Cite this dataset as
Dugdale, S.J., Field, R., Johnson, M., Mariani, M., Pugh, B., Schrodt, F., Thorne, C. (2023). High resolution burn severity data (derived from satellite imagery) for the South Fork McKenzie River in Oregon, USA, before and after a wildfire event, 2020 and 2021. NERC EDS Environmental Information Data Centre. (Dataset). https://doi.org/10.5285/8162887a-5481-440f-a7f2-427eee793efd
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This dataset is available under the terms of the Open Government Licence 
High resolution burn severity data (derived from satellite imagery) for the South Fork McKenzie River in Oregon, USA, before and after a wildfire event, 2020 and 2021
The data comprise Sentinel-2 derived burn severity rasters covering restored and unrestored reaches of the South Fork McKenzie river, Oregon USA. The data were collected in order to quantify differences in burn severity in restored and unrestored river reaches following the Holiday Farm wildfire in 2020.
Raw satellite imagery acquired in June 2020 and June 2021 was processed to calculate Normalised Burn Ratio (NBR), giving pre- and post-fire burn severity information. Data consist of 10 m .TIF raster imagery where a digital number gives a measure of burn severity; high NBR values indicate healthy vegetation, whereas lower values indicate burnt areas or bare ground.
The study was conducted by the University of Nottingham, in partnership with the US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council.
Raw satellite imagery acquired in June 2020 and June 2021 was processed to calculate Normalised Burn Ratio (NBR), giving pre- and post-fire burn severity information. Data consist of 10 m .TIF raster imagery where a digital number gives a measure of burn severity; high NBR values indicate healthy vegetation, whereas lower values indicate burnt areas or bare ground.
The study was conducted by the University of Nottingham, in partnership with the US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council.
Publication date: 2023-01-17
View record as
Format
TIFF
Spatial information
- Study area
-
- Spatial representation type
- Raster
- Spatial reference system
- WGS 84
Temporal information
- Temporal extent
-
2020-01-01 to 2021-12-30
Provenance & quality
20 m bands of Two Sentinel-2A images of study site (pre/post-fire; June 2020 and 2021) were super-resolved to 10m following Lanaras et al (2018)
These data were subsequently used to generate raster files depicting 10m resolution Normalised Burn Ratio (NBR) across South Fork McKenzie River, where NBR = (Sentinel Band B08 - B12) / (B08 + B12), following the methodology outlined in Keeley (2009). Subtraction of postfire from prefire NBR rasters facilitates estimation of burn severity by highlighting vegetation burn in relation to pre-burnt state.
Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319 https://doi.org/10.1016/j.isprsjprs.2018.09.018
Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18, 116-126. https://doi.org/10.1071/WF07049
These data were subsequently used to generate raster files depicting 10m resolution Normalised Burn Ratio (NBR) across South Fork McKenzie River, where NBR = (Sentinel Band B08 - B12) / (B08 + B12), following the methodology outlined in Keeley (2009). Subtraction of postfire from prefire NBR rasters facilitates estimation of burn severity by highlighting vegetation burn in relation to pre-burnt state.
Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319 https://doi.org/10.1016/j.isprsjprs.2018.09.018
Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18, 116-126. https://doi.org/10.1071/WF07049
Related
This dataset is included in the following collections
Supplemental information
Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319
Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18, 116-126.
Correspondence/contact details
Other contacts
- Rights holder
-
University of Nottingham
- Custodian
-
NERC EDS Environmental Information Data Centreinfo@eidc.ac.uk
- Publisher
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NERC EDS Environmental Information Data Centreinfo@eidc.ac.uk