Enhanced Forest Inventory System

A distributed processing system built to generate Enhanced Forest Inventories from aerial LiDAR data.

Overview

This project automated the processing of massive LiDAR point cloud datasets to extract forestry metrics at scale. Using Random Forest machine learning models trained on ground-truth plot data, the system predicts stand-level forest attributes across entire regions.

Technical Highlights

• Distributed Architecture: Scaled processing across multiple nodes to handle terabytes of point cloud data
• ML Pipeline: R-based Random Forest models for predicting tree height, volume, and species composition
• Integration: Connected with existing GIS workflows and client delivery systems

Impact

Reduced processing time for large acquisitions from weeks to days while improving prediction accuracy through iterative model refinement.