Normalized Difference Built-up Index Research Articles

Spatial Variability of Urban Heat Islands in Cairo City, Egypt using Time Series of Landsat Satellite Images

Global warming has obtained more attention because of increasing global mean surface temperature since the late 19th century. Urbanization is considered an important contributor to global warming in big cities. Cairo is one of the heavily populated cities in the world, which has rapid urbanization that resulted in remarkable temperature variation compared to the rural surrounding areas. This phenomenon is known as Urban Heat (UHIs) that influence both local and regional climate, environment, and socio-economic development. In this research time series of satellite images were used to map the spatial variability of Land Surface Temperature (LST) and Heat Islands in Cairo city. Historical and contemporary record of Landsat images nearly 16 dates from 1990 to 2014 were used to retrieve LST and land cover categories. Results showed that LST was highly influenced by Land use and land cover changes; heat island effect was dominant in urbanized areas, bare/desert land and industrial zones (e.g. Shobra El-khema). The industrial zones, in particular, were possibly high due to the aluminum roof material plus the thermal energy from production activities and high emissions of air pollutants. The pattern of spatial distribution of heat islands has significantly changed from bare/desert land and built up areas (as warmer) to cultivated land and water bodies (as colder). To analyze the relationship between Heat Islands and land-cover changes a quantitative correlation between LST and Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Build-up Index (NDBI) was employed. It was found that the correlation between LST and NDVI and MNDWI was negative with r 2 of 0.8 and 0.57 respectively; however, the correlation with NDBI was strongly positive with r 2 of 0.81. It is anticipated that the outcomes of this research will contribute to GEOSS for global integration and correlation.

Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

City/regional authorities are responsible for designing and structuring the urban morphology based on the desired land use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as the real gross domestic product (GDP). In Canada, the gross national income (GNI) mainly relies on the mining and manufacturing industries. In order to estimate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the relationship between the built-up area and the reported real GDP of seven major cities in Canada. The objectives of the study are: (1) to investigate the use of regression analysis between the built-up area derived from Landsat images and the industrial area extracted from Geographic Information System (GIS) data; and (2) to study the relationship between the built-up area and the socio-economic data (i.e., real GDP, total population and total employment). The experimental data include 42 multi-temporal Landsat TM images and 42 land use GIS vector datasets obtained from year 2005 to 2010 during the summer season (June, July and August) for seven major cities in Canada. The socio-economic data, including the real GDP, the total population and the total employment, are obtained from the Metropolitan Housing Outlook during the same period. Both the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used to determine the built-up areas. Those high built-up values within the industrial areas were acquired for further analysis. Finally, regression analysis was conducted between the real GDP, the total population, and the total employment with respect to the built-up area. Preliminary findings showed a strong linear relationship (R2= 0.82) between the percentage of built-up area and industrial area within the corresponding city. In addition, a strong linear relationship (R2= 0.8) was found between the built-up area and socio-economic data. Therefore, the study justifies the use of remote sensing and GIS data to model the socio-economic data (i.e., real GDP, total population and total employment). The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting a specific real GDP with respect to industrial areas.

Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data

In the present research work an integrated use of Landsat thermal data sets of year 2000 and 2013, field data and meteorological observation were used to assess the temporal changes in rising trends of urban heat island (UHI) in Noida city, India. The temperature estimation was performed on the basis of grid level analysis and compared with the land cover pattern for validation of temperature with reference to urban land use/land cover. During 2000, the total built up area was 28.17km2 which it further increased to 88.35km2 during 2013. Over the period of thirteen years from 2000 to 2013 it was observed that the built up area has increased by 26.94% of the total area (203km2). In order to study the relationship between UHI and land cover, statistical analysis was performed between temperature and Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Albedo and Emissivity. The correlation between NDVI, Emissivity and temperature was negative but NDBI, Albedo and temperature showed a positive correlation. Results showed that the change in temperature was mainly due to increase in impervious areas. The results of this study will be useful to the urban planners and environmentalists in formulating local policies.

Study of vulnerable district characteristics on urban heat island according to land use using normalized index - Focused on Daegu Metropolitan City residential district

Using normalized index composition, this study estimated biophysical environment characteristics of urban heat island (UHI) hotspots and identified vulnerable residential district. For this study, we used a regression tree model to examine nonlinear relationships between Land Surface Temperature (LST) and three satellite-based indicators within the UHI clusters: normalized difference vegetation index (NDVI), normalized difference build-up index (NDBI), and normalized difference bareness index (NDBaI). The results show that both NDBI and NDBal were strongly correlated with the variations of LST whereas NDVI had a weaker correlation with LST in Daegu. In addition, when we analyzed the UHI hotspots by land use type, we found that industrial district showed the highest risk type and central commercial district (CBD) had the second high risk. The most vulnerable residential district was ‘Nowon dong, Buk gu’. This site mostly comprised of quasi-residential area type, and we found the high building density ratio is the main reason of urban heat island.

Spectral index utility for summer urban heating analysis

The land surface temperature (LST) retrieval in an urban environment by thermal remote sensing is a widespread scientific topic and several studies have been made to point out the correspondence between spectral indices and LST pattern. This work evaluates the potential of the spectral indices introduced in the literature, more than 30, by assessing their correlation with the summer LST in the heterogeneous urban area of Rome, Italy, considering two different triplets of images acquired during 2009 and 2011 by the Landsat Thematic Mapper (TM). The spectral indices have been divided into vegetation and built up-soil indices employing the reflective TM bands, and then ranked on the basis of their linear and monotonic relationship with the LST. Vegetation indices have a strong negative correlation with LST: vegetation area (VA), nonlinear index, modified soil adjusted vegetation index exhibit a greater Pearson and Spearman correlation coefficient with LST. The more useful spectral indices for built up and soil analysis, exhibiting a greater positive correlation with LST, are the impervious surface area (ISA), the bare soil index, the index-based built-up index, and the normalized difference built-up index. Interesting indications of the impact on the spectral index performance of specific land-cover classes embedded in an urban environment, such as the bare soil and the water classes were pointed out; for example, the reduction of the ISA and VA capability to display the full dynamic range of the LST pattern.

Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing

Vegetation coverage has a significant influence on the land surface temperature (LST) distribution. In the field of urban heat islands (UHIs) based on remote sensing, vegetation indexes are widely used to estimate the LST–vegetation relationship. This paper devises two objectives. The first analyzes the correlation between vegetation parameters/indicators and LST. The subsequent computes the occurrence of vegetation parameter, which defines the distribution of LST (for quantitative analysis of urban heat island) in Kalaburagi (formerly Gulbarga) City. However, estimation work has been done on the valuation of the relationship between different vegetation indexes and LST. In addition to the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is attempted to explore the impacts of the green land to the build-up land on the urban heat island by calculating the evaluation index of sub-urban areas. The results indicated that the effect of urban heat island in Kalaburagi city is mainly located in the sub-urban areas or Rurban area especially in the South-Eastern and North-Western part of the city. The correlation between LST and NDVI, indicates the negative correlation. The NDVI suggests that the green land can weaken the effect on urban heat island, while we perceived the positive correlation between LST and NDBI, which infers that the built-up land can strengthen the effect of urban heat island in our case study. Although satellite data (e.g., Landsat TM thermal bands data) has been applied to test the distribution of urban heat islands, but the method still needs to be refined with in situ measurements of LST in future studies.

Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices

Since the mid-1990s, various spectral indices have been proposed for the rapid and accurate classification of built-up lands from satellite imageries. However, a comprehensive comparison between these indices as applied to various satellite imageries is still lacking. Hence, this study examines and compares the performance of six spectral indices in the classification and change detection of built-up lands from Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) imageries. It includes three mid-infrared (MIR)-based indices, i.e. the urban index (UI), the normalized difference built-up index (NDBI), and the index-based built-up index (IBI), two proposed visible (Vis)-based indices, i.e. the VrNIR-BI and VgNIR-BI or the visible red/green-based built-up indices, and one thermal infrared (TIR)-based index, i.e. the normalized difference impervious surface index (NDISI). In addition, a water index, i.e. the modified normalized difference water index (MNDWI), was also derived. Otsu's method was used to separate water from the non-water areas on the MNDWI map. Subsequently, a water mask was produced and used to mask all the built-up index maps, leaving only the non-water areas. Using the same thresholding method, the non-water areas of all the built-up index maps were classified into built-up and non-built-up classes. The classification accuracy was assessed using 5000 reference points for each image. The results show that the VrNIR-BI, with an overall accuracy of 92.50% (Landsat-7) and 92.28% (Landsat-8), and the VgNIR-BI, with 92.78% (Landsat-7) and 92.14% (Landsat-8) overall accuracy, were more robust and superior. They were more accurate than the other indices by up to 8% for the Landsat-7 ETM+ image and 10% for the Landsat-8 image OLI/TIRS image. The qualitative assessment results also supported these quantitative findings. The results also show indications that the detected spatiotemporal urban LUC changes (i.e. built-up expansions) based on the VrNIR-BI and VgNIR-BI were also the most accurate. These indices, i.e. the proposed Vis-based indices, have better potential in separating built-up lands from dry vegetation, which has been an important challenge in the application of spectral indices for classifying built-up lands from satellite imageries.

Analysis of the Urban Heat Island Effect in Shijiazhuang, China Using Satellite and Airborne Data

The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in the summer days to analyze the thermal spatial patterns and intensity of surface UHI (SUHI). Surface thermal characteristics were further examined by relating LST to percentage of imperious surface area (ISA%) and four remote sensing indices (RSIs), the Normalized Difference Vegetation Index (NDVI), Universal Pattern Decomposition method (VIUPD), Normalized Difference Built-up Index (NDBI) and Biophysical Composition Index (BCI). On the other hand, fives scenes of airborne TASI (Thermal Airborne Spectrographic Imager sensor) images were utilized to describe more detailed urban thermal characteristics of the downtown of Shijiazhuang city. Our results show that an obvious surface heat island effect existed in the study area during summer days, with a SUHI intensity of 2–4 °C. The analyses reveal that ISA% can provide an additional metric for the study of SUHI, yet its association with LST is not straightforward and this should a focus in future work. It was also found that two physically based indices, VIUPD and BCI, have the potential to account for the variation in urban LST. The results concerning on TASI indicate that diversity of impervious surfaces (rooftops, concrete, and mixed asphalt) contribute most to the SUHI, among all of the land cover features. Moreover, the effect of impervious surfaces on LST is complicated, and the composition and arrangement of land cover features may play an important role in determining the magnitude and intensity of SUHI. Overall, the analysis of urban thermal signatures at two spatial scales complement each other and the use of airborne imagery data with higher spatial resolution is helpful in revealing more details for understanding urban thermal environments.

Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping

Increasing the accuracy of thematic maps generated using satellite imagery is a crucial task in remote sensing. In this study, a product-level fusion (PLF) approach based on integration of different land-type maps generated using various satellite-derived indices including normalized difference water index (NDWI), normalized difference built-up index (NDBI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) is proposed to improve the accuracy of land cover mapping. The suitability of the proposed approach for land cover mapping is evaluated in comparison with two high-performance image classification techniques including support vector machine (SVM) and artificial neural network (ANN). The results show that the overall accuracy and kappa values of about 95.95 % and 0.95, 94.91 % and 0.94, and 85.32 % and 0.82 are achieved for the PLF, SVM, and ANN approaches, respectively. The results indicate superiority of the PLF approach than SVM and ANN techniques for land cover classification of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery, especially for the extraction of forest, rice, and citrus classes. However, SVM technique also provided reliable result for land cover mapping.

Urban Growth Monitoring and Analysis of Environmental Impacts on Bankura-I and II Block using Landsat Data

This study put forward a technique to estimate and monitor the urban built-up land features from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery taking into account of two blocks in Bankura District, West Bengal as examples. In this study three indices have been selected, viz., Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), and Normalized Differences Vegetation Index (NDVI) to represent three major urban land-use classes, built-up land, open water body and vegetation respectively. Consequently, the seven bands of an original Landsat image were reduced into three thematic-oriented bands derived from above indices. The three new bands were then combined to compose a new image. This considerably reduced data correlation and redundancy between original multispectral bands, and thus significantly avoided the spectral confusion of the above three land-use classes. As a result, the spectral signatures of the three urban land-use classes are more distinguishable in the new composite image than in the original seven-band image as the spectral clusters of the classes are well separated. Through logic calculation on the new image, the urban built-up lands were finally extracted with overall accuracy ranging from 91.5 to 98.5 percent. Therefore, the technique is effective and reliable. In addition, the advantages of over NDVI and over NDWI in the urban study are also discussed in this paper.

Remote Sensing and GIS Applications for Assessment of Urban Sprawl in Karachi, Pakistan

Over the past 30 years, Land development and consumption have been out of control and expanding out of order, especially to marginal areas of some metropolises in Pakistan. In this research, the dynamic change of urban sprawl and its spatial and temporal characteristics was analyzed for Karachi division, Pakistan. To achieve the goal different image processing techniques were applied i.e., (i) Supervised, (ii) Unsupervised image classification and (iii) Normalized Difference Built-Up Index on Landsat imageries for the years 1991, 2000 and 2013. Unsupervised classification technique was proved to be more effective with an overall agreement of 81%. The study concluded that from 1991-2000, the urbanization in Karachi was increased from 486-729.2 km 2 , whereas from 2000-2013 the urbanization was almost double i.e., 1582.5 km 2 . Apart from the derived results, this study also proved the potentials of remote sensing data and effectiveness of demonstrated/proposed techniques in urban geographic studies.

A Modified and Enhanced Normalized built-up Index using Multispectral and Thermal Bands

Objective: Mapping impervious surfaces using moderate resolution satellite images is a useful technique for supporting different fields including monitoring and evaluation, planning, statistical analysis and reporting and policy development. Due to the negative impact of impervious surfaces over urban climate, the development of new techniques using spectral indices is a key parameter to extract built up areas with high accuracy. Method: In this study we propose a new concept capitalizing the existing relationships between urban heat island effects and built up areas to produce a modified index, benefiting from the high reflectivity of thermal bands, the mean-infrared and near-infrared band. In addition, spatial enhancement of multispectral data is also used to improve the accuracy of the proposed index, our approach uses spectral reduction of dimensions in order to produce thematic indices (as an input data) instead of continuous images. Finding: Final result showed a high accuracy of built up areas compared to other spectral indices like normalized difference built-up index - NDBI and index -based built-up index - IBI, more than 10% compared to classic NDBI and 6% compared to modified index (IBI).

Remote Sensing Based Vegetation Indices Analysis to Improve Water Resources Management in Urban Environment

Normalized Difference Vegetation Index (NDVI) is estimated from Landsat 8 sensor acquired in December 2014 to drive the different water-related indices like as NDVI and its derivatives. Different vegetation indices (VIs) have been extracted exclusively. The temperature-vegetation index (TVX) space was constructed to investigate the influence of land changes over LST. In this paper, the effect of urban heat island is analyzed using the Landsat TM data in 2009 as a case study in Gulbarga City. The standard algorithms were applied to retrieve the land surface temperature (LST). The spatial pattern of LST in the study area is retrieved to characterize their local effects on urban heat island. In addition, the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is analyzed to explore the impacts of the green land and the build-up land on the urban heat island. The changes of Land surface temperature were related to many factors, including changes in land use, land surface parameters, seasonal variation, climatic condition and economic development, etc. The result showed that the land use change was an important driver for LST increase in the TVX space migrated from the dense-vegetation low temperature condition to the sparse vegetation-high temperature condition. The results showed that the interconnections between different VIs vary. Findings from the current work conducted are anticipated to contribute decisively toward an inclusive VIs assessment of its overall verification. It can be utilized for a multitude of water management applications since it is a valuable indicator of the surface moisture and evapotranspiration: the assessment of agricultural and urban water consumption; the negotiation and monitoring of water and alternative water management practices.

Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata (India).

Over the past five decades, the fragile wetland ecosystem surrounding the city of Kolkata has witnessed extensive changes in the name of urban development. In this study, we elaborate relationships among biophysical parameters and land surface temperature (LST) in Kolkata city and nearby surrounding areas where rapid urbanization has occurred. LST and associated surface physical characteristics were assessed using Landsat images acquired for the years 1989, 2006, and 2010. The satellite data was used to study the spatiotemporal urban footprint and correlation among normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI) and LST. Land use land cover (LULC) maps prepared using supervised classification had overall accuracy of 90, 88, and 86% and kappa coefficient of 0.8726, 0.8455, and 0.8212 for 1989, 2006, and 2010, respectively. The spatial expansion as a consequence of increasing urban population is 108.94km(2) over past two decades. The urban built-up in and around the city extends up to 88.71km(2) in 1989, 144.64km(2) in 2006, and 197.65km(2) in 2010. These changes have attributed in elevating surface temperature in the study region. Analysis of biophysical parameters shows LST and NDBI having a positive correlation, LST and NDVI having negative correlation, while NDBI and NDWI having a perfectly negative correlation. Satellite estimated temperatures of the surface show a warming trend evident from increasing mean surface temperature values from 27.36°C in 1989 to 30.025°C in 2006 and 33.023°C in 2010. The magnitude and extent of the estimates of LST are consistent with the urbanization pattern throughout the city and adjoining areas.

Spatial analysis of high-resolution urban thermal patterns in Vojvodina, Serbia

Main objective of this study was to establish a relationship between land cover and land surface temperature (LST) in urban and rural areas. The research was conducted using Landsat, WorldView-2 (WV-2) and Digital Mapping Camera. Normalised difference vegetation index and normalised difference built-up index were used for establishing the relation between built-up area, vegetation cover and LST for spatial resolution of 30 m. Impervious surface and vegetation area generated from Digital Mapping Camera from Intergraph and WV-2 were used to establish the relation between built-up area, vegetation cover and LST for spatial resolutions of 0.1, 0.5 and 30 m. Linear regression models were used to determine the relationship between LST and indicators. Main contribution of this research is to establish the use of combining remote sensing sensors with different spectral and spatial resolution for two typical settlements in Vojvodina. Correlation coefficients between LST and LST indicators ranged from 0.602 to 0.768.

A new index for mapping built-up and bare land areas from Landsat-8 OLI data

Remote sensing is a useful technology for monitoring the spatial distribution and expansion of built-up and bare land areas. One effective approach, known as the normalized difference built-up index (NDBI), has been promoted for identifying built-up areas based on Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) data. The successful launch of the Landsat-8 satellite has made possible the continued acquisition of high-quality data that meet requirements for observing land use and land cover (LULC), and whether or not NDBI approach can be used with Landsat-8 data needs further verification. In this study, we researched the reflectance spectral characteristics of different land cover types in different bands of Landsat-8 operational land imager (OLI) data and found that the trend of some built-up areas in the OLI data from the near-infrared band to the shortwave infrared band is different from that in the TM/ETM+ data. This different trend made the conventional NDBI approach unsuitable for Landsat-8 OLI data. We propose a new index, called the build-up and bare land areas index, for transforming Landsat-8 OLI data to map built-up and bare land areas automatically. This new index was used to detect the built-up and bare land areas in Zhengzhou (Henan, China). The accuracy assessment indicates that our index has much higher accuracy (90.8%) than the conventional NDBI approach does (57.4%).

The Use of Remote Sensing Technique to Predict Gross Domestic Product (GDP): An Analysis of Built-Up Index and GDP in Nine Major Cities in Canada

Abstract. City/regional authorities are responsible to design and structure the urban morphology based on the desired land-use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as Gross Domestic Product (GDP). In Canada, the gross national income mainly relies on mining and manufacturing industries. In order to facilitate new city development, this study aims to utilize remote sensing and GIS techniques to assess the relationship between the industrial area and the reported GDP in nine major cities in Canada. Free archive multi-temporal Landsat TM images and land use vector data were obtained for year 2005 and 2010 during the summer season, where the socio-economic data, such as GDP, population, and total employment are obtained from Metropolitan Housing Outlook for the same duration. The Landsat TM images were first atmospherically corrected and the built-up values were computed using the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) from the Landsat images. The high built-up values within the industrial areas were acquired for further analysis. Finally, a correlation analysis was conducted between the GDP, Population, and Total Employment with respect to the built-up areas. Preliminary findings show that the R2 between the percentage of built-up areas and industrial area within the corresponding city is 0.82. In addition, the R2 between the built-up areas and GDP ranges from 0.73 to 0.78. Consistent findings are observed in the similar correlation between the built-up areas and population, as well as the built-up areas and the employment, where the R2 is within 0.72 to 0.73. With the correlation found, we believe that results can be used as a generic indication for the federal/municipals authorities, which are aiming or target for a specific GDP with respect to the planned industrial area.

New normalized difference index for built-up land enhancement using airborne visible infrared imaging spectrometer imagery

In remote sensing imagery, various normalized difference indices are widely used for land cover mapping. Each index has its targeting cover type with a specialized data source. However, these indices are generally only studied in multispectral data. Hyperspectral images have become increasingly attractive due to their richness of spectrum information. A new index, i.e., Normalized Difference Built-up Index for Hyperspectral data (NDBIh), oriented to built-up land enhancement in hyperspectral remote sensing data is proposed. Spectral response curves of different cover types and possible calculation equations for NDBIh are obtained first. The equation having the best ability to differentiate built-up land from other areas is referred to as NDBIh. To evaluate the ability of our NDBIh, two other built-up indices, the conventional Normalized Difference Built-Up Index (NDBI) and the Index-based Built-Up Index (IBI), are compared with NDBIh both qualitatively and quantitatively. Experiments on airborne visible infrared imaging spectrometer data indicate that the NDBIh outperforms NDBI and IBI in identifying built-up land.

Extraction of impervious features from spectral indices using artificial neural network

An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %.

Built-up area extraction using Landsat 8 OLI imagery

The normalized difference built-up index (NDBI) has been useful for mapping urban built-up areas using Landsat Thematic Mapper (TM) data. The applicability of this index to the newer Landsat-8 Operational Land Imager (OLI) data was examined during this study, and a new method for built-up area extraction has been proposed. OLI imagery of urban areas of Lahore, Pakistan, was used to extract built-up areas through a modified NDBI approach and the proposed built-up area extraction method (BAEM). Instead of using individual bands, BAEM employed principal component analysis images of the highly correlated bands pertinent to NDBI computation. Through integration of temperature data, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), BAEM was able to improve the overall accuracy of built-up area extraction by 11.84% compared to the modified NDBI approach. Rather than employing the binary NDBI, NDVI and MNDWI images, continuous images of these indices were used, and the final output was recoded by determining the threshold value through a double-window flexible pace search (DFPS) method. Results indicate that BAEM was more accurate at mapping urban built-up areas when applied to OLI imagery as compared to the modified NDBI approach; omission and commission errors were reduced by 75.96% and 33.36%, respectively. Moreover, the use of DFPS improved robustness of the proposed approach by enhancing user control over the segmentation of the output.