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); MSc Forest & Nature Management (Copenhagen)