Identifying Major Wildfires Using Long-Term Multi-Source Data and Anomaly Detection Algorithms
- 1Department of Geography, Environmental Management & Energy Studies, University of Johannesburg, Johannesburg 2006, South Africa
- 2Earth Observation Directorate, South African National Space Agency, Pretoria 0001, South Africa
- 3Space Science Directorate, South African National Space Agency, Hermanus 7200, South Africa
Abstract
This study aimed to identify and characterise major wildfire events in South Africa (SA) between 2004 and 2023 by integrating long-term multi-source datasets with anomaly detection techniques. Biomass-burning emissions, such as black carbon from biomass burning (BCBB), organic carbon from biomass burning (OCBB), and carbon monoxide (CO), were retrieved from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis dataset, while burned area data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Isolation Forest (IF; contamination = 0.05) and the Generalised Extreme Studentised Deviate (GESD) test were applied independently and integrated at the decision level: single-method detections were classified as candidate anomalies, while agreement between the methods identified high-confidence anomalies. Calendar-month standardisation, Spearman rank correlation, Benjamini–Hochberg false-discovery-rate correction, and calendar-month-matched event composites were used to assess meteorological relationships. IF-selected 12 candidate months were identified throughout, whereas GESD identified a smaller subset. July 2007 was the only high-confidence burned area anomaly. High-confidence emission anomalies occurred in 2010, 2018, 2021, and 2023, predominantly between September and November, while the only high-confidence precipitation anomaly occurred in August 2006. After false-discovery-rate correction, the burned area was weakly associated with higher wind speed and lower daytime relative humidity. CO, BCBB, and OCBB were weakly associated with lower precipitation, lower daytime relative humidity, and higher wind speed, while surface air temperature (SAT) showed no significant relationships. Event composites displayed similar patterns, but none of the 16 comparisons remained statistically significant after correction. Spatial analyses showed that the July 2007 burned area anomaly was concentrated in eastern SA; CO and BCBB anomalies were prominent across the northern, central, and eastern interior, and the 2018 OCBB anomalies were concentrated in the southern Western Cape. Percentile-selected spatial composites demonstrated additional regional heterogeneity but were distinct from the IF-GESD consensus anomalies and were interpreted descriptively. The framework, therefore, provides transparent confidence stratification rather than evidence of superior predictive accuracy, while highlighting limitations arising from national monthly aggregation and differences in product resolution.
Related
- Data Quality Audit and Bivariate Temperature-Humidity Analysis of a Long-Term LoRaWAN Network: Anomaly Detection, PCA, Clustering and Machine Learning
- A South Atlantic atmospheric river dipole as a key precursor of Amazon wildfires
- Wildfires diminish vegetation control on surface runoff
- A Sentinel-2 benchmark dataset for deep-learning active-fire segmentation across 25 California wildfires, July 2020-August 2026
- Consistent drivers of lightning-ignited wildfires across global boreal forests
BibTeX
@article{mathaba2026identifying,
title = {{Identifying Major Wildfires Using Long-Term Multi-Source Data and Anomaly Detection Algorithms}},
author = {Kedibone Mathaba and Mahlatse Kganyago and Lerato Shikwambana and Michael Kosch},
journal = {Earth},
year = {2026},
month = sep,
doi = {10.3390/earth7050153},
url = {https://doi.org/10.3390/earth7050153},
note = {CC BY},
}
RIS
TY - JOUR
TI - Identifying Major Wildfires Using Long-Term Multi-Source Data and Anomaly Detection Algorithms
AU - Kedibone Mathaba
AU - Mahlatse Kganyago
AU - Lerato Shikwambana
AU - Michael Kosch
PY - 2026
DA - 2026/09/01
JO - Earth
DO - 10.3390/earth7050153
UR - https://doi.org/10.3390/earth7050153
AB - This study aimed to identify and characterise major wildfire events in South Africa (SA) between 2004 and 2023 by integrating long-term multi-source datasets with anomaly detection techniques. Biomass-burning emissions, such as black carbon from biomass burning (BCBB), organic carbon from biomass burning (OCBB), and carbon monoxide (CO), were retrieved from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis dataset, while burned area data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Isolation Forest (IF; contamination = 0.05) and the Generalised Extreme Studentised Deviate (GESD) test were applied independently and integrated at the decision level: single-method detections were classified as candidate anomalies, while agreement between the methods identified high-confidence anomalies. Calendar-month standardisation, Spearman rank correlation, Benjamini–Hochberg false-discovery-rate correction, and calendar-month-matched event composites were used to assess meteorological relationships. IF-selected 12 candidate months were identified throughout, whereas GESD identified a smaller subset. July 2007 was the only high-confidence burned area anomaly. High-confidence emission anomalies occurred in 2010, 2018, 2021, and 2023, predominantly between September and November, while the only high-confidence precipitation anomaly occurred in August 2006. After false-discovery-rate correction, the burned area was weakly associated with higher wind speed and lower daytime relative humidity. CO, BCBB, and OCBB were weakly associated with lower precipitation, lower daytime relative humidity, and higher wind speed, while surface air temperature (SAT) showed no significant relationships. Event composites displayed similar patterns, but none of the 16 comparisons remained statistically significant after correction. Spatial analyses showed that the July 2007 burned area anomaly was concentrated in eastern SA; CO and BCBB anomalies were prominent across the northern, central, and eastern interior, and the 2018 OCBB anomalies were concentrated in the southern Western Cape. Percentile-selected spatial composites demonstrated additional regional heterogeneity but were distinct from the IF-GESD consensus anomalies and were interpreted descriptively. The framework, therefore, provides transparent confidence stratification rather than evidence of superior predictive accuracy, while highlighting limitations arising from national monthly aggregation and differences in product resolution.
SN - 2673-4834
ER -