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The Atlantic Forest is a global biodiversity hotspot and a significant provider of ecosystem services to 65% of the Brazilian population. Due to being highly threatened, it is protected by federal law 11,428/2006, which establishes forest use restrictions based on native vegetation successional stages in the Atlantic Forest, with more advanced stages receiving more protection. The classification parameters are established at the state level. However, the parameters employed to classify forest fragments in different successional stages are subjective and imprecise, negatively impacting environmental permitting and related offset policies. Here, we critically assessed the major limitations in applying the 11,428/2006 law and presented alternatives for establishing a more transparent, applicable, legally safe, and effective protocol for identifying the conservation value of forest fragments. We also highlight problems related to sampling, indicators, and methodologies and present guidelines for revising the parameters for applying the Atlantic Forest law and associated state-level resolutions. We suggest an inclusive two-step analysis based on vegetation structure, forest cover history, biodiversity, ecosystem services (social), and landscape indicators. By employing a more technological approach and transferring part of the assessment responsibility to the state-level environmental agencies instead of allowing self-declared reports by landowners, our proposal focuses on the potential for evaluating ecological integrity among different successional classes by forest types. As nearly 90% of the remaining Brazilian Atlantic Forest is located within private lands, improving this legal instrument is essential for protecting the vulnerable biodiversity of this unique and threatened biome.

The use of multistep upscaling approaches in which field data are combined with data from multiple remote sensors that operate at different spatial scales (e.g., UAV LiDAR, GEDI, and Landsat) is becoming increasingly popular. In these approaches, a series of models are fitted linking the information from these different sensors, often resulting in improved predictions over large areas. Quantifying the uncertainty associated with individual models can be challenging as these models may not generate uncertainty estimates (e.g., machine learning models such as random forest), a problem that is further exacerbated if the results from multiple models are combined within a multistep upscaling methodology. In this article, we describe a nonparametric conformal approach to quantify uncertainty. This approach is straight-forward to apply, is computationally inexpensive (differently from bootstrapping), and generates improved predictive intervals. Importantly, this methodology can be used regardless of the number of models adopted in the upscaling approach and the nature of the intermediate models, as long as the final model can generate predictive intervals. We illustrate the improved empirical coverage of the conformalized predictive intervals using simulated data for a two-step upscaling scenario involving field, UAV LiDAR, and Landsat data. This simulation exercise shows how increasing uncertainty in the first stage model (which relates biomass field data to UAV LiDAR data) leads to an increase in the severity of uncertainty underestimation by naïve predictive intervals. On the other hand, conformalized predictive intervals do not exhibit this shortcoming. Finally, we illustrate uncertainty quantification for a multistep upscaling methodology using data from a large-scale carbon project in the Brazilian Amazon. Our validation exercise using these empirical data confirms the improved performance of the conformalized predictive intervals. We expect that the conformal approach described here will be key for uncertainty quantification as multistep upscaling approaches become increasingly more common.

Tropical deforestation drivers are complex and can change rapidly in periods of profound societal transformation, such as those during a pandemic. Evidence suggests that the COVID-19 pandemic has spurred illegal, opportunistic forest clearing in tropical countries, threatening forest ecosystems and their resident human communities. A total of 9583 km2 of deforestation alerts from Global Land Analysis & Discovery (GLAD) were detected across the global tropics during the first month following the implementation of confinement measures of local governments to reduce COVID-19 spread, which is nearly double that of 2019 (4732 km2). We present a conceptual framework linking tropical deforestation and the current pandemic. Zoonotic diseases, public health, economy, agriculture, and forests may all be reciprocally linked in complex positive and negative feedback loops with overarching consequences. We highlight the emerging threats to nature and society resulting from this complex reciprocal interplay and possible policy interventions that could minimize these threats.