Learn how Forest Data Partnership’s approach will build alignment of stakeholders to reach consensus around key datasets in the ever-expanding landscape of forest monitoring data.

2. Data is vital to tackling deforestation & accelerating restoration
Technology offers
unprecedented opportunities to
transform the way landscapes
are monitored and managed

3. We need to harness the full
potential of data and harmonize
the current fragmented data
landscape

4. The Forest Data Partnership
Unites organizations, governments and private sector
partners around trusted, transparent geospatial data
solutions that enable credible monitoring, verification and
disclosure of progress in reducing deforestation and
restoring degraded lands.

5. Five action-oriented work streams

6. Align process

7. ● Focal areas
○ Cocoa in West Africa
○ Palm Oil in Southeast Asia
○ Cattle and Soy in the Amazon Basin
○ Restoration
● Who are we prioritizing? Mapping stakeholders based on:
○ Roles in how they interact with and/or produce data,
○ Their impact in affecting deforestation and restoration,
○ Their influence in the uptake and application of data
● Actors across supply chains and financial sectors, governments in
producer and consumer countries, civil society, technology and
service providers
○ Reaching beyond the usual suspects
○ Local and place-based affected stakeholders to ensure representation
Align Who?

8. Align On
What?
Current data challenges and obstacles to uptake and application
of data, such as:
● Gaps (the data doesn’t exist, or isn’t accessible)
● Conflicts (scales, methods)
● Accuracy
● Definitions
● Applications

9. Align - How
and Why?
●Tiered approach
○ Information gathering from specific stakeholders already
active in ongoing discussions
○ Targeted engagement strategies and business case for
participation for tougher to reach stakeholders
● Tailored to the context and priorities of specific
regions and supply chains
● Learning from existing MSAs to develop alignment
and decision-making processes
○ Interaction between different stakeholders
○ Ensuring representation and buy-in for outputs
(consultation vs. consensus)

10. Align on
Foundational Data
Gaps
Develop Inclusive
demand-driven
Innovations
Deploy Data
Delivery
Mechanisms
Restoration
Deforestation
Continuous improvement and
replication
Continuous improvement and
replication
Impact
Assessment
Research
Informs policy and
project development
How will this work?
Informs policy and
project development

11. Innovate
process

12. Commodity mapping at scale: spatial prediction

13. Anonymized Data
Raw Data Reference Data
Existing maps
Predicted Maps
(Value + Uncertainty)
Existing maps
Existing maps
Existing maps
Map 1
Map 2
Map N
ENSEMBLE
REF DATA
LCML
Private Aligned Partners Public
Commodity mapping at scale: harmonization

14. Which oil palm map should you believe? (Hint: trick question.)
Meta-learner*
* Wolpert, 1992, Stacked generalization.

15. Inclusive mapping at scale
From the Ground to the Cloud and
back
If the harmonized map is wrong,
users who are affected have an
opportunity to correct the record
by submitting more data to train
the meta-learner.
Cloud-based
analytics and
ML
Section #5
Local
communities,
sustainability
users, citizen
scientists
Local insights
and
knowledge, in-
situ
observations
More accurate,
timely
information
Ground measurements

16. A dynamic pathway for restoration monitoring & planning
Reporting
Monitoring
Planning

17. THANK YOU
https://forestdatapartnership.org