Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km²), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km²) and Fire_cci (14,924 km²). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km²), followed by TREES (7985 km²), MCD64A1 (7181 km²) and Fire_cci (1745 km²). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.

The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America’s drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking.

Habitat fragmentation often induces edge effects that can increase, decrease or have minimal effect upon the population density of a species, depending upon environmental conditions and the requirements of the species. Using a trapping study and generalized linear mixed models, we evaluated edge effects on small tropical mammals living near roads, including two ground‐dwelling (Akodon sp. and Cerradomys subflavus) and two arboreal (Marmosops incanus and Rhipidomys sp.) species. We examined the relationship of these edge effects to environmental factors at both plot and patch scales. Generalist ground‐dwelling species were attracted to edges, with higher population densities recorded in habitats close to road or matrix edges where vegetation density was lower. In contrast, populations of the generalist arboreal species avoided edge habitats, their populations were found in greater density in habitats far from roads/matrix edges. Thus, our results show that patterns of edge habitat utilization were related to the ecological requirements of each species. These findings are especially important in the tropics, where demand for economic growth in many countries has accelerated the fragmentation process and has recently culminated in increased road construction and expansion. Fragmented habitats promote an increase in edge environments, and consequently will reduce the abundance of arboreal small mammal species, such as those used as models in this study.