Mapping typical LULC classes using spatiotemporal analysis and the thresholds of spectral optical satellite imagery indices: a case study in Algiers city

• Published in: Environmental monitoring and assessment 2024-12, Vol.197 (1), p.67
• Main Authors: Ghezali, Sana, Boukhemacha, Mohamed Amine
• Format: Article
• Language: English
• Subjects: Accuracy; Algeria; Atmospheric Protection/Air Quality Control/Air Pollution; Case studies; Cities; Classification; Conservation of Natural Resources - methods; Deforestation; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring - methods; Human-environment relationship; Impact analysis; Land cover; Land use; Mapping; Monitoring/Environmental Analysis; Remote sensing; Remote Sensing Technology; Satellite Imagery; Seasonal variation; Seasonal variations; Spatio-Temporal Analysis; Statistical analysis; Statistical methods; Urban sprawl; Urban studies; Urbanization; Vegetation; Vegetation index
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-024-13577-z

Land use and land cover (LULC) dynamics have a substantial impact on human–environment interactions. Nowadays, remote sensing imagery has emerged as a useful tool for mapping and tracking LULC changes. Spectral optical indices derived from remote sensing data can provide insightful information about vegetation health, urban expansion, water bodies, deforestation patterns, and many other applications. The present study examines the use of popular optical spectral indices: vegetation index (NDVI), water indices (NDWI and MNDWI), urban indices (UI and NDBI), and bare land index (MNDBI) in threshold-based classification for LULC mapping using Algiers (Algeria) as a case study, and assesses the potential impacts of their spatiotemporal (at a seasonal and annual temporal scales) variations associated with natural seasonal changes and/or the evolution of the city’s fabric. Here, a geo-statistical analysis of the values of the selected spectral indices at the level of each LU-class is conducted, threshold values (that account for seasonal variations) are proposed, and a classification approach (making use of best performing indices) is proposed and tested. Although fast and easy to implement, the proposed threshold-based LULC classification approach was successfully used for mapping LULC for the study zone with a high accuracy (an overall accuracy of 90.20 and a kappa of 0.84 for the demonstration year of 2017). The outcomes of the study heighten the potential and the limitations of the use of spectral indices for LULC mapping practices and consequent applications in environmental and urban studies.