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<p>The 2022 report from the United Nations Environment Programme exposes that climate and land-use changes are expected to increase the frequency and intensity of wildfires worldwide. This study analyzes burn severity in two experimental fires (B1 and B2) in the Paraná River wetlands, using the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) derived from Sentinel-2 and drone images at different spatial scales. The proposed approach enhances spatial resolution, enabling more detailed assessments of burn severity within a highly heterogeneous wetland environment. Results indicate that over 60% of B1 and 80% of B2 burned at low to moderate severity, a pattern closely associated with fuel characteristics. Drone-derived severity showed a positive correlation with fuel moisture and ash particle size. These findings underscore the relevance of integrating field measurements with remote sensing to improve burn severity estimates, particularly in dynamic ecosystems such as the Paraná River wetlands.</p>

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Assessing Experimental Burn Severity in the Paraná River Wetlands Using Drone and Satellite Data

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Elisabet Walker,
  • Virginia Venturini,
  • Zuleica Marchetti,
  • Aylen Carrasco Milián,
  • Reiman Alfonso,
  • Adriana Millán,
  • María Tibaldo,
  • Bibiana Bilbao

摘要

The 2022 report from the United Nations Environment Programme exposes that climate and land-use changes are expected to increase the frequency and intensity of wildfires worldwide. This study analyzes burn severity in two experimental fires (B1 and B2) in the Paraná River wetlands, using the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) derived from Sentinel-2 and drone images at different spatial scales. The proposed approach enhances spatial resolution, enabling more detailed assessments of burn severity within a highly heterogeneous wetland environment. Results indicate that over 60% of B1 and 80% of B2 burned at low to moderate severity, a pattern closely associated with fuel characteristics. Drone-derived severity showed a positive correlation with fuel moisture and ash particle size. These findings underscore the relevance of integrating field measurements with remote sensing to improve burn severity estimates, particularly in dynamic ecosystems such as the Paraná River wetlands.