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Forests 2014, 5(5), 1032-1052; doi:10.3390/f5051032
Article

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

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1 Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland 2 Centre of Excellence in Laser Scanning Research, Finnish Geodetic Institute, FI-02431 Masala, Finland 3 Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O. Box 15, 02431 Masala, Finland 4 Research Institute of Measuring and Modeling for the Built Environment, Aalto University, P.O. Box 11200, 00076 Aalto, Finland 5 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.
Received: 20 January 2014 / Revised: 13 May 2014 / Accepted: 14 May 2014 / Published: 22 May 2014
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Abstract

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.
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
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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