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Estimating Biomass and Carbon Sequestration

in Agroforestry Using Remote Sensing and Machine Learning

This case study examines an innovative approach to estimating biomass and assessing carbon sequestration potential in agroforestry systems by leveraging remote sensing, geospatial techniques, and machine learning. The methodology establishes a robust framework for biomass and carbon analysis, integrating advanced technologies with in-situ data collection.

Methodology

Data Sources and Inputs

  • In-Situ Biomass Measurements: Ground-truth data collected from sample plots provide the foundation for model training and validation.
  • Satellite Imagery: Sentinel-2 satellite data is utilized to extract critical spectral inputs. Vegetation indices such as NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index) are computed to analyze vegetation health and density.
  • Geospatial Layers: Agri Mask and Land Use Land Cover (LULC) data refine spatial classifications, delineating agroforestry zones with precision.

Machine Learning Models

Predictive models are developed using machine learning techniques to estimate biomass accurately. Key models include:

  • Random Forest (RF)
  • Gradient Boosting Machines (GBM)
  • Support Vector Machines (SVM)
  • Ensemble Techniques: XGBoost and LightGBM enhance prediction reliability by combining multiple models.

These models are trained and validated using the in-situ biomass data to ensure robustness and reliability.

Biomass and Carbon Estimation

  • Biomass Estimation: Machine learning predictions are based on spectral and spatial inputs, delivering accurate biomass estimates.
  • Carbon Conversion: Estimated biomass values are converted into net carbon content using standard biomass-to-carbon conversion factors.

Carbon Sequestration Analysis

  • Region-specific allometric equations are applied to calculate carbon sequestration potential.
  • Temporal data integration assesses changes in biomass and carbon stocks over time, providing insights into dynamic sequestration trends.

Outputs and Insights

The results are aggregated and presented at various administrative levels, including districts, tehsils, and villages. This spatially explicit data offers actionable insights into the role of agroforestry in carbon storage and climate change mitigation. Key deliverables include:

  • Biomass Distribution Maps: Visual representation of biomass density across agroforestry zones.
  • Carbon Stock and Sequestration Potential: Quantitative estimates of current carbon stocks and future sequestration capacity.
  • Temporal Analysis: Trends in biomass and carbon changes over time.

Policy and Practical Implications

This comprehensive framework provides policymakers, researchers, and stakeholders with actionable data to support sustainable land management. The findings can be integrated into national and regional climate action plans, enhancing agroforestry’s contributions to:

  • Climate change mitigation.
  • Ecosystem service enhancement.
  • Sustainable agricultural practices.

Conclusion

By combining remote sensing, geospatial analysis, and machine learning, this approach delivers a cutting-edge solution for biomass and carbon assessment in agroforestry systems. The methodology not only enhances accuracy but also provides scalable insights that can guide policy development and promote sustainable land-use practices.

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