UAV Autonomous Forestry Ops

Updated 12 December 2025

UAV-based autonomous forestry operations are autonomous systems using advanced sensors like cameras, LiDAR, and IMUs to perform accurate mapping, navigation, and inventory in complex forests.
They employ integrated perception techniques—including visual-inertial SLAM, deep learning, and semantic mapping—to ensure robust obstacle detection and real-time mission planning in GNSS-denied environments.
These platforms enable scalable forestry management by delivering high-speed navigation, precise forest inventory, and efficient task execution through adaptive planning and multi-modal sensor fusion.

Unmanned Aerial Vehicles (UAVs) are transforming autonomous forestry operations by enabling efficient, scalable, and safe mapping, monitoring, and intervention in complex, GNSS-denied environments. Recent research demonstrates robust real-time navigation, dense 3D mapping, precise inventory, and responsive task execution in under-canopy and unstructured forest conditions, leveraging visual-inertial SLAM, LiDAR, deep learning, and adaptive planning architectures. Systematic evaluation, both in simulation and in the field, confirms high reliability, safety, and performance of these integrated UAV solutions.

1. Autonomy Architectures for Forest Environments

UAV-based autonomous forestry systems integrate multiple sensing, estimation, mapping, and planning modalities to operate robustly in unstructured, dense forests. Onboard architectures typically comprise:

Visual-Inertial Navigation: Cameras (stereo or monocular) and IMU sensors enable simultaneous localization and mapping (VI-SLAM), compensating for GNSS denial by estimating MAV state (pose, velocity, biases) in real time. High-speed front ends (e.g., OKVIS2) process keyframes via sliding-window batch optimization, combining IMU residuals, stereo reprojection, and loop closure terms. Background global bundle adjustment ensures trajectory drift correction with smooth local odometry (Laina et al., 14 Mar 2024).
Dense Volumetric Submaps: Occupancy mapping is implemented through multi-resolution octrees, where each submap is anchored to a set of keyframes and rigidly updated on pose corrections. Depth measurements, generated by on-board CNNs or raw stereo, are fused into active submaps using occupancy log-odds updates per ray-cast. This modular mapping policy allows for scalable, drift-tolerant planning (Laina et al., 14 Mar 2024).
LiDAR Semantic SLAM: Direct cylinder (tree trunk) and ground-plane landmark extraction from LiDAR range images, semantically segmented via networks such as RangeNet++, geometrically constrains robot pose and map consistency. A two-stage optimization aligns ground (z, roll, pitch) and trunks (x, y, yaw), decoupling DOFs for robustness to partial observations. Semantic SLAM (SLOAM) creates storage- and compute-efficient maps at scale (Liu et al., 2021).
Task-Specific Navigation: For structure-poor or plantation environments, navigation can be driven by egocentric control strategies (e.g., ForaNav), which lock onto visually detected trees and execute heading corrections or dead-reckoning recovery, inspired by insect foraging (Kuang et al., 4 Mar 2025).
High-Level Planning: Path planning is orchestrated by OMPL-based Informed-RRT* algorithms or multi-level coverage planners (boustrophedon decomposition, global voxelized JPS, local jerk-limited motion planning), supporting both reactive avoidance and optimal trajectory generation (Laina et al., 14 Mar 2024, Liu et al., 2021).

These architectures enable sub-kilogram UAVs with on-board NVIDIA Jetson-class compute to safely and repeatably traverse forests at 3 m/s in densities exceeding 400 stems/ha (Laina et al., 14 Mar 2024).

2. Sensing, Mapping, and Perception Strategies

Dense forest operations demand robust perception pipelines tuned to clutter, varied lighting, and ambiguous structure:

Camera-Only Solutions: Stereo-IR cameras (e.g., Intel RealSense D455) feeding OKVIS2-based SLAM and CNN-based depth (5 Hz) enable full autonomy without LiDAR, providing accurate state estimates and volumetric maps through consistent VI-SLAM-keyframe submaps (Laina et al., 14 Mar 2024, Karjalainen et al., 21 Jan 2025).
LiDAR-Centric Approaches: 3D spinning LiDARs (e.g., Livox Mid-360) offer 360°/59° FOV and high point rates, sustaining accurate point-to-plane SLAM (FAST-LIO2) at latencies <10 ms. Occupancy grid mapping is augmented by unknown-cell inflation, dense ray-casting for sky and small-branch detection, and efficient incremental frontier generation for safe corridor planning (Liu et al., 29 Mar 2025).
Semantic Tree Modeling: LiDAR data is segmented into cylinders (trunks) and ground-plane primitives, which are indexed in KD-trees and associated over time to track and spatially anchor both map and pose. This modeling supports hyper-sparse semantic representations (~2 MB/km²) for the autonomy loop, compared with voxel occupancy approaches (~1.2 GB/km²) (Liu et al., 2021).
Depth Estimation under Dense Foliage: Stereo matching methods evaluated on forestry datasets evidence domain-specific failure modes, such as negative-disparity predictions (RAFT-Stereo, ETH3D), and highlight the requirement for foundation models (DEFOM) with superior cross-domain smoothness, or iterative geometry-aware techniques (IGEV++) with sharper boundary preservation for real-time collision avoidance (Lin et al., 3 Dec 2025).

Perception performance depends critically on sensor placement, algorithmic class, and post-processing strategies tailored to vegetation-dense, under-canopy domains.

3. Planning, Control, and Trajectory Management

Safe and efficient UAV operation in forests demands tightly integrated planning and control stacks:

Trajectory Anchoring and Re-Planning: After loop closures, the reference trajectory must be deformed using a weighted rigid transformation over recent keyframe anchors. Each segment is collision-checked in the current submap union, with invalid segments triggering fast RRT*-based re-planning (Laina et al., 14 Mar 2024).
Model Predictive Control (MPC): Time-parameterized, quintic-segment trajectories are tracked at up to 40 Hz by onboard MPC, ensuring robust path following and real-time safety enforcement (Laina et al., 14 Mar 2024, Liu et al., 29 Mar 2025).
Local Corridor Generation: Convex safe corridors, inflated by the MAV radius, guide MPC and further restrict motion to dynamically reachable, obstacle-free subsets of the map.
Task Scheduling and Optimization: Large-scale inspection or intervention points are sequenced via offline Traveling Salesman Problem (TSP) solvers (LKH heuristic), with distances based on A*-computed paths in global occupancy grids. This dual-phase approach (human-in-the-loop for waypoint capture, followed by fully autonomous execution) achieves 33–57% reductions in total trajectory length and flight time relative to manual operation (Liu et al., 29 Mar 2025).

Such integrated planning and control schemes enable the execution of kilometer-scale missions, safe traversal in forest plots up to 2000 trees/ha, and high-density stem survey and mapping (Liu et al., 2021, Karjalainen et al., 21 Jan 2025).

4. Deep Learning for Tree and Branch Perception

Deep learning methods are pivotal for autonomous object recognition, segmentation, and inventory in forestry:

Tree Detection and Inventory: End-to-end Mask R-CNN architectures, trained with UAV-acquired RGB or multispectral data, delineate crowns and assign species and health status with per-class F1 up to 0.80 (IoU ≥ 0.5). U-Net and DeepLabv3+ architectures support semantic segmentation of species across UAV and mission scales (Troles et al., 2023).
Branch and Obstacle Segmentation: Fine-resolution branch segmentation (U-Net+MiT-B4, 256–1024 px inputs) achieves top IoU/Dice/Boundary-F1 metrics, enabling robust collision avoidance, safe path corridor determination, and automated pruning with high accuracy and connectivity preservation (Lin et al., 5 Dec 2025).
Depth Prediction: Advanced stereo matching approaches (DEFOM/IGEV++) are specifically benchmarked on forestry datasets for their ability to handle repeated textures, thin-branch ambiguities, and strong occlusion gradients, establishing practical guidelines for selection under task and compute constraints (Lin et al., 3 Dec 2025).
Task-Driven Detection Pipelines: In resource-limited settings, hierarchical HOG+SVM pipelines (ForaNav) operate at >90% detection accuracy on embedded ARM CPUs at 9 FPS, reliably guiding MAVs to within 10 cm of trees without prior mapping (Kuang et al., 4 Mar 2025).

Continual retraining, model selection based on trade-offs between recall, connectivity, and resource constraints, and temporal spatial filtering are essential for robust UAV deployment across variable lighting, canopy density, and mission roles.

5. Operational Applications and Scaling Strategies

Autonomous UAVs support diverse forestry tasks with increasing robustness, adaptability, and scaling efficiency:

Forest Inventory and Structure Analysis: Miniaturized stereo-vision or LiDAR-equipped MAVs are validated for dense 3D photogrammetric mapping, trunk detection, and diameter-at-breast-height (DBH) estimation achieving RMSE as low as 1.16 cm (5.74%) for small stems, competitive with manual methods (Karjalainen et al., 21 Jan 2025). Mask R-CNN pipelines generalize to large-scale inventories and health mapping (Troles et al., 2023).
Wildfire Monitoring and Suppression: Swarm protocols, realized as leader-follower coalition models or massed refill-capable multirotor “rain effects,” efficiently cover large fires, monitor fronts, and suppress tens of meters of active fire line per hundred UAVs via carefully scheduled sorties, validated with energy-balance and cellular automata fire-propagation models (Afghah et al., 2019, Ausonio et al., 2020).
Distributed Search, Rescue, and Exploration: Multi-UAV collaborative SLAM systems perform GPS-denied mapping using laser/IMU/altimeter sensors, sharing highly compressed tree-based submaps and leveraging cycle-consistent multiway matching (CLEAR) for robust loop closure and global map fusion (Tian et al., 2019).
Decision Support and Data Fusion: End-to-end pipelines integrate UAV RGB and multispectral data, satellite imagery, soil moisture sensors, and existing cadastral inventories, with results visualized in interactive web applications for operational forester task planning, assessment, and adaptive intervention (Troles et al., 2023).
On-Board and Edge Compute Acceleration: Model serialization, fully on-board inference, tile-based parallelization, and containerization (Docker, Kubernetes, Jetson-class deployment) enable real-time or near-real-time decision-making in the field, crucial for scalability and operational reliability (Ataş et al., 2022, Laina et al., 14 Mar 2024).

Key challenges include domain adaptation to lighting, seasonality, and stand structure, the need for multi-temporal and multi-modal ground truths, robust sensor fusion strategies, and safety-layer integration for high-density, long-duration, or hazardous environments.

6. Limitations, Benchmarks, and Future Directions

Field deployments and systematic benchmarking expose critical task- and scene-dependent trade-offs:

Domain-Specific Failure Modes: Stereo fusion networks (e.g., RAFT-Stereo) show catastrophic failure in forestries with negative disparities, necessitating rigorous cross-domain validation prior to operationalization (Lin et al., 3 Dec 2025).
Perception Gaps: Thin or occluded branches, dynamic lighting, and variable foliage introduce drift, false positives, and mapping discontinuities. Exploration of multi-sensor (e.g., integrated vision+LiDAR) and multi-view temporal fusion is ongoing (Karjalainen et al., 21 Jan 2025, Liu et al., 29 Mar 2025).
Data and Model Sharing: Availability of open-source implementations (kr_autonomous_flight, sloam, PerformanceTreeBranchSegmentation) and public forestry datasets (e.g., Canterbury) drives reproducibility and accelerates progress in benchmarking (Lin et al., 3 Dec 2025, Lin et al., 5 Dec 2025).
Swarm Scaling and Redundancy: Real-time leader-follower, distributed coalition, and multi-UAV SLAM/CSLAM methods are under active investigation for robust, large-area coverage in both inventory and rapid incident response (Afghah et al., 2019, Tian et al., 2019).
Operational Integration: Emphasis is shifting toward close coupling with legacy forest management systems (GIS, cadastre) and cloud-driven analytics for continuous feedback, predictive planning, and adaptive learning (Troles et al., 2023).

Ongoing research targets hardware-in-the-loop validation, enhancement of drift and loop-closure robustness, increased endurance, and extension to generalize across forests, tree species, and global climate zones.

References:

(Laina et al., 14 Mar 2024) Scalable Autonomous Drone Flight in the Forest with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
(Kuang et al., 4 Mar 2025) ForaNav: Insect-inspired Online Target-oriented Navigation for MAVs in Tree Plantations
(Liu et al., 2021) Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
(Lin et al., 3 Dec 2025) Generalization Evaluation of Deep Stereo Matching Methods for UAV-Based Forestry Applications
(Liu et al., 29 Mar 2025) LiDAR-based Quadrotor Autonomous Inspection System in Cluttered Environments
(Karjalainen et al., 21 Jan 2025) Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone
(Lin et al., 5 Dec 2025) Performance Evaluation of Deep Learning for Tree Branch Segmentation in Autonomous Forestry Systems
(Troles et al., 2023) Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data
(Ataş et al., 2022) Development of Automatic Tree Counting Software from UAV Based Aerial Images With Machine Learning
(Afghah et al., 2019) Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles
(Ausonio et al., 2020) Drone swarms in fire suppression activities
(Tian et al., 2019) Search and Rescue under the Forest Canopy using Multiple UAVs