Enhanced Built-up And Bareness Index Research Articles

Land Surface Temperature and Thermal Comfort in the Cities of Punjab, India: Assessment Based on Remote Sensing Data

Objective: The present study aims to investigate thermal comfort in two cities in Punjab, India based on land surface temperature (LST), urban hot spots (UHS) and urban thermal field variance index (UTFVI). Method: Landsat 8 OLI/TIRS data are used to derive land surface temperature (LST), normalized difference vegetation index (NDVI) and enhanced built-up and bareness index (EBBI) for the year 2019. UTFVI reflects urban thermal conditions and demarcates comfort and discomfort zones in the cities. Findings: The results revealed that the mean LST (μ) of Ludhiana and Amritsar cities is 32.80 °C and 30.70 °C, respectively. LST shows a strong negative correlation with NDVI (-0.710 for Amritsar and -0.754 for Ludhiana) and a positive correlation with EBBI (0.531 for Amritsar and 0.541 for Ludhiana). About 57 and 52 per cent of geographical areas in Ludhiana and Amritsar city respectively are experiencing bad to worst ecological conditions. Novelty: (i) The study derived LST-based thermal comfort for the summer month in Amritsar and Ludhiana cities of Punjab which provide important information to urban planners and policymakers to design sustainable urban development policies to mitigate heat-related issues. (ii) Such information can be used to take steps to improve the situation in smart cities like Amritsar and Ludhiana. Keywords: Land surface temperature (LST), urban thermal field variance index (UTFVI), NDVI, Landsat 8, Punjab

Spatio-temporal dynamics of land use/cover and land surface temperature in Prayagraj city, India

The spatial monitoring of variation in land cover and its mapping support the local authorities and decision-makers in strengthening comprehensive and sustainable plans for future urbanization and sustainability of the city. This research work was carried out to evaluate the change in the land cover pattern and its impact on the land surface temperature of Prayagraj city from 2000 to 2020. This study also examined the correlation between land surface temperature (LST) and other improved urban biophysical indices, such as soil-adjusted vegetation index (SAVI) and enhanced built-up and bareness index (EBBI), to determine the significant factors affecting the LST of the urban area. The result indicates that the built-up area and cropland have been escalated by 10 and 6.5%, whereas tree cover, water bodies and fallow land were reduced by 12, 1.38 and 1.27%, respectively. LST was increased by 1.47°C between 2000 and 2020 due to an increase in the built-up area. LST showed a positive correlation with EBBI but a negative one with vegetation indices (normalized difference vegetation index, enhanced vegetation index and SAVI) and water indices (MNDWI). The results indicate that the land use pattern governs LST and any change in land use will ultimately change the LST.

Assessment of the sustainability of landcovers due to artisanal mining in Jos area, Nigeria.

Environmental sustainability ensures that minerals are responsibly exploited to meet the present needs without depriving the future generations the ability to meet their needs. Unfortunately, environmental sustainability and artisanal mining seem unattainable in recent years with aggressive mining operations. It is on this premise that geospatial techniques with proven role in environmental studies were employed to assess the environmental sustainability due to artisanal mining in Jos area, Plateau State, Nigeria. Land surface temperature (LST) and seven geospatial indices used for land use land cover (LULC) estimation were generated. The mean LST values showed a steady increasing pattern from 23.98 to 25.88°C and 29.46°C in 1984, 2002, and 2020 respectively, as a result of exposed outcrops occasioned by mining and the expansion of mining communities. The mean value of the Normalized Difference Vegetation Index (NDVI) depicts a considerable increase from 0.179 in 1984 to 0.458 in 2002 and a slight decline to 0.438 in 2020. This is congruent with the mean Optimized Soil-Adjusted Vegetation Index (OSAVI) values, thus revealed that the Jos area is not densely vegetated implying that the course to revegetate the region has not been achieved to the tune of woodland populated vegetation. The resulting maps from Modified Normalized Difference Water Index (MNDWI) revealed a consistent decline in the mean values - 0.349, - 0.391, and - 0.411 in 1984, 2002, and 2020 respectively. Mineral recovery, mineral processing, and seasonal variations could be some of the reasons waterbodies are one of the most stressed natural resources in the study area. The mean values of Enhanced Built-up and Bareness Index (EBBI) decreased from 0.282 in 1984 to 0.202 in 2002, but increased from 0.202 in 2002 to 0.230 in 2020, which corroborated with the findings of NDBI. It was deduced that built-up areas in the study area are very low. This may be attributed to urban migration and the migration of artisanal miners to new mining sites across the state or country. The values of bare land mapped using Dry Bare Soil Index (DBSI) corresponds with that of Normalized Difference Bareness Index (NDBaI) and showed that bare land has reduced on the Jos Plateau due to improved vegetation growth. This study provided essential input and referential information for proper decision making on environmental sustenance, environmental management, and mineral resource conservation.

Analysis of Decadal Land Use Changes and Its Impacts on Urban Heat Island (UHI) Using Remote Sensing-Based Approach: A Smart City Perspective

The land surface temperature (LST) pattern is regarded as one of the most important indicators of the environmental consequences of land use/land cover change. The possible contribution of land surface to the warming phenomenon is being investigated by scientists across the world. This research focuses on variations in surface temperature and urban heat islands (UHIs) over the course of two seasons, i.e., winter and summer. Using remotely sensed datasets and geospatial techniques, an attempt was made to analyze the spatiotemporal variation in urban heat islands (UHIs) and its association with LULC over Chandigarh from 2000 to 2020. The Enhanced Built-up and Bareness Index (EBBI), Dry Built-up Index (DBI), and Dry Bare-Soil Index (DBSI) were used to identify built-up areas in the city. The results revealed an increase of 10.08% in BA, whereas the vegetation decreased by 4.5% over the study period, which is in close agreement with the EBBI, DBI, and DBSI assessments. From 2000 to 2020, the UHI intensities increased steadily in both the summer and winter seasons. Dense built-up areas such as the industrial unit of the city possessed the highest UHIindex (>0.7) values.

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.

Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques.

Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapidly growing urban area within a short time is a prime factor for administrators for better environmental assessment and sustainable development of urban areas. Traditional survey approaches, on the other hand, are unable to meet the demand for rapid urban land management development, and there is a pressing need to develop new methods to address the limitations of existing ones. This study compares various urban spectral indices to other existing approaches in order to determine which index provides a better representation of the impervious area in the urban watershed. Landsat 8 OLI (Operational Land Imager) satellite images acquired on 15 March 2014 and 31 March 2020 are used in the present study. Indices, namely Modified Built-up Index (MBUI), SwiRed Index (SwiRed), and Enhanced Built-up and Bareness Index (EBBI), were utilized to extract impervious areas. Thresholding of indices is done by comparing them with 1000 reference points taken from Google Earth imagery of the respective years. The accuracy of the urban indices is assessed by comparing the results with high-resolution Google Earth Satellite Images. The impervious area is extracted from spectral indices and other remote sensing techniques such as maximum likelihood classification and support vector machine classification techniques. The overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed for the 2014 dataset was found to be 95.1%, 90.8%, 83.9%, 78.9%, and 87.3%, respectively, and the overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed was found to be 96%, 89.2%, 89.1%, 85.5%, and 92.6%, respectively. Impervious areas in the heterogeneous urban environment can be monitored in a better way and within lesser time by using spectral indices generated using Landsat 8 OLI (Operational Land Imager) satellite data.

Tourism Effect on the Spatiotemporal Pattern of Land Surface Temperature (LST): Babolsar and Fereydonkenar Cities (Cases Study in Iran)

The purpose of this study is to investigate the effect of tourism on Land Surface Temperature (LST), an issue which has rarely been considered in the tourism development literature. In this research, remote sensing techniques have been used to analyze the changes in the LST and spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) and Enhanced Built-Up and Bareness Index (EBBI). The data used were based on Landsat Collection 1 Surface Reflectance (SR) images taken in June and August. They were analyzed over 32 years in the years 1987, 1993, 1999, 2009, 2014 and 2019. The study area included the cities of Babolsar and Fereydonkenar and their suburbs in Mazandaran Province in the north of Iran and south of the Caspian Sea. First the tourism zones were separated from other land use zones and then the changes in land use and LST in each of the zones were studied for each year based on the trend of 32-year change. The results of Pearson correlation in the whole area for each main land use zone showed that there was a significant inverse relationship between the LST and the NDVI and MNDWI indices. This relationship was direct and significant for the EBBI index. Moreover, the results of one-way analysis of variance (ANOVA) and Tukey test showed that the LST changes in the tourism zones during the study period were significantly different from the other zones, so that the tourism zones always experienced lower LST. The findings also showed that, in the tourism zones, the values of the NDVI and MNDWI indices showed an increasing trend compared to the urban zone. Therefore, increasing the values of these indices due to the development of green space and its regular irrigation in tourism zones has led to a significant decrease in the LST. The applied results of this research in the urban planning and tourism literature indicate that any model of physical development such as urban development does not necessarily lead to an increase in the LST, and this is entirely dependent on the physical design strategies.

A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

Land use/land cover (LU/LC) change dynamics using indices overlay method in Gautam Buddha Nagar District-India

This study is aimed to analyze the dynamics of land use/ land cover (LU/LC) change in a newly created special economic zone, Gautam Buddha Nagar district during 2003–2015. The Landsat satellite data has been used to map the LU/LC pattern of 2003 and 2015 of the study area, using indices overlay method. Consequently, the indices overlay have been created using three land-use indices, i.e. modified normalized difference water index (MNDWI), soil adjusted vegetation index (SAVI), and enhanced built-up and bareness index (EBBI), and then the maximum likelihood classifier (MLC) has been used for the LU/LC classification. The result illustrates that the built-up area (419.35%) and open land (388.36%) have increased during 2003–2015 while the cropland (− 34.38%), scrubland (− 73.25%), and water bodies (− 58.37%) have declined. Further, northern parts of the district have experienced maximum change in the LU/LC while the southern parts have experienced comparatively low change. The study also reveals that the increase in the built-up area occurred mostly at the cost of cropland and scrubland. The statistical analysis shows that the EBBI and SAVI have high relationships with LU/LC while the MNDWI has a comparatively low relationship. The study concludes that cropland and scrubland are the main LU/LC types that get transformed due into the built-up area the study area and the SAVI, MNDWI, and EBBI are the good indicators in the study of LU/LC classification and change analysis.

Land Cover Influences on LST in Two Proposed Smart Cities of India: Comparative Analysis Using Spectral Indices

Elucidating the impact of Land Surface Temperature (LST) is an important aspect of urban studies. The impact of urbanization on LST has been widely studied to monitor the Urban Heat Island (UHI) phenomenon. However, the sensitivity of various urban factors such as urban green spaces (UGS), built-up area, and water bodies to LST is not sufficiently resolved for many urban settlements. By using remote sensing techniques, this study aimed to quantify the influence of urban factors on LST in the two traditional cities (i) Panaji and (ii) Tumkur of India, proposed to be developed as smart cities. Landsat data were used to extract thematic and statistical information about urban factors using the Enhanced Built-up and Bareness Index (EBBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI). The multivariate regression model revealed that the value of adjusted R2 was 0.716 with a standard error of 1.97 for Tumkur city, while it was 0.698 with a standard error of 1.407 for Panaji city. The non-parametric correlation test brought out a strong negative correlation between MNDWI and LST with a value of 0.83 for Panaji, and between SAVI and LST with a value of 0.77 for Tumkur. The maximum percentage share of cooling surfaces are water bodies in Panaji with 35% coverage and green spaces in Tumkur with 25% coverage. Apparently, the UGS and water bodies can help in bringing down the LST, as well as facilitating healthy living conditions and aesthetic appeal. Therefore, the significance of ecosystem services (green spaces and water bodies) should be given priority in the decision-making process of sustainable and vibrant city development.

LAND SURFACE TEMPERATURE PROFILING AND ITS RELATIONSHIPS WITH LAND INDICES: A CASE STUDY ON LUCKNOW CITY

Abstract. Globally, 54.5% of the total population was living in urban settings in 2016 and a projection indicates that if the same trend goes, then this population will be 60% in 2030. Natural land has been converted to impervious space rapidly which is altering the climate change. The main focus of the present paper is the study of Land Surface Temperature (LST) dynamics and its relationship with Land Indices, viz., Normal Difference Vegetation Index (NDVI) which is found negative, Normal Difference Built-up Index (NDBI) which is found positive, Enhanced Built-up and Bareness Index (EBBI) which is found positive in Lucknow city on both time points of 1993 and 2019. This study also includes the effects of land indices on LST profiling in nine different parts and eight different directions to explore the spatial dynamics of city landscape. The NDVI is found higher in the southern side than any other parts of the city in 2019 because of high vegetation growth which resulted in reduction of LST by 4.42 °C to 5.76 °C as compared to parts of the city. The results of NDBI and EBBI exhibit high built-up growth in the landscape of the Lucknow city especially from city center to 13 kms (least growth in south-eastern side) from 1993 to 2019. The results indicate intensification of LST in the range of 0.26 °C to 2.24 °C between city centre and city periphery from 1993 to 2019. The findings of the present study will help urban planners and policy makers to adopt suitable measures for sustainable planning for Lucknow city landscape to reduce the adverse effects of LST.

Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000 – 2018 over a sub-tropical Indian City

Rapid urbanization and expansion of cities have a significant impact on the climate. It is essential to understand the impact of the land use land cover changes (LULC) on the regional climate variations over cities. This work mainly focuses on the changes in hotspot zones, UHIs during two different seasons - winter and summer. An attempt has been made to study the spatiotemporal variation in the urban heat islands (UHIs) and their relationship with LULC during 2000–2018, over Chandigarh (30° 43′ 59″ N, 76° 46′ 46″ E), a planned sub-tropical Indian city, using the remote sensing techniques. The indices such as Enhanced Built-up and Bareness Index (EBBI), Dry Built-up Index (DBI) and Dry Bare-Soil Index (DBSI) are employed to differentiate the built-up areas (BA) from the drylands (DL) effectively. The changes observed in the LULC reveals an increase of 10% in the BA at the expense of the vegetation during 2000–2018, which is in close agreement with the EBBI, DBI and DBSI assessments. A steady increase is observed in the UHI intensities for both summer and winter seasons during the study period. The UHIindex is noticed to be higher for the DL than that of the BA.

Geospatial Assessment of Flood Hazard Along the Tamil Nadu Coast

During November–December 2015, very heavy rainfall caused severe flood in Southern Tamil Nadu that resulted in severe damages with huge economic losses as per news agency Times of India. Remote sensing data from Sentinel-1 synthetic aperture radar (SAR) and Landsat-8. Operational land imager (OLI) images together with ancillary information such as rainfall and demographic data were used in the current study to assess the extent and impact of flooding. The SAR data are used to map the flood or inundation zones. Landsat-8 OLI is used to extract built-up area affected by the flood employing three methods: built-up area extraction method (BAEM), BAEM with Enhanced Built-up and Bareness Index (EBBI), and modified Normalized Difference Built-up Index (NDBI) approach. The classification accuracies obtained for these three approaches were 89, 83.5, and 78% for BAEM (using EBBI), BAEM, and NDBI, respectively. Aerial comparison of built-up area extracted using BAEM (using EBBI) shows the best accuracy with respect to the built-up area obtained from very high-resolution imagery. This extracted built-up area BAEM (using EBBI) method was used to estimate the extent of inundation covering the built-up area. Further the flooding risk at village level was assessed using the population density and flooding area. Built-up area extracted was also overlaid with flooding area to highlight actual built-up areas under risk due to flood.

Modification of urban built-up area extraction method based on the thematic index-derived bands

This research aims to develop new automatic and quicker spectral signature analysis tools to separate urban built-up area and determines urban area changes. Nowadays, researcher uses thematic index-derived bands for automatic urban data extraction. The extraction of urban built-up land can be automatically done with New Built-Up Index (NBUI) although it has a limitation on separating built-up land and water body. This study attempts to obtain the maximum accuracy of the extraction by merging several indices including Enhanced Built-Up and Bareness Index (EBBI), Soil Adjusted Vegetation Index (SAVI), Modified Normalized Difference Water Index (MNDWI), New Water Index (NWI) and Normalized Different Pond Index (NDPI) and compared its accuracy with NBUI. The results showed that merging EBBI, SAVI, MNDWI and NDPI produces the highest accuracy of 98.21% by addition and subtraction. The combined application of EBBI, SAVI and NWI also gives a good effect for extracting urban built-up areas and has 94.64% mapping accuracy.

Analysis for Mapping of Built-Up Area Using Remotely Sensed Indices – A Case Study of Rajarhat Block in Barasat Sadar Sub-Division in West Bengal (India)

Abstract Present study investigated mapping and monitoring urban land areas from Landsat8 satellite data using remotely sensed indices. The normalized difference built-up index (NDBI), Enhanced Built-Up and Bareness Index (EBBI), Index-based built-up index (IBI), urban index (UI), normalized difference bareness index (NDBaI) were used to extract the built-up area. The NDBI was more effective at discriminating built-up areas and at increasing accuracy (overall accuracy of 76.45 % and kappa accuracy of 57 %) of the built-up density percentage than other remotely sensed indices. Evidence on built-up area change geographically would permit urban planner and decision makers to comprehend and appraise urban growth pattern in regards to land cover dynamics.

The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis.

Rapid and irregular urbanization is an essential issue in terms of environmental assessment and management. The dynamics of landscape patterns should be observed and analyzed by local authorities for a sustainable environment. The aim of this study is to determine which spectral urban index, originated from old Landsat missions, represents impervious area better when new generation Earth observation satellite Landsat 8 data are used. Two datasets of Landsat 8, acquired on 2 September 2013 and 10 September 2016, were utilized to investigate the consistency of the results. In this study, commonly used urban indices namely normalized difference built-up index (NDBI), index-based built-up index (IBI), urban index (UI), and enhanced built-up and bareness index (EBBI) were utilized to extract impervious areas. The accuracy assessment of urban indices was conducted by comparing the results with pan-sharpened images, which were classified using maximum likelihood classification (MLC) method. The kappa values of MLC, IBI, NDBI, EBBI, and UI for 2013 dataset were 0.89, 0.79, 0.71, 0.59, and 0.49, respectively, and the kappa values of MLC, IBI, NDBI, EBBI, and UI for 2016 dataset were 0.90, 0.78, 0.70, 0.56, and 0.47, respectively. In addition, area information was extracted from indices and classified images, and the obtained outcomes showed that IBI presented better results than the other urban indices, and UI extracted impervious areas worse than the other indices in both selected cases. Consequently, Landsat 8 satellite data can be considered as an important source to extract and monitor impervious surfaces for the sustainable development of cities.

An Improved Optimal Segmentation Threshold Algorithm and Its Application in the Built-up Quick Mapping

For purpose of improving the accuracy of the built-up quick mapping, this paper proposed an improved optimal segmentation threshold algorithm, namely the improved double-window flexible pace search (IDFPS) approach, by redesigning the valuation criteria and the sampling method based on the double-window flexible pace search (DFPS) approach. Moreover, the Normalized Difference Built-up Index (NDBI), the Index-based Built-up Index (IBI), the Enhanced Built-up and Bareness Index (EBBI) and the Urban Index (UI) inversed from Landsat 5 TM images were used for quick mapping by the IDFPS approach and the DFPS approach in different geographical areas. Results from the experiments exemplified by Chongqing (a mountain city) and Chengdu (a plain city) showed that the IDFPS approach was comprehensively superior to the DFPS approach. The IDFPS approach had more than 4.30% higher overall accuracy and 0.12 higher Kappa coefficients than the DFPS approach when both were implemented simultaneously at both the above-mentioned study areas. Besides, a new discovery in this paper was found that the UI had a better performance with higher overall accuracy and Kappa coefficient, lower omission error and commission error than the NDBI, IBI and EBBI because of the strong relationship between the UI and the density of built-up land. This new method has an important reference value for built-up quick mapping and some other applied researches.

Urban Built-up Area Extraction and Change Detection of Adama Municipal Area using Time-Series Landsat Images

Urban built-up area information is required in various applications of land use planning and management. However, urban built-up area extraction from moderate spatial resolution Landsat time-series data is challenging because of significant intra-urban heterogeneity and spectral confusion between other landcover types. This paper proposes a technique to extract urban built-up area from time-series Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imageries and determines urban area changes between 1984 to 2015 of Adama Municipal Area of Ethiopia. The study selected three indices, the Enhanced Built-Up and Bareness Index (EBBI), Soil Adjusted Vegetation Index (SAVI) and Modified Normalized Difference Water Index (MNDWI), to represent three major urban land-use classes: built-up and barren/bare land, open waterbody, and vegetation, respectively. The built-up area was extracted by taking the difference between EBBI, SAVI and MNDWI to remove the vegetation and water noises, and the resulted index image was spectrally segmented to separate built-up area from the non-urban built-up lands. The derived index was used to map built-up area for 1984, 1995, 2005 and 2015 periods. The expansion of the built-up area has been revealed as a major change in the area when city area expanded substantially by 293% between 1984 to 2015 periods. The advantage of the method was to use almost the entire spectral range of Landsat imageries which cause less spectral confusion between land cover classes and hence resulted in higher accuracies compared to other indices. The method was effective and simple to implement, and can be used for built-up extraction in other areas.

RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments

Understanding land use land cover change (LULCC) is a prerequisite for urban planning and environment management. For LULCC studies in urban/suburban environments, the abundance and spatial distributions of bare soil are essential due to its biophysically different properties when compared to anthropologic materials. Soil, however, is very difficult to be identified using remote sensing technologies majorly due to its complex physical and chemical compositions, as well as the lack of a direct relationship between soil abundance and its spectral signatures. This paper presents an empirical approach to enhance soil information through developing the ratio normalized difference soil index (RNDSI). The first step involves the generation of random samples of three major land cover types, namely soil, impervious surface areas (ISAs), and vegetation. With spectral signatures of these samples, a normalized difference soil index (NDSI) was proposed using the combination of bands 7 and 2 of Landsat Thematic Mapper Image. Finally, a ratio index was developed to further highlight soil covers through dividing the NDSI by the first component of tasseled cap transformation (TC1). Qualitative (e.g., frequency histogram and box charts) and quantitative analyses (e.g., spectral discrimination index and classification accuracy) were adopted to examine the performance of the developed RNDSI. Analyses of results and comparative analyses with two other relevant indices, biophysical composition index (BCI) and enhanced built-up and bareness Index (EBBI), indicate that RNDSI is promising in separating soil from ISAs and vegetation, and can serve as an input to LULCC models.

Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area

Remotely sensed imagery is a type of data that is compatible with the monitoring and mapping of changes in built-up and bare land within urban areas as the impacts of population growth and urbanisation increase. The application of currently available remote sensing indices, however, has some limitations with respect to distinguishing built-up and bare land in urban areas. In this study, a new index for transforming remote sensing data for mapping built-up and bare land areas is proposed. The Enhanced Built-Up and Bareness Index (EBBI) is able to map built-up and bare land areas using a single calculation. The EBBI is the first built-up and bare land index that applies near infrared (NIR), short wave infrared (SWIR), and thermal infrared (TIR) channels simultaneously. This new index was applied to distinguish built-up and bare land areas in Denpasar (Bali, Indonesia) and had a high accuracy level when compared to existing indices. The EBBI was more effective at discriminating built-up and bare land areas and at increasing the accuracy of the built-up density percentage than five other indices.