How to Automate Control Plot Generation at Scale (Reshaping VM47 Carbon Modeling: Part 1)
- Denada Permatasari
- 1 hour ago
- 3 min read
Watch the video version (with further explanation + narration) of this topic.
As carbon markets continue to evolve, the complexity of methodologies like VM47 (Improved Forest Management) presents both opportunities and challenges for project developers. One of the most significant bottlenecks? The intensive computational requirements for dynamic baseline control plot automation across large landscapes.
Having worked with numerous carbon project developers, I've witnessed firsthand how traditional approaches to VM47 modeling often fall short when dealing with the methodology's demanding data requirements.
Today, I want to share insights on how advanced cloud computing platforms are transforming this landscape.
I'm Johann Wah, co-founder of Nika. This is my third article in my Geospatial Machine Learning Series, where I explain concepts and applying Machine Learning techniques into geospatial workflows. You can read the first and second articles respectively!
The VM47 Challenge: Scale Meets Complexity
VM47 projects typically span areas exceeding 100 square kilometers, requiring comprehensive analysis of forest dynamics across vast landscapes. This creates several critical challenges:
Computational Limitations: Most desktop environments simply cannot handle the memory and processing requirements. Even robust workstations with up to 24GB RAM struggle with the data-intensive operations required for proper control plot selection and validation.
Memory Constraints: Platforms like Google Earth Engine, while powerful, impose 1GB memory limits that prove inadequate for large-scale VM47 applications.
Time Efficiency: Manual processes for control plot generation can take weeks, creating project development bottlenecks.
The Solution: Cloud-Based Automation
The breakthrough comes from leveraging cloud computing resources specifically designed for geospatial workloads. By scaling from standard 3-CPU configurations to 7-CPU systems with 54GB RAM, we can process entire VM47 projects in single automated runs.
All the videos in this article showing the techniques are done in NikaPlanet, though the same principles can be applied in your GIS software of choice.
Key Components of Automated VM47 Workflow
Project Area Segmentation: The algorithm begins by automatically dividing project boundaries into equal-sized plots, with minimum dimensions of 30x30 meters. The default for the Nika team is usually 100-meter plots (1 hectare) to provide an excellent balance between statistical power and computational efficiency. But it is customizable on the template if you choose to work with something else for various project use cases.
Temporal Framework Setup: The system automatically calculates ex-ante and ex-post periods based on project start years, ensuring proper temporal alignment for baseline calculations.
Donor Pool Creation: This is where the magic happens. The algorithm identifies suitable control areas outside project boundaries by analyzing:
Ecoregion compatibility
Protected area boundaries via WDPA data
Land cover characteristics using Dynamic World datasets
Jurisdictional constraints
In the next blog post, I’ll go over how I used NikaPlanet to automate the stocking index data extraction for all control plots and perform the relevant KNN matching to perform the dynamic baseline.
Interested in learning more about carbon project automation? Sign up at our waitlist here for access to the VM47 automation template with every subscription of NikaPlanet.
📞 Interested for a 30-minute call with me instead? Schedule a call using the button below!
About Nika
Nika is the company behind NikaPlanet, the planetary scale spatial data and AI platform. We're helping organizations move beyond legacy GIS systems to unlock the full potential of their spatial data with cloud-native technology, AI-powered analysis, and collaborative workflows. Our mission is to make spatial intelligence accessible and actionable for every organization.