Methodology

The pipeline

1. 01Select area & year. Choose one or more Kenyan counties, or draw a custom boundary, and an ESA CCI AGB reference year (or a range, averaged across every snapshot in it).
2. 02Build predictor stack. Earth Engine assembles a cloud-filtered, multi-source stack of optical, radar, terrain, climate and soil layers.
3. 03Sample & split. Reference carbon points are sampled from the stack and split into training and testing sets.
4. 04Train models. Random Forest, Gradient Tree Boosting and SVM each learn the link between predictors and carbon.
5. 05Map & validate. Estimates are mapped in carbon or biomass units, checked against held-out data (RMSE, MAE, R²), and summarised by county alongside tree-cover loss.

Data sources

All accessed and processed through Google Earth Engine.

Predictors

Models & carbon conversion

Three models are trained on reference points sampled from the predictor stack; the output is the unweighted mean of their predictions.

• Random Forest Bagged decision-tree ensemble
• Gradient Tree Boosting Sequentially boosted trees
• Support Vector Machine Kernel regression

Validation

Each run holds out held-out reference points and reports RMSE, MAE and R² against ESA CCI AGB v6.0, plus a predicted-vs-observed plot. The figures on the overview page are placeholders until a held-out run is loaded.

• R² 0.82
• RMSE 18 t/ha
• MAE 12 t/ha

The spread between the three models is mapped as a spatial uncertainty proxy, and Random-Forest / boosting feature importance is reported per run.

Limitations

• Signal saturation - optical and C-band radar lose sensitivity in dense, tall closed-canopy forest, so the highest biomass can be under-estimated.
• Sparse vegetation - very low-cover drylands sit near the noise floor; small absolute errors are large in relative terms.
• Cloud & data gaps - persistent cloud or missing scenes force wider compositing windows and raise uncertainty.
• Reference layer - trained against a modelled global product (ESA CCI AGB), not local field plots, so it inherits that product's biases until co-calibrated.
• Carbon fraction - a single IPCC default (0.47) is applied everywhere; species- and tissue-specific fractions vary by roughly ±0.03.
• Screening only - outputs support comparison and targeting, not verified carbon accounting or certification.

Before acting on the numbers

Use Carbon-Eye to screen, compare and target areas. Before any investment, crediting or certification decision:

• Validate with local field data
• Assess uncertainty
• Check land tenure and baselines
• Check leakage and permanence
• Follow the relevant methodology

Glossary

AGB

Above-ground biomass - the dry mass of living vegetation above the soil, in tonnes per hectare (t/ha).

Carbon stock

Carbon held in that biomass; AGB × 0.47 (IPCC default carbon fraction). Reported in t C/ha.

CO₂e

Carbon-dioxide equivalent; carbon stock × 3.67 (44/12), the mass of CO₂ that carbon represents.

MRV

Measurement, Reporting and Verification - the process of quantifying and checking carbon changes for climate reporting.

FREL / FRL

Forest Reference (Emission) Level - a national baseline against which REDD+ results are measured.

REDD+

Reducing Emissions from Deforestation and forest Degradation, plus conservation, sustainable management and enhancement of carbon stocks.

NDVI / EVI / SAVI

Vegetation indices from red and near-infrared reflectance that track greenness and canopy density (EVI and SAVI reduce soil and atmosphere effects).

NDMI / NDRE

Moisture and red-edge indices - sensitive to canopy water content and chlorophyll.

SAR backscatter

The fraction of a radar pulse returned to the sensor; VH (cross-polarised) responds to vegetation volume and structure.

Ensemble

The mean of the Random Forest, Gradient Tree Boosting and SVM predictions, used to reduce reliance on any single model.

References & further reading

• ESA Climate Change Initiative - Biomass
• IPCC 2006 Guidelines for National GHG Inventories, Vol. 4 (AFOLU)
• GFOI - Integration of remote sensing and ground data
• Copernicus Sentinel missions
• Google Earth Engine
• Global Forest Watch