10/16/2024

IKI supports Brazil to improve environmental protection and forest restoration

Opencast mining at the tree line

At the G20 Amazon Conference, an IKI project presented innovative forest restoration efforts in Brazil and Indonesia, using open Copernicus satellite data to support transparent forest monitoring.

As part of the G20 Ministerial Meeting of Science, a G20 Conference on the Amazonas took place in Manaus on the 17-18, September 2024. Under the motto “Open Innovation to strengthen international North-South cooperation in Science, Technology & Innovation”, the RESTORE+ project was invited to present its results. Supported by the International Climate Initiative (IKI), RESTORE+ promotes the development of open knowledge to use European Copernicus satellite data to help to protect and restore forests in Brazil and Indonesia. Open source software and open science will be used to modernise forest monitoring systems in Brazil and Indonesia. Transparent and reproducible forest monitoring systems are key to building the confidence by international partners on the commitment in Brazil and Indonesia to promote nature-based solutions.

The effort will be led by Prof. Dr. Gilberto Camara, an internationally recognised scientist who was Director of the National Institute for Space Research (INPE) and former Director of the Secretariat of the Group on Earth Observations (GEO). The studies and technologies funded by the IKI will be fully open source, based on open data from the Copernicus programme. They will be available to all Brazilian institutions and experts, as well as to Indonesia and other countries, especially Amazonian countries that want to use Copernicus data for environmental and biodiversity action.

New government plans and advanced technology boost Brazil's forest protection efforts

A color-coded map with large areas in dark green, contrasting with scattered patches in red, yellow, beige, and white. The green spaces dominate, while the smaller colored patches appear irregularly distributed across the center of the image. The overall style resembles a heat map or abstract pattern.
Comparison between visual interpretation and machine learning classification in part of the state of Rondonia (Brazil). The map shows natural forest areas in green and deforestation up to the year 2022 in white. The dark lines are deforested areas visually identified by INPE’s interpreters for 2022. The machine learning method identified deforestation in 2022 in the coloured areas. Brown areas indicate total deforestation. Red areas are places where tree cuts have been followed by fire. Light green areas are places where most of the forest has been cleared, but some trees remain. Note that the dark lines (visual interpretation) match the coloured areas (machine learning results). This shows that this new approach matches the quality and accuracy of the previous approach while increasing transparency, reproducibility, consistency, scalability, timely monitoring of deforestation and reducing costs.

Since 2012, the IKI has supported Brazilian researchers to develop land use models that provided critical information for Brazil’s Nationally Determined Contribution (NDC) and the Bonn Challenge pledge of 12 million ha of forest landscape restoration (FLR), submitted to the Paris Agreement in 2015. Brazilian scientists examined evidence-based policies needed for tropical forest restoration and the drivers of deforestation and degradation.

The new Brazilian government, elected in 2022, has drawn up important plans to protect the environment in all of Brazil’s biomes. These plans include key actions to prevent deforestation and degradation in the Amazon and Cerrado biomes. Brazilian scientists are developing advanced machine learning techniques to improve the country’s monitoring system. These new technologies will use the large and open data sets from Europe's Copernicus satellite programme. Brazil will significantly improve its capacity to protect the environment. Using open data from the Copernicus satellite, Brazil will also be able to improve its mapping of tropical forest degradation and its causes, and identify priority areas for promoting restoration actions.

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