Version 1
: Received: 12 August 2024 / Approved: 13 August 2024 / Online: 13 August 2024 (11:32:41 CEST)
How to cite:
Gillespie, M.; Okin, G. S.; Meyer, T.; Ochoa, F. Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms. Preprints2024, 2024080865. https://doi.org/10.20944/preprints202408.0865.v1
Gillespie, M.; Okin, G. S.; Meyer, T.; Ochoa, F. Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms. Preprints 2024, 2024080865. https://doi.org/10.20944/preprints202408.0865.v1
Gillespie, M.; Okin, G. S.; Meyer, T.; Ochoa, F. Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms. Preprints2024, 2024080865. https://doi.org/10.20944/preprints202408.0865.v1
APA Style
Gillespie, M., Okin, G. S., Meyer, T., & Ochoa, F. (2024). Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms. Preprints. https://doi.org/10.20944/preprints202408.0865.v1
Chicago/Turabian Style
Gillespie, M., Thoralf Meyer and Francisco Ochoa. 2024 "Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms" Preprints. https://doi.org/10.20944/preprints202408.0865.v1
Abstract
Accurate fire severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where vegetation experiences above-ground combustion, but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush fire severity and post-burn coppicing/regeneration events of woody vegetation in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.5% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
