Automating Forest Inventory and Tree Counting with Metashape and AI: A Smarter Way to Monitor Forests

Why Automate Forest Inventory?

Forests cover nearly one-third of the Earth’s land surface, yet large-scale inventory remains a challenge. Manual surveys are often impractical in remote or dense forest areas. Automating the process brings several benefits:

• Time-saving: Large areas can be surveyed in hours instead of weeks.
• Cost-efficient: Reduces the need for field teams and manual data collection.
• Improved accuracy: AI algorithms reduce human error in counting and classification.
• Repeatability: Enables regular monitoring to track growth, disease, or deforestation.

Workflow: From Drone to Data

The automation process begins with aerial imagery. Here’s a typical workflow to automate forest inventory using Metashape and AI:

1. Data Collection with Drones

Use a drone equipped with a high-resolution RGB or multispectral camera to capture overlapping aerial images. The ideal flight parameters include:

• 80% forward and 70% side overlap
• Altitude: 60–120 meters above ground
• Flying during clear daylight conditions for shadow-free imagery

2. Photogrammetric Processing in Agisoft Metashape

Import the drone imagery into Metashape and process it to generate a dense point cloud, digital surface model (DSM), and orthomosaic. Key steps include:

• Align Photos
• Build Dense Cloud
• Build DEM (Digital Elevation Model)
• Generate Orthomosaic

Metashape allows for high-accuracy georeferencing using ground control points (GCPs) if needed.

3. Tree Detection with AI and Python

Once the orthomosaic and point cloud are exported, AI algorithms can detect and classify individual trees. This can be achieved using Python-based libraries like:

• scikit-image or OpenCV for image segmentation
• TensorFlow or PyTorch for object detection models (e.g. YOLO, Faster R-CNN)
• QGIS plugins such as “Forestry Tools” for canopy height extraction

Developers often use canopy height models (CHM) derived from DSM and DEM layers to isolate tree crowns and count their occurrences.

Use Case: Counting Trees in a 100-Hectare Plantation

In a real-world test, a drone survey over a 100-hectare eucalyptus plantation produced over 3,000 images. After processing with Metashape, a high-resolution orthomosaic and DSM were exported. A Python script using a YOLOv5 model detected and counted tree crowns with 95% accuracy in under 30 minutes—compared to several days of manual work.

Benefits for Forestry and Environmental Monitoring

Automated tree counting and forest inventory supports a range of applications:

• Forest Management: Monitoring tree growth, density, and health over time
• Carbon Stock Estimation: Calculating biomass and carbon sequestration potential
• Illegal Logging Detection: Identifying missing or felled trees in protected areas
• Biodiversity Tracking: Mapping species distribution with multispectral data

Challenges and Considerations

While powerful, the automation process isn’t without its challenges:

• Complex Canopies: Dense or overlapping trees may require more advanced segmentation techniques.
• Data Quality: Image resolution and lighting significantly impact AI accuracy.
• Training Datasets: Custom AI models need quality training data specific to the tree species and region.

Future Trends: AI + Multispectral + Cloud

Emerging technologies are further enhancing automated forest inventory. Multispectral cameras allow vegetation health analysis, while cloud-based platforms such as Google Earth Engine enable scalable processing. As AI models improve, fully autonomous forest monitoring systems are becoming a reality.

Conclusion

Combining Agisoft Metashape with drones and AI unlocks a new level of efficiency in forest inventory and tree counting. It empowers forestry professionals, environmental agencies, and researchers to gain precise insights quickly and cost-effectively. As technology continues to evolve, expect automation to play an even greater role in the sustainable management of our world’s forests.