Abstract
Forest fires are significant catastrophic events that affect the landscape and vegetation in forested lands. They cause loss of biodiversity, land degradation & ecological imbalance. As the forest fires cause extreme damage to the habitat, it is of utmost necessity to assess the impact of fire on canopy/vegetation. Post-fire assessment is an essential element for finding the effects of fire on vegetation and implementing mitigation strategies. In this article, a Post-fire burn severity assessment was carried out with high-resolution multi-spectral images such as Sentinel-2 and Landsat-8 employing Google Earth Engine (GEE) to locate the burnt areas and fire severity. Three commonly used fire severity indices based on pre-fire Normalized Burn Ratio (NBR) and post-fire NBR, namely differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), and Relativized dNBR (RdNBR) are computed and compared based on their accuracy with the active fire points provided by MODIS & VIIRS. Both Sentinel-2 and Landsat-8 exhibited a similar trend in mapping burn severity. The RdNBR resulted in high accuracy over heterogeneous landscapes with 61.52% for Sentinel-2 and 64.1% for Landsat-8 followed by dNBR (41.67% for Sentinel-2 and 47.44% for Landsat-8) and weak performance by RBR with 32.69% for Sentinel-2 and 26.92% for Landsat-8. Hence RdNBR burn severity maps are considered highly appropriate for mapping burnt areas. Even though severity analysis from both Sentinel-2 and Landsat-8 is at an acceptable level, the Landsat based burn severity maps provided an adequate assessment of the degree of damage.
Similar content being viewed by others
References
Adagbasa GE, Adelabu SA, Okello TW (2018) Spatio-temporal assessment of fire severity in a protected and mountainous ecosystem. IGARSS 2018–2018 IEEE Int Geosci Remote Sens Symp 6572–6575. https://doi.org/10.1109/igarss.2018.8518268
Cardil A, Mola-Yudego B, Blázquez-Casado Á, González-Olabarria JR (2019) Fire and burn severity assessment: Calibration of Relative Differenced Normalized Burn Ratio (RdNBR) with field data. J Environ Manag 235:342–349. https://doi.org/10.1016/j.jenvman.2019.01.077
Cocke AE, Fulé PZ, Crouse JE (2005) Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data. Int J Wildl Fire 14:189–198. https://doi.org/10.1071/WF04010
DeFries R, Achard F, Brown S et al (2007) Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environ Sci Policy 10:385–394. https://doi.org/10.1016/j.envsci.2007.01.010
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 29:1053–1073. https://doi.org/10.1080/01431160701281072
Konkathi P, Shetty A (2019) Assessment of burn severity using different fire indices: A case study of Bandipur National Park. Proc 2019 IEEE Recent Adv Geosci Remote Sens Technol Stand Appl TENGARSS 2019 151–154. https://doi.org/10.1109/TENGARSS48957.2019.8976036
Konkathi P, Shetty A, Kolluru V et al (2019) Static fire risk index for the forest resources of Karnataka. Int Geosci Remote Sens Symp 6716–6719. https://doi.org/10.1109/IGARSS.2019.8898522
Lutes DC, Keane RE, Caratti JF et al (2006) FIREMON: Fire effects monitoring and inventory system. Gen Tech Rep. RMRS-GTR-164-CD. https://doi.org/10.2737/RMRS-GTR-164
Miller JD, Thode AE (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
Miller JD, Knapp EE, Key CH et al (2009) Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens Environ 113:645–656. https://doi.org/10.1016/j.rse.2008.11.009
Parks SA, Dillon GK, Miller C (2014) A new metric for quantifying burn severity: The relativized burn ratio. Remote Sens 6:1827–1844. https://doi.org/10.3390/rs6031827
Quintano C, Fernández-Manso A, Fernández-Manso O (2018) Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. Int J Appl Earth Obs Geoinf 64:221–225. https://doi.org/10.1016/j.jag.2017.09.014
Rahman S, Chang H, Hehir W et al (2018) Inter-comparison of fire severity indices from moderate (MODIS) and moderate-to-high spatial resolution ( LANDSAT 8 & SENTINEL-2A ) satellite sensors Department of Environmental Sciences, Macquarie University, Australia NSW Rural Fire Service, 15 Car. 2881–2884
Suresh Babu KV, Roy A, Aggarwal R (2018) Mapping of forest fire burned severity using the sentinel datasets. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci XLII 5:469–474. https://doi.org/10.5194/isprs-archives-xlii-5-469-2018
Tran BN, Tanase MA, Bennett LT, Aponte C (2018) Evaluation of spectral indices for assessing fire severity in Australian temperate forests. Remote Sens 10. https://doi.org/10.3390/rs10111680
Venkatesh K, Preethi K, Ramesh H (2020a) Evaluating the effects of forest fire on water balance using fire susceptibility maps. Ecol Indic 110. https://doi.org/10.1016/j.ecolind.2019.105856
Venkatesh K, Ramesh H, Das P (2020b) Modelling stream flow and soil erosion response considering varied land practices in a cascading river basin. J Environ Manage 264:110448. https://doi.org/10.1016/j.jenvman.2020.110448
Wagle N, Acharya TD, Kolluru V et al (2020) Multi-temporal land cover change mapping using google earth engine and ensemble learning methods. Appl Sci 10:1–20. https://doi.org/10.3390/app10228083
Yathish H, Athira KV, Preethi K et al (2019) A Comparative Analysis of Forest Fire Risk Zone Mapping Methods with Expert Knowledge. J Indian Soc Remote Sens 47:2047–2060. https://doi.org/10.1007/s12524-019-01047-w
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by: H. Babaie.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Konkathi, P., Shetty, A. Inter comparison of post-fire burn severity indices of Landsat-8 and Sentinel-2 imagery using Google Earth Engine. Earth Sci Inform 14, 645–653 (2021). https://doi.org/10.1007/s12145-020-00566-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-020-00566-2