vegetation, and, in particular, extending the range of overgrown areas, which are sometimes referred to as “urban green areas.” In this paper, the moisture condition and its mitigating effect on Land Surface Temperature in urban areas were examined in Warsaw, Poland, using satellite data. To do so, the so-called “Triangle Method” was employed. The triangle method is based on a Land Surface Temperature–Normalized Difference Vegetation Index (LST-NDVI) scatterplot to calculate the Temperature Vegetation Dryness Index (TVDI) and its modification–quadratic Temperature Vegetation Dryness Index (qTVDI). This article discusses, in detail, the usefulness of the triangle method for the analyses of built-up areas. The drought satellite indices TVDI and qTVDI compared with those of LST, NDVI, and NDBI (Normalized Difference Building Index). The study shows that the triangle method based on LST-NDVI scatterplot analysis is a promising tool for establishing moisture conditions over urban areas and for studying the effect of vegetation impact on urban heat islands. Detailed analysis shows that over an urban area, qTVDI shows better agreement with LST than classic TVDI.

Spatiotemporal change analysis of land use/land cover in NCT of Delhi, India using geospatial technology

Increasing anthropological and economic activity has transformed human–environment connections. Using remote sensing and geographic information system (GIS), severe issues connected to rapid growth, such as supplemental infrastructure, informal residents, demolition of ecological construction, shortage of natural resources, and environmental contamination, have been studied for a fast-growing metropolis like Delhi. Industrialization accelerates urbanization, resulting in land transformations, which are one of the major uses of natural resources. In a fast-growing city like Delhi, the development has been rapid, and it is important to investigate the factors behind these shifts. For the present study, remote sensing and GIS models are used for land use and land cover (LULC) change detection. The applied model in the study uses the satellite datasets of two different satellites, i.e., Landsat 5 (TM) of 2010 and Landsat 8 (OLI) of 2021, from November with little or no cloud cover, which could block the LULC features. The study compares the temporal LULC map and analyzes the change and increase in urbanization for two periods, 2010 and 2021. In the study, it is revealed that urbanization causes constant shifts in vegetation, built-up area ratio, and land-use patterns. To confirm the same, indices like the normalized difference vegetation index and the normalized difference building index are also studied. Inequitable land use is a major contributor to environmental degradation. The spatial datasets from two time periods used in the study and the database results are helpful in extensive LULC investigations, land use planning, spatial growth, and urbanization patterns in the NCT of Delhi. Further, the change detection model used in the study was supported by the standard accuracy assessment of the Kappa coefficient. Overall accuracy in 2010 was 89.52% with a Kappa statistic of 0.863 and 89.92% with a Kappa statistic of 0.868 in 2021. Such studies using remote sensing and GIS are extremely helpful in understanding and monitoring urban sprawl patterns.

Analysis and Research on Temporal and Spatial Variation of Color Steel Tile Roof of Munyaka Region in Kenya, Africa

In Africa, the distribution of color steel tile roof (CSTR) can reflect the living standard of residents, and the analysis of its temporal and spatial changes can reflect the local changes in local living conditions. It is helpful to analyze the change of the local economic level. By using the satellite remote sensing image processing method to obtain the temporal and spatial change characteristics of CSTR and to analyze the changes in residents’ living conditions in Munyaka, Eldoret, Kenya, Africa, the model of multifeature decision tree method (DTM) extraction was established. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index (NDBI) were used to remove farmland from the difference of the CSTR. The Normalized Difference Surface Index (NCSI) was constructed, and the texture features were analyzed to eliminate wasteland and bare land, respectively. The research results show that the Kappa coefficient is 0.9223, and the user precision and mapping precision are 97.79% and 91.10%, respectively. At the same time, combined with the Erdoret municipal road project, the changes of CSTR before and after the project in 2016–2020 are studied. Compared the area change of CSTR in 2016–2018 with that in 2018–2020, the annual growth rate before the construction of the municipal road project is about 3.47%. After the completion of the project, the annual growth rate is 7.29%, more than twice the rate before the construction. This method can realize the dynamic monitoring of CSTR, reflect the changes of the residents’ living environment in the region, help analyze the improvement of poverty in Africa, and help understand the changes of African economic conditions.

Landsat observation of urban growth and land use change using NDVI and NDBI analysis

Landsat observation has numerous potentials as a quantitative approach in regional scale monitoring of urban growth and environmental change. To achieve this approach, three Landsat data of year 1991 (TM), 2005 (ETM+) and 2019 (OLI-TIRS) has been acquired, classified, and accurately assessed. The research assesses spatio-temporal urban growth, its pattern and land use land cover (LULC) changes of using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index (NDBI) analysis. NDVI were performed for vegetation monitoring especially on loss of vegetation land while NDBI were performed for identification of dense urban and built-up areas. The NDVI and NDBI density results show a significant decreased of vegetation land and a leap up increased of urban and built-up land use. This indicates a significant rapid growth development and a vast transformation of agricultural and forest land into low density development. A rapid urban growth of regional development corridor has significant influence on environment change especially to their periphery. The utilization of both NDVI and NDBI as surrogates has the capability to provide dynamic view and improve the accuracy of land use land cover change analysis. The study showed urban growth has quadrupled from 1991 to 2019 with most rapid growth was from 1991 to 2005 due to greater low-density development and a discontinue growth pattern in the past years compare with much sustainable higher-density development in the recent years.

Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments

Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan.

Exploring the spatial pattern of urban heat island formation in relation to land transformation: A study on a mining industrial region of West Bengal, India

The Earth surface has transformed a lot by various deliberate and inadvertent anthropogenic activities; most profound among them are perhaps the changes in the land use land cover pattern of any area. It is considered to be responsible for many irreversible changes in the regional heat budget and water balance. Expansion of the built up area found to be modified the surface interaction of solar radiation thereby alters the surface temperature pattern of any region. Present study has been carried out in the Jamuria community development (C D.) block of West Bengal to examine the relation between land use land cover (LULC) change and its impact on the rising land surface temperature (LST) by using multi temporal satellite data. Urbanization, industrialization and mining activity in the study area found to be alters the LULC to a great extent which seems to be responsible for the general rise in the ambient temperature and micro climatic variation. Definite association has been found to be exists between LST and LULC pattern of the study area. Rise in the LST pattern are considered to be spatially associated with those areas where the intensity of land use change is maximum. Correlation between them, land use indices like normalized difference vegetation index (NDVI), normalized difference built index (NDBI) etc and transect profile also confirmed these facts.

Impact of climate change on the Oueme basin in Benin

The world is currently facing disruption of the climate system related to global warming. Climate is a crucial factor in ecosystem assessment. Benin is considered a vulnerable area, particularly its south, which is a coastal zone. The current study evaluated the impact of climate on ecosystems in the Oueme basin. An understanding of indicator difference between 2000 and 2016 was provided. Afterwards, systems to calculate the sensitivity index (S) to accurately identify the extremely sensitive area within the basin were designed for 2016. Indeed, detecting sensitive areas is a suitable vehicle to promote sustainability. Then, using ten indicators, mainly obtained from satellite images, grouped into three factors (climate, land use, and warming), and based on multicriteria methods, specifically the analytic hierarchy process (AHP) and principal component analysis (PCA), the index S was performed. Results revealed decreases in normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) and increases in land surface temperature (LST), normalized difference building index (NDBI), and potential evapotranspiration (PET). According to the AHP system, percentages of 39%, 46%, and 15% were determined to be the lowly, moderately, and extremely sensitive areas. In comparison, the PCA system identified ratios of 44%, 41%, and 15% as, respectively, the lowly, moderately, and extremely sensitive areas. Spatially and regardless of the established systems, the southern Oueme basin (Littoral, Atlantique, and Oueme) was consistently identified as extremely sensitive. In contrast, the northern Oueme basin (Donga and Borgou) was stable. Referring to Pearson's correlation, climate (extremely) and land use (moderately) were highlighted to be considered threats to ecosystems in the Oueme basin.