Expanding the Soy Moratorium to the Cerrado biome would prevent the direct conversion of 3.6 million ha of intact Cerrado to soy.

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.

Tropical forests regulate the global water and carbon cycles and also host most of the world’s biodiversity. Despite their importance, they are hard to survey due to their location, extent, and particularly, their cloud coverage. Clouds hinder the spatial and radiometric correction of satellite imagery and also diminishing the useful area on each image, making it difficult to monitor land change. For this reason, our purpose is to identify the cloud detection algorithm best suited for the Amazon rainforest on Sentinel–2 images. To achieve this, we tested four cloud detection algorithms on Sentinel–2 images spread in five areas of the Amazonia. Using more than eight thousand validation points, we compared four cloud detection methods: Fmask 4, MAJA, Sen2Cor, and s2cloudless. Our results point out that FMask 4 has the best overall accuracy on images of the Amazon region (90%), followed by Sen2Cor’s (79%), MAJA (69%), and S2cloudless (52%). We note the choice of method depends on the intended use. Since MAJA reduces the number of false positives by design, users that aim to improve the producer’s accuracy should consider its use.

Most of the world's nations (around 130) have committed to reaching net‐zero carbon dioxide or greenhouse gas (GHG) emissions by 2050, yet robust policies rarely underpin these ambitions. To investigate whether existing and expected national policies will allow Brazil to meet its net‐zero GHG emissions pledge by 2050, we applied a detailed regional integrated assessment modelling approach. This included quantifying the role of nature‐based solutions, such as the protection and restoration of ecosystems, and engineered solutions, such as bioenergy with carbon capture and storage. Our results highlight ecosystem protection as the most critical cost‐effective climate mitigation measure for Brazil, whereas relying heavily on costly and not‐mature‐yet engineered solutions will jeopardise Brazil's chances of achieving its net‐zero pledge by mid‐century. We show that the full implementation of Brazil's Forest Code (FC), a key policy for emission reduction in Brazil, would be enough for the country to achieve its short‐term climate targets up to 2030. However, it would reduce the gap to net‐zero GHG emissions by 38% by 2050. The FC, combined with zero legal deforestation and additional large‐scale ecosystem restoration, would reduce this gap by 62% by mid‐century, keeping Brazil on a clear path towards net‐zero GHG emissions by around 2040. While some level of deployment of negative emissions technologies will be needed for Brazil to achieve and sustain its net‐zero pledge, we show that the more mitigation measures from the land‐use sector, the less costly engineered solutions from the energy sector will be required. Our analysis underlines the urgent need for Brazil to go beyond existing policies to help fight climate emergency, to align its short‐ and long‐term climate targets, and to build climate resilience while curbing biodiversity loss.

A geração dos resíduos sólidos urbanos (RSU) no Brasil vem aumentando ao longo do tempo, com alterações na qualidade do resíduo, o que levanta discussões a respeito do gerenciamento e da importância em se destinar estes resíduos adequadamente. Atualmente as questões que envolvem a gestão dos RSU têm enfoque central no cumprimento de políticas públicas. A partir da implementação efetiva da Política Nacional de Resíduos Sólidos (PNRS) - lei nº 12.305/2010 é que se esperam mudanças no panorama dos resíduos sólidos no Brasil. Por conta disso, este trabalho faz um levantamento bibliográfico que tem como objetivo abordar aspectos sobre os RSU no Brasil, considerando aqueles esperados para acontecer no setor público após a implementação da PNRS. Os dados apresentados e discutidos permitiram entender a evolução da gestão dos RSU no Brasil e alguns desafios para o cumprimento das determinações da PNRS, como, por exemplo, a expansão da coleta e tratamento dos RSU, o papel mais ativo da sociedade nos processos que envolvem a separação dos resíduos e a dificuldade da erradicação de lixões e sua substituição por aterros sanitários. Em geral, apesar de terem ocorrido melhoras no gerenciamento dos RSU, estas não foram suficientes para que as mudanças propostas pela PNRS fossem cumpridas e para que se estabeleça uma gestão integrada de RSU mais eficaz e sustentável tanto ao meio ambiente quanto à sociedade, denotando a emergência de soluções estruturais no setor de gerenciamentos dos RSU no Brasil.