Normalized Difference Built-up Index Research Articles

Native plants on experimental urban green roofs support higher community-level insect abundance than exotics

Urbanization is one of the multiple factors leading to global insect declines. As urbanization grows, green roofs represent a nature-based solution that could provide habitat for plants and animals within cities. Several characteristics of green roofs could enhance insect biodiversity. Nevertheless, little is known about the effects of plant origin on insect abundance from a multi-taxa perspective. Thus, our main goal was to evaluate the effects of plant origin, local resources, and urbanization level on green roofs’ overall insect abundance, the abundance of different taxonomic orders, and community composition. Using experimental green roofs, we compared insect communities between native and exotic plants across an urbanization gradient, in Córdoba city, central Argentina. On the roofs of 30 houses, we installed two blocks of a modular extensive green roof system, with either native or exotic plant species (6 species each). In March 2019, we used yellow pan traps and pitfall traps (N = 360) to sample insects and measured flower abundance and plant cover at each plant treatment. The urbanization level of each house was estimated by the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and the Land Surface Temperature (LST). A total of 35,257 insects from 12 orders were registered, with Diptera, Hymenoptera, and Hemiptera as the dominant groups. Native plants supported significantly higher total insect abundance on both types of traps, independently from the urbanization level, flower abundance, or plant cover of each roof. The abundance of most of the taxonomic orders was higher in natives as well, but particular effects of the covariables were detected for certain groups. In addition, plant origin was an important factor for insect composition. Our results highlight that green roof design should prioritize native plants to enhance insect conservation while achieving more sustainable cities.

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Urbanization strongly correlates with land use land cover (LULC) dynamics, which further links to changes in land surface temperature (LST) & urban heat island (UHI) intensity. Each LULC type influences UHI differently with changing climate, therefore knowing this impact & connection is critical. To understand such relations, long temporal studies using remote sensing data play promising role by analysing the trend with continuity over vast area. Therefore, this study is aimed at machine learning centred spatio-temporal analysis of LST and land use indices to identify their intra-urban interaction during 1991–2021 (summer) in Imola city (specifically representing small urban environment) using Landsat-5/8 imageries. It was found that LST in 2021 increased by 38.36% from 1991, whereas average Normalised Difference Built-up Index (NDBI) increased by 43.75%, associating with increased thermal stress area evaluated using ecological evaluation index. Major LULC transformations included green area into agricultural arable-land and built-up. Finally, the modelled output shows that built-up & vegetation index have strongly impacted LST. This study, help to understand the relative impact of land-use dynamics on LST at intra-urban level specifically with respect to the small urban settings. Further assisting in designing and regenerating urban contexts with stable configuration, considering sustainability and liveable climate, for benefit of health of public and fragile population in particular.

Prediction of CO pollutant in Mashhad metropolis, Iran: Using multiple linear regression

AbstractGiven the importance of carbon monoxide (CO) in acute health threats, studies have been conducted in different countries and cities (272 cities in China and 337 cities on a global scale) on the relationship between daily mortality and CO while the spatial analysis of factors affecting CO pollutant in cities, especially in developing countries, has rarely been done. Accordingly, this research has measured the effect of five environmental‐social variables (ESVs) on the spatial distribution of CO pollutants in the metropolis of Mashhad, Iran. CO concentration data were collected in 23 air pollutant monitoring stations in an area of 356 km2 in 2019. Then, the relationship between five variables and CO pollutant were measured using linear and multiple regression by Sentinel 2A and 3 satellite images in ArcGIS and TerrSet software. The results show that the mean CO concentration averages at 1.56 ppm in the whole city. But its range varies between 0.171 and 2.907 ppm, which is a low figure compared with presented standards and does not indicate a critical situation. The results of multiple regression indicate that 42% of the variance in CO concentration is explained by independent variables. Among five independent variables, the beta value of the land surface temperature (LST) and digital elevation model (DEM) variables is negative and positive for the other three variables, including population density, normalised difference vegetation index (NDVI) and normalised difference built‐up index (NDBI). It should be noted that the strongest correlating variable is population density. Prediction of the spatial distribution of CO pollutants shows the division of the city into three areas: (1) the south and southwest slopes of the city with a low concentration; (2) central areas with a medium concentration; and (3) northern and northeastern areas of the city with a high concentration where low‐income groups reside and there are more worn‐out vehicles, motorcycles and industrial workshops. Areas with high CO concentration need more attention from urban managers.

Land Use and Cover Change Detection in Shankharapur Municipality, Kathmandu Using Spectral Indices

The surging increase of the urban population has been accompanied by a sharp increase in urban built-up areas. The growth of population contributing to rapid expansion of built-up area in recent decades has caused a substantial Land Use Land Cover (LULC) change across Nepal and in particular Kathmandu Valley (KV). In this study, a Normalized Difference Vegetative Index (NDVI) and Normalized Difference Built-up Index (NDBI) was applied to three Landsat imagery collected over time (2002, 2013, and 2022) and one Sentinel-2 imagery that provided recent and historical LULC conditions for, Shankharapur municipality that lies in the eastern part of Kathmandu. The three-land use land cover categories were identified and mapped from the value of NDBI and NDBI. We found that over a period of 20 years (from 2002 to 2022), the Shankharapur municipality has lost 14.64% and 25.97% of its forests and sparse vegetation, and increase in the settlement/open land by 5.48% and 226.73% as indicated by NDVI and NDBI for Landsat imagery respectively. The increase in settlement/open land can be summed to the augmented activities like constructing new building and increase forest and land defragmentation including construction of road and land planning to fulfill the demand of influx of people after the earthquake of 2015. The results of NDVI and NDBI from Sentinel-2 imagery also support the increase in settlement/open land and decrease in forest and sparse vegetation. Also, agriculture cover increased by 4.25 sq.km between 2002 to 2013 and increased by 4.85 sq.km. from 2013 to 2022 as indicated by NDVI derived from Landsat imagery. However, the significant amounts of losses of forest and sparse vegetation during 20 years have been absorbed by the expanding urbanized areas and agriculture land as more land is subjected to the built-up and land planning along with the wood-logging as a result of aftermath of the earthquake, 2015, where population has increased by 19.4 % in the span of 10 years from 2011 to 2021 and is never retreating in terms of changing land cover. Therefore, such trends if unchecked can result in loss of biodiversity and ecosystem services associated with deteriorating conditions for human well-being.

Assessing Land Use/Land Cover Changes and Urban Heat Island Intensification: A Case Study of Kamrup Metropolitan District, Northeast India (2000–2032)

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

Modeling medium resolution evapotranspiration using downscaling techniques in north-western part of India

The present investigation provides a modeling solution to downscale MODIS-based evapotranspiration (ET) at a 30 m spatial resolution from its original 500 m spatial resolution using meteorological and Landsat 8 (Operational Land Imager, OLI) data by employing downscaling models. The nine indices namely Surface Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Infrared Index for Band 7 (NDIIB7) were calculated from Lansat 8 data at 30 m spatial resolution. The multiple linear regression (MLR) and Least Square Support Vector Machine (LS-SVM) models were developed to generate the relationship between MODIS 500 m ET and Landsat indices at 500 m scale. Further, these develop models were used to estimate 30 m ET based on 30 m Landsat 8 indices. The performance of developed models (MLR and LS-SVM) was carried out using correlation coefficient (CC), Nash-Sutcliffe coefficient (NASH) efficiency, Root Mean Square Error (RMSE) and Normalised Mean Square Error (NMSE). Penman–Monteith (PM) method was used to estimate the ET using observed station data. The results show that lowest ETO was observed in the month of December while it was maximum in the month of May. Using the performances indices, it was found that LS-SVM model slightly outperformed than MLR model. However, the downscaled model overestimates ET in comparison to the Penman-Monteith method. Further, the significant correlation was found between MODIS ET and LS-SVM ET at all the stations.

Impacts of urban land use and land cover change on wetland dynamics in Jimma city, southwestern Ethiopia

Abstract Wetland has been substantially decreased by anthropogenic activities worldwide, requiring urgent conservation action. The present study aimed to analyze the effects of land use land cover change on wetland dynamics from 1992 to 2022 in Jimma City, Southwestern Ethiopia. Landsat TM of 1992, ETM+ of 2000, ETM+ of 2009 and OLI/TIRS of 2022 were used in this study. Landsat images were classified by using supervised classification with maximum likelihood algorithm. The results revealed that grassland, wetland and vegetation land cover classes declined by 2,304.9 ha, 1410.2 ha and 820.5 ha, respectively whereas farm land, settlement and bare land increased by 2,199 ha, 2,111 ha and 225.4 ha, respectively. Results show that the normalized difference water index (NDWI) has positive and negative strong relationship with normailized difference vegetation index (NDVI) and normalized difference built up index (NDBI) with the coefficent determination (R2) of 0.96 and 0.98, respectively. Due to rapid urbanization and declining vegetation cover in the study area, about 98% of wetland land cover in the study area has been lost over the past decades. Therefore, the governmental and non-governmental organizations should give special attention to wetland restoration and management in the study area.

The Application of Geospatial Technologies in the Study of Vegetation Cover, Assessment and Comparison of Plant Diversity in the Al-Jadriyah Complex “University of Baghdad and Al-Nahrain”

one of the most important distinguish evaluation of plant species features of environmentally sustainable universities that are concerned with improving the life quality. The study was aimed to apply geospatial techniques in the evaluation of plant diversity at the level of species, genus and plant families in Baghdad and Al-Nahrain Universities. And the application of remote sensing technolog and geographic information systems ArcGIS v 10.3 tudy area capturedRepresented by the satellite image of the s on 28/1/2020 by the Sentinel-2B which is based on multi-spectral Image (MSI) with a spatial resolution of 10x10m2. It contains 13 spectral bands with the TIFF extension. From the results of the field survey of Al-Nahrain University, it was found that the total number of plants reached 577 plants, which included 25 plant species, 22 plant genus, and 19 plant families, compared to the total number of plants in the University of Baghdad, which was 3717 plants that belong to 70 plant species, 63 genus, and 35 plant families. The results of the vegetation cover index showed that the vegetation cover values ranged from -2 to 0.7, distributed between high density vegetation cover between 0.5 to 0.7, dense 0.3 to 0.5, low density 0 to 0.3, and poor vegetation cover 0.1. to zero. As for the water channel around the academic domain, buildings and roads, their values ranged from zero to -2, respectively. The results of the Normalized Difference Built-up Index ranged between 0 and -0.6, noting that the standard range of the index includes values below zero. Whenever the values go from -1 or less, they refer to building covers, roads, bridges, sidewalks, walkways, parking lots and other urban facilities and concrete blocks..

Bi-temporal characterization of terrestrial temperature in relation to urban land use land cover dynamics and policies in Kozhikode Urban Area, India

The pattern of Land Use Land Cover (LULC) is shifting in an unbalanced manner due to urban growth. When cities flourish, vegetated regions are altered into impervious concrete masses. This along with other factors increases the Land Surface Temperature (LST) in urban areas, leading to the emergence of Surface Urban Heat Islands (SUHIs). This study seeks to examine how the evolving patterns of LULC impact the intensity of LST and its correlation with the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) in the rapidly expanding Kozhikode Urban Area (KUA). This is studied using a combination of geographic information systems, satellite remote sensing, and statistical analysis. Satellite images of Landsat 5 (TM) and Landsat 8 (OLI & TIRS) of the years 1993 and 2018 were used, respectively. ArcGIS software has been used to map the LULC pattern, LST, NDVI and NDBI. The supervised technique with the maximum likelihood algorithm is used to derive the LULC pattern, while the mono-window algorithm has been used to derive the LST. MS Excel, SPSS, and OrginPro were used for statistical analysis. The results reflect a decrease of 73.88 per cent in total vegetated areas from 1993 to 2018, while the built-up area expanded from 19.95 km2 to 132.96 km2. The mean LST increased by 2.65 °C from 1993 to 2018. Pearson correlation revealed LST is negatively correlated with NDVI (r = −0.541, P < 0.5 in 1993, r = −0.616, P < 0.5 in 2018) and positively correlated with NDBI (r = 0.664, P < 0.5 in 1993, r = 0.612, P < 0.5 in 2018). As a result, this paper urges urban planners and policymakers in Kozhikode to urgently address the disproportionate LULC patterns emerging in the city and implement more sustainable development measures and policies like urban greening with proper planning and effective stakeholder engagement to control the rising LST.

Assessing the impact of urbanization on groundwater quality of lahore region, Pakistan.

Land use land cover (LULC) dynamics is an important aspect of environmental studies. Lahore is one of the wide-ranging urban cities in the world experiencing rapid development in the form of unplanned urban growth and industrialization, which leads to many adverse consequences. This research focuses on the study of spatio-temporal variability of urbanization and its impact on the water quality index (WQI) in Lahore city using remote sensing (RS) and geographical information systems (GIS). Landsat images (Landsat 7 ETM+, Landsat 8 OLI) between 2005 to 2021 were used to observe the changes in urban growth over seventeen years. GIS is used to create the LULC, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) maps, to study the urbanization impact on the WQI. The results of this study indicate that the groundwater quality of metropolitan Lahore city has significantly dropped within 17years. The extent of the built-up area has been expanded from 22.4% to 953.04% with an increase in the poor WQI area from 1.95% to 37.89%, reveals a general decline in groundwater quality with urbanization. Indeed, the trends observed by the linear regression modelling showed a positive and negative correlation (R2 = 0.67 and -0.74) of WQI with % of urban and vegetation areas respectively. GIS and RS tools have been found effective in assessing spatio-temporal phenomena of urbanization and its impact on groundwater quality. Furthermore, this research would be very helpful in making decisions for managing groundwater resources and illegal urban expansion in Lahore city.

Machine Learning Classifier Evaluation for Different Input Combinations: A Case Study with Landsat 9 and Sentinel-2 Data

High-resolution multispectral remote sensing images offer valuable information about various land features, providing essential details and spatially accurate representations. In the complex urban environment, classification accuracy is not often adequate using the complete original multispectral bands for practical applications. To improve the classification accuracy of multispectral images, band reduction techniques are used, which can be categorized into feature extraction and feature selection techniques. The present study examined the use of multispectral satellite bands, spectral indices (including Normalized Difference Built-up Index, Normalized Difference Vegetation Index, and Normalized Difference Water Index) for feature extraction, and the principal component analysis technique for feature selection. These methods were analyzed both independently and in combination for the classification of multiple land use and land cover features. The classification was performed for Landsat 9 and Sentinel-2 satellite images in Delhi, India, using six machine learning techniques: Classification and Regression Tree, Minimum Distance, Naive Bayes, Random Forest, Gradient Tree Boosting, and Support Vector Machine on Google Earth Engine platform. The performance of the classifiers was evaluated quantitatively and qualitatively to analyze the classification results with whole image (comprehensive feature) and small subset (targeted feature). The RF and GTB classifiers were found to outperform all others in the quantitative analysis of all input combinations for both Landsat 9 and Sentinel-2 datasets. RF achieved a classification total accuracy of 96.19% for Landsat and 96.95% for Sentinel-2, whereas GTB achieved 91.62% for Landsat and 92.89% for Sentinel-2 in all band combinations. Furthermore, the RF classifier achieved the highest F1 score of 0.97 in both the Landsat and Sentinel datasets. The qualitative analysis revealed that the PCA bands were particularly useful to classifiers in distinguishing even the slightest differences among the feature class. The findings contribute to the understanding of feature extraction and selection techniques for land use and land cover classification, offering insights into their effectiveness in different scenarios.

Effects of the built-up index and land surface temperature on the mangrove area change along the southern Sumatra coast

Mangrove trees have a crucial role in providing habitat and breeding for various aquatic species and averting coastal erosion caused by strong waves, tidal surges, and storm surges. Currently, the mangrove area along the Sungsang coast in South Sumatra, Indonesia, has been degraded by human activities like agriculture, fish ponds, and other infrastructure developments. In this study, we used the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and land surface temperature (LST) analysis for observing urban development and vegetation cover in the Sungsang mangrove area. This study aimed to analyze the effects of NDBI and NDVI on the LST change in the Sungsang mangrove area. Results revealed that the mangrove vegetation significantly reduced (by 23%) during 2013–2022 years, in contrast to the built-up development and the LST gradually increased. The area with a temperature value greater than 20 °C was characterized by mangrove degradation, expanding around the peripheral area. There was a strong positive correlation between LST and NDBI (r = 0.90, p < 0.01), and a moderate correlation between LST and NDVI (r = 0.74, p < 0.05). Thus, the built-up index greatly influenced the vegetation area within the study site. This study assists the authority in comprehending their recent mangrove status and promoting sustainable actions for mangrove vegetation and urban management.

The Impact of the Trans-Java Toll Road Development on Spatial Planning in the Northern Region of Java Island: A Study Utilizing NDBI and Google Earth Images

The primary objective of toll roads is to stimulate economic growth. As the pivotal road network on Java Island, the Trans-Java Toll Road has a significant impact on regional development and spatial planning. However, there has been a lack of research focusing on the specific influence of this toll road on spatial planning. Therefore, this study aims to comprehensively examine the spatial and temporal implications of the Trans-Java Toll Road. To achieve this goal, the Normalized Difference Built-Up Index (NDBI) method is employed using satellite imagery. This method is widely recognized for its effectiveness in detecting changes in built-up areas through remote sensing techniques. The calculated NDBI values reveal substantial expansion of built-up areas in the cities and regencies traversed by the Trans-Java Toll Road. Notably, two specific areas with the highest rates of change are investigated: Gresik Regency and Bekasi Regency, where the built-up areas have experienced remarkable growth of over 186.9% and 61.8% respectively over a five-year period. To validate these findings, Google Earth imagery is utilized for two different years, namely 2016 and 2021. The observed changes in these regions are predominantly attributed to the economic growth facilitated by the establishment of industrial areas. Consequently, the expansion of industrial zones and settlements has resulted in a notable reduction in wetland areas and a simultaneous increase in built-up areas.

City densification and temporal dynamics of traditional inner core of Ibadan, Nigeria

The conversion of vegetation land cover contributes to the retention of solar radiation in the environment, resulting in the formation of the urban heat island. This study analyses the distribution pattern of urban heat island in the traditional core of Ibadan amidst the rapid urbanization experienced. The remote sensing tool was used to analyse the trend of land surface temperature, normalized difference built-up index, and normalized difference vegetation index for the traditional Ibadan's core between the year 2000 and the year 2020. This reveals that increasing built-up areas will continue to strengthen the effects of urban heat island in the traditional core of Ibadan, while vegetated land covers will weaken the effects of urban heat island. This is because anthropogenic activities resulting from rapid urbanisation has adversely altered the natural landscape in the traditional core of Ibadan. This alteration manifests in converting vegetation land covers into physical developments and other impervious surfaces by the increasing urban population. Thus, increasing the land surface temperature. The maximum average land surface temperature of 35.34℃, 36.62℃ and 31.86℃ were record for the years 2000, 2013 and 2020 respectively. This study further recommends that urban greening and proper urban planning should be encouraged in the traditional core of Ibadan. Keywords: Land Surface Temperature, Normalized Difference Built-up Index, Normalized Difference Vegetation Index, traditional core, urban heat island

Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale.

Due to expanding populations and thriving economies, studies into the built environment's thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3°C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST's effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30°C and 29.91°C, respectively, with a mean LST of 25.1°C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31°C and 25.35°C, respectively, with a mean LST of 21.33°C. There was a 3.8°C drop in the LST of this grid. The minimum and maximum NDVI were also - 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.

Evaluating the Spatiotemporal Dynamics of Land Surface Temperature in Relation to the Land Use/Land Cover changes in Nag-Hammadi District, Egypt, using Remote Sensing and GIS

In this study, three multi-temporal remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) and Landsat -8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) in 1990, 2005, and 2020 were used. The maximum likelihood classifier (MLC) was opted to classify land use and land cover (LULC). Land surface temperature (LST) and LULC spectral indices i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Latent Heat Index (NDLI) and Bare Soil Index (BSI) have been computed and their relationships were examined. The overall accuracy of LULC was more than 93%. The analyses showed a notable transformation in LULC over the study period. For instance, built-up areas increased 103.7% with a rate of 45.5 ha/year and agriculture land increased by 28.9% with a rate of 186.4 ha/year. Whereas, bare soil was sharply decreased by 36.4% at a rate of 227.7ha/year. The minimum and maximum LST values increased by 2.9°C and 4.9°C, respectively, from 1990 to 2020. Furthermore, LST has a negative relationship with NDVI and NDLI (NDVI: 1990: r2 = 0.62; 2005: r2 = 0.62; 2020: r2 = 0.65. NDLI: 1990: r2 = 0.79; 2005: r2 = 0.78; 2020: r2 = 0.61) and a positive relationship with NDBI and BSI (NDBI: 1990: r2 = 0.68; 2005: r2 = 0.73; 2020: r2 = 0.44. BSI: 1990: r2 = 0.77; 2005: r2 = 0.78; 2020: r2 = 0.53). These results provided useful information about LULC changes and its impact on LST, which are necessary for experts and land-use planners to formulate sustainable LST mitigation policies, create an environmental comfort in Nag-Hammadi district, and other geographical locations with similar conditions.

Where boundaries become bridges: Mosquito community composition, key vectors, and environmental associations at forest edges in the central Brazilian Amazon.

Risk of spillover and spillback of mosquito-borne viruses in the neotropics, including yellow fever, dengue, Zika (Flaviviridae: Flavivirus), chikungunya, and Mayaro (Togaviridae: Alphavirus) viruses, is highest at ecotones where humans, monkeys, and mosquitoes coexist. With a view to identifying potential bridge vectors, we investigated changes in mosquito community composition and environmental variables at ground level at distances of 0, 500, 1000, and 2000 m from the edge of a rainforest reserve bordering the city of Manaus in the central Brazilian Amazon. During two rainy seasons in 2019 and 2020, we sampled 9,467 mosquitoes at 244 unique sites using BG-Sentinel traps, hand-nets, and Prokopack aspirators. Species richness and diversity were generally higher at 0 m and 500 m than at 1000 m and 2000 m, while mosquito community composition changed considerably between the forest edge and 500 m before stabilizing by 1000 m. Shifts in environmental variables mainly occurred between the edge and 500 m, and the occurrence of key taxa (Aedes albopictus, Ae. scapularis, Limatus durhamii, Psorophora amazonica, Haemagogus, and Sabethes) was associated with one or more of these variables. Sites where Ae. aegypti and Ae. albopictus were detected had significantly higher surrounding mean NDBI (Normalized Difference Built-up Index) values than sites where they were not detected, while the opposite was true for Sabethes mosquitoes. Our findings suggest that major changes in mosquito communities and environmental variables occur within 500 m of the forest edge, where there is high risk for contact with both urban and sylvatic vectors. By 1000 m, conditions stabilize, species diversity decreases, and forest mosquitoes predominate. Environmental variables associated with the occurrence of key taxa may be leveraged to characterize suitable habitat and refine risk models for pathogen spillover and spillback.

ENVIRONMENTAL SUSTAINABILITY ASSESSMENT OF A HIMALAYAN CATCHMENT WITH LAND COVER INDICES AND LST RELATIONSHIP USING PRINCIPAL COMPONENT ANALYSIS – A GEOSPATIAL APPROACH

Abstract. Environmental sustainability assessment is a crucial part of the management of natural resources. Remote Sensing based environmental land cover indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), and its associated Land Surface Temperature (LST) are the major governing factors for the environmental processes that happen on the surface of the earth . These NDVI, NDWI, NDBI, NDMI, and LST are generated for 2020 using the Landsat satellite datasets. The process-based relationship among them is complex and involves various parameters but may be easily represented by multiple linear regression models. Principal Component Analysis (PCA) is one such type that efficiently handles and evaluates the contribution of each of these factors to each other based on the sampling units. The study area is the upper Ramganga catchment in the Indian Himalayas, consisting of 117 sub-catchments. These catchment units (samples) are entangled with these environmental factors. The results of the PCA reveal the relationship between each of the environmental factors and their priority. Based on the uncorrelated factors priority suggestion from the PCA, catchment units were classified as high, moderate, or low categories based on their dominance in the relationship among the factors. These spatial variations in the environmental factors can help to assess the sustainability of resources in the Himalayan catchment.

Investigating land use land cover changes and their effects on land surface temperature and urban heat islands in Sharqiyah Governorate, Egypt

The Urban Heat Island (UHI) issue is a result of the undesirable effects of urban growth on the environment, such as temperature rises and landscape changes that cause environmental dangers. Thus, the purpose of this research is to investigate the effect of Land Use Land Cover (LULC)change on Land Surface Temperature (LST) and then study UHI in Sharqiyah from 2001 to 2022 using remote sensing data.This data was collected from the Landsat satellite and Moderate Resolution Imaging Spectroradiometer (MODIS) 11A Thermal sensors. A Mono-Window Algorithm was used on Landsat 8 and 9 data to estimate the LST. To determine the LST and UHI, the thermal band was utilized. LULC maps were created using the Support Vector Machine (SVM) classification technique. To evaluate various LULC indicesin the Sharqiyah and find their correlation with LST, the spectral indices Normalized Difference Vegetation Index (NDVI), Normalized Difference Bare LandIndex (NDBaI), and Normalized Difference Built-up Index (NDBI) were obtained from the processing of multispectral Landsat data. To checkdata sources, air temperature measurements for Sharqiyah werealso acquired. The results show that urban expansion has increased in a noticeable trend. The built-up area increased by 18.9% during the research phase, and the region's mean LST increased within 3.98℃. The UHI threshold temperature increased by 4.27℃. This research is critical for Planning Engineers and environmental scientists to realize LULC variations effects on LST and to suggest suitable political steps to regulate urbanization in Sharqiyah Governorate.

Identification of climate change impact and thermal comfort zones in semi-arid regions of AP, India using LST and NDBI techniques

Andhra Pradesh, in southern India, frequently experiences warm summers and winters. However, the year-round greater relative humidity and the positioning of the summer hot spots are linked to actual weather heat exhaustion during the warmest months. Climate change is a significant—and frequently disregarded—factor in the relationship between climate and mortality. With the use of the Land Surface Temperature (LST) and Normalized Difference Built-up Index (NDBI), we sought to identify the climatic change in this research. Additionally, we attempted to identify the Thermal Comfort Zones (TCZs) in Andhra Pradesh, South India's semi-arid regions. Arc GIS and ERDAS Imagine were used to process the satellite data of Landsat 4–5 (TM & MSS), Landsat 7 ETM+, and Landsat 8 OLI for the development of LST and NDBI. The research was conducted for the past thirty years from 1990 to 2020. The resultant thermal comfort zones map was classified into four zones, Zone - I (Safe zone), Zone – II (Moderately safe zone), Zone – III (Risk Zone), and Zone -IV (Highly Risk Zone). The relation between the LST and NDBI is also revealed using correlation regression analysis in the study region. It has been noted that urban centre and high-density regions had greater LST than rural locations. The study finds that although this differs from one ecological zone to another and the distribution of LST intensity in the urban area depends on its changing LULC, various types of land cover within an urban region might impact the spatial pattern of urban LST. The main conclusions of this study might help policymakers understand the need of enabling more sustainable urban development in cities.