Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images

• Published in: International journal of remote sensing Vol. 29; no. 4; pp. 1053 - 1073
• Main Authors: Escuin, S., Navarro, R., Fernández, P.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.02.2008; Taylor and Francis
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Teledetection and vegetation maps; Iberian Peninsula; Southern Europe; Andalusia Spain; Europe; Spain; Thematic Mapper; maps; Vegetation index; Space remote sensing; Characteristic; Landsat; accuracy; vegetation; cartography; evaluation; Reflection spectrometry; Wildfire; fires; displacements; imagery; segmentation; discrimination; Vegetation fire; near infrared spectroscopy; Pixel; Threshold
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160701281072

In this work, the capacity of NBR and NDVI indices derived from LANDSAT TM/ETM images has been analysed for fire severity assessment. For this purpose, three fires occurring in southern Spain were studied. Firstly, the displacements of burned and unburned pixels in the pre-/post-fire NIR-MIR and NIR-R bi-spectral spaces were analysed with the aim of establishing which of the two indices was the most sensitive for discriminating severity levels. Then, the capacity of the two indices, both from a uni-temporal (post-fire) and bi-temporal perspective (pre and post-fire), to discriminate three severity levels was studied. Based on the results, it was decided that the most suitable way to assess wildfire severity by index segmentation was to discriminate between unburned and burned pixels according to their NBR pre-/post-fire difference values (dNBR), and, subsequently, to distinguish between pixels with an extreme and moderate severity based on the NBR post-fire values. The thresholds calculated for these indices permitted fire severity mapping with an accuracy of 86.42% (±4.31%). These thresholds could be extrapolated to other fires with similar characteristics although a calculation of their own specific thresholds could improve the accuracy of the fire severity map obtained.