Index-based Built-up Index Research Articles

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 %.

Determining the impact of urban components on land surface temperature of Istanbul by using remote sensing indices

For the past 60years, Istanbul has been experiencing an accelerated urban expansion. This urban expansion is leading to the replacement of natural surfaces by various artificial materials. This situation has a critical impact on the environment due to the alteration of heat energy balance. In this study, the effect upon the urban heat island (UHI) of Istanbul was analyzed using 2009 dated Landsat 5 Thematic Mapper (TM) data. An Index Based Built-up Index (IBI) was used to derive artificial surfaces in the study area. To produce the IBI index, Soil-Adjusted Vegetation Index, Normalized Difference Built-up Index, and Modified Normalized Difference Water Index were calculated. Land surface temperature (LST) distribution was derived from Landsat 5 TM images using a mono-window algorithm. In addition, 24 transects were selected, and different regression models were applied to explore the correlation between LST and IBI index. The results show that artificial surfaces have a positive exponential relationship with LST rather than a simple linear one. An ecological evaluation index of the region was calculated to explore the impact of both the vegetated land and the artificial surfaces on the UHI. Therefore, the quantitative relationship of urban components (artificial surfaces, vegetation, and water) and LST was examined using multivariate statistical analysis, and the correlation coefficient was obtained as 0.829. This suggested that the areas with a high rate of urbanization will accelerate the rise of LST and UHI in Istanbul.

Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China

Worldwide urbanization has accelerated expansion of urban built-up lands and resulted in substantial negative impacts on the global environments. Precisely measuring the urban sprawl is becoming an increasing need. Among the satellite-based earth observation systems, the Landsat and ASTER data are most suitable for mesoscale measurements of urban changes. Nevertheless, to date the difference in the capability of mapping built-up land between the two sensors is not clear. Therefore, this study compared the performances of the Landsat-7 ETM+ and ASTER sensors for built-up land mapping in the coastal areas of southeastern China. The comparison was implemented on three date-coincident image pairs and achieved by using three approaches, including per-band-based, index-based, and classification-based comparisons. The index used is the Index-based Built-up Index (IBI), while the classification algorithm employed is the Support Vector Machine (SVM). Results show that in the study areas, ETM+ and ASTER have an overall similar performance in built-up land mapping but also differ in several aspects. The IBI values determined from ASTER were consistently higher than from ETM+ by up to 45.54% according to percentage difference. The ASTER also estimates more built-up land area than ETM+ by 5.9–6.3% estimated with the IBI-based approach or 3.9–6.1% with the SVM classification. The differences in the spectral response functions and spatial resolution between relative spectral bands of the two sensors are attributed to these different performances.

A Study of Enhanced Index-based Built-up Index Based on Landsat TM Imagery

A new method for extraction of built-up land information both in suburban area and in urban district by using Landsat TM/ETM+ imagery is proposed in this paper.Firstly,to suppress the information of bare soil with the middle-infrared(TM7),near-infrared(TM4) and green band(TM2),it is necessary to build a secondary index,which is called normalized difference bareness and built-up index(NDBBI).At the same time,to enhance the information of bare soil from existing indices of bare soil index(BSI)and modified normalized difference water index(MNDWI),another secondary index is built,which is called enhanced bare soil index(EBSI).Finally,the indices of NDBBI,EBSI,SAVI and MNDWI are applied to constructing a new index for delineating built-up land features in satellite imagery,which is called enhanced index-based built-up index(EIBI).The new index(EIBI)can be employed to extract the built-up land information both in suburban area and in urban district.This approach has been successful in Fuzhou and Zhangzhou experimental regions.Built-up features can be extracted objectively and sufficiently with the accuracy above 90%.

Urban Expansion and Heat Island Dynamics in the Quanzhou Region, China

Urban spatial expansion in the Quanzhou region of southeastern China has been accelerated over the past 20 years. This has caused land cover changes and, thus, has significant impacts on the local ecosystem and climate. To study the urban expansion and heat island dynamics of the region over the past 20 years, multitemporal Landsat TM images of 1987, 1996, and 2006 were used. The estimation of the urban expansion was assisted by the index-based built-up index (IBI) through enhancing built-up land features in the images. The urban-heat-island (UHI) effect was assessed using the urban-heat-island ratio index (URI). Multitemporal analysis indicates that the great increase in urban area has resulted in the development of UHIs in the region. Regression statistics reveal that built-up land has a positive exponential relationship with land surface temperature (LST). Therefore, the increase in built-up land percentage can exponentially accelerate the rise of LST.

A new index for delineating built‐up land features in satellite imagery

A new index derived from existing indices – an index‐based built‐up index (IBI) – is proposed for the rapid extraction of built‐up land features in satellite imagery. The IBI is distinguished from conventional indices by its first‐time use of thematic index‐derived bands to construct an index rather than by using original image bands. The three thematic indices used in constructing the IBI are the soil adjusted vegetation index (SAVI), the modified normalized difference water index (MNDWI) and the normalized difference built‐up index (NDBI). Respectively, these represent the three major urban components of vegetation, water and built‐up land. The new index has been verified using the Landsat ETM+ image of Fuzhou City in southeastern China. The result shows that the IBI can significantly enhance the built‐up land feature while effectively suppressing background noise. A statistical analysis indicates that the IBI possesses a positive correlation with land surface temperature, but negative correlations with the NDVI and the MNDWI.