A Crown Guess and Selection Framework for Individual Tree Detection From ALS Point Clouds

Abstract

Individual tree detection from airborne laser scanning (ALS) point clouds is the basis for forestry inventory and further applications. In the past decade, many methods have been developed to localize tree instances in ALS point clouds. These methods rely on empirical rules and field measurements that may change from plot to plot. Besides, most existing methods cannot consider multiple clues (e.g., shape priors and neighboring trees) under the same framework, which makes them not flexible and extensible. In this letter, we devise a new point-based and model-driven framework named “crown guess and selection”. This framework first generates crown candidates automatically, and then the qualities of candidates and their neighboring information are both considered. Finally, expected crowns are selected from candidates simultaneously. The proposed framework is tested and evaluated in a benchmark dataset. We also compare the new framework with several existing methods, and it turns out that the proposed framework outperforms others in terms of model flexibility and detection accuracy.

Keywords

• Airborne laser scanning (ALS) point clouds
• model-driven
• tree crown
• tree instances
• tree localization