Burn indices from Landsat 8 : Restrictions on Its Application
Abstract
This study aims to understand the widely potential use of burn indices; Normalized Burn Ratio (NBR), Normalized Burn Ratio Thermal (NBRT), Burn Area Index (BAI), Differenced Normalized Burn Ratio (dNBR), and Relative differenced Normalized Burn Ratio (RdNBR). Landsat 8 imageries recorded in March and April 2016, the peak of burning period in Mae Hong Son Province are investigated. Three aspects were analyzed; 1) the accuracy or consistency of mapping burned area compared to the reference burned area in 2016 reported by Geo-Informatics and Space Technology Development Agency (Public Organization) or GISTDA 2) the burn severity and 3) the identification of burn impacted land uses/land covers. Results from this study show that NBR and RdNBR had higher corresponded (around 60%) to the reference than other burned indices. Five burn severity levels; Enhanced Regrowth, Unburnt, Low Severity, Moderate Severity and High Severity were identified clearly by dNBR. Although RdNBR could be used to classify 5 level of burn severity, but some overlapping between classes were found. Other indices may applicable in separating burn and unburnt areas but they have poor classification ability on burn severity levels. Identifying burnt impacted areas, RdNBR presented the most reliable differentiation of water bodies and built-up and others in compare with other indices. The results from this study can be benefit to those interested researchers and related agencies in selecting the suitable identification of burn index for further use or developing techniques. Keywords : burn Index ; Landsat 8 ; Mae Hong Son ; dNBR ; RdNBRReferences
Cardil, A., Mola-Yudego, B., Blázquez-Casado, A., & González-Olabarria, J. R. (2019). Fire and burn severity assessment: Calibration of Relative Differenced Normalized Burn Ratio (RdNBR) with field data.
J Environ Manage, 235, 342-349. https://doi.org/10.1016/j.jenvman.2019.01.077
Chen, W., Moriya, K., Sakai, T., Koyama, L., & Cao, C.X. (2016). Mapping a burned forest area from Landsat TM data by multiple methods, Geomat Nat Haz Risk, 7(1), 384-402, DOI: 10.1080/19475705.2014.925982
Chuvueco, E., Martin, M.P., & Palacios, A. (2002). Assessment of different spectral indices in the red–near-infrared spectral domain for burned land discrimination. Int. J. Remote Sens., 23 (23), 5103-5110. https://doi.org/10.1080/01431160210153129
Collins, L., Griffioen, P., Newell, G., & Mellor, A. (2018). The utility of Random Forests for wildfire severity mapping. Remote Sens. Environ., 216, 374-384. https://doi.org/10.1016/j.rse.2018.07.005
Department of National Parks, Wildlife and Plant Conservation, (2020). Fire statistics 2019. Retrieved May 22, 2020, from http://portal.dnp.go.th/Content/firednp?contentId=15705
ENVI. (2019). Burn Indices Tutorial. Retrieved Jan 21, 2020,
from http://enviidl.com/help/Subsystems/envi/Content/Tutorials/Tools/BurnIndicesTutorial.htm
Epting, J., Verbyla, D., & Sorbel, B. (2005). Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sens. Environ., 3 (4), 328-339. https://doi.org/10.1016/j.rse.2005.03.002
Escuin, S., Navarro, R., & Fernández, P. (2008). Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. Int. J. Remote Sens., 29 (4), 1053-1073. https://doi.org/10.1080/01431160701281072
Fornacca, D., Ren, G., & Xiao, W. (2018). Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China. Remote Sensing, 10(8), 1196. DOI: 10.3390/rs10081196
GISTDA. (2015). Manual of Using Geoinformatics data for fire and smoke monitoring. Retrieved September 30, 2019, from https://www.gistda.or.th/main/sites/default/files/e-magazine/fire_manual_final_indd_2-20150210.pdf
GISTDA. (2016). Summary of forest fires and haze in 2016 from MODIS satellite systems and area burned from Landsat -8. Retrieved September 30, 2019, from http://fire.gistda.or.th/fire_report/Fire_2559.pdf
GISTDA. (2020). Fire Report 2559 - 63. Retrieved June 15, 2020, from http://fire.gistda.or.th/fire_report/
Harris Geospatial Solutions, Inc. (2018). Burn Indices Tutorial. Retrieved October 14, 2019, from http://enviidl.com/help/Subsystems/envi/Content/Tutorials/Tools/BurnIndicesTutorial.htm
Keeley, J. E. (2009). Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int J Wildland Fire, 18(1), 116–126. https://doi.org/10.1071/WF07049
Klinger, R.C., McKinley, R., Brooks, M.L. (2019). An evaluation of remotely sensed indices for quantifying burn severity in arid ecoregions. Int J Wildland Fire, 28(12), 951-968. https://doi.org/10.1071/WF19025
Maehongson Provincial Office (2018). Geographic & Climate. Retrieved June 10, 2018, from http://www.maehongson.go.th/en/about-us/geographic-climate.html
Miller, J. D., & Thode, A. E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens. Environ., 109, 66–80.
https://doi.org/10.1016/j.rse.2006.12.006
Liu, S., Zheng, Y., Dalponte, M., & Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. Eur. J. Remote Sens., 53(1), 104-112 https://doi.org/10.1080/22797254.2020.1738900
Rozario, P., Madurapperuma, B., & Wang, Y. (2018). Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica. Remote Sensing, 10(9), 1427. DOI: 10.3390/rs10091427
Schepers, L., Haest, B., Veraverbeke, S., Spanhove, T., Vanden Borre, J., & Goossens, R. (2014). Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX). Remote Sensing, 6(3), 1803–1826. DOI: 10.3390/rs6031803
Tran, B., Tanase, M., Bennett, L., & Aponte, C. (2018). Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests. Remote Sensing, 10(11), 1680. DOI: 10.3390/rs10111680
USGS. (2019). Landsat Normalized Burn Ratio. Retrieved October 14, 2019, from https://www.usgs.gov/land-resources/nli/landsat/landsat-normalized-burn-ratio
Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., & Goossens, R. (2010). The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in GreeceS. Remote Sens. Environ., 114, 2548–2563. DOI: 10.1016/j.rse.2010.05.029
Wang, S., Baig, M. H. A., Liu, S., Wan, H., Wu, T., & Yang, Y. (2018). Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index. Int J Wildland Fire, 27(4):217-227. https://doi.org/10.1071/WF17069
Wasser, L., & Cattau, M. (2017). Lesson 4. Work with the Difference Normalized Burn Index - Using Spectral Remote Sensing to Understand the Impacts of Fire on the Landscape. In Earth Analytics Course: Learn Data Science. Retrieved August 24, 2019, from https://www.earthdatascience.org/courses/earth-analytics/multispectral-remote-sensing-modis/normalized-burn-index-dNBR/
J Environ Manage, 235, 342-349. https://doi.org/10.1016/j.jenvman.2019.01.077
Chen, W., Moriya, K., Sakai, T., Koyama, L., & Cao, C.X. (2016). Mapping a burned forest area from Landsat TM data by multiple methods, Geomat Nat Haz Risk, 7(1), 384-402, DOI: 10.1080/19475705.2014.925982
Chuvueco, E., Martin, M.P., & Palacios, A. (2002). Assessment of different spectral indices in the red–near-infrared spectral domain for burned land discrimination. Int. J. Remote Sens., 23 (23), 5103-5110. https://doi.org/10.1080/01431160210153129
Collins, L., Griffioen, P., Newell, G., & Mellor, A. (2018). The utility of Random Forests for wildfire severity mapping. Remote Sens. Environ., 216, 374-384. https://doi.org/10.1016/j.rse.2018.07.005
Department of National Parks, Wildlife and Plant Conservation, (2020). Fire statistics 2019. Retrieved May 22, 2020, from http://portal.dnp.go.th/Content/firednp?contentId=15705
ENVI. (2019). Burn Indices Tutorial. Retrieved Jan 21, 2020,
from http://enviidl.com/help/Subsystems/envi/Content/Tutorials/Tools/BurnIndicesTutorial.htm
Epting, J., Verbyla, D., & Sorbel, B. (2005). Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sens. Environ., 3 (4), 328-339. https://doi.org/10.1016/j.rse.2005.03.002
Escuin, S., Navarro, R., & Fernández, P. (2008). Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. Int. J. Remote Sens., 29 (4), 1053-1073. https://doi.org/10.1080/01431160701281072
Fornacca, D., Ren, G., & Xiao, W. (2018). Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China. Remote Sensing, 10(8), 1196. DOI: 10.3390/rs10081196
GISTDA. (2015). Manual of Using Geoinformatics data for fire and smoke monitoring. Retrieved September 30, 2019, from https://www.gistda.or.th/main/sites/default/files/e-magazine/fire_manual_final_indd_2-20150210.pdf
GISTDA. (2016). Summary of forest fires and haze in 2016 from MODIS satellite systems and area burned from Landsat -8. Retrieved September 30, 2019, from http://fire.gistda.or.th/fire_report/Fire_2559.pdf
GISTDA. (2020). Fire Report 2559 - 63. Retrieved June 15, 2020, from http://fire.gistda.or.th/fire_report/
Harris Geospatial Solutions, Inc. (2018). Burn Indices Tutorial. Retrieved October 14, 2019, from http://enviidl.com/help/Subsystems/envi/Content/Tutorials/Tools/BurnIndicesTutorial.htm
Keeley, J. E. (2009). Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int J Wildland Fire, 18(1), 116–126. https://doi.org/10.1071/WF07049
Klinger, R.C., McKinley, R., Brooks, M.L. (2019). An evaluation of remotely sensed indices for quantifying burn severity in arid ecoregions. Int J Wildland Fire, 28(12), 951-968. https://doi.org/10.1071/WF19025
Maehongson Provincial Office (2018). Geographic & Climate. Retrieved June 10, 2018, from http://www.maehongson.go.th/en/about-us/geographic-climate.html
Miller, J. D., & Thode, A. E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens. Environ., 109, 66–80.
https://doi.org/10.1016/j.rse.2006.12.006
Liu, S., Zheng, Y., Dalponte, M., & Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. Eur. J. Remote Sens., 53(1), 104-112 https://doi.org/10.1080/22797254.2020.1738900
Rozario, P., Madurapperuma, B., & Wang, Y. (2018). Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica. Remote Sensing, 10(9), 1427. DOI: 10.3390/rs10091427
Schepers, L., Haest, B., Veraverbeke, S., Spanhove, T., Vanden Borre, J., & Goossens, R. (2014). Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX). Remote Sensing, 6(3), 1803–1826. DOI: 10.3390/rs6031803
Tran, B., Tanase, M., Bennett, L., & Aponte, C. (2018). Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests. Remote Sensing, 10(11), 1680. DOI: 10.3390/rs10111680
USGS. (2019). Landsat Normalized Burn Ratio. Retrieved October 14, 2019, from https://www.usgs.gov/land-resources/nli/landsat/landsat-normalized-burn-ratio
Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., & Goossens, R. (2010). The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in GreeceS. Remote Sens. Environ., 114, 2548–2563. DOI: 10.1016/j.rse.2010.05.029
Wang, S., Baig, M. H. A., Liu, S., Wan, H., Wu, T., & Yang, Y. (2018). Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index. Int J Wildland Fire, 27(4):217-227. https://doi.org/10.1071/WF17069
Wasser, L., & Cattau, M. (2017). Lesson 4. Work with the Difference Normalized Burn Index - Using Spectral Remote Sensing to Understand the Impacts of Fire on the Landscape. In Earth Analytics Course: Learn Data Science. Retrieved August 24, 2019, from https://www.earthdatascience.org/courses/earth-analytics/multispectral-remote-sensing-modis/normalized-burn-index-dNBR/
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2021-06-01
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