Normalized Difference Built-up Index Research Articles

Hydrogeochemical Mechanism Associated with Land Use Land Cover Indices Using Geospatial, Remote Sensing Techniques, and Health Risks Model

Land is a vital component of nature around the world and is essential for humans, terrestrial plants, and animals. However, urbanization is growing worldwide and the groundwater quality in urban areas is declining due to rapid development, industrialization, and pollution. Hence, it is important to determine the hydrogeochemistry and changes concerning land use and land cover (LULC). This study was conducted to investigate the hydrogeochemical mechanism of Sargodha, one of Pakistan’s fastest-growing cities, during 2015 and 2021 under two different LULC indices named normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). Forty-eight groundwater samples were collected in 2015 and 2021 to assess groundwater quality for domestic use and irrigation. The Piper diagram revealed that (Ca2++Mg2+–HCO3−+CO32−) and (Na++K+–SO42−+Cl−) were the main components of hydrogeochemistry in both years. The Gibbs plot and silicate weathering of groundwater samples indicated that rock dominance and silicate weathering played a major role in aquifers. Additionally, the results showed that 4% of the groundwater in 2015 and 21% of the groundwater samples in 2021 were unfit for human consumption. However, 37% and 40% of samples were considered not fit for irrigation in 2015 and 2021, respectively. The LULC, NDVI, and NDBI clearly revealed higher urban areas in 2021 compared to 2015. The relationship between groundwater parameters and land use land cover indices (NDVI and NDBI) explained that none had a major relationship. The non-carcinogenic risk showed health quotient HQ < 1, indicating no severe health risk due to nitrate (NO3−) exposure in the city. The current study suggests conducting future investigations considering a larger scale to recommend efficient management strategies, urbanization planning, and ensuring safe irrigation and drinking water to prevent groundwater pollution.

Controls of Land Surface Temperature between and within Local Climate Zones: A Case Study of Harare in Zimbabwe

Urban growth-related changes in land use and land cover have segmented urban areas into zones of distinct surface and air temperatures (i.e., Local Climate Zones—LCZ). While studies have revealed inter-LCZ temperature variations, understanding controls of variations in Land Surface Temperature (LST) within LCZs has largely remained uninvestigated. In view of the need for LCZ-specific heat mitigation strategies, this study investigated factors driving LST variations within LCZs. To achieve this, an LCZ map for Harare was developed and correlated with LST, both derived using Landsat 8 data. The contribution index (CI) was then used to determine the relative contribution of LCZs to cooling and warming of the city. The contribution of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Urban Index (UI), and Aspect and Elevation as quantitative measures of surface controls of LST were investigated between and within LCZs. LST generally increased with built-up density and reduced with increases in surface water and vegetation. The study showed that the cooling effect of water bodies was reduced in contribution to their insignificant proportion of the study area. At the city scale, NDVI, MNDWI, NDBI, and UI had the strongest influence on LST (correlation coefficient > 0.5). At the intra-LCZ scale, the contribution of these surface properties remained significant, though to varied extents. The study concluded that surface wetness is a significant cooling determinant in densely built-up LCZs, while in other LCZs, it combines with vegetation abundance and health to mitigate elevated surface temperature. Aspect and elevation had low but significant correlations with LST in most LCZs. The study recommends that intra-LCZ controls of LST must be considered in heat mitigation efforts.

Modeling the Surface Urban Heat Island (SUHI) to study of its relationship with variations in the thermal field and with the indices of land use in the metropolitan area of Granada (Spain)

• 1°) Increases in LST (2.2°C) and SUHI (0.6°C) between 1985 and 2020 are confirmed. • 2°) An increase in the UHS (20.4%) is situated in the areas having the highest SUHI. • 3°) There is an increase of 26.2% in class 6 and a reduction in class 1 of -25.5%. • 4°) Areas without vegetation, industrial and commercial are the great focuses of UHS. Understanding just how the increase in the Earth's Surface Temperature (LST) is related to alterations of the urban climate —Surface Urban Heat Island (SUHI) or Urban Hotspots (UHS)— and with the deterioration of cities´ environmental quality has become a great challenge. Societies worldwide seek actions that might break these trends and improve the quality of life of local inhabitants. In this research, with the help of Landsat 5, 7 and 8 satellite images, the evolution of land use/cover (LULC), LST and SUHI were studied over a long period, from 1985 to 2020, in the metropolitan area of the city of Granada (Spain). The aim was to evaluate how these variables, together with the Urban Index (UI), Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Proportion of Vegetation (PV), have influenced the variability of the UHS and the level of thermal comfort according to the Urban Thermal Field Variance Index (UTFVI). Reported as results, corroborated by statistical analysis, are mean increases in LST (2.2°C), SUHI (0.6°C), UHS (20.4%), and class 6 of the UTFV (26.2%). NDBI and UI are associated with high variations in LULCs. These have suffered increases in built-up and bare soil coverage, and decreases in water bodies, vegetation and farmland coverage.

Analysis of the Spatio-temporal variations of thermal patterns of Shiraz city by satellite images and GIS processing

In recent years, climate change has caused an increase in the air temperature and consequently the temperature of the earth's surface. The temperature of the earth's surface has had various effects on urban life and has caused the formation of thermal islands in cities. Thermal islands are one of the important criteria in regional planning. The city of Shiraz is considered to be a large population center in the southwest of Iran on the one hand and one of the important tourism centers of Iran on the other hand, so the purpose of this research is to monitor the spatial form of thermal islands in this city. The satellite images of Shiraz city were first collected and extracted in the hot periods of the year from 1985-to 2020 using the data of Landsat’s 4 and 5 (TM), 7 (ETM+), and 8 (OLI/TIRS). After required preprocessing, the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI), and the urban heat dispersion index were determined by the Urban Thermal Field Variance Index (UTFVI). The regions with UHI, the variation of the temperature in the city, and the relationship between the LST and surface vegetation were evaluated based on the results of image processing to determine the UHIs of Shiraz. The temporal variations of the temperature pattern of Shiraz city indicated that about 12.76 km2 was added to the area of the fourth temperature class from 1985 to 2020. Results of NDVI evaluation in the studied period showed a decline in the vegetation from 22.28 km2 in 1985 to 17.54 km2 in 2020 due to alteration of the land use which can explain the formation and increase of the UHIs in the mentioned regions. At the beginning of the investigated period (1985), the temperature of the earth's surface was 17°Celsius and reached 21°Celsius at the end of the period (2020). The monitoring of the spatial form of the surface temperature of the earth shows that heat islands are moving from the outskirts of the city towards the city center. UTFVI also revealed that the awful regions (very hot temperatures) are mainly concentrated in the west and north-west to the south west of Shiraz (including some parts of districts 9 and 10), south east of district 7, and north of district 1.

Automated Mapping of Urban Heat Island to Predict Land Surface Temperature and Land use/cover Change Using Machine Learning Algorithms: Mansoura City

Urban heat island (UHI) frequency and emergence are strongly associated with variations in land use/land cover (LU/LC) and land surface temperature (LST). This study investigates the impact of LU/LC class changes on LST based on the distribution of UHI spot maps in Mansoura city, Egypt, using Landsat satellite images from 1991 to 2021. Based on these estimated LU/LC and LST maps, machine learning algorithms, cellular automata, and artificial neural network approaches were used to predict future changes in LU/LC and LST for 2031. The influence of UHI may be quantified using the urban thermal field variance index (UTFVI). The geographic information system (GIS) add-in UHI calculator ArcGIS tool was created because we considered the spatial consequences of employing remote sensing data. This tool includes all the methods and procedures for calculating LST and UHI. The analysis revealed a positive correlation between LST and normalized difference built-up index and a negative correlation between LST and normalized difference vegetation index. The forecasted results for 2031 also show that the built-up area will grow roughly 20%, with a considerable drop in vegetation by 18%. If the city's fast urbanization continues, more than 40% of Mansoura will have land surface temperatures above 45°C by 2031. Avoiding dense built-up areas and growing vegetation spaces remain efficient means of minimizing the influence of UTFVI in urban construction practice. Therefore, this research will help to achieve sustainable development by providing essential insights into the complicated interplay between diverse aspects of urban settings and promoting city competency.

Spatial Variation and Relation of Aerosol Optical Depth with LULC and Spectral Indices

In the current study area (Faridabad, Gurugram, Ghaziabad, and Gautam Buddha Nagar), the aerosol concentration is very high, adversely affecting the environmental conditions and air quality. Investigating the impact of Land Use Land Cover (LULC) on Aerosol Optical Depth (AOD) helps us to develop effective solutions for improving air quality. Hence, the spectral indices derived from LULC ((Normalized difference vegetation index (NDVI), Soil adjusted vegetation index (SAVI), Enhanced vegetation index (EVI), and Normalized difference build-up index (NDBI)) with Moderate Resolution Imaging Spectroradiometer (MODIS) Multiangle Implementation of Atmospheric Correction (MAIAC) high spatial resolution (1 km) AOD from the years 2010–2019 (less to high urbanized period) has been correlated. The current study used remote sensing and Geographical Information System (GIS) techniques to examine changes in LULC in the current study region over the ten years (2010–2019) and the relationship between LULC and AOD. A significant increase in built-up areas (12.18%) and grasslands (51.29%) was observed during 2010–2019, while cropland decreased by 4.42%. A positive correlation between NDBI and SAVI (0.35, 0.27) indicates that built-up soils play an important role in accumulating AOD in a semi-arid region. At the same time, a negative correlation between NDVI and EVI (−0.24, −0.15) indicates the removal of aerosols due to an increase in vegetation. The results indicate that SAVI can play an important role in PM2.5 modeling in semi-arid regions. Based on these findings, urban planners can improve land use management, air quality, and urban planning.

Assessment of LULC change dynamics and its relationship with LST and spectral indices in a rural area of Bengaluru district, Karnataka India

In recent decades, rural areas in the surroundings of metropolitan cities have undergone substantial development in India. This has resulted in changes in land use and land cover (LULC) patterns having a significant impact on hydrological processes and climate change. Therefore, comprehending such modifications is critical for the decision-making process, and the development of a sustainable environment. The current study focused on comprehending the spatio-temporal dynamics of LULC changes and their repercussions on variations in Land Surface Temperature (LST) profiles over a two-decade period in the Dodballapur Taluk, Bengaluru Rural District of Karnataka State in India. The study also investigates the relationship between LULC changes, Land Surface Temperature (LST), and spectral indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). Landsat 7 ETM + satellite images acquired in the years, 2001, 2010, and 2019 were used in the study. The Supervised Maximum Likelihood Classifier (MLC) technique was adopted to classify images and assess spatio-temporal LULC changes and analyse transformations of several LULC classes viz. barren land (BL), built-up land (BUL), crop land (CL), pasture (P), uncultivated land (UCL), shrub land (SL), and water bodies (WB). The accuracy assessment ensured the veracity of the image classification. The results showed that a) built-up, crop, and shrub land have been increased by 75.54%, 38.18%, and 52.57% respectively, b) barren, pasture, and uncultivated land have been increased by 22.94%, 4.89%, and 50.62% respectively; and c) the area covered with surface water bodies has been significantly reduced by 635.58%. The analysis of LST changes revealed a 2.09 °C increase in mean LST with a variance of 2.75 °C, showing considerable fluctuations in cold and hot weather conditions. The regression study of scatterplots for LST-NDVI revealed triangular space (R2 = −0.234), LST-NDBI revealed right-angled triangle space (R2 = 0.567), and LST-NDWI also exhibited right-angled triangular space (R2 = −0.533). Intriguingly, the relationship between NDVI and NDBI exhibited a crescent shaped scatterplot with R2 value of −0.448. The findings of the study can assist policymakers and decision-makers in developing and implementing policies for sustainable rural development and water resource management.

Prediction and monitoring of LULC shift using cellular automata-artificial neural network in Jumar watershed of Ranchi District, Jharkhand.

Jumar watershed of Ranchi district is agrarian in nature. The unplanned and exponentially growing urban sprawl has become one of the probable threats in achieving sustainable development goals (SDG-15). The purpose of this research study is to monitor the urban sprawl in Jumar watershed within three decades i.e. from the year 1990 to 2021. Land use land cover (LULC) change has been monitored using satellite data from LANDSAT (4, 5 and 8). Various indices are calculated like normalised difference vegetation index (NDVI), normalised difference built-up index (NDBI), normalised difference water index (NDWI) and built-up index (BUI) to monitor LULC change in the area. For prediction of urban sprawl, cellular automata and artificial neural network (CA-ANN) with GIS application technique is used. The model is validated by using Kappa coefficient. The prediction results showed increase in built-up area by 8.23 sq. km in the next decade. The built-up and barren land together increase up to 42.85 sq. km by 2030 and 34.61 sq. km in 2021. The NDVI for 3-decade period showed significant decrease in the healthy vegetation and increase in sparse vegetation. The NDBI showed a slight increase in urban area but massive increase in uncultivated and barren land. NDWI showed a decrease in area of the surface water. The LULC studies showed a major shift from healthy vegetation to agriculture and then to barren land. To assess the impact of urbanisation on water quality, water samples are taken seasonally from J1to J11 sampling locations and are analysed as per APHA procedure. The sites are classified as urban, semi urban and rural area as per their location. The water quality index (WQI) varied between 42.14 to 61.42 during pre-monsoon, 62.20 to 68.7995 during monsoon and 43.48 to 60.12 during post-monsoon. The quality of water is found poor in all seasons at all sampling sites. The water is found highly turbid and alkaline throughout the year. Overall, it can be concluded that the water needs to be pre-treated for drinking purposes throughout the year.

Estimating the Population of a Middle Eastern City Based on a Normalized Difference Built-Up Index and Urban Morphology

The aim of this study is to introduce a method for estimating the population of a Middle Eastern city, the city of Hillah, in the absence of a census which was last conducted in 1997. The method incorporates the normalized difference built-up index and land use-land cover information to identify residential areas for four years 1987, 1997 (the last census), 2008, and 2018 using Landsat imagery. Dasymetric mapping is employed to rezone residential pixels to the city's administrative districts or mahallahs. Mahallah populations for each year are estimated using local and historical urban morphology characteristics combined with standardized housing and household sizes. The result of this study is a range of estimates for mahallah populations, but a reasonable estimate of the city of Hillah's population is developed. The use of historical information, urban design, and environmental data with remote sensing methods in a Middle Eastern context provides an added tool in the arsenal of population estimation techniques.

Impacts of the spatial configuration of built-up areas and urban vegetation on land surface temperature using spectral and local spatial autocorrelation indices

ABSTRACT Understanding how the spatial configuration of land cover patterns of built-up areas and urban vegetation affect urban surface temperatures is crucial for improving the sustainability of cities as well as optimizing urban design and landscape planning. Because of their capability to detect distinct surface thermal features, satellite data have proved useful in exploring the impacts of spatial configuration of land cover on land surface temperature (LST). In this study, we examine how the spatial configuration of built-up and urban vegetation affects the LST in the Harare metropolitan city, Zimbabwe. In order to achieve this objective, we combined the LST, local spatial statistics of Getis-Ord Gi* and local Moran’s I statistic, Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI) derived from multi-date Landsat satellite data (1994, 2001 and 2017). The results of local Moran’s I statistic showed moderate and negative correlations between LST and Landsat derived NDVI. Overall, these results of local Moran’s I statistic demonstrate that clustered vegetation tend to lower LST, providing thermal comfort conditions. In contrast, clustered spatial arrangements of NDBI based on the Getis-Ord Gi* elevate LST, implying that continued clustered built-up expansion has the potential to increase urban surface temperatures.

Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan.

This research aims to assess the urban growth and impact on land surface temperature (LST) of Lahore, the second biggest city in Pakistan. In this research, various geographical information system (GIS) and remote sensing (RS) techniques (maximum likelihood classification (MLC)) LST, and different normalized satellite indices have been implemented to analyse the spatio-temporal trends of Lahore city; by using Landsat for 1990, 2004, and 2018. The development of integrated use of RS and GIS and combined cellular automata-Markov models has provided new means of assessing changes in land use and land cover and has enabled the projection of trajectories into the future. Results indicate that the built-up area and bare area increased from 15,541 (27%) to 23,024 km2 (40%) and 5756 km2 (10%) to 13,814 km2 (24%). Meanwhile, water area and vegetation were decreased from 2302 km2 (4%) to 1151 km2 (2%) and 33,961 km2 (59%) to 19,571 km2 (34%) respectively. Under this urbanization, the LST of the city was also got affected. In 1990, the mean LST of most of the area was between 14 and 28 ℃, which rose to 22-28 ℃ in 2004 and 34 to 36 ℃ in 2018. Because of the shift of vegetation and built-up land, the surface reflectance and roughness of each land use land cover (LULC) class are different. The analysis established a direct correlation between Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI) with LST and an indirect correlation among Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Built-up Index (BI) with LST. The results are important for the planning and development department since they may be used to guarantee the sustainable utilization of land resources for future urbanization expansion projects.

Investigating the Relationship between Land Use/Land Cover Change and Land Surface Temperature Using Google Earth Engine; Case Study: Melbourne, Australia

The rapid alteration to land cover, combined with climate change, results in the variation of the land surface temperature (LST). This LST variation is mainly affected by the spatiotemporal changes of land cover classes, their geospatial characteristics, and spectral indices. Melbourne has been the subject of previous studies of land cover change but often over short time periods without considering the trade-offs between land use/land cover (LULC) and mean daytimes summer season LST over a more extended period. To fill this gap, this research aims to investigate the role of LULC change on mean annual daytime LST in the hot summers of 2001 and 2018 in Melbourne. To achieve the study’s aim, LULC and LST maps were generated based on the cost-effective cloud-based geospatial analysis platform Google Earth Engine (GEE). Furthermore, the geospatial and geo-statistical relationship between LULC, LST, and spectral indices of LULC, including the Normalised Difference Built-up Index (NDBI) and the Normalised Difference Vegetation Index (NDVI), were identified. The findings showed that the mean daytime LST increased by 5.1 °C from 2001 to 2018. The minimum and maximum LST values were recorded for the vegetation and the built-up area classes for 2001 and 2018. Additionally, the mean daytime LST for vegetation and the built-up area classes increased by 5.5 °C and 5.9 °C from 2001 to 2018, respectively. Furthermore, both elevation and NDVI were revealed as the most influencing factors in the LULC classification process. Considering the R2 values between LULC and LST and their NDVI values in 2018, grass (0.48), forest (0.27), and shrubs (0.21) had the highest values. In addition, urban areas (0.64), bare land (0.62), and cropland (0.61) LULC types showed the highest R2 values between LST regarding their NDBI values. This study highlights why urban planners and policymakers must understand the impacts of LULC change on LST. Appropriate policy measures can be proposed based on the findings to control Melbourne’s future development.

Assessment of Intra-Urban Heat Island in a Densely Populated City Using Remote Sensing: A Case Study for Manila City

Changes in the environment occur in cities due to increased urbanization and population growth. Sustainable Development Goal (SDG) 11 is intrinsically linked to the environment, one facet of which is the need for universal access to secure, inclusive, and accessible green and public places. As urban heat islands (UHI) have the potential to negatively influence cities and their residents, existing resources and data must be used to identify and quantify these effects. To address this, we present the use of satellite-derived (2013–2022) and meteorological data (2014–2020) to assess intra-urban heat islands in Manila City, Philippines. The assessment includes (a) understanding the temporal variability of air temperature measurements and outdoor thermal comfort based on meteorological data, (b) comparative and correlative analysis between common Land-Use Land Cover indicators (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI)) and Land Surface Temperature (LST), (c) spatial and temporal analysis of LST using spatial statistics techniques, and (d) generation of an intra-urban heat island (IUHI) map with a recommended class of action using a suitability analysis model. Finally, the areas that need intervention are compared to the affected population, and suggestions to enhance the thermal characteristics of the city and mitigate the effects of UHI are established.

Gray squirrels consume anthropogenic food waste most often during winter.

The online version contains supplementary material available at 10.1007/s42991-022-00326-3.

Mapping build-up area density using normalized difference built-up index (ndbi) and urban index (ui) wetland in the city banjarmasin

Density and undeveloped land can be identified using digital transformation through remote sensing data. This study aims to map the distribution of building densities with the Normalized Difference Built-Up Index (NDBI) and Urban Index (UI), analyze the comparison of building densities of the NDBI and UI methods, and analyze the relationship between the transformation of NDBI and UI on building densities in Banjarmasin City. The data used to obtain building density image Landsat 8 Oli Tirs. The method used to separate the built area and non-building area using digital classification. The area was developed from the results of the multispectral classification filtered with NDBI and UI transformation for the classification of the building density level. The results of this study indicate that building density has a positive relationship with the transformation of NDBI and UI because the high building density in the transformation of NDBI and UI has a high value. Non-built area and constructed land shows an NDBI accuracy of 91.4505% and 92.5359% accuracy of UI, there is a difference of 1.0854% greater accuracy UI. Overall accuracy exceeding 80% indicates very high accuracy for building density mapping.

Simulating land surface temperature using biophysical variables related to building density and height in Dar Es Salaam, Tanzania

Land use and land cover dynamics are pivotal to communicating land surface temperature (LST) scenarios. This study characterises the influence of biophysical variables on LSTs in the Dar es Salaam Metropolitan City (DMC). Landsat images were analysed using geographically weighted regression (GWR) and ordinary least square (OLS) models to determine biophysical variables (soil adjusted vegetation index, normalized difference built-up index, and normalised difference bareness index) and LST relationships. The GWR analysis resultsrevealed that LST had a weak to strong negative correlation with the soil adjusted vegetation index, a moderate positive correlation with normalized difference built-up index, and a low positive correlation with the normalised difference bareness index. GWR predicted LST better than OLS, with coefficient of determination -R2 values of 55%, 80%, and 62% for 1995, 2009, and 2017, respectively. In addition, higher model residuals values were observed in high building density compared to low building density areas. This study provides a broad understanding of the biophysical variables’ impact on LST in DMC and provides reference for site-specific urban land-use planning and designing strategies for LST mitigation.

An STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data.

A change in an urban built-up area can reflect the process of urbanization and the development of a city. At present, multi-source remote sensing data extraction of built-up areas based on the human settlement index (HSI) has achieved relatively good results but the existence of noise, such as light spillover in the night-time light remote sensing data, seriously affects the accuracy of the HSI. In this paper, a high-precision human settlement index (STP-HSI) method based on spatio-temporal remote sensing and point-of-interest (POI) data is presented to improve the classification accuracy in urban built-up areas extractions. First, to correct light spillover, a new night-time light index the fuzzy c-means spatio-temporal point (FCM-STP) based on fuzzy c-means clustering is proposed, which integrates the spatio-temporal characteristics and uses night light video imaging data from Luojia-1 and POI data. Then, based on the FCM-STP index, the HSI is updated to the STP-HSI index. Finally, a random forest algorithm is used to extract the urban built-up areas, and the random forest feature database is composed of normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and STP-HSI index features and texture features. To develop and evaluate the accuracy of the new method for built-up areas extraction with multi-source data, three test sites located in the cities of China (Guangzhou, Xiamen and Nanjing) are used. The experimental results show that our method outperforms the single-source multi-spectral (Landsat 8) data extraction results, the overall accuracy is improved by up to 7.52%, and the kappa coefficient is improved by up to 14%. Compared with the HSI index, the maximum contribution rates of the STP-HSI increased by 25.74%. These experimental results show that the method in this paper is feasible.

Identifying Urban Heat Effect through Satellite Image Analysis: Focusing on Narayanganj Upazila, Bangladesh

The rapid growth of population and land use cover change are closely connected. Narayanganj Sadar Upazila is the first Growing City in Bangladesh. Land use and land cover change are very first. Global warming, increased greenhouse gas emissions, and other environmental issues have become critical issues to address in recent times. Because of the Unplanned Expansion of urban areas, the LULC pattern is changing, and this kind of adverse (LST increasing, Heat Island Growth) impact is increasing. In this area, the amount of vegetation is decreasing day by day. The objectives of this study are to identify land use land cover (LULC) dynamics for the year 2001 to 2021, identify Urban Heat Islands from the value of land surface temperature (LST) and identify hotspots based on normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI) and land surface temperature (LST) doing weighted overlay among them. To determine the land cover pattern, and change at Narayanganj Upazila in the years 2001, 2011, and 2021, the land cover type was divided into four categories (build-up, vegetation, water bodies, and barren soil) in geographic information system (GIS) and Earth Resources Data Analysis System (ERDAS) Imagine 2014. The overall accuracy of LULC in 2001, 2011, and 2021 was 90.08, 91.34, and 92.02, respectively. And the value of the kappa coefficient for the years 2001, 2011, and 2021 was 0.91, 0.89, and 0.90, respectively. This study demonstrates an increase of 14.22% in built-up area and destruction of 15.5% of vegetation, 3.26% of barren soil, and 1.05% of the waterbody in the previous 21 years in Narayanganj Upazila. This study will help the administration, agricultural directorates, Pourashava office, and city corporation authority to take necessary measures to mitigate the adverse impacts of land cover change. They can make new rules and regulations on the construction of buildings, restrictions on filling water bodies and measures to conserve vegetation.

Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China

Urban ecological environment is the basis of citizens’ survival and development. A rapid and objective urban ecological environment assessment (UEEA) plays an important role in the urban sustainable development and environment protection. This study established an improved urban ecological comfort index (UECIIMP), which is based on our previous UECI and fully composed of four remote sensing indicators: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), land surface temperature (LST), and aerosol optical depth (AOD), representing the greenness, dryness, heat, and atmospheric turbidity, respectively. Combining the entropy method and random forest (RF) algorithm, the weights of four indicators were calculated. To improve the accuracy of UECIIMP, the gap-filled quarterly mean results of each indicator with 30m resolution were obtained using the harmonic analysis of time series (HANTS) method and spatial-temporal information fusion based on non-local means filter (STNLFFM). UECIIMP was applied to the Hefei-Nanjing-Hangzhou Region to explore its spatiotemporal changes and response characteristics. Results show that the weights of UECIIMP fluctuate slightly (within 10%) before and after sensitivity analysis, with good stability and reliability. UECIIMP in Hangzhou > Hefei ≈ Nanjing, spring ≈ autumn > summer ≫ winter. From 2009 to 2019, UECIIMP has improved in all 33 districts of the Hefei-Nanjing-Hangzhou Region. The significant improvement of UECIIMP in 2014–2019 is 4.3 times than that in 2009–2014. The correlation between UECIIMP and economic index indicates that economic development has a positive impact on the urban ecological environment. The significant degradation of UECIIMP in the urban expansion area demonstrates a negative impact on the local environment from urban expansion.

ASSESSMENT OF THE IMPACT OF URBANIZATION GROWTH ON THE CLIMATE OF BAGHDAD PROVINCE USING REMOTE SENSING TECHNIQUES.

This study was aimed to recognize the spatial and temporal features of urban development and its impact on the climate of Baghdad City. The analytical method of the study relies on changes in Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Land Surface Temperature(LST); GIS technology was used to measure these statistics. Landsat (5,8) and Sentinel2A imagery were used to detect the change of urbanization growth, vegetation change, and land surface temperature during the study period from 2001 to 2020, whereas used the supervised classification technique for determination for LULC variations. The results showed significant changes among the LULC classes in the studied periods, as most of the LULC changes were caused by human activities. The most prominent changes in LULC were the urban expansion on agricultural land, continuously in all years, which led to the decline of vegetation resulting from land degradation. The Building area increased from around 863 km2 in 2001 to 1469 km2 in 2020 concentrated mainly in the center, the northeastern and southeastern part of the city. Moderate plants decrease from around 88.5 km2 in 2001 to 59 km2 in 2020 and dense plants decreased from 0.0252 km2 in 2001 to 0.0135 km2 in 2020. This has led to negative effects on the climate where temperature rates increased from (26-47) degrees Celsius in 2001 to (32-56) degrees Celsius in the last year of the study, the highest temperatures were recorded in urban growth areas and areas without vegetation.