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Forest complexity decoded: how ai and drone lasers read the canopy’s hidden language | Newswise - Newswise

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Newswise — Canopy structural complexity (CSC) describes how leaves, branches, and trunks are arranged in space—a property that influences everything from light penetration and microclimate to wildlife habitat and carbon cycling. Traditional field surveys rely on plot‑level metrics like tree height variation or canopy cover, but these two‑dimensional or discrete measures miss the true three‑dimensional internal arrangement of canopy elements. LiDAR has revolutionized forest monitoring by capturing millions of points that map canopy volume in 3D, yet extracting pure canopy from these dense point clouds remains a major obstacle. Most methods depend on fully labeled training data—point‑by‑point annotations that are prohibitively expensive and time‑consuming for large‑scale surveys across diverse terrain. Given these challenges, there is a pressing need to develop efficient and generalizable methods for canopy extraction and structural quantification from large‑scale, unlabeled forest point clouds.

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