MRV Systems
MRV System Design with AI Integration
End to end monitoring, reporting, and verification systems that combine field data, remote sensing, and AI assisted classification.
The challenge
MRV System Design with AI Integration
Carbon and biodiversity projects live or die on MRV. Weak baselines, unaddressed leakage, and unverifiable permanence sink credits at validation. A credible MRV system connects field measurement to remote sensing to a registry and can defend every number.
How I work
The approach
A field verified method, not a desk exercise.
01
Design the architecture
Define baselines, monitoring indicators, spatial and temporal scope, and the evidence chain from field to registry.
02
Integrate sensing and AI
Wire in Sentinel 2 classification and AI assisted change detection, calibrated against field plots.
03
Build verification protocols
Set the field verification and QA steps that let the system survive third party audit.
What you get
Deliverables
Architecture
MRV system design
REDD+ or NbS MRV architecture with defensible baselines.
Carbon
Article 6 / VCM fit
Alignment with Article 6 and voluntary market verification expectations.
Sensing
AI classification
Satellite classification calibrated to ground plots.
QA
Verification protocols
Field verification and quality assurance procedures.
My MSc research and field record centre on forest governance and measurement in the Congo Basin and Cameroon. I design MRV that reflects how forests and communities actually behave, not just how a model assumes they do.
MSc Tropical Forestry (TU Dresden) and MSc Forest & Nature Management (Copenhagen)