Forest Data Partnership

The Forest Data Partnership strengthens collaboration and application around global monitoring of commodity-driven deforestation, forest degradation and restoration efforts across the globe.
The Forest Data Partnership: a collective approach for ending deforestation and accelerating restoration

Governments and companies around the world are pledging to help end deforestation and accelerate restoration in order to avert the worst impacts of climate change, protect against biodiversity loss and safeguard the many benefits of forests to people and nature.

In order for governments and companies to establish meaningful plans to meet these pledges, and monitor and demonstrate progress, it is essential that all parties have accurate and timely information about forest extent, land use and the drivers of land use change including deforestation. This information needs to be available in a transparent and consistent format so that all parties can compare and collectively assess their progress toward meeting these pledges.

The Forest Data Partnership aims to halt and reverse forest loss from commodity production by collaboratively improving global monitoring and supply chain tracking and accelerating restoration. It aligns partners around the data and ensures access for stakeholders across sectors to consistent, validated open-source geospatial forest-risk commodity data.

The Forest Data Partnership will develop a consistent geospatial data ecosystem that will enable all actors — local, government, producers, traders and financiers — to access consistent, open-source, publicly available and validated geospatial data related to forest-risk commodities and restoration. In doing so, the Partnership will facilitate credible and systematic monitoring, verification and disclosure to drive progress in reducing deforestation and restoring degraded lands.

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Engage Partners
and Stakeholders
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Align on
Foundational
Data Gaps
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Innovate
Demand-Driven

Approaches

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Deploy Data
Delivery
Mechanisms
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Assess Impact
Innovating Demand-Driven Approaches

We work with you to compile, harmonize and mobilize the best available data using the ensemble method, a machine learning technique that combines several base models to produce an optimal, predictive model. Our ensemble includes the best available data sets, benefiting from diverse inputs

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If you have field, ground or training data, we can use it to train the ensemble, improving
prediction in your area of interest.
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If you have map products to contribute to the ensemble, our custom pipeline will illuminate where your model is the strongest.
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Through our network of trusted observers, we conduct field inventories to train and validate our ensemble or use crowd-sourced open data.