Copernicus NDVI What NDVI actually is and why it’s used NDVI, Copernicus & Satellite Verification NDVI (Normalized Difference Vegetation Index) is a numerical indicator derived from satellite imagery that estimates vegetation “greenness” and vigor . Formula ​ Red band : vegetation absorbs this for photosynthesis Near-Infrared (NIR) : vegetation reflects this strongly if healthy Value range NDVI value Interpretation -1 to 0 Water, snow, clouds 0–0.2 Bare soil, rocks 0.2–0.4 Sparse vegetation, grass 0.4–0.6 Healthy vegetation 0.6–0.9 Dense forest canopy Key point: NDVI does not detect “trees” directly — it detects photosynthetically active biomass . Copernicus & Sentinel-2: what data you actually get Sentinel-2 key specs Spatial resolution : 10 m (Red, NIR) → NDVI is 10×10 m per pixel Temporal resolution : Every 5 days globally Cost : Free, open data (huge win for DAOs) Bands : 13 spectral bands (visible + infrared) This means: One pixel ≈ 100 m² You cannot see individual saplings You can see land cover trends over time What NDVI can verify  Strong, defensible claims These are blockchain-safe claims you can attach to smart contracts: 1. Vegetation increase over time “NDVI increased from 0.21 → 0.47 over 18 months in polygon X” This is robust , auditable, and repeatable. 2. No-regression guarantees “NDVI did not fall below baseline for 12 months” Useful for: Anti-deforestation clauses Delayed DAO payouts 3. Seasonal survival trends By comparing same season year-over-year , you can show: Whether vegetation persists Whether the area is stable or degrading 4. Area-based verification You can verify: Hectares showing sustained vegetation Continuity of canopy cover , not just spot planting Canopy cover analysis (beyond basic NDVI) NDVI alone is not enough for forest-quality claims. Typically you combine: Techniques used in practice NDVI thresholding e.g. NDVI > 0.5 = likely canopy Time-series smoothing filters out noise & weather artifacts Change detection ΔNDVI between years Masking exclude water, agriculture, urban areas What canopy analysis can say “This area transitioned from low biomass to sustained canopy” “Vegetation density increased and stabilized” What it cannot say Tree species Forest age Biodiversity quality Native vs invasive plants Limitations you MUST be explicit about (to avoid greenwashing) 1. Cloud cover  Optical satellites cannot see through clouds Tropical regions can lose 30–60% of observations Mitigation: Cloud masking Multi-month aggregation DAO rules that accept “data gaps” On-chain implication: Smart contracts must tolerate missing data windows. 2. Resolution constraints (10 m) Young trees <2–3 years may be invisible Agroforestry can look like forest Shrubs can inflate NDVI You cannot claim: “10,000 trees planted” 3. Species identification is impossible NDVI: Does not know what is growing Does not know why it’s growing A monoculture plantation can score higher NDVI than a biodiverse forest. Mitigation strategies Combine with: Field audits Community attestations NGO validation NFTs Use NDVI as one oracle , not the only one 4. Cause vs correlation NDVI increase ≠ successful reforestation It could be: Natural regrowth Agricultural cycles Irrigation changes Honest framing: “This project contributed to vegetation recovery as verified by satellite indices” Not: “This project restored a forest ecosystem” 6. What claims are DAO-safe vs DAO-dangerous Safe, verifiable, professional claims “Vegetation cover increased” “NDVI stabilized above baseline” “No major deforestation detected” “Land cover change confirmed by Copernicus” Dangerous claims (avoid on-chain) Exact tree counts Species restoration Biodiversity recovery Carbon sequestration without models + field data