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

Abstract

Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel’s finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems.

Assessment of Small-Extent Forest Fires in Semi-Arid Environment in Jordan Using Sentinel-2 and Landsat Sensors Data

“…Sentinel-2 (MSI) data can be recommended as a key Earth observation data source in forest resources assessment and monitoring [34]. Howe et al [35] assess the accuracy of Sentinel-and Landsat-derived burn indices for 26 fires that burned between 2016 and 2019 in western North America. They concluded that burn severity mapping could significantly benefit from the integration of Sentinel imagery to Landsat-forest surveillance data by increasing imagery availability (image every 5 days), and that Sentinel's higher spatial resolution can improve opportunities for inspecting finer-scale fire impact across ecosystems.…”

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

“…No study has been published for the 2021 Muli fire, and the area burned in Muli County in 2021 has not been monitored in the remote sensing fire product MOD14A2 resolution due to its large spatial resolution (1 km). Therefore, this method can extract the burning area of smaller fires more accurately, but the extraction accuracy is lower for larger fires such as the 2019 fire, which may be potentially due to fire having heterogeneous effects at scales finer than 30 m, particularly in non-stand replacing fires, resulting in high sub-pixel variability in tree mortality ( Schroeter & Foster, 2004 ; Howe et al, 2022 ) and frequency of smaller-sized unburned patches. 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.…”

“…There have been studies using the decision tree to build a combination of the burning index and OTSU thresholds to extract burned areas ( Tianchi, Fuqin & Yan, 2020 ; Li et al, 2021a ; Tang et al, 2021 ; Tian et al, 2022 ), which can achieve better accuracy results. There are also comparative studies on the extraction of the burned area from Sentinel-2 and Landsat 8 data sources, and they found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems ( Mallinis, Mitsopoulos & Chrysafi, 2018 ; Zhang et al, 2019 ; Howe et al, 2022 ). However, the extraction and evaluation of burned areas with different damage using Sentinel-2 are lacking, so assessing the method’s applicability is necessary.…”

Assessing the Use of Burn Ratios and Red-Edge Spectral Indices for Detecting Fire Effects in the Greater Yellowstone Ecosystem

“…This can be attributed to a number of variables, including the spatial resolution of Landsat images, the radiometric resolution of the sensors used during the time of these studies being inadequate to capture the slight variations in radiance, and the lack of spectral bands in the red-edge region. Previous research has shown slight improvement in the performance of Sentinel-2 NBR-based indices when compared to Landsat 8 NBR-based indices [24,25]. However, this research was limited to indices that could be calculated by both sensor systems, which eliminates the use of red-edge indices.…”