Evaluating a New Relative Phenological Correction and the Effect of Sentinel-Based Earth Engine Compositing Approaches to Map Fire Severity and Burned Area

Abstract

The remote sensing of fire severity and burned area is fundamental in the evaluation of fire impacts. The current study aimed to: (i) compare Sentinel-2 (S2) spectral indices to predict field-observed fire severity in Durango, Mexico; (ii) evaluate the effect of the compositing period (1 or 3 months), techniques (average or minimum), and phenological correction (constant offset, c, against a novel relative phenological correction, rc) on fire severity mapping, and (iii) determine fire perimeter accuracy. The Relative Burn Ratio (RBR), using S2 bands 8a and 12, provided the best correspondence with field-based fire severity (FBS). One-month rc minimum composites showed the highest correspondence with FBS (R2 = 0.83). The decrease in R2 using 3 months rather than 1 month was ≥0.05 (0.05–0.15) for c composites and <0.05 (0.02–0.03) for rc composites. Furthermore, using rc increased the R2 by 0.05–0.09 and 0.10–0.15 for the 3-month RBR and dNBR compared to the corresponding c composites. Rc composites also showed increases of up to 0.16–0.22 and 0.08–0.11 in kappa values and overall accuracy, respectively, in mapping fire perimeters against c composites. These results suggest a promising potential of the novel relative phenological correction to be systematically applied with automated algorithms to improve the accuracy and robustness of fire severity and perimeter evaluations.

Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment

“…However, the larger the area, the greater the heterogeneity of fire images presented, so it is also a question of whether to choose a more accurate way, such as deep learning, to extract the burning area in the method for a higher spatial resolution. In addition, the accuracy of this method for extracting heavily burned area is higher than that of mildly burned area, which is similar to the study of Zhang et al (2021) and Silva-Cardoza et al (2022) , In addition, the novelty of this method lies in the rapid acquisition of before and after fire images through the GEE cloud computing platform. GEE and the developed algorithm are very attractive and suitable for providing near real-time burned area maps in that both burned areas and the different levels of burn severity can be identified automatically and without using fixed threshold values.…”

Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America

“…While the MTBS program already announced a plan to begin integrating Sentinel imagery upon availability of a harmonized Landsat and Sentinel dataset [24], to date, no formal analysis of Sentinel's performance for burn severity mapping has been conducted in the United States or Canada. Regional studies in other parts of the world generally show the suitability of Sentinel for burn severity mapping, including Australia [37,38], Bolivia [39], Greece [25], India [40], Italy [41,42], Mexico [43], Portugal [44], Spain [45][46][47][48], and Siberia [49]. However, the methods used to map and assess burn severity estimates are highly variable, with a limited number of studies directly comparing Landsat and Sentinel using field-validated data.…”

Efecto de la severidad de incendio en la estructura y regeneración del bosque tropical de pino-implicaciones para su restauración

“…El efecto de los incendios en la cobertura vegetal puede categorizarse mediante la evaluación de su severidad. Esta categorización se puede realizar primero con herramientas de percepción remota para luego hacer una comprobación en el campo (Key & Benson 2006, Parks et al 2018, Silva-Cardoza et al 2022. Para categorizar la severidad de incendio en el campo se utilizan varios indicadores: (i) el nivel de consumo de combustibles superficiales, por ejemplo la hojarasca y el material leñoso caído, (ii) la presencia de vegetación en el sotobosque, como las hierbas, arbustos y los árboles jóvenes, (iii) el soflamado de las copas, (iv) la mortalidad de árboles adultos y (v) los cambios en la densidad y en el área basal (Agee 1996).…”