Tree Counts
March 2001
Leveraging machine learning algorithms, the collected data is processed to identify and count individual trees within the forest landscape.
LiDAR-based Tree Segmentation
Machine Learning with LiDAR for Tree Segmentation
How it Works
Machine learning algorithms can be trained to recognize patterns in LiDAR point clouds that correspond to individual trees.
Characteristics
Feature Extraction: Features such as point density, height variance, and other 3D geometrical features can be extracted from LiDAR point clouds.
Point Cloud Segmentation: Algorithms like Random Forests, Support Vector Machines, or deep learning methods can be trained on these features to segment point clouds into individual tree crowns.
Machine Learning with Optical Data for Tree Segmentation
How it Works
Machine learning algorithms can identify trees by learning spectral and spatial patterns in optical images.
Characteristics
Feature Extraction: Spectral features such as color bands, and textural features like "smoothness," can be extracted.
Image Segmentation: Algorithms are trained on these features to segment the 2D image into patches corresponding to individual trees.
Key Differences
Type of Data: LiDAR provides 3D structural information, whereas optical data provides 2D spectral information.
Feature Set: LiDAR-based ML models may use 3D geometric features, while optical-based models may rely on spectral and textural features.
Computational Complexity: Processing 3D point clouds typically requires more computational resources compared to 2D optical images.
Both LiDAR and optical data have their own advantages and limitations when used with machine learning algorithms for the task of tree detection and segmentation. The choice between the two will often be dictated by the specific requirements and constraints of the application at hand.