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In the Brazilian Amazon, multiple logging activities are undergoing, involving different actors and interests. They shape a disturbance gradient bound to the intensity and frequency of logging, and forest management techniques. However, until now, few studies have been carried out at the landscape scale taking into account these multiple types of logging and this disturbance gradient. Here we address this issue of how to account for the multiple logging activities shaping the current forest landscape. We developed an inexpensive and efficient remote sensing methodology based on Landsat imagery to detect and track logging activity based on the monitoring of canopy openings. Then, we implemented a set of remote sensing indicators to follow the different trajectories of forest disturbance through time. Using these indicators, we emphasized five major spatial and temporal disturbance patterns occurring in the municipality of Paragominas (State of Pará, Brazilian Amazon), from well-managed forests to highly over-logged forests. Our disturbance indicators provide observable evidence for the difference between legal and illegal patterns, with some illegal areas having suffered more than three explorations in fifteen years. They also clearly underlined the efficiency of Reduced Impact Logging (RIL) techniques applied under Forest Stewardship Council (FSC) guidelines to reduce the logging impacts in terms of canopy openings. For these reasons, we argue the need to promote legal certified logging to conserve forests, as without them, many actors mine the forest resources without any concerns for future stocks. Finally, our remote tracking methodology, which produces easy to interpret disturbance indicators, could be a real boon to forest managers, including for conservationists working in protected areas and stakeholders dealing with international trade rules such as RBUE (Wood regulation of European Union) or FLEGT (Forest Law for Enforcement, Governance and Trade). [ABSTRACT FROM AUTHOR] more...

In the agricultural frontiers of Brazil, the distinction between forested and deforested lands traditionally used to map the state of the Amazon does not reflect the reality of the forest situation. A whole gradient exists for these forests, spanning from well conserved to severely degraded. For decision makers, there is an urgent need to better characterize the status of the forest resource at the regional scale. Until now, few studies have been carried out on the potential of multisource, freely accessible remote sensing for modelling and mapping degraded forest structural parameters such as aboveground biomass (AGB). The aim of this article is to address that gap and to evaluate the potential of optical (Landsat, MODIS) and radar (ALOS-1 PALSAR, Sentinel-1) remote sensing sources in modelling and mapping forest AGB in the old pioneer front of Paragominas municipality (Para state). We derived a wide range of vegetation and textural indices and combined them with in situ collected AGB data into a random forest regression model to predict AGB at a resolution of 20 m. The model explained 28% of the variance with a root mean square error of 97.1 Mg·ha−1 and captured all spatial variability. We identified Landsat spectral unmixing and mid-infrared indicators to be the most robust indicators with the highest explanatory power. AGB mapping reveals that 87% of forest is degraded, with illegal logging activities, impacted forest edges and other spatial distribution of AGB that are not captured with pantropical datasets. We validated this map with a field-based forest degradation typology built on canopy height and structure observations. We conclude that the modelling framework developed here combined with high-resolution vegetation status indicators can help improve the management of degraded forests at the regional scale. [ABSTRACT FROM AUTHOR] more...