Next Article in Journal
Analyzing Glacier Surface Motion Using LiDAR Data
Previous Article in Journal
UAV-Based Oblique Photogrammetry for Outdoor Data Acquisition and Offsite Visual Inspection of Transmission Line
Previous Article in Special Issue
Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics?
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(3), 279; doi:10.3390/rs9030279

Calibrating Satellite-Based Indices of Burn Severity from UAV-Derived Metrics of a Burned Boreal Forest in NWT, Canada

1
Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa K1S 5K2, ON, Canada
2
Centre for Geomatics, Government of the Northwest Territories, Yellowknife X1A 2L9, NT, Canada
3
Canadian Forest Service, Natural Resources Canada, Edmonton T6H 3S5, AB, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Diofantos Hadjimitsis, Ioannis Gitas, Luigi Boschetti, Kyriacos Themistocleous and Prasad S. Thenkabail
Received: 9 February 2017 / Revised: 9 March 2017 / Accepted: 13 March 2017 / Published: 16 March 2017
View Full-Text   |   Download PDF [25156 KB, uploaded 16 March 2017]   |  

Abstract

Wildfires are a dominant disturbance to boreal forests, and in North America, they typically cause widespread tree mortality. Forest fire burn severity is often measured at a plot scale using the Composite Burn Index (CBI), which was originally developed as a means of assigning severity levels to the Normalized Burn Ratio (NBR) computed from Landsat satellite imagery. Our study investigated the potential to map biophysical indicators of burn severity (residual green vegetation and charred organic surface) at very high (3 cm) resolution, using color orthomosaics and vegetation height models derived from UAV-based photographic surveys and Structure from Motion methods. These indicators were scaled to 30 m resolution Landsat pixel footprints and compared to the post-burn NBR (post-NBR) and differenced NBR (dNBR) ratios computed from pre- and post-fire Landsat imagery. The post-NBR showed the strongest relationship to both the fraction of charred surface (exponential R2 = 0.79) and the fraction of green crown vegetation above 5 m (exponential R2 = 0.81), while the dNBR was more closely related to the total green vegetation fraction (exponential R2 = 0.69). Additionally, the UAV green fraction and Landsat indices could individually explain more than 50% of the variance in the overall CBI measured in 39 plots. These results provide a proof-of-concept for using low-cost UAV photogrammetric mapping to quantify key measures of boreal burn severity at landscape scales, which could be used to calibrate and assign a biophysical meaning to Landsat spectral indices for mapping severity at regional scales. View Full-Text
Keywords: forest fire; burn severity; composite burn index; normalized burn ratio; unmanned aerial vehicle; UAV; UAS; Landsat forest fire; burn severity; composite burn index; normalized burn ratio; unmanned aerial vehicle; UAV; UAS; Landsat
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Fraser, R.H.; van der Sluijs, J.; Hall, R.J. Calibrating Satellite-Based Indices of Burn Severity from UAV-Derived Metrics of a Burned Boreal Forest in NWT, Canada. Remote Sens. 2017, 9, 279.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top