Normalized Difference Built-up Index Research Articles

Seguimiento de los cambios en el uso del suelo y la cobertura del suelo utilizando tecnicas de teledeteccion y SIG en la Isla de Phu Quoc, Provincia de Kien Giang, Vietnam

The changes in land use and land cover (LULC) are among the primary drivers of global environmental changes in many developing countries. In this study, LULC changes were assessed on Phu Quoc Island, located in Kien Giang province, Vietnam, from 2001 to 2022. The study utilized remote sensing and Geographic Information System (GIS) technology using Landsat images for the years 2001, 2009, and 2022. Image classification for each year was conducted through supervised classification using a maximum likelihood classifier, with the main LULC classes being forests, bare land, agricultural areas, water bodies, and built-up areas. The accuracy of the classification was evaluated using the kappa coefficient, achieving values consistently above 0.8 for all three images. Over the 20-year period (2001-2022), the area of forest, agriculture, and water bodies decreased by 14.90 km2, 30.96 km2, and 0.64 km2, respectively. Meanwhile, the areas of bare land and built-up areas increased by 22.22 km2 and 24.28 km2, respectively. Additionally, this study employed the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) to quickly assess LULC changes, obtaining results consistent with the supervised classification. The findings underscore the importance of closely monitoring LULC changes to facilitate effective natural resource management and maintain a sustainable environment.

Urban land use, land cover change and urban microclimate dynamics in Addis Ababa, Ethiopia

Land surface temperature (LST) increases and urban heat island (UHI) variability are the major urban climatology problems arising in urban development. This study attempts to assess the effects of urban land use and land cover change on microclimate dynamics in Addis Ababa city. Three different sets of remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005) and Landsat 8 OLI/TIRS (2021) were used for the study. LSTs were retrieved from Landsat5 TM and Landsat7 ETM+ using a mono window,and the thermal infrared band (TR-10) of Landsat–8 was used to retrieve LST. Regression and correlation analyses of the LST, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were performed in SPSS V23. The study also examined the different residential urban morphology types (UMTs) of the LST and NDVI. The selected built-up blocks of UMTs included apartments, villas and mud houses. These UMTs are extracted by digitizing them from the Google Earth explorer. The results from this study showed that the proportion of urban green space (UGS) to other LULC types decreased from 120.4 km2 in 1990 to 76.26 km2 in 2021. However, the built-up area increased at a rate of 216.5 km2 (39.03%) from 1990 to 2021. The rapid expansion of built-up land in the study area was the main factor influencing the increase in LST. The residential UMTs exhibited significant differences in mean LSTs and NDVIs. The results indicate that UMT inhibited by Villia had the highest mean NDVI value and that the highest mean LST was observed in Apartment. The results of multiple linear regression analysis clearly indicate that built-up and green vegetation contributed 92.2% of the LST variations with R2 = 0.92 and VIF ≤ 10 in Addis Ababa city. The results of the study indicate that strengthening public participation in urban greening and optimizing the NDVI and NDBI are important strategies for mitigating the effects of microclimate change and that sustaining urban development and providing better quality of life for the urban population are important.

Assessing the Impact of Land Use and Land Cover Changes on Surface Temperature Dynamics Using Google Earth Engine: A Case Study of Tlemcen Municipality, Northwestern Algeria (1989–2019)

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Monitoring and predicting development of built-up area in sub-urban areas: A case study of Sleman, Yogyakarta, Indonesia

Monitoring built-up areas in the previous year and possible predictions for the following year are important in planning regional development and controlling the expansion of built-up areas. This study detects changes in the built-up area (2018–2022). It predicts the future (2026) using Landsat satellite imagery in the Sleman Regency, Yogyakarta Special Region, Indonesia study area. Mapping built-up areas is identified using the Normalized Difference Built-Up Index (NDBI). Vegetation conditions were analyzed using the Normalized Difference Vegetation Index (NDVI). Changes in the built-up area are predicted using the CA-Markov chain model for 2026. The prediction is calibrated by comparing the simulated map with the results of the classification of built-up areas in 2022. The research findings show that the built-up area has increased by 12.84 % from 2018 to 2022 and is predicted to increase by 15.48 % in 2026. The existence of built-up areas has an influence on land surface temperatures where the analysis results show a moderate correlation between NDBI and LST, namely 2018 (R2 = 0.401), 2019 (R2 = 0.323), 2020 (R2 = 0.401), 2021 (R2 = 0.415), and 2022 (R2 = 0.384). The higher the NDBI value, the higher the LST value, and vice versa. Therefore, regional development planning, mainly built-up areas, is an important recommendation for decision-makers in the study area.

Fine-scale habitat suitability modelling of Northern red muntjac (Muntiacus vaginalis) in the Chitwan Annapurna Landscape, Nepal

Abstract Factors associated with the habitat suitability of northern red muntjac (Muntiacus vaginalis) especially outside protected areas in the human-dominated landscape are still lacking. Fine-scale environmental variables can influence the habitat suitability of the animals. This study aimed to explore the different eco-geographic fine-scale variables for the distribution of the northern red muntjac; and predict suitable habitats using the maximum entropy (Maxent) model in the Chitwan Annapurna landscape (CHAL). The presence points of the northern red muntjac (n = 265) were collected between 2018 to 2021 using 150 transects of various lengths in four blocks. Density-based occurrence points rarify and performance-based variable selection were applied to improve the output of the model. The model was evaluated based on the area under the curve (AUC) value of operator characteristic (ROC) and analyzed on the basis of the response curve, the relative importance of variables, Jackknife test and suitability map. Results indicated the model was statistically satisfactory (mean AUC > 0.75). The distance to the nearest cropland was the most contributed variable followed by Normalized Difference Built-up Index (NDBI), distance to developed/settlement area and distance to grassland that explained suitability of Northern red muntjac. The species distribution model predicted 6.52% highly suitable and 23.77% suitable area for northern red muntjac. Therefore, this area is important for the muntjac and provides a possible alternative habitat for other wild animals outside the protected areas. Our research suggests that human dominated landscape should be prioritized in management plans for the conservation of muntjac.

Surface Temperature Distribution Through Vegetation Density and Building growth in Makassar City

The high temperature phenomenon is an event where the temperature in the city area is hotter than the surrounding area. Indonesia is located in a tropical climate, so several cities have the potential for temperatures to rise during the dry season. The city of Makassar is one of the cities with a high level of urbanization. With residents' enthusiasm for looking for work in the city, the land around the city has been converted into built-up land. Conditions like this make the existence of green open space uneven. This research aims is to determine the distribution of high-temperature phenomena through a spatial approach using remote sensing technology and geographic information systems. This research uses Landsat imagery for 2022 with the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) methods. The results of the research explain that the large number of existing buildings has caused an increase in urban temperatures, from an initial temperature of 29.93 oC to 39.51 oC.The increase in temperature occurs throughout the year, with the hottest peak temperature reaching 37 oC on April 15, 2022. The condition of vegetation density decreased by 0.05, starting from 0.66 in 2012 to 0.61 in 2022; this is in line with the increase in building area to 15,368 ha in 2022. This condition makes Makassar City one of the hottest cities in Indonesia. With an increasingly high need for built-up land and high mobility, it has the potential to accelerate temperature changes, so it is necessary to provide green open space in densely built areas by creating public open spaces. open private.

LAND USE MAPPING USING MAXIMUM LIKELIHOOD CLASSIFICATION AND REMOTE SENSING INDICES: CASE STUDY AIN-ABID CONSTANTINE (ALGERIA)

Land use is an essential theme in monitoring environmental phenomena. The supervised maximum likelihood classification algorithm has been shown to provide the best results from remotely sensed data. This work is aimed at the application of a supervised classification (maximum likelihood) based on a priori knowledge of the terrain under study and information extracted from the two remote sensing indices NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) for mapping land use of the area of Ain Abid Constantine (located in eastern Algeria) for the year 2020. The obtained result showed that this city is an agricultural area with a percentage of 68.49% of agricultural land and a low percentage of 2.02% of Buildings.

Exploring Urban Heat Distribution and Thermal Comfort Exposure Using Spatiotemporal Weighted Regression (STWR)

With rapid urbanization, many cities have experienced significant changes in land use and land cover (LULC), triggered urban heat islands (UHI), and increased the health risks of citizens’ exposure to UHI. Some studies have recognized residents’ inequitable exposure to UHI intensity. However, few have discussed the spatiotemporal heterogeneity in environmental justice and countermeasures for mitigating the inequalities. This study proposed a novel framework that integrates the population-weighted exposure model for assessing adjusted thermal comfort exposure (TCEa) and the spatiotemporal weighted regression (STWR) model for analyzing countermeasures. This framework can facilitate capturing the spatiotemporal heterogeneities in the response of TCEa to three specified land-surface and built-environment parameters (i.e., enhanced vegetation index (EVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNDWI)). Using this framework, we conducted an empirical study in the urban area of Fuzhou, China. Results showed that high TCEa was mainly concentrated in locations with dense populations and industrial regions. Although the TCEa’s responses to various land-surface and built-environment parameters differed at locations over time, the TCEa illustrated overall negative correlations with EVI and MNDWI while positive correlations with NDBI. Many exciting spatial details can be detected from the generated coefficient surfaces: (1) The influences of NDBI on TCEa may be magnified, especially in rapidly urbanizing areas. Still, they diminish to some extent, which may be related to the reduction in building construction activities caused by the COVID-19 epidemic and the gradual improvement of urbanization. (2) The influences of EVI on TCEa decline, which may be correlated with the population increase. (3) Compared with NDBI, the MNDWI had more continuous and stable significant cooling effects on TCEa. Several mitigation strategies based on the spatiotemporal heterogeneous relationships also emanated. The effectiveness of the presented framework was verified. It can help analysts effectively evaluate local thermal comfort exposure inequality and prompt timely mitigation efforts.

ASSESSMENT OF URBAN DYNAMICS – A CASE STUDY ON BERHAMPORE MUNICIPALITY AND ITS SURROUNDINGS, WEST BENGAL, INDIA

Berhampore was a former Armenian-to-British colony and historic commercial centre, had steady expansion during the colonial era, a still-ongoing trend. The natural landscape is transforming from the centre to the periphery in an extremely unpredictable way due to rapid urbanization. The current study examines the urban growth of Berhampore town in West Bengal, India, using geospatial techniques. To understand the spatiotemporal dynamics of the urban landscape from remotely sensed data, four indices are used: the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), the Built-up Index (BUI), and the Modified Normalized Difference Water Index (MNDWI).The result of these indices shows the unsustainable urbanization in this region. Over the course of thirty years, the built-up area increased by nearly 7.80 percent, depleting the prime vegetative cover, water bodies and, in some cases, the barren land. According to the study, it is helpful to determine current urban growth and development so that local planning authorities may control growth and development in accordance with the ecological or environmental carrying capacity of the region.

Assessing the land use dynamics and thermal environment using geospatial techniques in the industrial city of Chotanagpur Plateau Region, India.

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world.

Geospatial Assessment of Oil Spill’s Impact in Obio/Akpor Local Government Area Rivers State, Nigeria

Environmental degradation resulting from oil spills has emerged as a critical issue requiring immediate attention. This study uses geospatial techniques to investigate the impact of oil spills in Obio/Akpor Local Government Area of Rivers State. Landsat images, administrative map of the study area and historical oil spill data were data used in carrying out this study. The images were pre-processed through atmospheric and geometric corrections. Spatial analysis of four remote sensing indices-Normalized Difference Vegetation Index (NDVI) for assessing vegetation health, Normalized Difference Water Index (NDWI) for detecting water bodies, Land Surface Temperature (LST) for measuring surface temperature, and Normalized Difference Built-Up Index (NDBI) for identifying built-up areas-was conducted using the raster calculator in ArcGIS. The study employed the Analytical Hierarchy Process (AHP) to assign weights to these indices based their relative significance regarding oil spill impact. Subsequently, reclassification and integration of this information were done to generate Oil Spill Index (OSI) maps for 2003, 2013, and 2023. Validation of the Oil Spill Index (OSI) of various points before and after an oil spill incident was performed, specifically contrasting 2003 with 2013 and 2023 OSI values. This was to affirm the accuracy and predictive capability of the OSI maps in identifying oil spill-prone areas. Findings revealed OSI values for 2003 and 2013, indicating a range of 1.04 to 3.48 for 2003, with a noticeable increase observed in 2013, expanding the range to 3.08 to 4.72. Similarly, a comparison of OSI values between 2003 and 2023 indicates OSI values, ranging from 1.64 to 3.92 in 2003, then in 2023 after the oil spill incidents, there were notable changes in the OSI values, with the range shifting to 3.2 to 4.84. This suggests a significant increase in the impact of oil spill on the affected areas over the two-decade. This comprehensive index map serves as a tool for monitoring and assessing the effects of oil spills on the study area, thereby achieving the research objectives effectively.

Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery

<p>The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method, (iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91%.</p>

Multitemporal Analysis of the Relationship Between Vegetation Density and Built-Up Land to the Urban Heat Island Phenomenon in Cirebon City

Abstract The attractiveness of Cirebon City as a target for urbanization lies in its strategic location and significant development, particularly in tourism, the service industry, and culture. As a result, vegetation has been transformed into urban development, leading to a subsequent rise in land surface temperatures and causing the Urban Heat Island (UHI) phenomenon. UHI is prevalent in urban areas and diminishes in peripheral regions. This research aims to investigate the correlation between vegetation density and built-up areas to the UHI phenomenon in Cirebon City, utilizing Landsat-8 data for the years 2014 and 2023. Normalized Difference Built-Up Index (NDBI) is used to identify built-up areas, vegetation density is identified with the Normalized Difference Vegetation Index (NDVI) algorithm, and Land Surface Temperature (LST) data is obtained from thermal band extraction. The results of the LST analysis were used to describe the Urban UHI phenomenon. Findings throughout the study period revealed significant changes in built-up land and vegetation density, while dynamic changes occurred in LST and UHI. The results also show that there is a positive correlation between the built-up land index and UHI, which indicates a unidirectional relationship. Meanwhile, the vegetation density index to UHI is negatively correlated, which means it shows an inverse relationship.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Assessing the Impact of Urban Expansion on Forest Cover using LULC Maps, NDVI, and NDBI: A Case Study of Kathmandu District

Rapid urbanization poses significant challenges to the delicate balance between urban development and environmental conservation. This study investigates the impact of urban expansion on forest cover in the Kathmandu District, employing a comprehensive approach that integrates Land Use/Land Cover (LULC) maps, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-Up Index (NDBI). The aim of the study area is to provide insights into the dynamic interplay between urban growth and its consequences on the region's ecological landscape. LULC maps derived from remote sensing data offer a spatially explicit representation of land-use changes over time, enabling the identification of areas where urbanization has encroached upon forest area. The NDVI, a vegetation health indicator, serves as a quantitative measure of changes in the density and health of vegetation cover, aiding in the assessment of ecological impacts. Complementing this, the NDBI highlights intensity of built-up areas, allowing for a nuanced understanding of the urbanization process. This study employs NDBI and NDVI to dynamically assess urban growth and land use cover change, enhancing analytical accuracy. Findings reveal a substantial 89.9 km² increase in built- up areas between 2013 and 2016, coupled with a minor 18.4 km² increase between 2016 and 2021, reflecting continuous urban expansion driven by population growth and development initiatives. Concurrently, forest area marginally increased by 2.3 km² in 2016, suggesting reforestation and conservation efforts, yet a subsequent 23.5 km² decline by 2021, due to deforestation, urban encroachment, and natural disasters such as wildfires. Notably, vacant land decreased by 92.3 km² in 2016, absorbed by built- up areas and forest expansion, underscoring the urgent need for spatial planning to accommodate the growing population and its evolving needs.

Dynamics of Heat Island Intensity in a Rapidly Urbanizing Area and the Cooling Effect of Ecological Land: A Case Study in Suzhou, Yangtze River Delta

Ecological land could play an important function in climate regulation to mitigate urban heat islands (UHIs) and enhance the quality of the living environment. In this work, we chose Suzhou as our case study for urban agglomeration in the Yangtze River Delta (YRD), eastern China. In this city, we analyzed the dynamics of heat island intensity from 2000 to 2021 by retrieving land surface temperature (LST). Subsequently, we examined the relationship between the urban thermal environment pattern and land use change, and finally, we explored the cooling effect provided by ecological land. The results indicated that, in 2000, the city’s UHI effect primarily centered around the central urban region as a singular patch; however, since 2014, the patch UHI effect in the central urban region has been mitigated, and the original small hotspots have converged into a large, contiguous expanse spreading outward. As the shift has occurred from low- to high-temperature zones, the proportion of conversion between ecological land has been decreasing, while the opposite trend has been seen for the proportions of ecological land transferred out and for unchanged artificial surfaces. The normalized difference built-up index was found to be the main contributor to the UHI effect, followed by the normalized difference vegetation index. These findings provide novel insights into the regulation of ecosystem services during urban expansion and offer a reference for improving the function of the cooling effect through urban renewal activities and the optimization of spatial planning.

Community-based assessment of the dynamics of urban landscape characteristics and ecosystem services in the rainforest and guinea savanna ecoregions of Nigeria

Understanding the dynamics of urban landscapes and their impacts on ecological well-being is crucial for developing sustainable urban management strategies in times of rapid urbanisation. This study assesses the nature and drivers of the changing urban landscape and ecosystem services in cities located in the rainforest (Akure and Owerri) and guinea savannah (Makurdi and Minna) of Nigeria using a combination of remote sensing and socioeconomic techniques. Landsat 8 datasets provided spatial patterns of the normalised difference vegetation index (NDVI) and normalised difference built-up index (NDBI). A household survey involving the administration of a semi-structured questionnaire to 1552 participants was conducted. Diminishing NDVI and increasing NDBI were observed due to the rising trend of urban expansion, corroborating the perception of over 54% of the respondents who noted a decline in landscape ecological health. Residential expansion, agricultural practices, transport and infrastructural development, and fuelwood production were recognised as the principal drivers of landscape changes. Climate variability/change reportedly makes a 28.5%–34.4% (Negelkerke R2) contribution to the changing status of natural landscapes in Akure and Makurdi as modelled by multinomial logistic regression, while population growth/in-migration and economic activities reportedly account for 19.9%–36.3% in Owerri and Minna. Consequently, ecosystem services were perceived to have declined in their potential to regulate air and water pollution, reduce soil erosion and flooding, and mitigate urban heat stress, with a corresponding reduction in access to social services. We recommend that urban residents be integrated into management policies geared towards effectively developing and enforcing urban planning regulations, promoting urban afforestation, and establishing sustainable waste management systems.

Spatial distribution and environmental correlations of Culex pipiens pallens (Diptera: Culicidae) in Haidian district, Beijing.

Culex pipiens pallens Coquillett, 1898 (Diptera: Culicidae) was the dominant health threat to mosquito species in Beijing, and it is important to unravel the spatial distribution and environmental correlations of Cx. pipiens pallens in Beijing. 3S technology methods and spatial statistics were used to clarify the distribution pattern. Subsequently, linear and spatial regression were performed to detect the environmental factors linked with the density of Cx. pipiens pallens. The same "middle peak" spatial distribution pattern was observed for Cx. pipiens pallens density at the community, subdistrict, and loop area levels in our study area. In addition, there were various correlated environmental factors at the community and subdistrict scales. At the community scale, the summary values of the Modified Normalized Difference Water Index (MNDWI) in 2 km buffer zone (MNDWI_2K) were negatively correlated, and the summary values of Normalized Difference Built-up Index (NDBI) in 800 m buffer zone (NDBI_800) was positively correlated to the Cx. pipiens pallens density. However, the summary values of Normalized Difference Vegetation Index and Nighttime Light Index significantly affected Cx. pipiens pallens density at the subdistrict scale. Our findings provide insight into the spatial distribution pattern of Cx. pipiens pallens density and its associated environmental risk factors at different spatial scales in the Haidian district of Beijing for the first time. The results could be used to predict the Cx. pipiens pallens density as well as the risk of lymphatic filariasis (LF) infection, which would help implement prevention and control measures to prevent future risks of biting and LF transmission in Beijing.

Examining the relationship of major air pollutants with land surface parameters and its monthly variation in Indian cities using satellite data

The relationship of land surface parameters with the air pollutants is worth exploring. The present study investigates the relation of Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) with six air pollutants such as Aerosol Index (AI), Carbon monoxide (CO), formaldehyde (HCHO), Sulphur Dioxide (SO2), Nitrogen dioxide (NO2) and ozone (O3) from January to December month of year 2022 in four cities situated at varying climatic zones of India. Except for O3, which exhibits lower concentration in winter and greater in summer, the air pollutants concentration showed lower values during monsoon season and higher during summer and post-monsoon seasons. The relationship of LST, NDBI and NDVI with different air pollutants were found to vary throughout the year in the four cities. The magnitude of correlation coefficient (R) was found greater for AI and NO2 as compared to other pollutants depicting greater impact of land surface parameters on concentration of AI and NO2. The surface urban cool island effect in Bikaner showed strong negative relation of LST with NO2 with magnitude of R greater than 0.41. The surface urban heat island formation in Varanasi showed strong positive correlation with the air pollutants such as AI, CO, NO2 and O3 with magnitude of R value greater than 0.61, 0.31, 0.59 and 0.32 for AI, CO, NO2 and O3, respectively. Even though, the correlation of LST with air pollutants varied with seasons and cities, NDVI showed negative correlation with most of the air pollutants in the cities except in Bikaner where vegetation content is negligible. Thus, increasing the amount of vegetation in a city can improve the air quality by lowering the quantity of air pollutants there.