Geomatics and Environmental Engineering, vol. 20, no. 3

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Vol. 20 No. 3 (2026): Geomatics and Environmental Engineering

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Submitted
Jun 20, 2025
Accepted
Feb 6, 2026
Published
Mar 23, 2026
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Optimizing Built-Up Area Extraction in Semi-Arid Regions Using Sentinel-2A Imagery: A Comparative Analysis of Spectral Indices and PCA-Based Classification in Batna, Algeria

https://doi.org/10.7494/geom.2026.20.3.25
Wahiba Touati
University of Batna 2, Institute of Earth Sciences and the Universe, Laboratory of Natural Hazards and Territory Planning, Algeria
https://orcid.org/0009-0005-6422-9095
Mahdi Kalla
University of Batna 2, Institute of Earth Sciences and the Universe, Laboratory of Natural Hazards and Territory Planning, Algeria
https://orcid.org/0000-0001-6380-8777
Lemya Kacha
University of Batna 1, Institute of Architecture and Urbanism, Laboratory of the Child, City and Environment, Algeria
https://orcid.org/0000-0002-2527-5356

Corresponding Author(s) : Wahiba Touati

wahiba.touati@univ-batna.dz

Geomatics and Environmental Engineering, Vol. 20 No. 3 (2026): Geomatics and Environmental Engineering

Abstract

Accurate detection of built-up areas in semi-arid regions is vital for urban planning and environmental monitoring. However, built-up surfaces and bare soils often produce very similar spectral responses. As a result, this similarity causes confusion in satellite image classification. Additionally, spectral overlap among urban materials, bare soil, and sparse vegetation further complicates detection. This study evaluates several spectral indices, including DBSI, NDTI, NDVI, BRBA, and BSI, combined with Principal Component Analysis (PCA) to enhance built-up area extraction from Sentinel-2A imagery. Images captured during the driest season were selected to maximize spectral contrast. Three classification schemes based on Support Vector Machine (SVM) were tested. The first scheme used DBSI, NDTI, and NDVI. The second used BRBA, NDTI, and NDVI. The third relied on PCA-derived components. The results indicate that the PCA-based approach achieved the highest classification accuracy at 95%. In comparison, the DBSI/NDTI/NDVI combination reached 93%, while the BRBA/NDTI/NDVI scheme achieved 92%. Therefore, PCA helps reduce spectral confusion and enhances the identification of built-up areas in semi-arid environments. Overall, combining multiple spectral indices with dimensionality reduction offers a reliable method for urban analysis using Sentinel-2 imagery.

Keywords

built-up area extraction Sentinel-2A imagery semi-arid regions PCA spectral indices SVM classification dimensionality reduction
Touati, W., Kalla, M., & Kacha, L. (2026). Optimizing Built-Up Area Extraction in Semi-Arid Regions Using Sentinel-2A Imagery: A Comparative Analysis of Spectral Indices and PCA-Based Classification in Batna, Algeria. Geomatics and Environmental Engineering, 20(3), 25–49. https://doi.org/10.7494/geom.2026.20.3.25
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Author Biographies

Mahdi Kalla, University of Batna 2, Institute of Earth Sciences and the Universe, Laboratory of Natural Hazards and Territory Planning, Algeria

Dr. Kalla Mahdi is a faculty member at the Institute of Earth Sciences and Univere, University of Batna 2, Algeria. He obtained his PhD in Geography from Université Paris 7 in 1995. His research focuses on geomorphology, natural hazards, and GIS-based assessment of territorial vulnerability in semi-arid environments. He has published several scientific articles and presented extensively at national and international conferences on issues related to erosion, floods, and spatial analysis.

Lemya Kacha, University of Batna 1, Institute of Architecture and Urbanism, Laboratory of the Child, City and Environment, Algeria

Dr. Lemya Kacha is an Associate Professor (Doctor of Engineering) at the Institute of Architecture and Urbanism, University of Batna 1, Algeria. She earned her PhD (Doctor of Engineering) from Nagoya Institute of Technology, Japan in March 2014. Her research focuses on urban morphology, visual complexity in streetscapes, and the application of Kansei engineering and fractal analysis in architectural and spatial design. She has published numerous scientific articles and a book chapters, and has actively presented her work at national and international conferences. Her contributions have been recognized with a Best Paper Award in 2015 from from the Journal of Asian Architecture and Building Engineering (JAABE).

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