Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery

Abstract

Accurate built-up area extraction is one of the most critical issues in land-cover classification. In previous studies, various techniques have been developed for built-up area extraction using Landsat images. However, the efficiency of these techniques under different technical and geographical conditions, especially for bare and sandy areas, is not optimal. One of the main challenges of built-up area extraction techniques is to determine an optimum and stable threshold with the highest possible accuracy. In many of these techniques, the optimum threshold value fluctuates substantially in different parts of the image scene. The purpose of this study is to provide a new index to improve built-up area extraction with a stable optimum threshold for different environments. In this study, the developed Automated Built-up Extraction Index (ABEI) is presented to improve the classification accuracy in areas containing bare and sandy surfaces. To develop and evaluate the accuracy of the new method for built-up area extraction with Landsat 8 OLI reflective bands, five test sites located in the Iranian cities (Babol, Naqadeh, Kashmar, Bam and Masjed Soleyman), eleven European cities (Athens, Brussels, Bucharest, Budapest, Ciechanow, Hamburg, Lyon, Madrid, Riga, Rome and Porto) and high resolution layer imperviousness (HRLI) data were used. Each site has varying environmental and complex surface coverage conditions. To determine the optimal weights for each of the Landsat 8 OLI reflective bands, the pure pixel sets for different classes and the improved gravitational search algorithm (IGSA) optimization were used. The Kappa coefficient and overall error were calculated to evaluate the accuracy of the built-up extraction map. Additionally, the ABEI performance was compared with the urban index (UI) and normalized difference built-up index (NDBI) performances. In each of the five test sites and eleven cities, the extraction accuracy of the built-up areas using the ABEI was higher than that using the UI, and NDBI (P-value of 0.01). The relative standard deviations of the optimal threshold values for the ABEI and UI were 27 and 155% (at five test sites) and were 16 and 37% (at eleven European cities), respectively, which indicates the stability of the ABEI threshold value when the location and environmental conditions change. The results of this study demonstrated that the ABEI can be used to extract built-up areas from other land covers. This index is effective even in bare soil and sandy areas, where other indices experience major challenges.

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

“…However, satellite imagery can be used to address these challenges. In previous studies, various spectral indices and methods have been proposed for ISC modelling and built-up land extraction [51,53,61].…”

“…Some studies have shown that BCI can be effective in demonstrating spatial changes in the ISC in urban environments [24,70]. Firozjaei, Sedighi, Kiavarz, Qureshi, Haase and Alavipanah [61] showed that the ABEI is more effective than other indices for separating built-up lands from other land covers, especially bare lands. However, in this study, the ABEI accuracy for DSAHII quantification was lower than that of other indices.…”

“…The characteristics of selected cities are summarized in Table 1. To select these cities, various criteria including (1) geographical conditions, (2) surface characteristics, (3) climatic conditions, (4) varied physical size, and (5) population density were considered [61,62]. The characteristics of selected cities are summarized in Table 1.…”

“…In previous studies, various spectral indices and methods, such as the urban index (UI) [49,50], the normalized difference bareness index (NDBaI) [50], the normalized difference built-up index (NDBI) [51], the index-based built-up index (IBI) [52], the modified NDBI [53], the band ratio for built-up area (BRBA) and the normalized built-up area index (NBAI) [54], the built-up index (BUI) [55], the new built-up index (NBI) [56], the bare soil index (BI) [57], soil index (SI), normalized built-up and bare soil index (NBBSI) [58], the built-up area extraction method (BAEM) [59], the enhanced built-up and bareness index (EBBI) [60], and the automated built-up extraction index (ABEI) [61], have been proposed for the extraction of built-up and impervious lands. The advantages of these indices 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 [61]. Therefore, using these indices can be useful for SAHI quantification.…”

Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data

“…NASA successfully launched the Landsat 8 satellite on 11 February 2013, with the OLI sensor onboard, which collects data from nine spectral bands. Apart from the 8th panchromatic band (15 m), the remaining bands have a spatial resolution of 30 m. The first seven commonly used bands were selected for the experimentation, i.e., coastal, blue, green, red, NIR, SWIR1, and SWIR2 [36,37]. The Landsat 8 OLI images were obtained from the Geospatial Data Cloud site (http://www.gscloud.cn/search, 22 February 2020).…”