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

Abstract

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 urban heat island (SUHI) spatiotemporal pattern in comparison with NDBI before/after COVID-19 pandemic in Sleman Regency, Indonesia (Withdrawal Notice)

“…For past analysis, urban areas were identified by supervised classification; hence, the current generation automatically extracted it from the index transformation. Although it has limitations, the urban built-up image transformation resulting from NDBI is highly accurate (Hidayati et al, 2018;Muhaimin et al, 2022;Suharyadi et al, 2022). This study implemented a built-up index range to reflect Sleman Regency's urbanization.…”

Comparative analysis of urban heat island phenomenon in residential and industrial areas of Jakarta and Surabaya

“…Additionally, the transformation of vegetated land into built-up areas, detected using build-up indices like NDBI, IBI, and NBI, is correlated with temperature increases. Building indices provide insights into the development of building density in urban areas, influencing heat distribution and UHI intensity [15].…”

A New High-Resolution Rural Built-Up Land Extraction Method Based on Artificial Surface Index with Short-Wave Infrared Downscaling

“…Challenges still existed in determining the optimal parameters during the segmentation process and the generalization of segmentation methods across different study areas and data sources [14]. Machine learning algorithms have been widely used to acquire distribution and dynamical changes of built-up areas using medium-to high-resolution images [15][16][17]. In recent years, deep learning techniques have received much attention for building extraction from high-resolution or very-high-resolution (VHR) images [4,18,19].…”