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Open AccessArticle

Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest

1
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
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Department of Earth and Environmental Science, Macquarie University, Sydney, NSW 2109, Australia
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Faculty of Geoinformatics, Munich University of Applied Sciences, Karlstr. 6, D-80333 Munich, Germany
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Faculty of Environment and Natural Resources, University of Freiburg, 79085 Freiburg im Breisgau, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2614; https://doi.org/10.3390/rs11222614
Received: 5 September 2019 / Revised: 31 October 2019 / Accepted: 2 November 2019 / Published: 8 November 2019
(This article belongs to the Section Forest Remote Sensing)
Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees. View Full-Text
Keywords: segmentation; forest structure analysis; dead wood; ALS point cloud; feature importance analysis segmentation; forest structure analysis; dead wood; ALS point cloud; feature importance analysis
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MDPI and ACS Style

Amiri, N.; Krzystek, P.; Heurich, M.; Skidmore, A. Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest. Remote Sens. 2019, 11, 2614.

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