Potential of UAV images as ground-truth data for burn severity classification of Landsat imagery: approaches to an useful product for post-fire management

Authors

  • M. Pla Centre Tecnològic Forestal de Catalunya (CTFC-InFOREST) https://orcid.org/0000-0002-7060-6783
  • A. Duane Centre Tecnològic Forestal de Catalunya (CTFC-InFOREST); Centre de Recerca Ecològica i Aplicacions Forestals
  • L. Brotons Centre Tecnològic Forestal de Catalunya (CTFC-InFOREST); Centre de Recerca Ecològica i Aplicacions Forestals; Consejo Superior de Investigaciones Científicas (CSIC)

DOI:

https://doi.org/10.4995/raet.2017.7140

Keywords:

severity, wildfires, Landsat, UAV, RdNBR

Abstract

Mapping fire severity is determinant to understand landscape evolution after a wildfire and provides useful information for decision making during post fire management. Quantitative fire severity mapping from relative changes in Normalized Burn Ratio index (RdNBR) is not actually being incorporated into decision making processes, being more useful the categorization in severity levels (high, moderate and low). However, the most common mapping severity methodologies based on the definition of RdNBR thresholds from field information are not always possible due to lack of field data or because the published thresholds are unsatisfactory in new regions. The boom in the use of UAVs (Unmanned Aerial Vehicle) has raised these platforms as potential tools for validation of remote sensing data. This paper presents the potential of UAVs as ground truth information in forest fires. From the photointerpretation of high resolution RGB images, the Aerial Severity Proportion Index (ASPI) has been created. Non-linear regression models between RdNBR and ASPI allows to delimitate of thresholds for the classification of Landsat images and to obtain qualitative severity maps. Validation with random points presents a kappa index of 0,5 and a relative accuracy of 70,8%. Therefore, UAV images become a very useful tool for wildfire severity mapping and for fill the gap between remote sensing information and expensive field ground campaigns

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References

Aguirre-Gómez, R., Salmerón-García, O., GómezRodríguez, G., Peralta-Higuera, A. 2017. Use of unmanned aerial vehicles and remote sensors in urban lakes studies in Mexico. International Journal of Remote Sensing, 38(8-10). https://doi.org/10.1080 /01431161.2016.1264031

Banu, T.P., Borlea, G.F., Banu, C. 2016. The Use of Drones in Forestry. Journal of Environmental Science and Engineering, B5, 557-562. https://doi. org/10.17265/2162-5263/2016.11.007

Burnham, K.P., Anderson, D.R. 2002. Model Selection and Multimodel Inference: A Practical InformationTheoretical Approach. New York: Springer-Verlag.

Cansler, C.A., McKenzie, D. 2012. How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods. Remote Sensing, 4(2), 456–83. https://doi.org/10.3390/rs4020456

Chen, X., Vogelmann, J.E., Rollins, M., Ohlen, D., Key, C. H., Yang, L., Huang, C., Shi, H. 2011. Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. International Journal of Remote Sensing. 32(23). https://doi.org/10.1080/01 431161.2010.524678

Díaz-Delgado, R., Lloret, F. and Pons, X. 2003. Influence of Fire Severity on Plant Regeneration by Means of Remote Sensing Imagery. International Journal of Remote Sensing, 24(8), 1751–1763. https://doi.org/10.1080/01431160210144732

Dunford, R., K. Michel, M. Gagnage, Piégay, H., Trémelo, M. L. 2009. Potential and Constraints of Unmanned Aerial Vehicle Technology for the Characterization of Mediterranean Riparian Forest. International Journal of Remote Sensing, 30(19), 4915–4935. https://doi.org/10.1080/01431160903023025

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. International Journal of Remote Sensing, 29(4), 1053–1073. https://doi. org/10.1080/01431160701281072

Lentile, L.B., Holden, Z.A., Smith, A.M.S., Falkowski, M.J., Hudak, A.T., Morgan, P., Lewis, S.A., Gessler, P.E., Benson, N.C. 2006. Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects. International Journal of Wildland Fire, 15, 319–345. https://doi.org/10.1071/WF05097

Long, J.S., Freese, J. 2005. Regression Models for Categorical Dependent Variables Using Stata. College Station, TX: Stata Press, Second Edition.

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

Näsi, R., Honkavaara, E., Lyytikäinen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., Viljanen, N., Kantola, T., Tanhuanpää, T., Holopainen, M. 2015. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sensing, 7(11), 15467– 15493. https://doi.org/10.3390/rs71115467

Palomo, I., Martín-López, B., Potschin, M. HainesYoung, R., Montes, C. 2013. National Parks, Buffer Zones and Surrounding Lands: Mapping Ecosystem Service Flows. Ecosystem Services, 4, 104–116. https://doi.org/10.1016/j.ecoser.2012.09.001

Parks, S.A., Gregory K. D., Miller, C. 2014. A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio. Remote Sensing, 6(3), 1827–1844. https://doi.org/10.3390/rs6031827

Pons, X., Solé-Sugrañes. L. 1994. A Simple Radiometric Correction Model to Improve Automatic Mapping of Vegetation from Multispectral Satellite Data. Remote Sensing of Environment, 48(2), 191–204. https://doi.org/10.1016/0034-4257(94)90141-4

Puliti, S., Olerka, H., Gobakken, T., Næsset, E. 2015. Inventory of Small Forest Areas Using an Unmanned Aerial System. Remote Sensing, 7(8), 9632–9654. https://doi.org/10.3390/rs70809632

Sardà-Palomera, F., Bota, G., Viñolo, C., Pallarés, O., Sazatornil, V., Brotons, L., Sardà, F. 2012. FineScale Bird Monitoring from Light Unmanned Aircraft Systems. Ibis, 154(1), 177–183. https://doi. org/10.1111/j.1474-919X.2011.01177.x

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), 125–36. https://doi.org/10.1071/ WF9960125

Yi, S. 2017. FragMAP: a tool for long-term and cooperative monitoring and analysis of small-scale habitat fragmentation using an unmanned aerial vehicle. International Journal of Remote Sensing, 38 , Iss. 8-10. https://doi.org/10.1080/01431161.20 16.1253898

Published

2017-12-05