Volume 11, Issue 2 (12-2025)
KJES 2025, 11(2): 342-365 |
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Shojaei M J, Milan A. Detection and classification of environmental anomalies using multi-temporal sentinel-2 satellite imagery and lightweight machine learning. KJES 2025; 11 (2) :342-365
URL: http://gnf.khu.ac.ir/article-1-2964-en.html
URL: http://gnf.khu.ac.ir/article-1-2964-en.html
1- Shahid Beheshti University
2- Shahid Beheshti University ,a_milan@sbu.ac.ir
2- Shahid Beheshti University ,
Abstract: (420 Views)
Monitoring vegetation cover changes and environmental anomalies is essential for ecosystem management, precision agriculture, and early warning systems. Given the complexity of spatiotemporal environmental patterns, multi-temporal satellite data offer an efficient approach to track gradual and abrupt changes. In this study, time-series data from the Sentinel-2 sensor and spectral indices NDVI, EVI, and NBR are used as indicators sensitive to chlorophyll content and severe disturbances, along with lightweight, unsupervised algorithms Isolation Forest, Local Outlier Factor (LOF), and One-Class Support Vector Machine to identify anomalies. Model performance evaluation shows that the Isolation Forest algorithm provides the most balanced and robust performance (Accuracy = 0.886, Precision = 0.065, Recall = 0.250, F1 = 0.103). The Local Outlier Factor algorithm demonstrated higher sensitivity to localized patterns but had lower stability in noisy data (F1 = 0.069). The One-Class SVM adopted a more conservative labeling approach and was mostly effective at detecting severe disturbances, especially with the NBR index (F1 = 0.035). Overlap and Distinction Analysis of Indices that NDVI captures gradual chlorophyll decline, EVI performs better in dense vegetation, and NBR plays a crucial role in identifying severe events such as wildfires and droughts. The concurrent use of these indices broadens the detectable range from subtle fluctuations to large-scale disturbances. Findings show that integrating freely available Sentinel-2 data with lightweight machine learning models yields a scalable, reproducible, and efficient framework for large-scale environmental anomaly monitoring. This approach minimizes reliance on costly field data and enables practical applications in drought monitoring, environmental-crisis management, ecosystem health assessment, and smart agricultural planning. For future work, integrating moisture- and temperature-related spectral data, higher temporal resolution time series, and metaheuristic parameter tuning is recommended to further enhance robustness and accuracy.
Keywords: Environmental anomalies, spectral indices, machine learning, multi-temporal satellite imagery, lightweight algorithms.
Type of Study: Original Research |
Subject:
Remote sensing and GIS
Received: 2025/11/6 | Accepted: 2025/12/26 | Published: 2025/12/31
Received: 2025/11/6 | Accepted: 2025/12/26 | Published: 2025/12/31
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