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Article

High-Resolution Estimates of Fire Severity—An Evaluation of UAS Image and LiDAR Mapping Approaches on a Sedgeland Forest Boundary in Tasmania, Australia

1
School of Science, RMIT University, Melbourne, VIC 3001, Australia
2
School of Geography, Planning, and Spatial Sciences, University of Tasmania, Hobart, TAS 7001, Australia
3
Bushfire and Natural Hazards Cooperative Research Centre, East Melbourne, VIC 3004, Australia
*
Author to whom correspondence should be addressed.
Current address: School of Science, RMIT University, Melbourne, VIC 3001, Australia.
Received: 3 February 2021 / Revised: 13 March 2021 / Accepted: 14 March 2021 / Published: 18 March 2021
(This article belongs to the Special Issue Bushfire in Tasmania)
With an increase in the frequency and severity of wildfires across the globe and resultant changes to long-established fire regimes, the mapping of fire severity is a vital part of monitoring ecosystem resilience and recovery. The emergence of unoccupied aircraft systems (UAS) and compact sensors (RGB and LiDAR) provide new opportunities to map fire severity. This paper conducts a comparison of metrics derived from UAS Light Detecting and Ranging (LiDAR) point clouds and UAS image based products to classify fire severity. A workflow which derives novel metrics describing vegetation structure and fire severity from UAS remote sensing data is developed that fully utilises the vegetation information available in both data sources. UAS imagery and LiDAR data were captured pre- and post-fire over a 300 m by 300 m study area in Tasmania, Australia. The study area featured a vegetation gradient from sedgeland vegetation (e.g., button grass 0.2m) to forest (e.g., Eucalyptus obliqua and Eucalyptus globulus 50m). To classify the vegetation and fire severity, a comprehensive set of variables describing structural, textural and spectral characteristics were gathered using UAS images and UAS LiDAR datasets. A recursive feature elimination process was used to highlight the subsets of variables to be included in random forest classifiers. The classifier was then used to map vegetation and severity across the study area. The results indicate that UAS LiDAR provided similar overall accuracy to UAS image and combined (UAS LiDAR and UAS image predictor values) data streams to classify vegetation (UAS image: 80.6%; UAS LiDAR: 78.9%; and Combined: 83.1%) and severity in areas of forest (UAS image: 76.6%, UAS LiDAR: 74.5%; and Combined: 78.5%) and areas of sedgeland (UAS image: 72.4%; UAS LiDAR: 75.2%; and Combined: 76.6%). These results indicate that UAS SfM and LiDAR point clouds can be used to assess fire severity at very high spatial resolution. View Full-Text
Keywords: photogrammetry; UAS; LiDAR; 3D remote sensing; vegetation; RPAS; drone; structure; fuel structure; fire severity photogrammetry; UAS; LiDAR; 3D remote sensing; vegetation; RPAS; drone; structure; fuel structure; fire severity
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MDPI and ACS Style

Hillman, S.; Hally, B.; Wallace, L.; Turner, D.; Lucieer, A.; Reinke, K.; Jones, S. High-Resolution Estimates of Fire Severity—An Evaluation of UAS Image and LiDAR Mapping Approaches on a Sedgeland Forest Boundary in Tasmania, Australia. Fire 2021, 4, 14. https://0-doi-org.brum.beds.ac.uk/10.3390/fire4010014

AMA Style

Hillman S, Hally B, Wallace L, Turner D, Lucieer A, Reinke K, Jones S. High-Resolution Estimates of Fire Severity—An Evaluation of UAS Image and LiDAR Mapping Approaches on a Sedgeland Forest Boundary in Tasmania, Australia. Fire. 2021; 4(1):14. https://0-doi-org.brum.beds.ac.uk/10.3390/fire4010014

Chicago/Turabian Style

Hillman, Samuel, Bryan Hally, Luke Wallace, Darren Turner, Arko Lucieer, Karin Reinke, and Simon Jones. 2021. "High-Resolution Estimates of Fire Severity—An Evaluation of UAS Image and LiDAR Mapping Approaches on a Sedgeland Forest Boundary in Tasmania, Australia" Fire 4, no. 1: 14. https://0-doi-org.brum.beds.ac.uk/10.3390/fire4010014

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