Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

The accurate detection of burned forest area is essential for post-fire management and assessment, and for quantifying carbon budgets. Therefore, it is imperative to map burned areas accurately. Currently, there are few burned-area products around the world. Researchers have mapped burned areas directly at the pixel level that is usually a mixture of burned area and other land cover types. In order to improve the burned area mapping at subpixel level, we proposed a Burned Area Subpixel Mapping (BASM) workflow to map burned areas at the subpixel level. We then applied the workflow to Sentinel 2 data sets to obtain burned area mapping at subpixel level. In this study, the information of true fire scar was provided by the Department of Emergency Management of Hunan Province, China. To validate the accuracy of the BASM workflow for detecting burned areas at the subpixel level, we applied the workflow to the Sentinel 2 image data and then compared the detected burned area at subpixel level with in situ measurements at fifteen fire-scar reference sites located in Hunan Province, China. Results show the proposed method generated successfully burned area at the subpixel level. The methods, especially the BASM-Feature Extraction Rule Based (BASM-FERB) method, could minimize misclassification and effects due to noise more effectively compared with the BASM-Random Forest (BASM-RF), BASM-Backpropagation Neural Net (BASM-BPNN), BASM-Support Vector Machine (BASM-SVM), and BASM-notra methods. We conducted a comparison study among BASM-FERB, BASM-RF, BASM-BPNN, BASM-SVM, and BASM-notra using five accuracy evaluation indices, i.e., overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), intersection over union (IoU), and Kappa coefficient (Kappa). The detection accuracy of burned area at the subpixel level by BASM-FERB’s OA, UA, IoU, and Kappa is 98.11%, 81.72%, 74.32%, and 83.98%, respectively, better than BASM-RF’s, BASM-BPNN’s, BASM-SVM’s, and BASM-notra’s, even though BASM-RF’s and BASM-notra’s average PA is higher than BASM-FERB’s, with 89.97%, 91.36%, and 89.52%, respectively. We conclude that the newly proposed BASM workflow can map burned areas at the subpixel level, providing greater accuracy in regards to the burned area for post-forest fire management and assessment.

Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.

Comparing the Ability of Burned Area Products to Detect Crop Residue Burning in China

Burning crop residues is a common way to remove them during the final stages of crop ripening in China. To conduct research effectively, it is critical to reliably and quantitatively estimate the extent and location of a burned area. Here, we investigated three publicly available burned area products—MCD64A1, FireCCI 5.1, and the Copernicus Burnt Area—and evaluated their relative performance at estimating total burned areas for cropland regions in China between 2015 and 2019. We compared these burned area products at a fine spatial and temporal scale using a grid system comprised of three-dimensional cells spanning both space and time. In general, the Copernicus Burnt Area product detected the largest annual average burned area (37,095.1 km2), followed by MCD64A1 (21,631.4 km2) and FireCCI 5.1 (12,547.99 km2). The Copernicus Burnt Area product showed a consistent pattern of monthly burned areas during the study period, whereas MCD64A1 and FireCCI 5.1 showed frequent changes in monthly burned area peaks. The greatest spatial differences between all three products occurred in Northeast and North China, where cultivated land is concentrated. The burned area detected by Copernicus in Xinjiang Province was larger than that detected by the other two products. In conclusion, we found that all three products underestimated the amount of crop residues present in a burned area. This limits the ability of end users to understand fire-related impacts and burned area characteristics, and hinders them in making an informed choice of which product is most appropriate for their application.

Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems

The catastrophic impact of wildfires on the economy and ecosystems of Mediterranean countries in recent years, along with insufficient policies that favor disproportionally high funding for fire suppression, demand a more comprehensive understanding of fire regimes. Satellite remote sensing products support the generation of relevant burned-area (BA) information, since they provide the means for the systematic monitoring of large areas worldwide at low cost. This research study assesses the accuracy of the two publicly available MODIS BA products, MCD64A1 C6 and FireCCI51, at a national scale in a Mediterranean country. The research period covered four fire seasons, and a comparison was conducted against a higher-resolution Sentinel-2 dataset. The specific objectives were to assess their performance in detecting fire events occurring primarily in forest and semi-natural lands and to investigate their spatial and temporal uncertainties. Monthly fire observations were processed and analyzed to derive a comprehensive set of accuracy metrics. We found that fire size has an impact on their detection accuracy, with higher detection occurring in fires larger than 100 ha. Detection of smaller (<100 ha) fires was favored by the 250 m FireCCI51 product, but not from MCD64A1 C6, which exhibited less than 50% detection probability in the same range. Their spatial estimates of burned area exhibited a fairly satisfactory agreement with the reference data, reaching an average of 78% in detection rate. MCD64A1 C6 exhibited a more consistent spatial performance overall and better temporal accuracy, whereas FireCCI51 did not substantially outperform the former despite its finer resolution. Additional research is required for a more rigorous assessment of the variability of these burned area products, yet this research provides further insight and has implications for their use in fire-related applications at the local to the national scale.

Burning in southwestern Brazilian Amazonia, 2016-2019

Fire is one of the most powerful modifiers of the Amazonian landscape and knowledge about its drivers is needed for planning control and suppression. A plethora of factors may play a role in the annual dynamics of fire frequency, spanning the biophysical, climatic, socioeconomic and institutional dimensions. To uncover the main forces currently at play, we investigated the area burned in both forested and deforested areas in the outstanding case of Brazil's state of Acre, in southwestern Amazonia. We mapped burn scars in already-deforested areas and intact forest based on satellite images from the Landsat series analyzed between 2016 and 2019. The mapped burnings in already-deforested areas totalled 550,251 ha. In addition, we mapped three forest fires totaling 34,084 ha. Fire and deforestation were highly correlated, and the latter occurred mainly in federal government lands, with protected areas showing unprecedented forest fire levels in 2019. These results indicate that Acre state is under increased fire risk even during average rainfall years. The record fires of 2019 may continue if Brazil's ongoing softening of environmental regulations and enforcement is maintained. Acre and other Amazonian states must act quickly to avoid an upsurge of social and economic losses in the coming years.