Next Article in Journal
3D Geostrophy and Volume Transport in the Southern Ocean
Next Article in Special Issue
Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features
Previous Article in Journal
Identifying Flood Events over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations, Hydrological Models and In Situ Data
Previous Article in Special Issue
Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(5), 714; https://doi.org/10.3390/rs10050714

Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity

1
Natural Resources Institute Finland, FI-00790 Helsinki, Finland
2
Finnish Geospatial Research Institute, National Land Survey of Finland, FI-02430 Masala, Finland
3
Faculty of Information Technology, University of Jyväskylä, FI-40014 Jyväskylän yliopisto, Jyväskylä, Finland
4
VTT Microelectronics, FI-02044 VTT, Espoo, Finland
*
Author to whom correspondence should be addressed.
Received: 21 March 2018 / Revised: 29 April 2018 / Accepted: 4 May 2018 / Published: 5 May 2018
View Full-Text   |   Download PDF [7403 KB, uploaded 5 May 2018]   |  

Abstract

Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated reflectance mosaics and was tested along with the mosaics based on original image digital number values (DN). Two alternative classifiers, a k nearest neighbor method (k-nn), combined with a genetic algorithm and a random forest method, were tested for predicting the tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. The combination of VNIR, SWIR, and 3D features performed better than any of the data sets individually. Furthermore, the calibrated reflectance values performed better compared to uncorrected DN values. These trends were similar with both tested classifiers. Of the classifiers, the k-nn combined with the genetic algorithm provided consistently better results than the random forest algorithm. The best result was thus achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features; the proportion of correctly-classified trees was 0.823 for tree species and 0.869 for tree genus. View Full-Text
Keywords: hyperspectral imagery; tree species recognition; photogrammetry; dense point cloud; reflectance calibration; UAV; random forest; genetic algorithm; machine learning hyperspectral imagery; tree species recognition; photogrammetry; dense point cloud; reflectance calibration; UAV; random forest; genetic algorithm; machine learning
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Tuominen, S.; Näsi, R.; Honkavaara, E.; Balazs, A.; Hakala, T.; Viljanen, N.; Pölönen, I.; Saari, H.; Ojanen, H. Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity. Remote Sens. 2018, 10, 714.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top