Field-Validated Burn-Severity Mapping in North Patagonian Forests

Abstract

Burn severity, which can be reliably estimated by validated spectral indices, is a key element for understanding ecosystem dynamics and informing management strategies. However, in North Patagonian forests, where wildfires are a major disturbance agent, studies aimed at the field validation of spectral indices of burn severity are scarce. The aim of this work was to develop a field validated methodology for burn-severity mapping by studying two large fires that burned in the summer of 2013–2014 in forests of Araucaria araucana and other tree species. We explored the relation between widely used spectral indices and a field burn-severity index, and we evaluated index performance by examining index sensitivity in discriminating burn-severity classes in different vegetation types. For those indices that proved to be suitable, we adjusted the class thresholds and constructed confusion matrices to assess their accuracy. Burn severity maps of the studied fires were generated using the two most accurate methods and were compared to evaluate their level of agreement. Our results confirm that reliable burn severity estimates can be derived from spectral indices for these forests. Two severity indices, the delta normalized burn ratio (dNBR) and delta normalized difference vegetation index (dNDVI), were highly related to the fire-induced changes observed in the field, but the strength of these associations varied across the five different vegetation types defined by tree heights and tree and tall shrub species regeneration strategies. The thresholds proposed in this study for these indices generated classifications with global accuracies of 82% and Kappa indices of 70%. Both the dNBR and dNDVI classification approaches were more accurate in detecting high severity, but to a lesser degree for detecting low severity burns. Moderate severity was poorly classified, with producer and user errors reaching 50%. These constraints, along with detected differences in separability, need to be considered when interpreting burn severity maps generated using these methods.

Urban Fire Severity and Vegetation Dynamics in Southern California

“…In other words, the highest R 2 occurred for invasive fires that experienced both the greatest burn severity as well as canopy cover ( Figure 6B). The agreement in dNBR and dNDVI for high-severity fires has been shown in previous work, such as Franco et al [12]. Due to the aggressive nature of non-native vegetation, the rapid greening that occurs in the time between a wildfire and measurements (field-based or remote sensing) can often belie the true severity of a wildfire [15].…”

“…Since 2002, the number of fires under 5 km 2 in the urban riparian environment has increased in southern California [12]. This can be attributed to human ignition sources from transportation corridors, recreation, powerlines, and people experiencing homelessness [2,13,14].…”

Índices Espectrais e Temperatura de Superfície em Áreas Queimadas no Parque Estadual do Araguaia em Mato Grosso

“…A perda de biomassa verde (clorofila) reduz a atividade fotossintética e a absorção de luz, aumentando a refletividade das bandas do espectro visível deteriorando a estrutura celular, provoca uma redução no sinal no infravermelho próximo e infravermelho médio, em resposta a perda de umidade (Arenas et al, 2016). De modo geral, a assinatura espectral da superfície afetada pelo fogo é alterada, mudança essa associada principalmente ao teor de umidade e clorofila da vegetação, a refletância do vermelho e infravermelho próximo (NIR) diminui e no infravermelho de ondas curtas (SWIR) a refletância aumenta, influenciando no valor do índice NBR (Franco et al, 2020).…”

Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques?

“…Similarly, remote sensing techniques provide a widely used alternative to field-measured burn damage [14,[48][49][50][51][52][53]. Operational examples include the European Forest Fire Information System (EFFIS) and the Monitoring Trends in Burn Severity (MTBS) project in the USA.…”

“…Operational examples include the European Forest Fire Information System (EFFIS) and the Monitoring Trends in Burn Severity (MTBS) project in the USA. Among the most widely used techniques to estimate burn severity from satellite data, spectral indices stand out due to their simplicity [52][53][54][55][56][57][58]. In particular, using thresholds to classify differenced Normalized Burn Ratio (dNBR) [6] has become a standard to estimate burn severity from optical remotely sensed data [59], specifically from Landsat data [57,60,61].…”