Process Flow

Accurately quantifying forest coverage and estimating CO₂ sequestration potential is crucial for understanding the role of forests in mitigating climate change. Carbontribe leverages computer vision models and remote sensing technologies to classify forest and non-forest areas, monitor critical forest parameters, and estimate annual carbon sequestration rates. This methodology integrates high-resolution satellite imagery, machine learning algorithms, and ecological equations to provide a comprehensive, scalable solution for forest monitoring and carbon accounting.

Step 1: Data Acquisition and Preprocessing

• Satellite imagery data is collected from various sources such as remote sensing platforms.
• The collected data undergo preprocessing, including noise reduction, radiometric correction, atmospheric correction, and geographic alignment, to ensure compatibility with the computer vision models.

Step 2: Forest Classification Using Computer Vision

• Our computer vision model is trained to classify regions as forest or non-forest at pixel level. Training data includes labeled examples of both categories.
• The model processes high-resolution satellite images to identify forested areas based on spectral, textural, and structural features.
• The output is a classified map highlighting forest and non-forest regions at pixel-level.

Step 3: Monitoring Key Forest Parameters

• For areas classified as forest, the monitoring process aligns with IPCC guidelines to estimate forest biomass and carbon stocks accurately. More detailed information about what parameters and how exactly we conduct the monitoring can be found in the following chapters.
• Wherever possible, further literature reviews will be conducted to obtain detailed information such as country-specific factors, forest type-specific allometric equations, and regional emission factors. This ensures that monitoring results are both scientifically robust and tailored to local ecological contexts.
• These parameters are monitored over time at the specified frequency to track changes and trends in forest health and growth.

Step 4: CO₂ Sequestration Estimation

• Using the monitored parameters, relevant equations or biomass models are applied to estimate carbon storage.

Step 5: Blockchain Storage

• All relevant data, including forest classification outputs, monitored parameters, and CO₂ sequestration estimates, are securely stored on a decentralized blockchain platform.
• Data links, metadata, and timestamps are recorded to ensure traceability and tamper-proof documentation.

Step 6: Digital Asset Creation

• A digital asset in the form of a Non-Fungible Token (NFT) is created to represent the carbon sequestration results for a specific geographic area and time period.
• The NFT includes embedded data or links to external datasets, including: detected forest areas; parameter estimates and methods; scientific equations and values used in the calculations; and documentation on compliance with IPCC methodologies and any country-specific factors.
• The blockchain-stored data and NFT serve as a transparent and auditable foundation for issuing carbon credits.
• This process ensures that credits are backed by scientifically verified data, reducing the risk of fraudulent claims.