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Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification

Urban-Tree-Attribute Update Using Multisource Single-Tree Inventory

Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland
Centre of Excellence in Laser Scanning Research, Finnish Geodetic Institute, FI-02431 Masala, Finland
Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O. Box 15, 02431 Masala, Finland
Research Institute of Measuring and Modeling for the Built Environment, Aalto University, P.O. Box 11200, 00076 Aalto, Finland
Helsinki Metropolia, University of Applied Sciences, Civil Engineering and Building Services, P.O. Box 4000, 00079 Metropolia, Finland
Author to whom correspondence should be addressed.
Forests 2014, 5(5), 1032-1052;
Received: 20 January 2014 / Revised: 13 May 2014 / Accepted: 14 May 2014 / Published: 22 May 2014
The requirements for up-to-date tree data in city parks and forests are increasing, and an important question is how to keep the digital databases current for various applications. Traditional map-updating procedures, such as visual interpretation of digital aerial images or field measurements using tachymeters, are either inaccurate or expensive. Recently, the development of laser-scanning technology has opened new opportunities for tree mapping and attributes updating. For a detailed measurement and attributes update of urban trees, we tested the use of a multisource single-tree inventory (MS-STI) for heterogeneous urban forest conditions. MS-STI requires an existing tree map as input information in addition to airborne laser-scanning (ALS) data. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System (GNSS). Tree attributes were either measured from ALS or predicted by using metrics extracted from ALS data. Stem diameter-at-breast height (DBH) was predicted and compared to the field measures, and tree height and crown area were directly measured from ALS data at the two different urban-forest areas. The results indicate that MS-STI can be used for updating urban-forest attributes. The accuracies of DBH estimations were improved compared to the existing attribute information in the city of Helsinki’s urban-tree register. In addition, important attributes, such as tree height and crown dimensions, were extracted from ALS and added as attributes to the urban-tree register. View Full-Text
Keywords: urban forest; remote sensing; LiDAR; Airborne laser scanning; GIS; forest inventory; forest mapping; city planning; land-use planning urban forest; remote sensing; LiDAR; Airborne laser scanning; GIS; forest inventory; forest mapping; city planning; land-use planning
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MDPI and ACS Style

Saarinen, N.; Vastaranta, M.; Kankare, V.; Tanhuanpää, T.; Holopainen, M.; Hyyppä, J.; Hyyppä, H. Urban-Tree-Attribute Update Using Multisource Single-Tree Inventory. Forests 2014, 5, 1032-1052.

AMA Style

Saarinen N, Vastaranta M, Kankare V, Tanhuanpää T, Holopainen M, Hyyppä J, Hyyppä H. Urban-Tree-Attribute Update Using Multisource Single-Tree Inventory. Forests. 2014; 5(5):1032-1052.

Chicago/Turabian Style

Saarinen, Ninni, Mikko Vastaranta, Ville Kankare, Topi Tanhuanpää, Markus Holopainen, Juha Hyyppä, and Hannu Hyyppä. 2014. "Urban-Tree-Attribute Update Using Multisource Single-Tree Inventory" Forests 5, no. 5: 1032-1052.

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