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An Assessment of the Suitability of Sentinel-2 Data for Identifying Burn Severity in Areas of Low Vegetation

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Abstract

Forest fires result in a range of adverse Earth's eco-environment and economic impacts. It is crucial to timely and accurately assess the severity of a forest fire, because burn severity is the factor for post-fire vegetation recovery. On the 7th July 2015, a forest fire occurred in western Spain, Comunidad Valenciana, near the villages of Montán and Caudiel. The fire mainly affected low vegetation types such as scrublands and herbs. This study intended to evaluate the use of Sentinel-2 data for identifying burn severity within areas covered by low vegetation, with the single band, spectral index, and differential spectral index Sentinel-2 data assessed. The results confirmed that the use of near-infrared and short-wave infrared ranges of Sentinel-2 data was suitable for identifying burned and unburned areas of low vegetation. The use of the normalized difference vegetation index performed best in distinguishing between areas of highly and moderately damaged vegetation, whereas the use of the normalized burn ratio (NBR) and NBR2 performed best for distinguishing between areas of completely destroyed and moderately damaged vegetation. These preliminary research results indicated that Sentinel-2 data are useful for forest fire monitoring in areas with low vegetation.

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References

  • Amos, C., Petropoulos, G. P., & Ferentinos, K. P. (2019). Determining the use of Sentinel-2A MSI for wildfire burning and severity detection. International Journal of Remote Sensing, 40(3), 905–930. https://doi.org/10.1080/01431161.2018.1519284

    Article  Google Scholar 

  • Arnett, J. T. T. R., Coops, N. C., Daniels, L. D., & Falls, R. W. (2015). Detecting forest damage after a low-severity fire using remote sensing at multiple scales. International Journal of Applied Earth Observation and Geoinformation, 35, 239–246.

    Article  Google Scholar 

  • Bar, S., Parida, B. R., & Pandey, A. C. (2020). Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18, 100324. https://doi.org/10.1016/j.rsase.2020.100324

    Article  Google Scholar 

  • Brewer, C. K., Winne, J. C., Redmond, R. L., Opitz, D. W., & Mangrich, M. V. (2005). Classifying and mapping wildfire severity: A comparison of methods. Photogrammetric Engineering and Remote Sensing, 71(11), 1311–1320.

    Article  Google Scholar 

  • Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14(4), 711–722. https://doi.org/10.1080/01431169308904370

    Article  Google Scholar 

  • Chuvieco, E. (2009). Earth observation of wildland fires in mediterranean ecosystems. Springer.

    Book  Google Scholar 

  • Collins, L., Griffioen, P., Newell, G., & Mellor, A. (2018). The utility of Random Forests for wildfire severity mapping. Remote Sensing of Environment, 216, 374–384. https://doi.org/10.1016/j.rse.2018.07.005

    Article  Google Scholar 

  • Copernicus Global Land Service. (2014). CORINE land cover nomenclature illustrated guide, 57–60. https://land.copernicus.eu/user-corner/technical-library/copy_of_Nomenclature.pdf

  • Copernicus EMS. (2015). EMSR131. https://emergency.copernicus.eu/mapping/list-of-components/EMSR131

  • DellaSala, D. A., & Hanson, C. T. (2015). The ecological importance of mixed-severity fires: Nature’s phoenix. Elsevier.

    Google Scholar 

  • de Vasconcelos, S. S., Fearnside, P. M., de Alencastro Graça, P. M. L., Dias, D. V., & Correia, F. W. S. (2013). Variability of vegetation fires with rain and deforestation in Brazil’s state of Amazonas. Remote Sensing of Environment, 136, 199–209.

    Article  Google Scholar 

  • 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 Sensing of Environment, 96(3–4), 328–339. https://doi.org/10.1016/j.rse.2005.03.002

    Article  Google Scholar 

  • European Space Agency. (2020). SNAP supported plugins Sen2Cor. European Space Agency. http://step.esa.int/main/snap-supported-plugins/sen2cor/

  • Fernández-Manso, A., Fernández-Manso, O., & Quintano, C. (2016). SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. International Journal of Applied Earth Observation and Geoinformation, 50, 170–175. https://doi.org/10.1016/j.jag.2016.03.005

    Article  Google Scholar 

  • García, M. J. L., & Caselles, V. (1991). Mapping burns and natural reforestation using thematic mapper data. Geocarto International, 6(1), 31–37. https://doi.org/10.1080/10106049109354290

    Article  Google Scholar 

  • Ghimire, B., Rogan, J., Galiano, V., Panday, P., & Neeti, N. (2012). An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. Giscience and Remote Sensing, 49(5), 623–643. https://doi.org/10.2747/1548-1603.49.5.623

    Article  Google Scholar 

  • He, Y., Chen, G., De Santis, A., Roberts, D. A., Zhou, Y., & Meentemeyer, R. K. (2019). A disturbance weighting analysis model (DWAM) for mapping wildfire burn severity in the presence of forest disease. Remote Sensing of Environment, 221, 108–121.

    Article  Google Scholar 

  • Huang, H., Roy, D. P., Boschetti, L., Zhang, H. K., Yan, L., Kumar, S. S., et al. (2016). Separability analysis of Sentinel-2A multi-spectral instrument (MSI) data for burned area discrimination. Remote Sensing, 8(10), 873.

    Article  Google Scholar 

  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(88)90106-X

    Article  Google Scholar 

  • Keeley, J. E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18(1), 116–126. https://doi.org/10.1071/WF07049

    Article  Google Scholar 

  • Key, C. H., & Benson, N. (1999). The normalized burn ratio (NBR): A landsat TM radiometric MEASURE OF BURN severity. US Geological Survey Northern Rocky Mountain Science Center.

  • Key, C. H., & Benson, N. C. (2006). Landscape assessment: Remote sensing of severity, the normalized burn ratio. FIREMON: Fire Effects Monitoring and Inventory System. General Technical Report, RMRS-GTR-164-CD, (164 RMRS-GTR), 305–325.

  • Lasaponara, R. (2006). Estimating spectral separability of satellite derived parameters for burned areas mapping in the Calabria region by using SPOT-vegetation data. Ecological Modelling, 196(1–2), 265–270. https://doi.org/10.1016/j.ecolmodel.2006.02.025

    Article  Google Scholar 

  • Lima, T. A., Beuchle, R., Langner, A., Grecchi, R. C., Griess, V. C., Achard, F., & Achard, F. (2019). Comparing sentinel-2 MSI and Landsat 8 OLI imagery for monitoring selective logging in the Brazilian Amazon. Remote Sensing, 11(8), 961.

    Article  Google Scholar 

  • Mallinis, G., Mitsopoulos, I., & Chrysafi, I. (2018). Evaluating and comparing sentinel 2A and landsat-8 operational land imager (OLI) spectral indices for estimating fire severity in a mediterranean pine ecosystem of Greece. Giscience and Remote Sensing, 55(1), 1–18. https://doi.org/10.1080/15481603.2017.1354803

    Article  Google Scholar 

  • Mayer, B., & Kylling, A. (2005). Technical note: the libRadtran software package for radiative transfer calculations—Description and examples of use. Atmospheric Chemistry and Physics, 5(7), 1855–1877. https://doi.org/10.5194/acp-5-1855-2005

    Article  Google Scholar 

  • Meng, R., Wu, J., Schwager, K. L., Zhao, F., Dennison, P. E., Cook, B. D., et al. (2017). Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem. Remote Sensing of Environment, 191, 95–109. https://doi.org/10.1016/j.rse.2017.01.016

    Article  Google Scholar 

  • 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 Sensing of Environment, 109(1), 66–80. https://doi.org/10.1016/j.rse.2006.12.006

    Article  Google Scholar 

  • Montorio, R., Pérez-Cabello, F., Borini Alves, D., & García-Martín, A. (2020). Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests. Remote Sensing of Environment, 249, 112025. https://doi.org/10.1016/j.rse.2020.112025

    Article  Google Scholar 

  • Navarro, G., Caballero, I., Silva, G., Parra, P. C., Vázquez, Á., & Caldeira, R. (2017). Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 58, 97–106. https://doi.org/10.1016/j.jag.2017.02.003

    Article  Google Scholar 

  • Ngadze, F., Mpakairi, K. S., Kavhu, B., Ndaimani, H., & Maremba, M. S. (2020). Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape. PLoS ONE, 15(5), 1–13. https://doi.org/10.1371/journal.pone.0232962

    Article  Google Scholar 

  • Parks, S. A., Holsinger, L. M., Koontz, M. J., Collins, L., Whitman, E., Parisien, M. A., et al. (2019). Giving ecological meaning to satellite-derived fire severity metrics across North American forests. Remote Sensing, 11(14), 1–19. https://doi.org/10.3390/rs11141735

    Article  Google Scholar 

  • Pereira, J. M. C. (1999). A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping. In IEEE Transactions on Geoscience and Remote Sensing, 37(Part 1), (pp. 217–226).

  • Quintano, C., Fernández-Manso, A., & Fernández-Manso, O. (2018). Combination of landsat and sentinel-2 MSI data for initial assessing of burn severity. International Journal of Applied Earth Observation and Geoinformation, 64, 221–225.

    Article  Google Scholar 

  • Quintano, C., Fernández-Manso, A., & Roberts, D. A. (2013). Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sensing of Environment, 136, 76–88. https://doi.org/10.1016/j.rse.2013.04.017

    Article  Google Scholar 

  • Quintano, C., Fernández-Manso, A., & Roberts, D. A. (2020). Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm. Remote Sensing of Environment, 244, 111815.

    Article  Google Scholar 

  • Roteta, E., Bastarrika, A., Padilla, M., Storm, T., & Chuvieco, E. (2019). Development of a sentinel-2 burned area algorithm: generation of a small fire database for sub-Saharan Africa. Remote Sensing of Environment, 222, 1–17.

    Article  Google Scholar 

  • Rouse, J. W., Hass, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Final Report, RSC 1978–4, Texas A & M University, College Station, Texas.

  • Rozario, P. F., Madurapperuma, B. D., & Wang, Y. (2018). Remote sensing approach to detect burn severity risk zones in Palo Verde National Park Costa Rica. Remote Sensing, 10(9), 1–19. https://doi.org/10.3390/rs10091427

    Article  Google Scholar 

  • Schepers, L., Haest, B., Veraverbeke, S., Spanhove, T., Borre, J. V., & 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. https://doi.org/10.3390/rs6031803

    Article  Google Scholar 

  • Seydi, S. T., Akhoondzadeh, M., Amani, M., & Mahdavi, S. (2021). Wildfire damage assessment over australia using sentinel-2 imagery and MODIS land cover product within the google earth engine cloud platform. Remote Sensing, 13(2), 1–30. https://doi.org/10.3390/rs13020220

    Article  Google Scholar 

  • Teodoro, A., & Amaral, A. (2019). A statistical and spatial analysis of portuguese forest fires in summer 2016 considering landsat 8 and sentinel 2A data. Environments - MDPI, 6(3), 36.

    Article  Google Scholar 

  • Veraverbeke, S., Gitas, I., Katagis, T., Polychronaki, A., Somers, B., & Goossens, R. (2012a). Assessing post-fire vegetation recovery using red-near infrared vegetation indices: Accounting for background and vegetation variability. ISPRS Journal of Photogrammetry and Remote Sensing, 68(1), 28–39. https://doi.org/10.1016/j.isprsjprs.2011.12.007

    Article  Google Scholar 

  • Veraverbeke, S., Hook, S., & Hulley, G. (2012b). An alternative spectral index for rapid fire severity assessments. Remote Sensing of Environment, 123, 72–80. https://doi.org/10.1016/j.rse.2012.02.025

    Article  Google Scholar 

  • Veraverbeke, S., Lhermitte, S., Verstraeten, W. W., & Goossens, R. (2011). Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a mediterranean environment with landsat thematic mapper. International Journal of Remote Sensing, 32(12), 3521–3537. https://doi.org/10.1080/01431161003752430

    Article  Google Scholar 

  • Veraverbeke, S., Verstraeten, W. W., Lhermitte, S., & Goossens, R. (2010). Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece. International Journal of Wildland Fire, 19(5), 558–569. https://doi.org/10.1071/WF09069

    Article  Google Scholar 

  • Viedma, O., Meliá, J., Segarra, D., & García-Haro, J. (1997). Modeling rates of ecosystem recovery after fires by using landsat TM data. Remote Sensing of Environment, 61(3), 383–398. https://doi.org/10.1016/S0034-4257(97)00048-5

    Article  Google Scholar 

  • Warner, T. A., Skowronski, N. S., & Gallagher, M. R. (2017). High spatial resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery. International Journal of Remote Sensing, 38(2), 598–616. https://doi.org/10.1080/01431161.2016.1268739

    Article  Google Scholar 

  • White, J. D., Ryan, K. C., Key, C. C., & Running, S. W. (1996). Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 6(3), 125–136. https://doi.org/10.1071/WF9960125

    Article  Google Scholar 

  • Wilson, E. H., & Sader, S. A. (2002). Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80(3), 385–396. https://doi.org/10.1016/S0034-4257(01)00318-2

    Article  Google Scholar 

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Funding

This work was supported by the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (Grant No. 2020L0751). Thank Copernicus Emergency Management Service for the public information and data. We are grateful to the anonymous reviewers for providing comments and suggestions that greatly improved the article.

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Correspondence to Huifen Luo.

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Luo, H., Wu, J. An Assessment of the Suitability of Sentinel-2 Data for Identifying Burn Severity in Areas of Low Vegetation. J Indian Soc Remote Sens 50, 1135–1144 (2022). https://doi.org/10.1007/s12524-022-01518-7

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