CLASlite Publications

The planned construction of hundreds of hydroelectric dams in the Amazon basin has the potential to provide invaluable 'clean' energy resources for aiding in securing future regional energy needs and continued economic growth. These mega-structures, however, directly and indirectly interfere with natural ecosystem dynamics, and can cause noticeable tree loss. To improve our understanding of how hydroelectric dams affect the surrounding spatiotemporal patterns of forest disturbances, this case study integrated remote sensing spectral mixture analysis, GIS proximity analysis and statistical hypothesis testing to extract and evaluate spatially-explicit patterns of deforestation (clearing of entire forest patch) and forest degradation (reduced tree density) in the 80,000 km2 neighborhoods of the Brazil's Tucuruí Dam, the first large-scale hydroelectric project in the Amazon region, over a period of 25 years from 1988 to 2013. Results show that the average rates of deforestation were consistent during the first three time periods 1988-1995 (620 km2 per year), 1995-2001 (591 km2 per year), and 2001-2008 (660 km2 per year). However, such rate dramatically fell to half of historical levels after 2008, possibly reflecting the 2008 global economic crisis and enforcement of the Brazilian Law of Environmental Crimes. The rate of forest degradation was relatively stable from 1988 to 2013 and, on average, was 17.8% of the rate of deforestation. Deforestation and forest degradation were found to follow similar spatial patterns across the dam neighborhoods, upstream reaches or downstream reaches at the distances of 5 km-80 km, suggesting that small and large-scale forest disturbances may have been influencing each other in the vicinity of the dam. We further found that the neighborhoods of the Tucuruí Dam and the upstream region experienced similar degrees of canopy loss. Such loss was mainly attributed to the fast expansion of the Tucuruí town, and the intensive logging activities alongside major roads in the upstream reservoir region. In contrast, a significantly lower level of forest disturbance was discovered in the downstream region.

• Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
• Department of Ecology and Evolutionary Biology, Yale University, USA
• Department of Forest Sciences, Federal University of Lavras, Brazil
• Global Observation for Forest Cover and Land Dynamics (GOFC/GOLD) Land Cover Project Office, Wageningen University and Research Centre, The Netherlands

Forests are changing faster today than at any time since the Ice Age. The human enterprise has driven vast forest losses and a mounting wake of forest regrowth that defies historical biogeography. As the pace of change accelerates, so does the demand to monitor forests for conservation and resource policy.

When I was a graduate student in the 1990s, mapping Amazon deforestation was science at the bleeding edge. New reports of rainforest losses were trickling in, creating shock waves around the world. Although the problem of deforestation was known at the time, new satellite-based maps provided a strong propellant for rainforest action. The satellite of choice for deforestation monitoring was the National Aeronautics and Space Administration's Landsat, and images sold for more than $2,000. I vividly remember that breathtaking moment when my professor purchased three images for my thesis project. However, at least 250 cloud-free Landsat images are required to make one decent map of the Amazon each year. Back then only a few elite groups could afford such a trove of satellite data, and there were even fewer experts with their secret methods to convert impenetrable Landsat pixels into user-friendly maps.

• Department of Global Ecology, Carnegie Institution for Science, United States

Carbon-centric conservation strategies such as the United Nation's program to Reduce CO₂Emissions from Deforestation and Degradation (REDD+), are expected to simultaneously reduce net global CO₂ emissions and mitigate species extinctions in regions with high endemism and diversity, such as the Tropical Andes Biodiversity Hotspot. Using data from the northern Andes, we show, however, that carbon-focused conservation strategies may potentially lead to increased risks of species extinctions if there is displacement (i.e., "leakage") of land-use changes from forests with large aboveground biomass stocks but relatively poor species richness and low levels of endemism, to forests with lower biomass stocks but higher species richness and endemism, as are found in the Andean highlands (especially low-biomass non-tree growth forms such as herbs and epiphytes that are often overlooked in biological inventories). We conclude that despite the considerable potential benefits of REDD+ and other carbon-centric conservation strategies, there is still a need to develop mechanisms to safeguard against possible negative effects on biodiversity in situations where carbon stocks do not covary positively with species diversity and endemism.

• Departamento de Ciencias Forestales, Universidad Nacional de Colombia, Colombia
• Department of Biological Sciences, Florida International University, USA
• Instituto de Hidrologia, Metreologia y Estudios Ambientales –IDEAM, Colombia
• Instituto de Biología – Herbario Universidad de Antioquia, Universidad de Antioquia., Colombia

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including-in the latter case-x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha-1 when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

• Department of Global Ecology, Carnegie Institution for Science, United States of America

To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario.

• Forests and Climate Global Initiative, WWF-US, USA

Oil palm plantation expansion into tropical forests may alter physical and biogeochemical inputs to streams, thereby changing hydrological function. In West Kalimantan, Indonesia, we assessed streams draining watersheds characterized by five land uses: intact forest, logged forest, mixed agroforest, and young (<3 years) and mature (>10 years) oil palm plantation. We quantified suspended sediments, stream temperature, and metabolism using high-frequency submersible sonde measurements during month-long intervals between 2009 and 2012. Streams draining oil palm plantations had markedly higher sediment concentrations and yields, and stream temperatures, compared to other streams. Mean sediment concentrations were fourfold to 550-fold greater in young oil palm than in all other streams and remained elevated even under base flow conditions. After controlling for precipitation, the mature oil palm stream exhibited significantly greater sediment yield than other streams. Young and mature oil palm streams were 3.9°C and 3.0°C warmer than the intact forest stream (25°C). Across all streams, base flow periods were significantly warmer than times of stormflow, and these differences were especially large in oil palm catchments. Ecosystem respiration rates were also influenced by low precipitation. During an El Niño-Southern Oscillation-associated drought, the mature oil palm stream consumed a maximum 21 g O2 m^-2 d^-1 in ecosystem respiration, in contrast with 2.8 ± 3.1 g O2m^-2 d^-1 during nondrought sampling. Given that 23% of Kalimantan's land area is occupied by watersheds similar to those studied here, our findings inform potential hydrologic outcomes of regional periodic drought coupled with continued oil palm plantation expansion.

• Institute on the Environment, University of Minnesota, Saint Paul, Minnesota, USA
• School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut, USA
• Woods Institute for the Environment, Stanford University, Stanford, California, USA
• Department of Anthropology, Stanford University, Stanford, California, USA
• Santa Fe Institute, Santa Fe, New Mexico, USA
• Department of Geography, University of North Texas, Denton, Texas, USA
• Living Landscapes Indonesia, Pontianak, Kalimantan Barat, Indonesia

We use social ecological systems theory (SES) to analyse change in forest communities in the northern Bolivian Amazon. SES characterizes interdependent dynamics of social and ecological systems and we hypothesized it to be a useful frame to grasp dynamics of forest communities affected by changes in forest policies, regulations and institutions, as well as economic demands and conservation objectives. We analysed the long-term historical changes since the region became incorporated in the global tropical forest product value chain since the late 19th century and quantitatively analysed changes in 85 forest communities between 1997 and 2009. We collected information on 16 variables related to demographic, productive, and socio-economic characteristics. Results show that forest communities have experienced major changes and have adapted to these changes. Social thresholds, a key concept in SES, are consistent with multiple social economic forces experienced by forest communities. Detrimental feed-back effects of SES can be confronted when innovative exploration mechanisms, such as new productive chains are developed, or the agro-extractive cycles of current productive system are expanded. Competition among households, population growth and more profitable economic opportunities may threaten benign forms of forest products extraction that have persisted through various cycles of internal and external changes.

• Tropenbos International, The Netherlands
• Center for Integrated Area Studies, Kyoto University, Japan
• Utrecht University, The Netherlands
• Forest Ecology and Forest Management Group, Wageningen University and Research Center, The Netherlands

• Department of International Development and Environment Studies Norwegian University of Life Sciences, Norway

Figure 1. Map of Juma Sustainable Development Reserve, Amazon State. Villages in black are located outside the reserve borders, while those marked with grey are located within.

We conducted an analysis of deforestation and forest disturbance from 2005-2011 in Masoala National Park, the largest federal protected area in Madagascar. We found that the annual rate of forest change in 2010-2011 within the park (1.27%) was considerably higher than in 2005-2008 (0.99%), and was higher than the most recently published deforestation rate for all of Madagascar. Although deforestation and disturbance immediately following the 2009 coup d'état were lower than in the other time periods analyzed, the longer-term increase in forest change over the study period corroborates recent ground-based accounts of increased illegal activities in national parks, including logging of precious hardwoods. We also analyzed forest disturbance patterns in relation to rivers and travel distance from permanent villages. Forest disturbances were significantly closer to rivers than expected by chance, and 82% of disturbance was within the mean maximum travel distance to villages surrounding the park. Both results strongly indicate that most of the mapped disturbance in the study area is anthropogenic, despite two cyclones during the study period. High-resolution forest monitoring ensures that forest change statistics accurately reflect anthropogenic disturbances and are not inflated by forest losses resulting from natural processes.

• University of California, Berkeley, Department of Environmental Science, Policy and Management, USA
• Wildlife Conservation Society, Madagascar Program, Madagascar
• Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, USA
• Harvard University Center for the Environment, USA
• World Wildlife Fund-US, Conservation Science Program, USA

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests.

• Department of Global Ecology, Carnegie Institution for Science, United States
• Dirección General de Ordenamiento Territorial, and cAsesor de la Alta Dirección, Ministerio del Ambiente, Peru