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Published VersionCC BY V4.0, Open Access
Journal article
Tracking Post-Fire Vegetation Regrowth and Burned Areas Using Bitemporal Sentinel-1 SAR Data: A Google Earth Engine Approach in Heath Vegetation of Mooloolah River National Park, Queensland, Australia
Remote Sensing, Vol.17(12), pp.1-27
2025
Abstract
This study utilizes the unique capabilities of Sentinel-1 C-band synthetic aperture radar (SAR) data to map post-fire burned areas and monitor vegetation recovery in a heath-dominated Queensland National Park. Sentinel-1 SAR data were used due to their cloud-penetrating capability and frequent revisit times. Using Google Earth Engine (GEE), a bitemporal ratio analysis was applied to SAR data from post-fire periods between 2021 and 2023. SAR backscatter changes over time captured fire impacts and subsequent vegetation regrowth. This differentiation was further enhanced with k-means clustering. Validation was supported by Sentinel-2 dNBR and official fire history records. The dNBR provided a quantitative assessment of burn severity and was used alongside the fire history data to evaluate the accuracy of the burned area classification. While Sentinel-2 false-colour composite (FCC) imagery was generated for visualisation and interpretation purposes, the primary validation relied on dNBR and QPWS fire history records. The results highlighted significant vegetation regrowth, with some areas returning to near pre-fire biomass levels by March 2023. This approach demonstrates the sensitivity of Sentinel-1 SAR, especially in VV polarization, for detecting subtle changes in vegetation, providing a cost-effective method for post-fire ecosystem monitoring and informing ecological management strategies amid increasing wildfire events.
Details
- Title
- Tracking Post-Fire Vegetation Regrowth and Burned Areas Using Bitemporal Sentinel-1 SAR Data: A Google Earth Engine Approach in Heath Vegetation of Mooloolah River National Park, Queensland, Australia
- Authors
- Singh HarikeshPrashant Srivastava - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringPrasad Rajendra - Indian Institute of Technology BHUSanjeev Srivastava (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Remote Sensing, Vol.17(12), pp.1-27
- Publisher
- MDPI AG
- Date published
- 2025
- DOI
- 10.3390/rs17122031
- ISSN
- 2072-4292
- Copyright note
- © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Data Availability
- The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
- Grant note
- This research was supported by the University of the Sunshine Coast and the SmartSat CRC, whose activities are funded by the Australian Government’s CRC Program.
- Organisation Unit
- School of Science, Technology and Engineering; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 991138093702621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Environmental Sciences
- Geosciences, Multidisciplinary
- Imaging Science & Photographic Technology
- Remote Sensing
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