| Title | Rapid recent deforestation incursion in a vulnerable indigenous land in the Brazilian Amazon and fire-driven emissions of fine particulate aerosol pollutants |
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| Author | Oliveira, G; Chen, J M; Mataveli, G A V; Chaves, M E D; Seixas, H T; Cardozo, F S; Shimabukuro, Y E; He, L ; Stark, S C; dos Santos, C A C |
| Source | Forests vol. 11, issue 8, 829, 2020 p. 1-18, https://doi.org/10.3390/f11080829  Open Access |
| Image |  |
| Year | 2020 |
| Alt Series | Natural Resources Canada, Contribution Series 20200409 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf; html |
| Area | Brazil |
| Lat/Long WENS | -52.8333 -51.2500 -5.0000 -6.5000 |
| Subjects | Science and Technology; Nature and Environment; Society and Culture; fires; Amazonia; Forest fires; Deforestation |
| Illustrations | location maps; graphs; diagrams |
| Released | 2020 07 30 |
| Abstract | Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation,
land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to
perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM2.5), a primary human health
risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three
years (200 km2), which is clearly associated with an increase in the PM2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM2.5 was estimated to be 3594 ton year-1, while the most recent interval of 2017-2019 had an average
of 6258 ton year-1. This represented an increase of 58% in the annual average of PM2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an
ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within "protected areas" in the Brazilian Amazon. To reverse this scenario, the
implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential. |
| Summary | (Plain Language Summary, not published)
This study focuses on deforestation in the Brazilian Amazon, specifically in the Apyterewa Indigenous Land. Deforestation often involves the use of fire
to clear land for agriculture. The researchers aimed to understand the relationship between deforestation, land-use changes, and fire emissions in this region.
To do this, they used a geographic object-based image analysis (GEOBIA) to map land-use
and land-cover changes. They also used a model to estimate emissions of particulate matter with a diameter less than 2.5 µm (PM2.5), which can harm human health.
The study found that in the past three years, about 200 square kilometers of primary
forest were converted into agriculture within the Apyterewa Indigenous Land. This led to a significant increase in PM2.5 emissions from fires. From 2017 to 2019, PM2.5 emissions increased by 58%, contributing to air quality problems and health
risks.
These findings highlight the ongoing deforestation and forest degradation within supposedly protected areas in the Brazilian Amazon. To address this, sustainable agricultural practices and conservation policies promoting forest regrowth in
degraded areas are crucial. This research underscores the need to balance agricultural development with forest preservation in critical ecosystems like the Amazon. |
| GEOSCAN ID | 327223 |
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