How Do Emission Factors Contribute to the Uncertainty in Biomass Burning Emissions in the Amazon and Cerrado?

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 423 - 423

Published: April 4, 2025

Fires drive global ecosystem change, impacting carbon dynamics, atmospheric composition, biodiversity, and human well-being. Biomass burning, a major outcome of fires, significantly contributes to greenhouse gas aerosol emissions. Among these, fine particulate matter (PM2.5) is particularly concerning due its adverse effects on air quality health, substantial yet uncertain role in Earth’s energy balance. Variability emission factors (EFs) remains key source uncertainty estimates. This study evaluates PM2.5 sensitivity EFs variability Brazil’s Amazon Cerrado biomes over 2002–2023 using the 3BEM_FRP model implemented PREP-CHEM-SRC tool. We updated with values ranges from Andreae (2019), which reflect more comprehensive literature review than earlier datasets. The results reveal that annual average emissions varied by up 162% (1213 Gg yr−1 3172 yr−1) 184% (601 1709 yr−1). Average peak at grid-cell level reached 5688 Mg “Arc Deforestation” region under High-end EF scenario. Notably, forest areas increased time despite shrinking cover, indicating Amazonian forests are becoming vulnerable fire. In Cerrado, savannas primary land cover contributing total emissions, accounting for 64% 80%. These findings underscore importance accurate, region-specific improving models reducing uncertainties.

Language: Английский

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data

Sensors, Journal Year: 2024, Volume and Issue: 24(5), P. 1587 - 1587

Published: Feb. 29, 2024

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, five-class two dates (winter non-winter) of Sentinel-2 granule across seven international sites. The approach uses spectral, textural, distance decision functions combined modified ancillary layers (such as global impervious surface tree cover) create binary masks from which generate balanced set applied random forest classifier. For the masks, stepwise threshold adjustments were reflectance, spectral index values, Euclidean layers, 62 combinations evaluated. Global (all scenes) regional (arid, tropics, temperate) adaptive thresholds computed. An annual 95th 5th percentile NDVI composite was used provide temporal corrections functions, these compared against original model. accuracy assessment found that both two-date temporally corrected could accurately type within nine-class (68.4% vs. 73.1%), six-class (79.8% 82.8%), (80.1% 85.1%) schemes. Lastly, models manually labeled deep model (Esri), where they performed similar accuracies (five classes: Esri 80.0 ± 3.4%, region 85.1 2.9%). results highlight only performance in line an intensive approach, but also reasonably accurate can be created without full imagery.

Language: Английский

Fire effect on bamboo-dominated forests in Southwestern Amazon: impacts on tree diversity and forest structure

Revista Brasileira de Ciências Ambientais, Journal Year: 2024, Volume and Issue: 59

Published: Jan. 1, 2024

Severe droughts increase the forest flammability, especially if fires are recurrent. Considering that tend to alter structure and reduce biological diversity, we analyzed fire effect on tree plant community over a 10-year post-fire period. The study was carried out in two tropical fragments located eastern Acre State southwestern Brazilian Amazon. In each fragment, established three plots of 250 × 10 m2 an unburned burned forest. these plots, collected all individuals with DBH≥10 following RAINFOR protocol, censuses made 2011, 2014, 2016, 2017, 2019, 2020 2021. significantly reduced abundance, basal area, aboveground biomass species, altered species composition along temporal gradient. absence differences richness diversity between forests is probably related life cycle bamboo. results suggest that, years after fire, phytosociology have not yet fully recovered.

Language: Английский