End-to-end Monitoring, Reporting, and Verification system design for a Brazilian Amazon REDD+ project, integrating satellite imagery, AI classification, and blockchain-based carbon credit registry.
A project developer operating in the Brazilian Amazon biome, focused on forest conservation and community-based carbon credit generation under the Verra VCS and CCB standards.
45,000 hectares of tropical forest under conservation, surrounded by agricultural frontiers with high deforestation pressure and historical forest loss rates.
Integration of Sentinel-1 SAR, Sentinel-2 MSI, ALOS PALSAR, GEDI LiDAR, and FAO FRA 2020 data for multi-source forest carbon estimation.
Traditional field-based MRV is costly and slow in the Amazon. The client needed AI-assisted satellite classification to reduce costs while meeting VCS VMD0007 uncertainty requirements.
Blockchain-based carbon credit registry for transparent, auditable issuance and retirement tracking, with immutable satellite evidence.
The cost of traditional MRV (Monitoring, Reporting, and Verification) for REDD+ projects has been a persistent barrier to implementation. Köhl et al. (2020) analyzed the cost-effectiveness of MRV systems and found that field-based forest inventories can consume 20-30% of total project revenues, creating a "cost trap" that threatens project viability. Their work emphasized that integrating remote sensing with statistical sampling is essential for cost reduction. Köhl et al. (2020) — Ecological Economics
Asner (2011) demonstrated that airborne and satellite-based remote sensing can provide the spatial coverage needed for carbon stock estimation at a fraction of the cost of field-based inventories. Their Carnegie Airborne Observatory (CAO) approach mapped forest carbon at high resolution, establishing the scientific basis for remote-sensing MRV that underpins modern AI-enhanced systems. Asner (2011) — Environmental Research Letters
The challenge of forest degradation — distinct from deforestation — requires advanced detection capabilities. Reiche et al. (2021) developed Sentinel-1-based disturbance alerts for the Congo Basin, demonstrating that Synthetic Aperture Radar (SAR) can detect small-scale forest degradation events that optical sensors miss, particularly under persistent cloud cover. Reiche et al. (2021) — Environmental Research Letters
Perhaps most critically, Qin et al. (2021) showed that in the Brazilian Amazon, carbon loss from forest degradation now exceeds that from deforestation, fundamentally changing the MRV paradigm. Projects must monitor not just tree loss but canopy degradation, selective logging, and forest fragmentation — all of which require the multi-sensor, AI-powered approach we designed. Qin et al. (2021) — Nature Climate Change
Compiled 13 years of Sentinel-2, Sentinel-1, and ALOS PALSAR data. Established cloud-free composite baselines. Downloaded GEDI L4A biomass reference data for calibration zone.
Labeled 12,000 image patches with forest condition classes. Trained U-Net and Random Forest ensemble. Achieved 91.4% accuracy on validation set. Optimized for degradation detection sensitivity.
Established 120 permanent plots. Measured 4,800 trees across diameter classes. Conducted wood density analysis. Calibrated satellite AGB estimates against field data.
Integrated AI pipeline with blockchain registry. Deployed uncertainty quantification module. Conducted third-party audit. Achieved VCS validation readiness with ±12% uncertainty.
Time series of aboveground biomass (AGB) estimates with Monte Carlo uncertainty intervals (2010–2023).
Solid line = mean AGB estimate; shaded band = 95% confidence interval from Monte Carlo uncertainty propagation. Dashed line = linear trend (R² = 0.72, p < 0.001).
Three.js point cloud simulation of forest canopy structure derived from GEDI LiDAR and field plot data. Points represent canopy height and density.