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Article

Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

1
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
2
Department of Natural Resources and Environment and Institute of Agriculture, Natural Resources and Extension, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
3
Alaska Satellite Facility, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
4
National Park Service, Alaska Regional Office, Fairbanks, AK 99709, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(10), 1966; https://doi.org/10.3390/rs13101966
Received: 10 April 2021 / Revised: 14 May 2021 / Accepted: 17 May 2021 / Published: 18 May 2021
(This article belongs to the Special Issue Satellite Image Processing and Applications)
In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent. View Full-Text
Keywords: burn severity; wildfires; boreal forest; machine learning; spectral indices; Alaska burn severity; wildfires; boreal forest; machine learning; spectral indices; Alaska
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MDPI and ACS Style

Smith, C.W.; Panda, S.K.; Bhatt, U.S.; Meyer, F.J.; Badola, A.; Hrobak, J.L. Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest. Remote Sens. 2021, 13, 1966. https://doi.org/10.3390/rs13101966

AMA Style

Smith CW, Panda SK, Bhatt US, Meyer FJ, Badola A, Hrobak JL. Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest. Remote Sensing. 2021; 13(10):1966. https://doi.org/10.3390/rs13101966

Chicago/Turabian Style

Smith, Christopher W, Santosh K Panda, Uma S Bhatt, Franz J Meyer, Anushree Badola, and Jennifer L Hrobak. 2021. "Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest" Remote Sensing 13, no. 10: 1966. https://doi.org/10.3390/rs13101966

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