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Remote Sensing of Wildfire Using a Small Unmanned Aerial System: Post-Fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay, Mississippi/Alabama, USA

1
Geosystems Research Institute, Mississippi State University, Mississippi State, MS 39762, USA
2
Grand Bay National Estuarine Research Reserve, Moss Point, MS 39562, USA
*
Author to whom correspondence should be addressed.
Drones 2019, 3(2), 43; https://doi.org/10.3390/drones3020043
Received: 5 April 2019 / Accepted: 7 April 2019 / Published: 9 May 2019
PDF [5722 KB, uploaded 9 May 2019]

Abstract

Wildfires can be beneficial for native vegetation. However, wildfires can impact property values, human safety, and ecosystem function. Resource managers require safe, easy to use, timely, and cost-effective methods for quantifying wildfire damage and regeneration. In this work, we demonstrate an approach using an unmanned aerial system (UAS) equipped with a MicaSense RedEdge multispectral sensor to classify and estimate wildfire damage in a coastal marsh. We collected approximately 7.2 km2 of five-band multispectral imagery after a wildfire event in February 2016, which was used to create a photogrammetry-based digital surface model (DSM) and orthomosaic for object-based classification analysis. Airborne light detection and ranging data were used to validate the accuracy of the DSM. Four-band airborne imagery from pre- and post-fire were used to estimate pre-fire health, post-fire damage, and track the vegetation recovery process. Immediate and long-term post-fire classifications, area, and volume of burned regions were produced to track the revegetation progress. The UAS-based classification produced from normalized difference vegetation index and DSM was compared to the Landsat-based Burned Area Reflectance Classification. Experimental results show the potential of using UAS and the presented approach compared to satellite-based mapping in terms of classification accuracies, turnaround time, and spatial and temporal resolutions.
Keywords: wildfire; damage assessment; vegetation recovery wildfire; damage assessment; vegetation recovery
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 (CC BY 4.0).
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Samiappan, S.; Hathcock, L.; Turnage, G.; McCraine, C.; Pitchford, J.; Moorhead, R. Remote Sensing of Wildfire Using a Small Unmanned Aerial System: Post-Fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay, Mississippi/Alabama, USA. Drones 2019, 3, 43.

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