Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential

Abstract

Abstract:Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre-and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: The delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection but instead use a mean compositing approach in which all valid pixels (e.g., cloud-free) over a pre-specified date range (pre-and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared to the standard approach in which one pre-fire and one post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western United States. These validations are compared to Landsat-based fire severity datasets produced using only one pre-and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets generally show improved validation statistics compared to parallel versions in which only one pre-fire and one post-fire scene are used, though some of the improvements in some validations are more or less negligible. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e., fire perimeters) be produced independently and prior to applying our methods, we suggest that our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.

Incorporating biophysical gradients and uncertainty into burn severity maps in a temperate fire‐prone forested region

“…Past comparisons between RdNBR and dNBR have also found minor quantitative differences in performance between indices (Miller and Thode , Cansler and McKenzie ), as have comparisons that also include RBR (Parks et al. , , Whitman et al. ).…”

“…Similar performance among indices likely results from the fact that all three indices are slightly different derivatives of the normalized burn ratio (Key and Benson 2005); that is, they are all starting with the same information from Landsat bands that measure reflectance in near-infrared and shortwave-infrared wavelengths. Past comparisons between RdNBR and dNBR have also found minor quantitative differences in performance between indices Thode 2007, Cansler andMcKenzie 2012), as have comparisons that also include RBR (Parks et al 2014a, 2018b, Whitman et al 2018. Our findings support the idea that relative indices (e.g., RdNBR and RBR) fit better to field data than absolute indices (e.g., dNBR), as has been reported elsewhere (Miller and Thode 2007, Parks et al 2014a, Whitman et al 2018) and that canopy measures of burn severity are captured more accurately than forest-floor measures ).…”

Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests

“…We produced the spectral indices from Landsat imagery using the Google Earth Engine cloud computing platform [45]. For each spectral variable, we used the mean compositing approach described by Parks et al [50] to produce pre-and post-fire image composites. This approach selects all valid pixels (i.e., free of clouds, shadows, water, and snow) within a pre-specified time window (i.e., 'image season', Table 2) for one year prior to fire for pre-fire imagery and one year after fire for post-fire imagery.…”

“…Earth Engine is becoming a widely used tool for measuring and quantifying continental-to-global characteristics such as annual gross primary production [46] and changes in the distribution of surface water [47] and land cover [48]. Earth Engine has also been used for mapping satellite-derived fire extent [49] and fire severity [50]. Machine learning models such as Random Forest regression and classification [51] are available in Earth Engine, and as such, models can be built and executed within Earth Engine, including those describing fire severity [44].…”

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

“…Phenology-based threshold methods have increasingly gained public and scientific attention in geospatial science. The Phenology-based classification has shown a promising level of accuracy for mapping vegetation, crops, and land cover using moderate to high spatial resolution satellite data [24,27,[54][55][56] with aid of GEE cloud computing technology [23,53,[57][58][59][60][61]. There has been some research effort to study surface phenology of vegetation, rubber plantation, and cropland mapping in tropical regions [62][63][64].…”