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In this article we describe Chile’s transition from an agriculture productive model that originated in the 19th century into a more complex economic model that incorporates forest production, explaining the role of forest sciences in this process. Forest science has made great contributions to the country especially in terms of improving forestation and forest management techniques that have allowed the rapid expansion of the forestry industry and prevented soil erosion on degraded lands. However, native forests have been neglected and vast areas of forest have been replaced with exotic plantations. This process has highlighted the imperative need for developing a new productive model to assure not only a fair distribution of wealth but also the use of science-based sustainable forest management practices to protect native forest ecosystems nationwide. A national strategic plan for managing, conserving, and restoring native forests is needed not only to align the forest industry with sustainable development but also to develop sound climate change strategies to allow the country’s goal of becoming carbon neutral by 2050. Under this scenario forest science can play an important role by producing much needed evidence-based knowledge.

A fundamental pillar of community-driven restoration projects is motivation, without which initiatives are doomed to fail. However, technical knowledge and funding are also important factors for success. In this work, we present three case studies of post-fire restoration projects driven by local communities in Central Chile to assess whether motivation and proactive actions were sufficient to carry out successful restoration projects. We found that while in the three cases motivation was essential for starting restoration actions, the level of technical knowledge, the ability to connect with academic institutions, and the capability to secure funding played key roles in the projects' results. We identified three key factors that should be present to increase community-driven restoration success: (1) appropriate technical knowledge and expertise to set suitable restoration objectives and activities; (2) adequate capabilities and networks for procuring financial and technical resources; and (3) capacity to apply for available funding to ensure resources for later phases. Ensuring the concurrence of these three factors cannot be the sole responsibility of local communities. Instead, it should be a shared responsibility between government agencies and academic institutions to develop strategies that help develop links between experts and local communities.

Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. As a result, the restoration of degraded landscapes is essential to maintaining the functionality of ecosystems, especially those with greater social, economic, and environmental vulnerability. Nevertheless, policymakers are frequently challenged by deciding where to prioritize restoration actions, which usually includes dealing with multiple and complex needs under an always limited budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. In order to help decision-makers take informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social, and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing, and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called “RePlant alpha”, which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis with at least six indicators for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical.

Abstract. In an era of increasing wildfires frequency and intensity an accurate estimation of the emissions released to the atmosphere is essential to reduce their impacts. In this study, we improve the accuracy of our estimations by introducing field measurements of biomass and adapting the burning efficiency factors to different levels of burn severity computed from Sentinel-2 data. The biomass measured in the field complemented the data found in the literature. The emissions derived were compared with the emissions from the GFED product showing a good agreement, although GFED values were higher than ours, suggesting that GFED may overestimate the emissions due to their coarse resolution and the generalized factors applied to large ecosystems.

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