RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery
The precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/13/3132 |
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| author | Aakash Chhabra Christoph Rüdiger Marta Yebra Thomas Jagdhuber James Hilton |
| author_facet | Aakash Chhabra Christoph Rüdiger Marta Yebra Thomas Jagdhuber James Hilton |
| author_sort | Aakash Chhabra |
| collection | DOAJ |
| description | The precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cloud cover that limits their continuous assessments. This study utilises remotely sensed Synthetic-Aperture Radar (SAR) (Sentinel-1 backscatter) data as an alternative to optical-based imaging (Sentinel-2 scaled surface reflectance). SAR can penetrate clouds and offers high-spatial and medium-temporal resolution datasets and can hence complement the optical dataset. Inspired by the optical-based Vegetation Structural Perpendicular Index (VSPI), an SAR-based index termed RADAR-VSPI (R-VSPI) is introduced in this study. R-VSPI characterises the spatio-temporal changes in fuel load due to wildfire and the subsequent vegetation recovery thereof. The R-VSPI utilises SAR backscatter (σ°) from the co-polarized (VV) and cross-polarized (VH) channels at a centre frequency of 5.4 GHz. The newly developed index is applied over major wildfire events that occurred during the “Black Summer” wildfire season (2019–2020) in southern Australia. The condition of the fuel load was mapped every 5 (any orbit) to 12 (same orbit) days at an aggregated spatial resolution of 110 m. The results show that R-VSPI was able to quantify fuel depletion by wildfire (relative to healthy vegetation) and monitor its subsequent post-fire recovery. The information on fuel condition and heterogeneity improved at high-resolution by adapting the VSPI on a dual-polarization SAR dataset (R-VSPI) compared to the historic forest fuel characterisation methods (that used visible and infrared bands only for fuel estimations). The R-VSPI thus provides a complementary source of information on fuel load changes in a forest landscape compared to the optical-based VSPI, in particular when optical observations are not available due to cloud cover. |
| first_indexed | 2024-03-09T03:55:17Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T03:55:17Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1eaa88ee4db44692b73e5eb2f01180112023-12-03T14:20:42ZengMDPI AGRemote Sensing2072-42922022-06-011413313210.3390/rs14133132RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation RecoveryAakash Chhabra0Christoph Rüdiger1Marta Yebra2Thomas Jagdhuber3James Hilton4Department of Civil Engineering, Monash University, Clayton, VIC 3800, AustraliaDepartment of Civil Engineering, Monash University, Clayton, VIC 3800, AustraliaFenner School of Environment & Society, College of Science, The Australian National University, Canberra, ACT 2600, AustraliaMicrowaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, GermanyData-61, Commonwealth Scientific and Industrial Research Organisation, Clayton, VIC 3168, AustraliaThe precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cloud cover that limits their continuous assessments. This study utilises remotely sensed Synthetic-Aperture Radar (SAR) (Sentinel-1 backscatter) data as an alternative to optical-based imaging (Sentinel-2 scaled surface reflectance). SAR can penetrate clouds and offers high-spatial and medium-temporal resolution datasets and can hence complement the optical dataset. Inspired by the optical-based Vegetation Structural Perpendicular Index (VSPI), an SAR-based index termed RADAR-VSPI (R-VSPI) is introduced in this study. R-VSPI characterises the spatio-temporal changes in fuel load due to wildfire and the subsequent vegetation recovery thereof. The R-VSPI utilises SAR backscatter (σ°) from the co-polarized (VV) and cross-polarized (VH) channels at a centre frequency of 5.4 GHz. The newly developed index is applied over major wildfire events that occurred during the “Black Summer” wildfire season (2019–2020) in southern Australia. The condition of the fuel load was mapped every 5 (any orbit) to 12 (same orbit) days at an aggregated spatial resolution of 110 m. The results show that R-VSPI was able to quantify fuel depletion by wildfire (relative to healthy vegetation) and monitor its subsequent post-fire recovery. The information on fuel condition and heterogeneity improved at high-resolution by adapting the VSPI on a dual-polarization SAR dataset (R-VSPI) compared to the historic forest fuel characterisation methods (that used visible and infrared bands only for fuel estimations). The R-VSPI thus provides a complementary source of information on fuel load changes in a forest landscape compared to the optical-based VSPI, in particular when optical observations are not available due to cloud cover.https://www.mdpi.com/2072-4292/14/13/3132microwave remote sensingsynthetic aperture radarSentinel-1Sentinel-2wildfirefuel mapping |
| spellingShingle | Aakash Chhabra Christoph Rüdiger Marta Yebra Thomas Jagdhuber James Hilton RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery Remote Sensing microwave remote sensing synthetic aperture radar Sentinel-1 Sentinel-2 wildfire fuel mapping |
| title | RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery |
| title_full | RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery |
| title_fullStr | RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery |
| title_full_unstemmed | RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery |
| title_short | RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery |
| title_sort | radar vegetation structural perpendicular index r vspi for the quantification of wildfire impact and post fire vegetation recovery |
| topic | microwave remote sensing synthetic aperture radar Sentinel-1 Sentinel-2 wildfire fuel mapping |
| url | https://www.mdpi.com/2072-4292/14/13/3132 |
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