High-Resolution Forest Structure Mapping with Deep Learning to Evaluate Restoration Outcomes
This study presents a validated workflow that integrates PlanetScope imagery with a residual U‑Net to map canopy structure and monitor restoration outcomes in fire‑prone western U.S. forests. Independent validation with LiDAR confirms strong performance, enabling managers to track structural change, assess treatment effectiveness, and refine restoration strategies.
Subject Tags
- Land management
- Habitat restoration
- Forest
Abstract
Forest management interventions in fire-prone western U.S. forests aim to restore structural heterogeneity, yet tracking treatment efficacy at landscape scales remains a persistent challenge. Traditional monitoring tools often lack the spatial resolution or temporal frequency needed to assess fine-scale structural outcomes. While deep learning approaches for mapping canopy structure from high-resolution satellite imagery have advanced rapidly, their application to operational monitoring of restoration outcomes with independent validation remains limited. This study demonstrates and validates a scalable monitoring workflow that integrates high-resolution PlanetScope multispectral imagery (~4.77 m) with a residual U-Net convolutional neural network (CNN) to quantify canopy structure dynamics in support of forest restoration programs. Trained using 3 m canopy cover data from the California Forest Observatory (CFO) as a reference, the model accurately segmented forest canopy from openings across a large, independent test area of ~1761 km2, with an overall accuracy of 92.2%, and an F1-score of 95.1%. Independent validation against airborne LiDAR across 140 km2 of heterogeneous terrain confirmed operational performance (overall accuracy 85.9%, F1-score 0.77 for canopy gaps). We applied this framework to quantify structural changes within the North Yuba Collaborative Forest Landscape Restoration Program from 2020 to 2024, providing managers with actionable metrics to evaluate treatment effectiveness against historical reference conditions. The treatments created 564 acres of new openings, significantly increasing structural heterogeneity, with 56% of new open area located within 12 m of residual canopy. While treatment outcomes aligned with the goal of fragmenting dense canopy, the resulting large openings (>5 acres) slightly exceeded historical reference conditions for the area. This validated workflow translates high-resolution satellite imagery into timely, actionable metrics of forest structure, enabling managers to rapidly evaluate treatment impacts and refine restoration strategies in fire-prone ecosystems.
Citation
Hendershot, J. N., Estes, B. L., & Wilson, K. N. (2026). High-Resolution Forest Structure Mapping with Deep Learning to Evaluate Restoration Outcomes. Remote Sensing, 18(2), 346.
TNC Authors
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Nicholas Hendershot
Forest Ecologist, Northern California
The Nature Conservancy
Email: n.hendershot@tnc.org