BAIF Development Research Foundation
At a Glance
Industry
Nonprofit
Project Types
Data Analysis, Food and Agriculture
Year
2023
Location
Pune, Maharashtra
Summary
Abigith Baby devised a replicable methodology for the satellite-based estimation of tree aboveground biomass in Gujarat's WADI agroforestry sites.
Goals
The BAIF Development Research Foundation's Wadi (agriculture, horticulture, forestry) program enhances smallholder farmers' income and resilience by integrating trees into their farming systems.
To facilitate carbon credit generation from Wadis, an efficient GIS-based methodology was developed to estimate tree biomass. This replaced the need for extensive field surveys in large areas, which is a costly and time-consuming process. This model will enable BAIF to effectively estimate tree aboveground biomass across Gujarat’s WADI region.
Solutions
- GIS-Based Biomass Estimation Methodology: The project focused on developing a comprehensive methodology, offering a step-by-step procedure for accurate biomass estimation. This encompassed guidelines for field data collection, satellite imagery processing procedures, biomass modeling in Python, and rigorous data analysis.
- Biomass Modeling: A replicable tree biomass equation was derived through the meticulous modeling of field data. This equation was designed to be a valuable tool for future biomass estimations, ensuring consistency and accuracy in approach.
- Aboveground Biomass Estimation: Utilizing the developed formula, aboveground biomass for the many previous years can be precisely estimated. This capability provides historical insights and lays the foundation for ongoing and future assessments, contributing to a more sustainable and informed approach to agroforestry management.
Potential Impact
The project addressed the lack of information on biomass estimation procedures, offering BAIF a comprehensive, step-by-step methodology applicable to forestry and agroforestry for aboveground biomass estimation.
Once fully developed, this methodology is poised to contribute substantially to agroforestry by facilitating carbon estimation for carbon credits, which can consequently enhance farmers' income.