Figure 1.
Fire and deforestation in the Brazilian Amazon.
A) The Legal Brazilian Amazon showing reserves and World Fire Atlas hot pixels from 1996–2006. The high-impact forest is to the southeast and low-impact forest is to the northwest of the yellow boundary line. Roads mentioned in the text are labeled. B) PRODES deforestation polygons through 2005 against the background of annual rainfall from the WorldClim dataset. High-impact areas include the states of Rondônia [RO], Mato Grosso [MT], Tocantins [TO], Maranhão [MA] and the portion of Pará [PA] east of the Xingu River. Low-impact areas include the states of Acre [AC], Amazonas [AM], Roraima [RR], Amapá [AP] and the portion of Pará north and west of the Xingu river.
Figure 2.
Relationship between 1996–2006 hot pixels/100 km2 and their distance to roads.
A) low-impact and B) high-impact forests (low- and high- impact areas as shown in Fig. 1). Data are separated by whether fires are inside (grey) or outside (black) reserves. Fire rates were calculated on the basis of distance classes, but data points are offset from the class number for clarity (e.g., x values of 9 and 10 for class 10).
Figure 3.
Relationship between 2001–2003 hot pixels/100 km2 and their distance to roads for 3 different sensors, ATSR/AATSR, AVHRR, and MODIS.
A–C) low-impact and D–F) high-impact forests (low- and high- impact areas as shown in Fig. 1). Data are separated by whether fires are inside (grey) or outside (black) reserves. Fire rates were calculated on the basis of distance classes, but data points are offset from the class number for clarity (e.g., x values of 9 and 10 for class 10). All sensors detect at a 1-km2 resolution, but differ in detection algorithms and overpass times.
Table 1.
Significance values for analysis of covariance tests of the patterns of decline in hot pixels with road distance inside and outside of reserves in Fig. 2 (WFA) and Fig. 3 (sensor comparison).
Table 2.
Significance values for analysis of covariance tests of the differences between high-impact and low-impact forests (e.g., reserves in high-impact vs. reserves in low-impact) shown in Fig. 2 (WFA) and Fig. 3 (sensor comparison).
Figure 4.
Relationship of the Multivariate ENSO Index (MEI) and the incidence of hot pixels/100 km2.
Panels show (WFA, 1996–2005) hot pixels per year and the average yearly MEI A) within 10 km of roads (close) and B) more than 10 km from roads (far). Data are separated by whether fires are inside (grey) or outside (black) reserves. Significance values for analysis of covariance tests are as follows: inside reserves, close to roads: p<0.004; inside reserves, far from roads: p<0.004; outside reserves, close to roads: p<0.02; outside reserves, far from roads: p = 0.17.
Figure 5.
Differences in fire frequencies between fully protected parks, indigenous lands, and limited-use areas in the Brazilian Amazon.
Solid lines (right axis) show the percentage of each reserve type (each year) with at least 1 hot pixel. Dashed lines (left axis) show the average number of hot pixels/100 km2 in those reserves that do have at least 1 hot pixel. Grey stripes indicate ENSO years.
Figure 6.
Regional differences in reserve protection against fire.
A) Spatial distribution of reserves in the Brazilian Amazon. Close ups of areas in black squares where all reserve types are in close proximity, left to right: B) Rondônia, C) the BR-174 north of Manaus, and D) eastern Amazon (Maranhão and Pará). The WFA hot pixels for 1996–2006 are shown as red dots. Line colors denote reserve types: orange, indigenous lands; purple, limited use areas; green, fully protected parks. The background images are Landsat MrSID images (https://zulu.ssc.nasa.gov/mrsid/), and MODIS Blue Marble images, from the year 2000.