Index-based Built-up Index Research Articles

A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel imageries

This study aims to build a computer model to detect built-up land in the identified tsunami hazard zone based on Sentinel 2A imagery using the normalized built up area index (NBI), urban index (UI), normalize difference build-up index (NDBI), a modified built-up index (MBI), index-based builtup index (IBI) algorithms, optimized with machine learning Random Forest (RF) and extreme gradient boosting (XGboost) algorithms and the spatial patterns are predicted using the ordinary kriging (OK) method. Testing of the accuracy of the classification and optimization results was performed using the Kohen Kappa and overall accuracy functions. The results of the study show that a built-up land consisting of open land and water, settlements, industry areas, and agriculture and tourism areas can be identified using the parameters of built-up indices. The accuracy testings that were performed using overall accuracy and Kohen Kappa methods show that classification and prediction are highly accurate using XGboost machine learning, namely 91%. This study produces a novelty of finding, namely a computer model to detect and predict the spatial distribution of built-up land in 4 scales, i.e., very low, low, high, and very high based on NBI, UI, NDBI, MBI, IBI data extracted from Sentinel 2A imagery.

THE EFFECTS OF LAND COVER CHANGES ON LAND SURFACE TEMPERATURES

Abstract. The aims of this study are to detect the land-cover maps and land surface temperatures using Landsat time series and analyse the relation between the land-cover and land surface temperatures (LST) and their changes in time. For these purposes initially, land-cover maps were generated rapidly using land cover indices and automatic thresholding. The land-cover indices used in this study are Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Index-Based Built-Up Index (IBI), Modified Bare-Soil Index (MBI), Plastic-Mulched Landcover Index (PMLI), Plastic Greenhouse Index (PGI) and Normalized Burn Ratio Thermal (NBRT) Index. Then, using the thermal bands of Landsat satellites, LST maps were created. Finally, the land-cover and LST changes were examined. The Kumluca district of Antalya, which includes extensive greenhouse areas as well as urban, vegetation, bareland, water, was selected as study area. Between the years 2004 and 2021, within the study area the greenhouse areas increased significantly, the urban area expanded and some areas exposed to fire, especially the fire in 2016. Therefore, the images within this time period were used. The overall accuracies for land-cover maps were computed as 76%, 79%, 79%, 89% and 86% for the years 2004, 2009, 2013, 2017 and 2021, respectively. The results obtained from the study reveal that while greenhouse and urban areas were increased, the vegetation areas were decreased significantly within this time period. In addition, generally increases were observed for LST values of all land-cover classes and the highest LST values were detected for the burned, bareland, urban and greenhouse areas.

Correlation Analysis Between Urban Heat Island Intensity and Temperature Criticality Value in Denpasar City

The compactness of buildings in Denpasar resulted in the formation of urban heat islands (UHI), which can be evaluated through the Urban Thermal Field Variance Index (UTFVI) and Environment Criticality Index (ECI). ECI is the ratio of land surface temperature to the Normalized Difference Vegetation Index (NDVI). It can be transformed into Temperature Criticality Value (TCV) using air temperature and Index-based Built-up Index (IBI). This study aims to identify the UHI intensity, the impact of land cover changes, and its association with the TCV. The study employs Landsat 8 imagery and field measurements data, and the findings demonstrate that the study area was mainly composed of built-up areas that had grown from 2015 to 2021. TFVI indicates the most intense UHI (>0.02) in the built-up areas, whereas the mean value of NDVI suggested a reduction in vegetation density. The density of built-up areas (IBI) had increased, while vegetation had decreased. TCV in 2015 ranged from -11.15°C.IBI to 6.42°C.IBI; 2018 between -9.96°C.IBI to 6.79°C.IBI; and 2021 between -10.84°C.IBI to 6.87°C.IBI showed that the environment had become increasingly critical from 2015 to 2021. A transect analysis revealed that more vigorous UHI intensity, denser buildings, and a more critical environment were present in urban centers compared to the suburbs. The correlation coefficient (r) between TCV and UTFVI was relatively robust (0.75–0.82), indicating that the growth of UHI intensity was associated with a more critical environment. TCV has the strongest (r=0.99) and strong correlation (r>0,80) with Built-up Index but inverse correlation with NDVI. Therefore, limiting the expansion of built-up areas and increasing vegetation could help to control the environment's criticality.

Machine learning based combinatorial analysis for land use and land cover assessment in Kyiv City (Ukraine)

The main goal of this study is to evaluate different models for further improvement of the accuracy of land use and land cover (LULC) classification on Google Earth Engine using random forest (RF) and support vector machine (SVM) learning algorithms. Ten indices, namely normalized difference vegetation index, normalized difference soil index, index-based built-up index, biophysical composition index, built-up area extraction index (BAEI), urban index, new built-up index, band ratio for built-up area, bare soil index, and normalized built up area index, were used as input parameters for the machine learning algorithms to improve classification accuracy. The combinatorial analysis of the Sentinel-2 bands and the aforementioned indices allowed us to create four combinations based on surface reflectance characteristics. The study includes data from April 2020 to September 2021 and April 2022 to June 2022. The multitemporal Sentinel-2 data with spatial resolutions of 10 m were used to determine the LULC classification. The major land use classes such as water, forest, grassland, urban areas, and other lands were obtained. Generally, the RF algorithm showed higher accuracy than the SVM. The overall accuracy for RF and SVM was 86.56% and 84.48%, respectively, and the mean Kappa was 0.82 and 0.79, respectively. Using the combination 2 with the RF algorithm and combination 4 with the SVM algorithm for LULC classification was more accurate. The additional use of vegetation indices allowed to increase in the accuracy of LULC classification and separate classes with similar reflection spectra.

Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.

Exploring Built-Up Indices and Machine Learning Regressions for Multi-Temporal Building Density Monitoring Based on Landsat Series.

Uncontrolled built-up area expansion and building densification could bring some detrimental problems in social and economic aspects such as social inequality, urban heat islands, and disturbance in urban environments. This study monitored multi-decadal building density (1991–2019) in the Yogyakarta urban area, Indonesia consisting of two stages, i.e., built-up area classification and building density estimation, therefore, both built-up expansion and the densification were quantified. Multi sensors of the Landsat series including Landsat 5, 7, and 8 were utilized with some prior corrections to harmonize the reflectance values. A support vector machine (SVM) classifier was used to distinguish between built-up and non built-up areas. Regression algorithms, i.e., linear regression (LR), support vector regression (SVR), and random forest regression (RFR) were explored to obtain the best model to estimate building density using the inputs of built-up indices: Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), and NIR-based built-up index based on the red (VrNIR-BI) and green band (VgNIR-BI). The best models were revealed by SVR with the inputs of UI-NDBI-IBI and LR with a single predictor of UI, for Landsat 8 (2013–2019) and Landsat 5/7 (1991–2009), respectively, using separate training samples. We found that machine learning regressions (SVM and RF) could perform best when the sample size is abundant, whereas LR could predict better for a limited sample size if a linear positive relationship was identified between the predictor(s) and building density. We conclude that expansion in the study area occurred first, followed by rapid building development in the subsequent years leading to an increase in building density.

Surface biophysical features fusion in remote sensing for improving land crop/cover classification accuracy

Preparing up-to-date land crop/cover maps is important to study in order to achieve food security. Therefore, the aim of this study was to evaluate the impact of surface biophysical features in the land crop/cover classification accuracy and introduce a new fusion-based method with more accurate results for land crop/cover classification. For this purpose, multi-temporal images from Sentinel 1 and 2, and an actual land crop map prepared by Agriculture and Agri-Food Canada (AAFC) in 2019 were used for 3 test sites in Ontario, Canada. Firstly, surface biophysical features maps were prepared based on spectral indices from Sentinel 2 including Normalized Difference Vegetation Index (NDVI), Index-based Built-up Index (IBI), Wetness, Albedo, and Brightness and co-polarization (VV) and cross-polarization (VH) from Sentinel 1 for different dates. Then, different scenarios were generated; these included single surface biophysical features as well as a combination of several surface biophysical features. Secondly, land crop/cover maps were prepared for each scenario based on the Random Forest (RF). In the third step, based on the voting strategy, classification maps from different scenarios were combined. Finally, the accuracy of the land crop/cover maps obtained from each of the scenario was evaluated. The results showed that the average overall accuracy of land crop/cover maps obtained from individual scenario (one feature) including NDVI, IBI, Wetness, Albedo, Brightness, VV and VH were 66%, 68%, 63%, 60%, 57%, 62% and 58%, respectively, which by the surface biophysical features fusion, the overall accuracy of land crop/cover maps increased to 83%. Also, by combining the classification results obtained from different scenarios based on voting strategy, the overall accuracy increased to 89%. The results of this study indicate that the feature level-based fusion of surface biophysical features and decision level based fusion of land crop/cover maps obtained from various scenarios increases the accuracy of land crop/cover classification.

A multicity analysis of daytime Surface Urban Heat Islands in India and the US.

• In the US, urban land surface temperature (LST) was higher compared to rural LST. • Despite urbanization, urban LST in India was lower compared to rural LST. • Non-photosynthetic vegetation (NPV) and barren land types influence the results. • Built-up indices used here do not differentiate sparse vegetation and built-up surfaces. • New methods are needed for quantifying urbanization's impact on LST in India. Daytime surface UHI (SUHI) and the factors influencing it across highly populated cities in India and the United States (US) were evaluated. Urban-rural mean land surface temperature (LST) differences (ΔT S ) were calculated and correlated with vegetation and built-up surface densities, quantified using the Normalized Difference Vegetation Index (NDVI) and Index Based Built-Up Index (IBI) respectively. The results from the US show a conventional SUHI phenomenon, and LST decrease with increasing vegetation and decreasing built-up surfaces. However, in India, despite existing urban densities, urban areas are cooler than the rural surroundings. The average LST was higher and NDVI was lower compared to the US, and NDVI and LST correlations were weak. Furthermore, in India, the average IBI values calculated for built-up areas, cropland, and other sparse vegetation land covers were approximately equal. The prevalence of non-photosynthetic vegetation (NPV) and barren soil in India are impacting spectral indices and the correlations. Differences in ΔT S and spectral indices trends between India and US highlight the shortcomings of the indices used in appropriately identifying Indian LULC types and establish the need for localized SUHI research methods that can specifically analyze the impact of urbanization on SUHI in India.

A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks

Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments.

Land Subsidence in Beijing’s Sub-Administrative Center and Its Relationship with Urban Expansion Inferred from Sentinel-1/2 Observations

Beijing’s Sub-Administrative Center (BSAC) is located in the South-eastern Beijing Plain, which exhibits severe subsidence. The rapid urban expansion in recent years has aggravated land subsidence and threatens the safe operation of Beijing. First, this study applied the persistent scatterer-interferometric synthetic aperture radar (PS-InSAR) to extract BSAC subsidence time series data. Second, combined with the index-based built-up index (IBI), expansion intensity index (EII), and expansion gradient index (EGI), the spatiotemporal characteristics of urban expansion were retrieved from optical data. Finally, we examined the urban expansion effects on land subsidence at the regional and single-building scales. The results showed that the maximum subsidence velocity in the BSAC reached 121 mm/year from 2015 to 2018, and the urban construction land area increased by 22%. At the regional scale, there existed a positive correlation between land subsidence and EGI or EII. This indicated that urban expansion had a certain impact on land subsidence. Therefore, we further explored the relationship between construction and land subsidence at the single-building scale. The engineering construction effects on land subsidence were divided into three periods, namely, rapid settlement, rebound, and stable periods. Although construction had a significant influence on land subsidence, it did not cause subsidence mutation.

The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

Modeling the impact of the COVID-19 lockdowns on urban surface ecological status: A case study of Milan and Wuhan cities

The COVID-19 pandemic has caused unprecedent negative impacts on our society, however, evidences show a reduction of anthropogenic pressures on the environment. Due to the high importance of environmental conditions on human life quality, it is crucial to model the impact of COVID-19 lockdown on environmental conditions. Consequently, the objective of this study was to model the impact of COVID-19 lockdown on the urban surface ecological status (USES). To this end, the Landsat-8 images of Milan for three pre-lockdown dates (Feb 13, 2018 (MD1), April 18, 2018 (MD2) and Feb 3, 2020 (MD3)) and one date over the lockdown (April 14, 2020 (MD4)), and Wuhan for three pre-lockdown dates (Dec 17, 2017 (WD1), March 23, 2018 (WD2) and Dec 7, 2019 (WD3)) and one lockdown date (Feb 9, 2020 (WD4)) were used. First, pressure-state-response (PSR) framework parameters including index-based built-up index (IBI), vegetation cover (VC), vegetation health index (VHI), land surface temperature (LST) and Wetness were calculated. Second, by combining the PSR framework parameters based on comprehensive ecological evaluation index (CEEI), the USES were modeled on different dates. Thirdly, the USES during the COVID-19 lockdown was compared with the USES for pre-lockdown. The mean (standard deviation) of CEEI for Milan on MD1, MD2, MD3 and MD4 were 0.52 (0.12), 0.60 (0.19), 0.57 (0.13) and 0.45 (0.16), respectively. Also, these values for Wuhan on WD1, WD2, WD3 and WD4 were 0.63 (0.14), 0.67 (0.15), 0.60 (0.13) and 0.57 (0.13), respectively. Due to the lockdowns, the mean CEEI of built-up, bare soil and green spaces for Milan and Wuhan decreased by [0.18, 0.02, 0.08], [0.13, 0.06, 0.05], respectively. During the lockdown period, the USES improved substantially due to the reduction of anthropogenic activities in the urban environment.

Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

Prediction of Urban Heat Island Effect over Jinan City Using the Markov-Cellular Automata Model Combined with Urban Biophysical Descriptors

The feasibility of integrating urban biophysical descriptors into land surface temperature (LST) prediction in a rapidly urbanizing region, Jinan, China, was explored. We combined several models, including LST and an urban biophysical descriptor generation algorithm, through regression analysis and cellular automata-Markov modeling. First, the relationship between the indices, including the soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI), index-based built-up index (IBI), and the LST was established; then, the future LST was simulated and predicted. The results showed that LST has a negative correlation with SAVI (R2 = 0.905, P < 0.01) and MNDWI (R2 = 0.911, P < 0.01) while having a high positive correlation with IBI (R2 = 0.801, P < 0.01). Additionally, correlations between the simulated values and retrieval of different land use/cover types show good agreement, with a mean R from 0.6 to 0.9, such that this method can be used to simulate future urban heat island effects. If the development trend remains unchanged, the urban heat island effect of Jinan will continue to strengthen by 2030 because of the increase in impervious ground and reduction in green vegetation. These results can provide guidance for Jinan’s urban construction and planning, which promote sustainable urbanization.

Mapping Impervious Surface Distribution and Dynamics in an Arid/Semiarid Area-A Case Study in Ordos, China

The impervious surface area (ISA) has become an important parameter in the areas such as surface runoff, climate change, and socioeconomic analysis. However, using remote sensing data to accurately map ISA is still difficult due to its complexity in the arid and semi-arid areas with large amounts of bare soil, sparse vegetation, and a small ISA. In this study, we have selected Ordos city as study area and examined the spatial pattern and dynamics in the years of 1990, 1995, 2000, 2005, 2010, 2015, and 2019 by using multiple data sources with a combination method. The results indicate that the ISAs in 2010, 2015, and 2019 were extracted with overall accuracies of 90.6%, 89.2%, and 91.8% with kappa coefficients of 0.79, 0.76, and 0.82, respectively. The ISA extraction results of the Urban Index (UI), Normalized Difference Built-Up Index (NDBI), Index-based Built-Up Index (IBI), and Impervious Built-up Index from optical sensors (IBUIopt) were used for comparison. Meanwhile, a correlation analysis between socioeconomic factors and the ISA of the typical resource-based city of Ordos was conducted. The spatial pattern and dynamics of the ISA data in Ordos city are of great significance to the study of socioeconomic and environmental changes and provide a reference to examine the effect of ISAs in other resource-based cities.

Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method

In the Beijing Plain, land subsidence is one of the most prominent geological problems, which is affected by multiple factors. Groundwater exploitation, thickness of the Quaternary deposit and urban development and construction are important factors affecting the formation and development of land subsidence. Here we choose groundwater level change, thickness of the Quaternary deposit and index-based built-up index (IBI) as influencing factors, and we use the influence factors to predict the subsidence amount in the Beijing Plain. The Sentinel-1 radar images and the persistent scatters interferometry (PSI) were adopted to obtain the information of land subsidence. By using Google Earth Engine platform and Landsat8 optical images, IBI was extracted. Groundwater level change and thickness of the Quaternary deposit were obtained from hydrogeological data. Machine learning algorithms Linear Regression and Principal Component Analysis (PCA) were used to investigate the relationship between land subsidence and influencing factors. Based on the results obtained by Linear Regression and PCA, a suitable machine learning algorithm was selected to predict the subsidence amount in the Beijing Plain in 2018 through influencing factors. In this study, we found that the maximum subsidence rate in the Beijing Plain had reached 115.96 mm/y from 2016 to 2018. The land subsidence was serious in eastern Chaoyang and northwestern Tongzhou. In addition, the area where thickness of the Quaternary deposit reached 150–200 m was prone to more serious land subsidence in the Beijing Plain. In groundwater exploitation, the second confined aquifer had the greatest impact on land subsidence. Through Linear Regression and PCA, we found that the relationship between land subsidence and influencing factors was nonlinear. XGBoost was feasible to predict subsidence amount. The prediction accuracy of XGBoost on the subsidence amount reached 0.9431, and the mean square error was controlled at 15.97. By using XGBoost to predict the subsidence amount, our research provides a new idea for land subsidence prediction.

Assessing urban heat islands and thermal comfort in Noida City using geospatial technology

Incessant conurbation in Noida has exacerbated its microclimate, causing rise in the thermal anomalies within the city. Between 2011 and 2019, there is an immoderate increase in the hotspots and overall decrease in the thermal comfort of the city, evaluated using the Urban Thermal Field Variance Index (UTFVI). The study further explores the association between land surface temperature (LST) and spectral indices - Normalized Difference Vegetation Index (NDVI) and Index-based built-up Index (IBI) for both the years. The results highlight that there is an increase of 6.42 °C in eight years. The NDVI values have increased from 0.06 to 0.14 during these years, suggesting increase in the total urban greenness in the city. The observed increase in IBI values explains the rising LST of the city. LST exhibits negative correlation with NDVI ascertaining the cooling impact of green spaces and positive correlation with IBI highlighting the heating effect of built-up on urban microclimate. Measuring and monitoring the intensity of these UHI in space and time is important to analyse the thermal environment at the city scale. Identification of such anomalies in the urban thermal environment can provide the best input for equipping urban areas to be more resilient and sustainable.

Performance testing of selected spectral indices in automated extraction of impervious built-up surface features using Resourcesat LISS-III image

The increasing urban density and spatial expansion of urbanized areas result in changes from the natural landscape to impervious surface features. Remote sensing–based spectral indices provide an efficient method in the automated identification of land use and cover classes. However, a common challenge is the accurate extraction of built-up features from satellite images. The original Normalized Difference Built-up Index (NDBI) has been modified by several researchers in the anticipation of improvement of the built-up area classification. The indices adopted in the study are Index-based Built-up Index (IBI), Built Up Index (BUI), NDBI, Modified Built-up Index (MBI), and the newly developed Impervious Built-up Index (IBUI). These indices work on automated kernel-based probabilistic thresholding algorithm to group the index values into built-up and non-built-up areas. This study investigates the performance of the above mentioned five spectral indices on Resourcesat LISS III imageries over the Kolkata-Howrah urban agglomeration, India. The indices were compared and found based on the overall accuracy that the novel IBUI provided the maximum accuracy (94%) in extracting impervious built-up areas. Based on the overall accuracy and kappa statistic the IBUI was chosen for estimating spatio-temporal urban growth of the study area from 2003 to 2018 based on the same LISS-III sensor data. It was calculated that the urban built-up area has increased from 144 to 177 km2.

Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology

The surface anthropogenic heat island (SAHI) phenomenon is one of the most important environmental concerns in urban areas. SAHIs play a significant role in quality of urban life. Hence, the quantification of SAHI intensity (SAHII) is of great importance. The impervious surface cover (ISC) can well reflect the degree and extent of anthropogenic activities in an area. Various actual ISC (AISC) datasets are available for different regions of the world. However, the temporal and spatial coverage of available and accessible AISC datasets is limited. This study was aimed to evaluate the spectral indices efficiency to daytime SAHII (DSAHII) quantification. Consequently, 14 cities including Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome in Europe and Dallas, Seattle, Minneapolis, Los Angeles, Chicago, and Phoenix in the USA, were selected. A set of 91 Landsat 8 images, the Landsat provisional surface temperature product, the High Resolution Imperviousness Layer (HRIL), and the National Land Cover Database (NLCD) imperviousness data were used as the AISC datasets for the selected cities. The spectral index-based ISC (SIISC) and land surface temperature (LST) were modelled from the Landsat 8 images. Then, a linear least square model (LLSM) obtained from the LST-AISC feature space was applied to quantify the actual SAHII of the selected cities. Finally, the SAHII of the selected cities was modelled based on the LST-SIISC feature space-derived LLSM. Finally, the values of the coefficient of determination (R2) and the root mean square error (RMSE) between the actual and modelled SAHII were calculated to evaluate and compare the performance of different spectral indices in SAHII quantification. The performance of the spectral indices used in the built LST-SIISC feature space for SAHII quantification differed. The index-based built-up index (IBI) (R2 = 0.98, RMSE = 0.34 °C) and albedo (0.76, 1.39 °C) performed the best and worst performance in SAHII quantification, respectively. Our results indicate that the LST-SIISC feature space is very useful and effective for SAHII quantification. The advantages of the spectral indices used in SAHII quantification include (1) synchronization with the recording of thermal data, (2) simplicity, (3) low cost, (4) accessibility under different spatial and temporal conditions, and (5) scalability.

Analysis of Built-up Land Spatial Patterns Using Multitemporal Satellite Imagery in Pekalongan City

Regional growth is characterized by an increase in built-up land. An increase in built-up land can cause changes in land use such as vacant land turned into built-up land. One of the cities in Central Java that experienced an increase in built-up land was in the City of Pekalongan. Based on Pekalongan City Regulation Number 30 Year 2011, the National Spatial Planning stipulates that Pekalongan City is the Regional Activity Center. This causes the Pekalongan City to have the potential to increase the amount of built-up land. An increase in uncontrolled built-up land can cause negative impacts such as reduced water catchment areas so that the disruption of water resources conditions. Therefore, it is necessary to monitor the increase of built-up land in Pekalongan City and see its development spatial patterns. One of method for monitoring a city's built-up land uses the remote sensing method. This study uses an Index-based Built-up Index (IBI) algorithm. Based on the results of this study, it can be concluded that the city of Pekalongan experienced an increase in built-up land between 2013 and 2019. The largest increase in built-up land is in the range of 2017 to 2019 with an area of increase of 359.088 ha so that it can be obtained the speed of increase of built-up land by 170.544 ha/year. The spatial pattern of built-up land increased in 2017 to 2019 heading south because South Pekalongan Regency has a toll road that connects the main road with the toll road.