A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine

Abstract

A preliminary version of a global automatic burned-area (BA) algorithm at medium spatial resolution was developed in Google Earth Engine (GEE), based on Landsat or Sentinel-2 reflectance images. The algorithm involves two main steps: initial burned candidates are identified by analyzing spectral changes around MODIS hotspots, and those candidates are then used to estimate the burn probability for each scene. The burning dates are identified by analyzing the temporal evolution of burn probabilities. The algorithm was processed, and its quality assessed globally using reference data from 2019 derived from Sentinel-2 data at 10 m, which involved 369 pairs of consecutive images in total located in 50 20 × 20 km2 areas selected by stratified random sampling. Commissions were around 10% with both satellites, although omissions ranged between 27 (Sentinel-2) and 35% (Landsat), depending on the selected resolution and dataset, with highest omissions being in croplands and forests; for their part, BA from Sentinel-2 data at 20 m were the most accurate and fastest to process. In addition, three 5 × 5 degree regions were randomly selected from the biomes where most fires occur, and BA were detected from Sentinel-2 images at 20 m. Comparison with global products at coarse resolution FireCCI51 and MCD64A1 would seem to show to a reliable extent that the algorithm is procuring spatially and temporally coherent results, improving detection of smaller fires as a consequence of higher-spatial-resolution data. The proposed automatic algorithm has shown the potential to map BA globally using medium-spatial-resolution data (Sentinel-2 and Landsat) from 2000 onwards, when MODIS satellites were launched.

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

“…In contrast, for average composites, the initial post-fire severity was more diluted, likely by forest recovery responses (e.g., Figure 3b,d) and possibly also by increased fuel and soil moisture with post-fire precipitation (Figure 3j,l), which was undesirable for burned area perimeter and initial fire severity detection. This supports studies that have proposed the use of the minimum or maximum values (depending on the spectral index) for burned area perimeter mapping (e.g., [27,51,144]). For example, Roteta et al [26] reported that minimum NBR composites retained the pixel values when the burned signal was considered to be strongest, corroborated by our comparisons of post-fire minimum versus average NBR composites (Figure 3).…”

“…Dates immediately before and after each fire, based on MODIS and VIIRS active fire data, were used to define each composite search period (Table S2). We tested average and minimum compositing since they are the most widely used approaches in the literature (e.g., [19,27,28,41,42,44,45,48]). For each 1-and 3-month composite, three combinations of average (A) and minimum (M) for the pre-and post-fire images (AA, AM, and MM) were tested (see Table 4).…”

“…Regarding this challenge, several spectral indices using medium-resolution sensors have been developed [3]. These include several formulations of NBR indices based on NIR/SWIR bands (e.g., [24][25][26][27]), and more recently, spectral indices including Sentinel-2 red-edge bands such as CIre [28,29] or BAIS2 [30]. Nonetheless, the selection of the most appropriate indices from Sentinel-2 to map fire severity and perimeters is still an open research question, with a large variety of results depending on the ecosystems analyzed (e.g., [8,9,[31][32][33][34][35]).…”

“…This is the case in Mexico, a very ecologically diverse country with high fire activity (e.g., [36][37][38][39]), where previous studies evaluating spectral indices, validated with field data, to map fire severity and burned area perimeters, are lacking. Pixel-based compositing methods can provide a new analysis paradigm instead of depending on a single scene [40], resulting in an unprecedented opportunity for standardizing and automating fire severity and burned area evaluations [27,[41][42][43]. Nevertheless, the image composite techniques analyzed in the literature have largely varied, depending on the study goal, location, and period analyzed.…”

“…For example, mean composites have been used for extended assessments of fire severity [19,41,44,45] and mean, median, or medoid [46,47] composites have been also used to map burned area and recovery [47][48][49][50]. Likewise, minimum, or maximum composites [26,27,42,[51][52][53] have been used for mapping fire perimeters and trends of burned area [54] or fire severity [55]. Because of this variety of approaches, there is a need for a formal evaluation of the effect of compositing techniques (i.e., average or minimum) and compositing periods on their accuracy in mapping fire perimeters and fire severity.…”