Carlos Souza

The Amazon basin in transition

Agricultural expansion and climate variability have become important agents of disturbance in the Amazon basin. Recent studies have demonstrated considerable resilience of Amazonian forests to moderate annual drought, but they also show that interactions between deforestation, fire and drought potentially lead to losses of carbon storage and changes in regional precipitation patterns and river discharge. Although the basin-wide impacts of land use and drought may not yet surpass the magnitude of natural variability of hydrologic and biogeochemical cycles, there are some signs of a transition to a disturbance-dominated regime. These signs include changing energy and water cycles in the southern and eastern portions of the Amazon basin.

Boom-and-bust development patterns across the Amazon deforestation frontier.

Boom and Bust The Brazilian Amazon is renowned for its biodiversity and for its influence on climate regulation and geochemical cycles. It is also one of the countrys poorest regions. For decades, much economic development has been pursued through conversion of forest for agriculture and cattle-ranching. Rodrigues et al. (p. 1435) investigated whether this pattern of land use brings lasting prosperity by analyzing data on the economic development of nearly 300 municipalities across the deforestation frontier. Relative development, in terms of life expectancy, literacy, and standard of living, increases as deforestation begins but then declines again as the frontier passes through. As a result, pre- and postfrontier levels of development are similarly low, indicating a pattern of boom and bust. Rainforest loss in the Amazon is associated with ephemeral increase in people’s relative prosperity. The Brazilian Amazon is globally important for biodiversity, climate, and geochemical cycles, but is also among the least developed regions in Brazil. Economic development is often pursued through forest conversion for cattle ranching and agriculture, mediated by logging. However, on the basis of an assessment of 286 municipalities in different stages of deforestation, we found a boom-and-bust pattern in levels of human development across the deforestation frontier. Relative standards of living, literacy, and life expectancy increase as deforestation begins but then decline as the frontier evolves, so that pre- and postfrontier levels of human development are similarly low. New financial incentives and policies are creating opportunities for a more sustained development trajectory that is not based on the depletion of nature and ecosystem services.

Natural Resource Management in the Brazilian Amazon An integrated research approach

he Amazon region of Brazil contains billions of cubic meters of high-quality wood whose overall value after sawing would be several trillion dollars. Given this timber wealth, it is common to consider forestry as the natural vocation for Amazonia (Pandolfo 1974). Already, well over half of the wood consumed in Brazil comes from Amazonia, and this domestic demand for Amazonian roundwood is expected to grow (Verissimo et al. 1992). Foreign consumption of Amazonian wood, although low at present, is also likely to increase as Asian tropical hardwood stocks decline. Brazil, which possesses almost one-third of the worlds rain forest

Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation

Concerted political attention has focused on reducing deforestation, and this remains the cornerstone of most biodiversity conservation strategies. However, maintaining forest cover may not reduce anthropogenic forest disturbances, which are rarely considered in conservation programmes. These disturbances occur both within forests, including selective logging and wildfires, and at the landscape level, through edge, area and isolation effects. Until now, the combined effect of anthropogenic disturbance on the conservation value of remnant primary forests has remained unknown, making it impossible to assess the relative importance of forest disturbance and forest loss. Here we address these knowledge gaps using a large data set of plants, birds and dung beetles (1,538, 460 and 156 species, respectively) sampled in 36 catchments in the Brazilian state of Pará. Catchments retaining more than 69–80% forest cover lost more conservation value from disturbance than from forest loss. For example, a 20% loss of primary forest, the maximum level of deforestation allowed on Amazonian properties under Brazil’s Forest Code, resulted in a 39–54% loss of conservation value: 96–171% more than expected without considering disturbance effects. We extrapolated the disturbance-mediated loss of conservation value throughout Pará, which covers 25% of the Brazilian Amazon. Although disturbed forests retained considerable conservation value compared with deforested areas, the toll of disturbance outside Pará’s strictly protected areas is equivalent to the loss of 92,000–139,000 km2 of primary forest. Even this lowest estimate is greater than the area deforested across the entire Brazilian Amazon between 2006 and 2015 (ref. 10). Species distribution models showed that both landscape and within-forest disturbances contributed to biodiversity loss, with the greatest negative effects on species of high conservation and functional value. These results demonstrate an urgent need for policy interventions that go beyond the maintenance of forest cover to safeguard the hyper-diversity of tropical forest ecosystems.

Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon

Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000–2010 using high-resolution Landsat satellite imagery. Combining spectral mixture analysis, normalized difference fraction index, and knowledge-based decision tree classification, we mapped and assessed the accuracy to quantify forest (0.97), deforestation (0.85) and forest degradation (0.82) with an overall accuracy of 0.92. We show that 169,074 km2 of Amazonian forest was converted to human-dominated land uses, such as agriculture, from 2000 to 2010. In that same time frame, an additional 50,815 km2 of forest was directly altered by timber harvesting and/or fire, equivalent to 30% of the area converted by deforestation. While average annual outright deforestation declined by 46% between the first and second halves of the study period, annual forest degradation increased by 20%. Existing operational monitoring systems (PRODES: Monitoramento da Florestal Amazonica Brasileira por Satelite) report deforestation area to within 2% of our results, but do not account for the extensive forest degradation occurring throughout the region due to selective logging and forest fire. Annual monitoring of forest degradation across tropical forests is critical for developing land management policies as well as the monitoring of carbon stocks/emissions and protected areas.

Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD

Measuring forest degradation and related forest carbon stock changes is more challenging than measuring deforestation since degradation implies changes in the structure of the forest and does not entail a change in land use, making it less easily detectable through remote sensing. Although we anticipate the use of the IPCC guidance under the United Framework Convention on Climate Change (UNFCCC), there is no one single method for monitoring forest degradation for the case of REDD+ policy. In this review paper we highlight that the choice depends upon a number of factors including the type of degradation, available historical data, capacities and resources, and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field data (i.e. multi-date national forest inventories and permanent sample plot data, commercial forestry data sets, proxy data from domestic markets) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of techniques providing the best options. Developing countries frequently lack consistent historical field data for assessing past forest degradation, and so must rely more on remote sensing approaches mixed with current field assessments of carbon stock changes. Historical degradation estimates will have larger uncertainties as it will be difficult to determine their accuracy. However improving monitoring capacities for systematic forest degradation estimates today will help reduce uncertainties even for historical estimates.

The Fragmentation of Space in the Amazon Basin: Emergent Road Networks

In this article, we simulate forest fragmentation patterns by reference to the actual decision-making of the agents engaged in the fragmentation process itself. We take as our empirical case fragmentation in the Brazilian Amazon basin associated with road-building by loggers. Roads built by the private sector, particularly loggers, play a decisive role in the dynamics of frontier expansion in the Amazon. Our objective is to explain the manner in which logging roads manifest spatially, thereby creating fragmented landscapes in a small portion of the so-called “Terra do Meio,” a region of 300,000 km 2 in the heart of the Amazon basin. We combine geostatistical methods with GIS to replicate a common fragmentation pattern found in tropical forests known as dendritic. Such fragmentation has been identified as one of the three most common types observed in the Amazon basin. The model replicates the general dendritic pattern and many branching points of the network, although segments do not overlay precisely. The paper concludes with a discussion of steps necessary to develop a model that is fully effective in describing the spatial decision-making of loggers.

Temporal fluctuations in Amazonian deforestation rates

Tropical deforestation is one of the most important components of global change. Rates of deforestation in Brazil, the nation with the single largest concentration of tropical forest on Earth, have fluctuated widely over the last twenty years. Based on local knowledge, such fluctuations have been variously attributed to a wide range of factors such as the expansion of cattle ranching and soybean farming, infrastructural expansion and the proliferation of paved and unpaved roads, macroeconomic shocks to the Brazilian economy and international exchange rates. Many, if not all, of these arguments are plausible explanations for temporal variation in deforestation rates, but have to date not been subjected to rigorous statistical testing; this study investigates the potential impact of these variables on Brazilian tropical deforestation over the period 1990-2005. When analysed at the basin-wide scale, nearly all variables were highly inter-correlated through time and were also closely correlated with deforestation rate, but appropriate time-series analysis found no statistical evidence that any of the variables have systematically caused variation in deforestation rates. Power analysis showed that the variables may exert small or medium influences on deforestation rates, but the impacts, if present, are not strong. Future analyses of time series data at finer spatial scales that exploit spatiotemporal variation in deforestation rates and in the hypothesized predictor variables may find significant causal processes that arc overlooked when analysed at the basin-wide scale.

Predictive Modelling of Contagious Deforestation in the Brazilian Amazon

Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.

Mapping forest degradation in the Amazon region with Ikonos images

Ikonos images can provide unprecedented information about forest degradation caused by selective logging and forest surface fires in the Brazilian Amazon. Until now, the loss of the Brazilian rainforest has been estimated regularly through deforestation statistics from maps produced with Landsat images (INPE 2000). Selectively logged forests and burned forests have not been included in the basin wide statistics, but can be mapped with Landsat and SPOT images as well (Cochrane and Souza 1998, Stone and Lefebvre 1998, Souza and Barreto 2000, Asner et al. 2002, Monteiro et al. 2003). These moderate spatial resolution images do not provide detailed information about degraded forests. For example, forest structure and composition (e.g. crown size, liana density, tree species, etc) cannot be retrieved at these moderate spatial resolutions (i.e. 20–30 m pixel size). In addition, old degraded forests (i.e. .2 years) are difficult to distinguish from intact forests with both Landsat and SPOT images. This is one of the reasons forest degradation has been reported as ‘cryptic deforestation’ (Nepstad et al. 1999), and has been considered ‘invisible’ to Landsat images (Laurence and Fearnside 1999). Ikonos images, which can be acquired with resolution as fine as one metre, offer a new capability to map forest degradation in the Amazon region. The cover image (figure 1) is a unique example of the resolving spatial power of the Ikonos images, obtained by merging spectral bands 4 (0.757–0.853 mm), 3 (0.632–0.698 mm) and 2 (0.506–0.595 mm) with the panchromatic band (0.45–0.90 mm). In this example, all of the processes that cause forest degradation were captured in one snapshot. We will discuss the potential of the Ikonos images to map degraded forests in the Brazilian Amazon and evaluate whether forest degradation may become ‘cryptic’ to the Ikonos sensor.