Normalized Difference Built-up Index Research Articles

GEOSPATIAL APPROACH FOR URBAN ENVIRONMENTAL QUALITY ASSESSMENT

Abstract. Growing population and change in gentrification patterns of urban areas affect the environment quality, especially in the developing nations. Most cities are thereby, facing serious environmental sustainability challenges. A resilient urban planning is required to solve these environmental problems, which in turn need information that is not available in the desired scales, hence, making it difficult to comprehend. In this study, an approach is proposed to comprehend the environmental quality of a city at community level. Landsat 8 images are used to compute the biophysical indicators, namely, land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI) and normalized difference impervious surface index (NDISI) in ArcGIS software. These indicators are then integrated with the indicators from the government census data to compute the community level index for urban environmental quality (UEQI). The results depicted that the highest value of our developed index occurred in the places where there is more of greenery and less built-up area. Based on the obtained UEQI values, different areas of the city are divided into five categories. The index values suggest that urban greening significantly contribute to enhance the urban environmental quality. Also, it highlights the critical zones where intervention should be made by the planners and policymakers for the sustainability of the city.

LAND SUBSIDENCE MONITORING AND ANALYSIS IN FUZHOU BASED ON INSAR AND MULTISPECTRAL REMOTE SENSING TECHNOLOGY

Abstract. Interferometric Synthetic Aperture Radar (InSAR) technology has millimeter level measurement accuracy and has great advantages in urban land subsidence monitoring. Meanwhile, multispectral remote sensing technique can also provide a large amount of urban features changes information for analyzing the causes of land subsidence. In this study, SAR and multispectral images are both used to monitor and analyze land subsidence in Fuzhou of China. 115 scenes Sentinel-1 SAR images from May 2017 to May 2021 are used based on the Persistent Scatterers Interferometric (PSI) method to evaluate the land subsidence in Fuzhou, while Sentinel-2 multispectral images are used to evaluate several remote sensing indexes. During SAR data processing, Generic Atmospheric Correction Online Service for InSAR (GACOS) data is used to remove atmospheric errors for higher accuracy land subsidence. In order to analyze the relationship between the land subsidence and land cover changes in urban areas, the Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) and Modified Normalized Difference Water Index (MNDWI) of the main subsidence areas are obtained based on Sentinel-2 multispectral images from 2016 to 2021. In the end, it is found that the land subsidence in some areas exceeded 12 mm/year in Fuzhou. The time series of four areas with severe subsidence were analyzed, and the cumulative subsidence reached about 60 mm. Besides, the spatial distribution and temporal changes of vegetation, buildings and water bodies in these areas were obtained based on the multispectral data, it is found there is very less relationship between the land subsidence and the urban features. It is concluded that that the main causes of the land subsidence are the changes of land internal components such as groundwater and others.

Urban Greening Effect on Land Surface Temperature.

Urbanization has accelerated the conversion of vegetated land to built-up regions. The purpose of this study was to evaluate the effects of urban park configuration on the Land Surface Temperature of the park and adjacent areas. In urban parks, the study analyzed the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and the Land Surface Temperature (LST). The NDVI categorization process resulted in the development of a vegetation density distribution. The majority of Medan’s urban areas were categorized as low density, as seen by their low NDVI values. The NDBI values were significantly higher in the majority of the area. This shows that the majority of places are experiencing a decline in vegetation cover. The density of vegetation varies according to the placement of park components such as trees, mixed plants, recreation, and sports areas. According to LST data, the temperature in the urban park was cooler than in the surrounding areas. Although the surrounding areas are densely populated, urban parks are dominated by trees. Additionally, there is a green space adjacent to the park, which is a green lane that runs alongside the main roadways.

Enhancement of tree canopy cover for the mapping of forest from the Sentinel-2 imagery

A forest is a piece of land dominated by trees; factors like tree density, tree height, canopy cover, land use, and ecological function discriminate different forest types. The canopy cover is instrumental in the identification of forest in the satellite imagery. Multispectral satellite imageries are very useful in feature highlighting. The combination of bands is a powerful tool not only to enhance some earth features but also to suppress others. In our study, a novel index is proposed for the delineation of tree canopy using Sentinel-2 multispectral imagery. A new simple vegetation ratio is proposed for the extraction of the vegetated areas including forest, farms, and other green lands, the existing normalized difference built-up index is modified, and an open surface water index is utilized to formulate a novel tree canopy cover index for the identification of tree canopy.

Spatial evolution of land use and land cover for the Tatu-Bola Wildlife Refuge - Pernambuco, using orbital images

This article aims the analyze NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), and NDBI (Normalized Difference Built-up Index) radiometric indices for TM and OLI sensor scenes aboard the LANDSAT satellite. The study area is the Tatu-Bola Wildlife Refuge, created in March 2015 through State Decree number 41,546, and its surroundings. The study aimed to monitor the spatial and temporal evolution of land use and land cover in a period before and after the implementation of the Armadillo-Bola Wildlife Refuge, using indices of vegetation (NDVI), moisture (NDWI), and exposed soil (NDBI) and its relationship with the surroundings. TM and OLI images (orbit and point 217-066) were used on board the LANDSAT satellite 5 (23-05-1987, 15-05-1996, and 01-02-2005) and 8 (28-01-2015 and 17- 03-2021), radiometrically and geometrically corrected and obtained by accessing EarthExplorer, with the calculation of radiance and reflectance being performed, and the indices applied. In order to better identify the area with vegetation and exposed soil, the RGB composition was performed, with NDBI, NDVI, and NDWI indices for Landsat images and fusion of multispectral and panchromatic bands in WPM sensor images from the CBERS-4A satellite from 23- 08-2020 (orbit and point 198-124) and 18-09-2020 (199-124). Digital numbers of cloud and shadow pixels were excluded through supervised classification. Between 1987 and 2021, it was possible to observe a decrease in vegetation cover, especially around the RVS Armadillo-balla, and an increase in area, with NDVI values lower than 0.40. The Refuge area in Petrolina has the largest amount of deforested area. The use of RGB composition of the indices and the merged images from the CBERS-4A satellite, with a spatial resolution of 2 m, present a significant improvement in identifying areas of exposed soil and dry vegetation.

Environmental Investigation of Bahar Al-Najaf Region Using Sentinel-2 Images

This study aims to shed light on the indicators of environmental change and environmental impact assessment during the past five years in a representative area of the western part of Iraq (BAHAR-ALNAJAF). This is to understand the leading causes that led to the environmental changes from (2016 to 2020) due to the change in land cover in the study area. The paper refers to an environmental study for the study area using satellite data within the software environment (ArcGIS) and the application of remote sensing from two aspects: Ecological indices retrieval and the monitoring environment for land cover. Remote sensing and GIS software have been utilized to categorize (Sentinel-2) imagery into seven land use and land cover (LULC) classes: cropland land, orchards land, wetland, sandy area land, mixed barren land, built-up land, and water bodies. Supervised classification and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), and Normalized Difference Salinity Index (NDSI) were approved and utilized respectively to retrieve its class boundary. From a practical point of view, it was found that there is a rise in water levels in the Bahar Al-Najaf; this rising has led to the flooding of many built-up and vegetated lands. As a result, flooded land areas increased in 2020 to about 50% more than in 2016. Consequently, the built-up growth regions in the study area were very slow to change during the study period (2016-2020). The vegetation cover for 2020 is 56% higher than in 2016 because of the abundance of water and agricultural policy of this year.

Urban Green Space Planning and Development in Urban Cities Using Geospatial Technology: A Case Study of Noida

Abstract Urban planning, with special attention to green space development, offers a relatively simple and low-cost solution to the impacts of climate change and urbanization faced by urban centres. The present work examines the spatial variability of availability of adequate sites for the development of urban green amenities in Noida city. Multi-criteria assessment of potential locations has been accomplished using Analytical Hierarchical Process coupled with geospatial technology. Urban land use, physiographic factors (slope and elevation), accessibility (proximity to roads), and presence of grey, green and blue amenities (Normalised Difference Built-up Index, Normalised Difference Vegetation Index and proximity to water bodies, respectively) are the seven key criteria used to derive the final green space suitability map. A total of 46.47 % of the land was found to be in the category of highly and moderately suitable for greening the city, highlighting the potential of developing different forms of green spaces in the area. Such holistic city scale analysis of availability of potential sites for green space development can be utilised by the city administrators and urban planners for future land use planning and improving the distribution and spatial connectivity of the green spaces in the city with the common goals of better health, a cleaner environment, and climate change mitigation.

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity—Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000–2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000–2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth.

Assessing the Spatiotemporal Heterogeneity of Terrestrial Temperature as a Proxy to Microclimate and Its Relationship With Urban Hydro-Biophysical Parameters

Rapid urban land use and land cover changes have become a major environmental issue because of their ecological effects, including loss of green space and urban heat islands. Effective monitoring and management techniques are required. The Saudi Arabian twin city of Abha-Khamis Mushyet was selected as a case study for this research. As a result, the current study aimed to statistically and spatially investigate the relationship between land surface temperature (LST) and land use land cover based urban biophysical parameters such as normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). This study used random forest (RF) to classify LULC in 1990, 2000, and 2018. We also validated the LULC maps in a novel way. Using mono window algorithm techniques, we extracted LST for three periods. The dynamics of LULC, LST, and biophysical parameters were investigated using standard statistical graphs such as the heat map and the Sankey diagram. The correlation coefficient and the global bivariate Moran’ I approach were used to determine the association between LST and urban biophysical parameters. The relationship was then established in greater detail by categorizing the entire pixel into percentile classes and employing parallel coordinate plots. Finally, the association was built using GeoDA software and a conditional map. The LULC maps revealed a 334.4 percent increase in urban areas between 1990 and 2018. The built-up region is the largest stable LULC, with an 83.6 percent transitional probability matrix between 1990 and 2018. While 17.9%, 21.8%, 12.4%, and 10.5% of agricultural land, scrubland, exposed rocks, and water bodies were converted to built-up areas, respectively. The LST has increased rapidly over time because of LULC changes. The link between LST and urban biophysical parameters revealed that NDBI had a positive relationship, whereas NDWI and NDVI had a negative relationship. As a result, this study could be very important because it could help decision makers figure out how to lessen the effects of urban heat islands because of changes in LULC.

Analytical Study of Urban Heat Spot Patterns in Colombo District from 1988 – 2019 based on Landsat Data

Researching on urban heat island (UHI) is a hot topic among urban designers due to its adverse impacts. This paper focuses on studying spatial and temporal dynamicity of surface UHI in the Colombo district based on correlations between land surface temperatures (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) using Landsat data from 1988 to 2019. Image processing and statistical analysis were done using QGIS Desktop 3.16.0 and RStudio softwares respectively. The mean of LSTs were continuously increasing from 1988 to 2019. The highest LSTs were observed at the Colombo harbour area in both 1997 and 2007. After initiation of the port city project in 2015, these values have been increased rapidly around the Colombo port city area. The expansion of UHI area was 71.55% between 1988 to 2019, and they were distributed from the western coastal belt to the east along with the central part of the district. The urban hot spots (UHS) were compacted at harbour and port city area. Additionally, new hot spots have been generated since 2017 adjacent to “Seethagama”. These small pockets are too hot and not very conducive for human settlements. Parking lots, compacted built-up areas, and ongoing industrial construction areas influence the formation of UHS. Considering this critical situation, it is highly recommended that to move mitigation strategies like urban greening methods, cooling pavements and cooling roofs, etc. These results could be used towards a well-designed urban planning system to maintain the ecological balance within the study area.

Impact of anthropogenic activities on urban heat islands in major cities of El-Minya Governorate, Egypt

Urban heat islands (UHIs) represent one of the significant factors in regard to environmental and human health. UHIs are significantly changeable, responding to the land use type and the dominant anthropogenic activities. In this research, UHIs in El-Minya cities were identified based on Land Surface Temperature (LST) measurements. Thermal bands of Landsat imagery were processed to produce LST in the cities of El-Minya governorate in 2001, 2011, and 2021. In addition, the spectral indices; Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Normalized Difference Built-up Index (NDBI) were retrieved from the processing of multispectral Landsat imagery to assess different Land-cover units in the study area and to detect their correlation with LST/UHIs. Analysis of data indicated that LST variations are corresponding with different land-cover types. It was found that NDVI and NDWI have a strong negative influence on LST (R = −0.7 and −0.8 for both indices, respectively), while NDBI has a significant positive correlation (R = 0.85). Furthermore, the highest LST was detected at the cities of El-Minya, New-El-Minya, and Malawi, consequently. These regions have the greatest potential for UHIs formation among other El-Minya governorate cities. The year 2021 recorded the highest average LST with a value of 34.1 °C where the largest UHI area was observed in New-El-Minya (2.74Km2) in 2001, and (2.6 Km2) in 2021, as well as (1.12Km2) in El-Minya in 2011. It can be concluded that increasing LST and UHIs at different districts of El-Minya arise from urbanization and industrialization processes.

Comparison Normalized Dryness Built-Up Index (NDBI) with Enhanced Built-Up and Bareness Index (EBBI) for Identification Urban in Buleleng Sub-District

The research aims to determine how much accuracy is improved in developing settlements and distinguish the areas built up and vacant land in Buleleng, Bali district. This study used remote sensing methods to monitor and detect changes in the waking area using Landsat 8 OLI imagery. Identify settlement developments using the Normalized Difference Built-up Index (NDBI) and Enhanced Built-Up and Bareness Index (EBBI) algorithms. Both algorithms use red and infrared bands as the basis for identifying building differences. As a result, NDBI and EBBI have differences where the accuracy of EBBI is higher than NDBI by 84% and 82%. The difference in accuracy is influenced by the appearance of vegetation and clay-roofed buildings. Based on that, it can be concluded that in identifying the building, EBBI has a higher capacity compared to NDBI, but it must be ensured that in the use of EBBI, the area studied has a more dominant appearance of the building.

Assessing The Spatiotemporal Urban Green Cover Changes and Their Impact on Land Surface Temperature and Urban Heat Island in Lahore (Pakistan)

Urban vegetation has a decisive role in sustaining homogeneous Land Surface Temperature (LST) in a built-up environment. However, urban areas are facing rapid changes in land use/land cover (LULC) over the last few decades as green cover is being replaced by built-up structures. Consequently, LST is increasing and urban heat island (UHI) effects are expanding. In this context, this study was organized to assess urban green cover changes in Lahore and their impact on LST and UHI effects. For this, climate data was collected from the Pakistan Meteorological Department and Landsat images were acquired from Earth Explorer. LULC and LST maps were generated for 1990, 2000, 2010, and 2020 in ArcGIS 10.8. Also, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were computed to analyze the effects of vegetation and built-up areas on LST and UHI. The study found that over the last three decades, built-up area increased 113.85% by removing 392.78 km2 of green cover in the study area. Similarly, a rapid expansion of the high LST range and UHI effects was found towards the eastern and southern parts of the study area. Moreover, a negative correlation was found between LST and NDVI, whereas the correlation between LST and NDBI was found to be positive. Therefore, it was concluded that the continuation of green cover reduction is highly damaging because this might render the city more fragile ecologically. So, the study calls the attention of the responsible authorities for suitable measures against continuous green cover loss in the study area.

Evaluation of the prediction capability of AHP and F-AHP methods in flood susceptibility mapping of Ernakulam district (India)

Floods are one of the frequent natural hazards occurring in Kerala because of the remarkably high annual rate of rainfall. The objective of this study is to prepare the flood susceptibility maps of Ernakulam district by integrating remote sensing data, GIS, analytical hierarchy process (AHP), and fuzzy-analytical hierarchy process methods. Ernakulam is one of Kerala's most flood-prone districts. The development of this map can help to raise awareness about the risks of flooding. Factors such as slope angle, soil types (texture), land use/land cover, stream density, water ratio index, normalized difference built-up index, topographic wetness index, stream power index, aspect, and sediment transport index have been selected. The area of the final maps is grouped into five flood susceptible zones, ranging from very low to very high. The major reasons for flood occurrence in Ernakulam district are the combined effect of multiple factors such as excess silting, reduction of stream width due to anthropogenic activities, and changes in land cover and land-use pattern, lower slope, higher soil moisture content, lower stream capacity, and poor infiltration capacity of soils. The prepared map was validated using the receiver operating characteristic (ROC) curve method. The area under the ROC curve (AUC) values of 0.75 and 0.81 estimated by the ROC curve method for the AHP and F-AHP methods are considered acceptable and excellent, which confirms the prediction capability of the prepared maps. The very high susceptible zone constitutes around 19% of the district. This map is useful for land-use planners and policymakers to adopt strategies that will reduce the impact of flood hazards and damage in the future.

Assessing the effect of covid-19 lockdown on surface urban heat island for different land use /cover types using remote sensing

Rapid urbanization significantly affects the near-surface temperature for major cities worldwide. A greater understanding of spatial-temporal changes of the near-surface temperature has given better insights into global issues such as climate change, thermal discomfort, and energy consumption. This remote sensing-based study aims to investigate the spatial-temporal variations of land surface temperature before and after the lockdown, implemented in response to the spread of COVID-19, for three major Indian cities located in different climate zones viz., i) New Delhi (inland), ii) Hyderabad (inland), and iii) Mumbai (coastal). The land surface temperature variation is assessed for 2015-2020 on different land cover types, i.e., buildings, barren land, roads, vegetation, and water. Landsat 8 OLI/TIRS images are employed to classify land use/cover types using Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Land Surface Temperature maps. Results show that for New Delhi, Hyderabad, and Mumbai, the mean land surface temperature decreases by 5°C, 1.9°C, and 0.26°C, respectively, in April 2020 (after lockdown) in comparison with April 2019. Results for Mumbai do not vary significantly compared to the other two cities. Overall, the performed analysis presents evidence of the impact produced by the COVID-19 lockdown on the surface urban heat island for different land use/cover types.

Research on the spatiotemporal coupling relationships between land use/land cover compositions or patterns and the surface urban heat island effect.

Urbanization leads to changes in landscape configuration and land use/land cover (LULC) patterns, and these changes are important factors affecting the surface urban heat island (SUHI) effect. However, from the perspective of spatiotemporal changes, quantitative analytical results regarding the impacts of the LULC composition, configuration, and pattern in inland plateau lakeside cities on the SUHI effect, and the responsive relationships among these factors remain unclear. By combining satellite remote sensing data with analytical methods, such as urban-rural gradients, spatial statistics, and landscape pattern indices, the impacts of LULC changes on the SUHI effect in Kunming, China, are revealed. The results show the following. (1) The explosive growth in impervious surfaces (ISs) caused by urbanization, leading to changes in the LULC composition, configuration and pattern, is the main reason for the deterioration of the SUHI effect. Over the past 30years, Kunming's ISs have increased by 304.58 km2, SUHI has expanded by 764.26 km2, and the regional average land surface temperature (LST) has increased by 1°C. (2) This study also found that a large area of bare ground is another important reason for the sharp rise in LST, explaining why bare land (BL) has the highest average LST (28.72°C). (3) The pattern of LULC can well explain the spatial distribution characteristics of SUHIs. The normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), and LST have the same change curve along the urban-rural gradient, while the normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and LST have opposite trends. (4) ISs and water body (WB) are the main types of warming and cooling, respectively, but the warming effect of ISs is greater than the cooling effect of WB. From the average value of the correlation coefficient with LST, NDBI (0.84) > MNDWI (-0.63). (5) Kunming's remote sensing index values do not have simple linear relationships with the LST. NDBaI, NDBI, and LST show significant exponential relationships, and NDVI, MNDWI, and LST show significant quadratic polynomial relationships. (6) The dominant landscape type determines the correlation between the landscape shape index (LSI) and the LST of green spaces (GSs). (7) Adopting a simple and regular landscape layout can effectively reduce the SUHI effect. These research results could provide a scientific decision-making basis for the spatial urban planning and ecological construction of Kunming and could have practical significance for guiding the green, healthy, and sustainable development of the city.

Urban spatial temporal analysis and the urban heat island potential

The rate of urbanization has in changes in the urban spatial development, including in the city of Kediri. This study aims to analyze the distribution of urban spatial development in Kediri Regency/City, 2) Analyze the factors the factors that influence the spatial development, and 3) Analyze the UHI phenomenon and its relationship with urban spatial development. This study used Landsat 5 (1996) and 8 (2016, 2021) OLI to analyze Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and land surface temperature (LST). Ndbi and ndvi are used to determine the urban spatial development, meanwhile lst and ndvi are to analyse surface temperature. The analysis was conducted from 1996 to 2021. Urban spatial development in study area has a linear type along with the development of roads. Built-up land in the study area has increased rapidly in the last 5 years (2016 – 2021) by 83.8% compared to changes that occurred in 20 years (1996 – 2016). This is in line with the increase in urbanization, especially since the advent of the shopping centers. Temperature is increasing in line with the increase in built-up land. There is a potential UHI phenomenon in the study area as indicated by a significant temperature difference between urban and sub-urban. This makes it a challenge for urban spatial planning so that the study area remains a comfortable place to live.

Spatiotemporal dynamic of environmental indices of watershed sustainability in connection with land-use change

ABSTRACT Exploring spatial and temporal land-use changes is valuable for local governments to address issues of sustainability and planning policy where urbanization and industrialization are taking place. Besides anthropogenic effects, natural driving forces like climate change may also affect sustainability. However, such relationships have not been studied minutely. Hence, this study first investigates the land-use changes and their relationship with land surface temperature (LST) for the Shazand Watershed, Iran, in 1986, 1998, 2008, and 2016 coincided with supplementary industrialization stages. Furthermore, the relations among LST and other biophysical parameters, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI), and Normalized Difference Water Index (NDWI), were analyzed, and corresponding variations were explored. The results indicated that the mean LST of the study watershed has an increasing trend from 1986 to 2008 due to land-use change and drought intensification. Later, LST decreased in 2016. Lower LST was associated with irrigation farming and orchard, and higher LST was related to sparse oak forest areas. There was also a negative correlation between LST and NDVI. As a result, it was inferred that greenery declined LST. Conversely, a positive correlation was found between LST and NDBI resulting from the built-up areas. Since LST could influence biological, physical, chemical processes, it can therefore be supported as an effective index for environmental sustainability assessment.

Urban green space mediates spatiotemporal variation in land surface temperature: a case study of an urbanized city, Bangladesh.

Rapid urbanization with an increasing rate of urban built-up area is decreasing urban green space resulting in changing urban microclimate conditions showing increasing land surface temperature. A better understanding of these effects is important to formulate effective strategies in addressing the impact of increasing built-up area. Land surface temperature patterns in an urbanized city in Bangladesh (Mymensingh district) were investigated using Landsat satellite sensor data from 1988 to 2016. A total of nineteen Landsat satellite images were used to retrieve land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The radiative transfer equation (RTE) model was applied to derive LST for the years 1988, 1992, 1999, 2004, 2008, 2012, and 2016. Further, the Landsat-derived LST results were compared with MODIS Terra satellite outputs (MOD11A1) for the validation of our study results. Our results showed NDVI higher in 2008 and lower in 2004, LST maximum in 1988 and minimum in 2008, and NDBI higher in 2004 and lower in 2012. Seasonally, summer was characterized by higher LST and winter by lower LST, while NDVI was higher in autumn and lower in winter, however, NDBI was higher in winter and lower in autumn. Spatially, a relatively higher LST and NDBI was observed in the southwest, followed by central, and northern regions, whereas the trend was opposite for NDVI. Using Pearson's correlation, results showed a strong significant negative correlation between LST and NDVI and a positive significant correlation between LST and NDBI. Further, simple linear regression analysis revealed that LST decreased with increasing NDVI most quickly in 2012, followed by the years 2016, 2008, 1992, 1988, 1999, and 2004. On the other hand, LST increased with increasing NDBI most quickly in 1999, followed by the years2016, 1988, 1992, 2012, 2004, and 2008. Thus, long-term observation suggested that urbanization had driven a decrease in green space while simultaneously increasing the land surface temperature within an urbanized area. This study has concluded that the protection of urban green spaces is needed as an effective step toward addressing adverse effects of regional climate change and desertification.

ASSESSMENT OF URBAN ENVIRONMENTAL QUALITY: A CASE STUDY OF CASABLANCA, MOROCCO

Abstract. By 2050, Most of the world’s population will live in cities, this demographic explosion will lead to significant urban development at the expanse of natural land which may harm the environmental quality. Consequently, assessing and modeling the urban environmental quality (UEQ) is requisite for efficient urban sprawl control and better city planning and management. The present study proposes a methodology to model and assess the environment of the urban system by developing the urban environmental quality index (UEQI) based on remote sensing data. Five environmental indicators were derived from the Landsat OLI image namely, Modified Normalized Difference Impervious Surface Index (MNDISI), Modified Normalized Difference, Water Index (MNDWI), Normalized difference vegetation Index (NDVI), Normalized difference built-up Index (NDBI) and Soil adjusted vegetation index (SAVI). Using the Principal Component Analysis (PCA) the urban environmental quality index was computed for the 17 communes of Casablanca city. The UEQI values were spatially mapped under three classes (good, moderate, and poor). The results obtained from the analysis showed a significant difference in the term of UEQI values among the communes. In addition, the environmental quality is inadequate in communes with fewer green spaces and more impervious surfaces. The outcomes of this work can serve as an efficient tool to determine the most critical interventions to be made by the authority for current and future urban planning and land/resource management.