Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

نویسندگان

چکیده

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology describe the 3D structure forests at high resolution. We structurally complex, mixed-species Mediterranean test performance Handheld Laser Scanning (HLS) system estimate tree attributes within patch in central Spain. different stages HLS approach: field position, ground data collection, path design, point cloud processing, alignment between detected trees and measured reference trees, finally, assessment main structural diameter breast height (DBH) considering species size as control factors. surveyed 418 account for omission commission error rates over 1 ha plot divided into 16 sections scanned using two paths. HLS-based approach reached 88 92% detection rate best combination processing modes system. root mean squared errors DBH estimates varied species: Pinus pinaster were below 2 cm Scan 02. Quercus pyrenaica, Alnus glutinosa showed higher rates. observed good agreement ALS height, highlighting differences measurements. Despite complexity mixed area surveyed, our results show that highly efficient detecting locations, estimating DBH, supporting measurements confirmed with airborne used validation. This study one first studies conducted region, where variability allometries spacing presence natural regeneration pose challenges approach. feasible, time-efficient, mapping potential support monitoring programmes such national inventories lacking three-dimensional, remote sensing

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منابع مشابه

Laser Scanning in Forests

1 Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, FI-02431 Masala, Finland 2 Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland; E-Mail: [email protected] 3 Remote Sensing Laboratory, Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), SE-901 83 Umeå, Sweden; E-Mail: [email protected]

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2023

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs15051169