# Technical Report for Silvi: Key Development Questions and Next Steps

## Introduction

This report provides a concise overview of the essential tools, methodologies, and features needed to advance census-based and area-based carbon accounting.

It focuses on high-impact data types and functionalities to integrate into Silvi and Treekipedia, supporting MRV protocols such as CFC, Plan Vivo, and Verra VM0047.

---

## Question 1: Essential Tools and Features for Census-Based Accounting

To ensure that Silvi and Treekipedia excel in census-based carbon accounting, we recommend the following prioritized tools and features:

1. **Species Tracking and Allometric Modeling**
- **Goal**: Precisely monitor tree and species growth, using species-specific allometric models for accurate biomass and carbon estimates.
- **Implementation**: Integrate species-specific growth data and models (e.g., Global Allometric Model) into Treekipedia, enabling Silvi to utilize consistent, validated metrics.

2. **Remote Sensing and GIS Integration**
- **Goal**: Use remote sensing (e.g., LiDAR, NDVI) to validate census data, especially for hard-to-access areas.
- **Implementation**: Integrate tools like Google Earth Engine to track tree density and health, enhancing accuracy for large-scale validation.

3. **Automated Claim Validation**
- **Goal**: Streamline claim validation with automated, protocol-specific templates.
- **Implementation**: Use species and growth data from Treekipedia to create automated claim validation criteria in Silvi, reducing manual validation time.

## Table 1: Key Data Requirements for Census-Based and Area-Based Carbon Accounting

| **Data Type** | **Description** | **Source** | **Usage in System** |
|-------------------------|-----------------------------------------------------------------|--------------------------|----------------------------------------------------------|
| DBH (Diameter at Breast Height) | Tree trunk diameter measured at 1.3 meters above ground | Field data collection | Used in allometric equations for biomass estimation |
| Tree Height | Total height of each tree | Field data collection | Combined with DBH for volume and biomass calculations |
| Canopy Diameter | Width of tree canopy | Remote sensing, drones | Used for area-based biomass validation |
| Species Information | Specific species of each tree | Treekipedia database | Applies species-specific biomass and carbon models |
| Soil Organic Content | Organic carbon content in soil | Soil samples, lab tests | Supports below-ground carbon calculations |
| Carbon Fractions | Carbon percentages by tree part (e.g., stem, branch, leaf) | Treekipedia database | Used to calculate carbon content from biomass estimates |

+-------------------------------+
| Field Data Collection |
| |
| - DBH, Height, Canopy |
| - Tree Location (GPS) |
| - Soil Data |
+---------------+---------------+
|
v
+-------------------------------+
| Treekipedia Database |
| |
| - Stores specie-level data |
| - Integrates species-specific|
| allometric models |
+---------------+---------------+
|
v
+-------------------------------+
| Silvi Carbon Calculation |
| and Validation Module |
| |
| - Uses allometric equations |
| for biomass |
| - Calculates carbon storage |
+---------------+---------------+
|
v
+-------------------------------+
| MRV Protocol Compliance |
| |
| - Carbon credits issuance |
| - Ongoing validation |
+-------------------------------+

4. **Dynamic Carbon Calculation Models**
- **Goal**: Enable real-time carbon updates as trees grow.
- **Implementation**: Treekipedia provides predictive carbon data for Silvi, allowing for dynamic claim adjustments and accurate carbon estimates.

---

## Question 2: Bridging Census and Area-Based Accounting

To link census-based data with area-based requirements, we propose the following methods:

1. **Hybrid Data Protocol**
- **Goal**: Scale census data to cover larger regions by extrapolating from key points.
- **Implementation**: Use Treekipedia to group similar zones and apply census-derived averages to estimate area-based biomass.

+-------------------------------+
| Sample Census-Based Data |
| |
| - DBH, Height, Species |
| - Soil Organic Content |
+---------------+---------------+
|
v
+-------------------------------+
| Treekipedia Aggregation and |
| Data Grouping |
| |
| - Groups similar zones |
| - Averages sample data |
+---------------+---------------+
|
v
+-------------------------------+
| Scaling Algorithm |
| |
| - Extrapolates data across |
| larger areas |
+---------------+---------------+
|
v
+-------------------------------+
| Area-Based Carbon Estimate |
| |
| - Total carbon for large |
| project area |
+-------------------------------+

2. **Zonal Scaling Algorithms**
- **Goal**: Convert grouped census data into area-based estimates.
- **Implementation**: Apply scaling algorithms within Silvi to generate carbon data across different zones using species data from Treekipedia.

3. **Protocol Templates for Compliance**
- **Goal**: Use modular templates to standardize census and area-based data reporting.
- **Implementation**: Treekipedia provides protocol-specific templates for Silvi, ensuring compliance with CFC, Plan Vivo, and Verra VM0047.

4. **Remote Verification Support**
- **Goal**: Enhance validation by integrating remote sensing to cross-reference census data.
- **Implementation**: Use remote sensing data in Treekipedia and Silvi to support area-based verification across large-scale projects.

---

## Question 3: Modeling Species-Specific Attributes with Treekipedia Data

For species-specific growth and carbon modeling, we suggest:

## Table 2: Summary of Species-Specific Carbon Calculation Steps

| **Step** | **Description** | **Data Required** |
|-------------------------------------|-------------------------------------------------------------------------------------------------|------------------------------------------------------------|
| Calculate Part-Specific Biomass | Use measurements for DBH, height, and species to estimate biomass for stem, branches, and leaves| DBH, height, species, allometric model |
| Apply Carbon Fractions | Multiply part-specific biomass by species-specific carbon fractions | Biomass values for each part, carbon fractions |
| Sum Carbon Content for Total Storage| Add carbon content of each part to get total carbon sequestration | Part-specific carbon content (stem, branches, leaves, roots)|

1. **Allometric Models and Growth Curves**
- **Goal**: Use species-specific models to predict biomass and carbon sequestration accurately.
- **Implementation**: Integrate allometric and growth models in Treekipedia for species-driven validation in Silvi.

2. **Species-Specific Carbon Sequestration Models**
- **Goal**: Estimate carbon across growth stages.
- **Implementation**: Use Treekipedia’s carbon data in Silvi to validate storage dynamically, aligning credits with real-time growth.

+-----------------------------+
| Treekipedia Species Data |
| |
| - DBH, Height, Canopy |
| - Species-specific data |
+--------------+--------------+
|
v
+-----------------------------+
| Part-Specific Biomass |
| Calculations |
| |
| - Stem Biomass |
| - Branch Biomass |
| - Leaf Biomass |
+--------------+--------------+
|
v
+-----------------------------+
| Apply Carbon Fractions |
| |
| - Stem: 0.48 |
| - Branch: 0.5 |
| - Leaf: 0.4 |
+--------------+--------------+
|
v
+-----------------------------+
| Total Carbon Content |
| |
| - Sum of part-specific |
| carbon content |
+-----------------------------+

3. **Ecosystem Service Metrics**
- **Goal**: Quantify additional ecological benefits beyond carbon.
- **Implementation**: Treekipedia integrates services like biodiversity and soil health, enhancing the ecological value of Silvi’s carbon credits.

## Table 3: Data Relationships for Silvi and Treekipedia Integration

| **Data Category** | **Relationship** | **Linked Data Category** | **Description** |
|-------------------------|-------------------------------|-----------------------------------|----------------------------------------------------------------|
| Restoration Area | Contains | Tree | Restoration areas include individual trees measured for carbon |
| Restoration Area | Associated with | Biodiversity Hotspot | High-priority zones for restoration based on biodiversity |
| Tree | Measured by | Carbon Sequestration Data | Each tree measurement contributes to total carbon storage data |
| Tree | Located in | Restoration Area | Links each tree to its designated restoration area |
| Data Submission | Verified by | Blockchain Hash | Secures each data point, ensuring it is tamper-proof |
| Data Submission | Processed by | Smart Contract | Smart contracts verify and approve submissions for carbon credits |

## Table 4: Smart Contract Criteria for Data Validation and Credit Issuance

| **Criteria** | **Condition** | **Action upon Verification** |
|-------------------------------------|-----------------------------------------------------------|------------------------------------------------------------|
| Data Completeness | All required fields (DBH, height, species) are provided | Proceed to carbon calculation |
| Accuracy Validation | Matches data from remote sensing (canopy diameter, health)| Data is flagged as “Verified” in Silvi |
| Protocol Compliance | Meets MRV protocol standards (e.g., CFC, Plan Vivo) | Credits issued and recorded on blockchain |
| Data Integrity | Hash matches blockchain record | Credits released, payment processed via smart contract |

---

## Conclusion and Next Steps

### Immediate Priorities
1. **Implement DBH and Height Tracking**: Core data for all protocols.
2. **Validate Soil Type Detection**: Explore image-based or electromagnetic soil identification.
3. **Focus on Species-Driven Claim Validation**: Prioritize automated species validation for the CFC protocol.

### Ongoing Refinement
- **Living Document**: Regularly update documentation with insights and feedback.
- **Feature Roadmap**: Implement high-impact features like claim validation and continuous monitoring for enhanced MRV compliance.

This report provides a structured approach for Silvi and Treekipedia, ensuring practical, scalable steps toward optimized carbon credit accounting.