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ABSTRACT In a particular region within western Amazonia, Amazonian manatees (Trichechus inunguis) live in a floodplain environment that becomes inhospitable for them during the annual low-water season. To flee from it, they undergo a dangerous migration to a refuge while water levels are dropping fast. Our aim was to better understand the role of depth variation in this migratory process. We analyzed the sole tracking data on wild manatees (n=10 males), 30 years of Landsat images, a 14-year hydrograph and a 3-D bathymetric model. Migratory routes contained shallower segments, here called 'migratory bottlenecks', which dried out at the end of most lowering-water seasons, blocking the passage to the refuge. Manatees began migrating just in time to traverse the bottlenecks furthest away, suggesting they fine-tuned their departure so as to maximize time within the foraging home range without compromising safety. They apparently achieved this by estimating depth at the bottlenecks. Moreover, a bottleneck was created in >15 years, illustrating the environment's dynamism and the challenge this imposes upon manatees. Our results are probably generalizable to most of the species' range. We contend manatees possess an updatable cognitive map of their environment and are behaviorally plastic. Current dam-building plans, if implemented, would create more bottlenecks and make flooding less predictable, increasing manatee mortality from unsuccessful migrations. It would also partition the species into small populations, each prone to short-term extinction. The natural outcome would be the second species-level collapse. Economic growth should not come at the expense of the extinction of the iconic manatee.

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

O presente trabalho demonstra a aplicabilidade de imagens de sensoriamento remoto e de métodos de processamento de imagens digitais para definição de locais adequados à instalação de sistemas telemétricos de monitoramento de variáveis ambientais em sistemas aquáticos, localizados em regiões de difícil acesso. A técnica consiste essencialmente da aplicação de operações Booleanas entre mapas da pluma do Rio Amazonas e de zonas inundadas do Lago de Curuai em diferentes etapas do ciclo hidrológico. A localização exata para o sistema de monitoramento telemétrico será vital para o desenvolvimento de modelos de troca de gases traço entre a planície de inundação Amazônica e a atmosfera.The present work illustrates the application of remote sensing and image processing methods to define appropriate sites for installing buoy moored telemetric systems at the surface of Amazon floodplain lakes for long-term limnologic-micrometeorologic monitoring. The technique consists essentially of Boolean operations over Amazon plume maps and historic inundation of the Curuai Lake at distinct stages of the hydrologic cycle. The precise location for the long-term monitoring is vital to the development of models concerning air-water trace gas exchange in the Amazon floodplains.

Este artigo se propõe a apresentar exemplos de questões científicas que puderam ser respondidas no contexto do Projeto LBA (Large Sale Biosphere-Atmosphere Experiment in Amazonia) graças à contribuição de informações derivadas de sensoriamento remoto. Os métodos de sensoriamento remoto permitem integrar informações sobre os vários processos físicos e biológicos em diferentes escalas de tempo e espaço. Nesse artigo, são enfatizados aqueles avanços de conhecimento que jamais seriam alcançados sem a concorrência da informação derivada de sensoriamento.This paper aims to assess the contribution of remote sensing technology in addressing key questions raised by the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). The answers to these questions foster the knowledge on the climatic, biogechemical and hydrologic functioning of the Amazon, as well as on the impact of human activities at regional and global scales. Remote sensing methods allow integrating information on several processes at different temporal and spatial scales. By doing so, it is possible to perceive hidden relations among processes and structures, enhancing their teleconnections. Key advances in the remote sensing science are summarized in this article, which is particularly focused on information that would not be possible to be retrieved without the concurrence of this technology.

Técnicas de sensoriamento remoto são fundamentais para o monitoramento das mudanças de uso da terra, principalmente em áreas extensas como a Amazônia. O mapeamento de uso da terra, geralmente é realizado por métodos de classificação manual ou digital pixel a pixel, os quais consomem muito tempo. Este estudo aborda a aplicação do modelo linear de mistura em uma imagem Landsat-TM segmentada para o mapeamento das classes de uso da terra na região do reservatório de Tucuruí-PA para os anos de 1996 e 2001.Remote sensing techniques are mandatory for monitoring land use changes in large areas such as the Amazon. Land use mapping is usually performed by both manual and digital pixel based classification methods which are cost and time-consuming. In this study an image segmentation approach is applied to unmix TM-Landsat images for mapping land use classes in Tucuruí-PA reservoir region to 1996 and 2001.