AI for Forestry

Description

Use remote sensing and LiDAR to inventory forests, segment trees, estimate biomass, and map species and disturbances.

When to use

You are measuring, mapping, or monitoring forests using field plots, aerial/satellite imagery, or LiDAR point clouds.

Usage

• Estimate forest inventory variables (tree counts, DBH, height, volume) from field and remote-sensing data.
• Detect and segment individual tree crowns from CHM or LiDAR point clouds.
• Predict above-ground biomass by regressing LiDAR structural metrics against field plots.
• Classify forest types and disturbance (fire, insects, harvest) from multi-temporal imagery.

Steps

1. Collect field inventory plots, airborne/satellite imagery, and LiDAR point clouds for the forest area.
2. Preprocess LiDAR (ground classification, CHM, normalization) and extract structural features per plot.
3. Train a tree-crown segmentation or detection model and validate counts against field inventory.
4. Build an AGB regression model using LiDAR metrics and independent field-measured biomass.
5. Classify forest species and disturbance from spectral/temporal features and validate with aerial photo interpretation.
6. Map uncertainty, integrate with forest management systems, and update with new acquisitions.

Code pattern

import numpy as np
from sklearn.ensemble import RandomForestRegressor

# LiDAR/structural features per plot
X = features[["h_mean", "canopy_density", "intensity_mean", "chm_max"]]
y = field_measured_agb

rfr = RandomForestRegressor(n_estimators=300, random_state=42)
rfr.fit(X, y)

Tuning notes

• Normalize and classify LiDAR point clouds; remove non-ground returns carefully.
• Use CHM-based or point-cloud deep learning (e.g., PointNet, Point Transformer) for ITD.
• Consider mixed-effects or hierarchical models to pool limited field plots.
• Validate against independent inventory plots and propagate uncertainty.

Verification

1. Predict AGB from LiDAR features and report RMSE and R2 vs field plots.
2. Segment individual tree crowns and compare counts to field inventory.
3. Map forest disturbance and validate with aerial photo interpretation.

References

• https://doi.org/10.1007/s40725-024-00234-4
• https://doi.org/10.1007/s40725-024-00223-7
• https://doi.org/10.3390/rs11111260
• https://doi.org/10.3390/electronics13204139