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Sensors 2008, 8(8), 5037-5054; doi:10.3390/s8085037

Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images

University of Joensuu, Faculty of Forest Sciences, P.O.Box 111, FI-80101 Joensuu, Finland
Finnish Forest Research Institute, Joensuu Research Unit, P.O Box 68, FI-80101 Joensuu, Finland
Author to whom correspondence should be addressed.
Received: 5 August 2008 / Revised: 21 August 2008 / Accepted: 22 August 2008 / Published: 25 August 2008
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
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The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM) was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF) and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90th percentile and the proportion of laser points reflected at the 60th height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species. View Full-Text
Keywords: Airborne laser scanning; digital aerial images; aspen; individual tree detection; tree species classification Airborne laser scanning; digital aerial images; aspen; individual tree detection; tree species classification

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

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MDPI and ACS Style

Säynäjoki, R.; Packalén, P.; Maltamo, M.; Vehmas, M.; Eerikäinen, K. Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images. Sensors 2008, 8, 5037-5054.

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