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Streszczenie:
Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single treelevel information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMATM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection.

Słowa kluczowe:
mobile device, tree attributes , LiDAR, Photogrammetry

Streszczenie:
Precise forest inventory in difficult terrain remains challenging due to mobility constraints and canopy occlusion. This paper presents an affordable air-ground mobile mapping system combining shoulder-mounted double LiDAR with a lightweight FPV drone-based LiDAR. We developed an extrinsic calibration method for dual orthogonally-mounted sensors and implemented a comprehensive processing pipeline incorporating LiDAR odometry, pose graph SLAM, and multi-view Normal Distributions Transform. Field ex-periments demonstrate successful air-ground data fusion for tree stem detection and dendrometric parameter extraction. The system was validated in extreme environments including a cave survey, proving versatility in difficult terrain. All software components are released as open-source tools: https://github.com/MapsHD/HDMapping.

Słowa kluczowe:
UAV LiDAR, Forest Inventory, Under-Canopy Mapping, SLAM, Open Source

Słowa kluczowe:
leśnictwo, sztuczna inteligencja, uczenie maszynowe