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Article

Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing

1
Global Change Ecology, Universities of Bayreuth, Würzburg & Augsburg, D-95440 Bayreuth, Germany
2
Department of Remote Sensing, University of Würzburg, D-97074 Würzburg, Germany
3
Biogeographical Modelling, BayCEER, University of Bayreuth, D-95440 Bayreuth, Germany
4
Bavarian Forest National Park, D-94481 Grafenau, Germany
5
Terrestrial Ecology, Technische Universität München, D-85354 Freising, Germany
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German Remote Sensing Data Centre, German Aerospace Centre DLR, D-82234 Wessling, Germany
7
Biogeography, BayCEER, University of Bayreuth, D-95440 Bayreuth, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2012, 4(9), 2818-2845; https://doi.org/10.3390/rs4092818
Received: 5 August 2012 / Revised: 14 September 2012 / Accepted: 17 September 2012 / Published: 21 September 2012
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially the combination of LiDAR and hyperspectral data is expected to deliver valuable information. In this study spatial patterns of vascular plant community composition and α-diversity of a temperate montane forest in Germany were analysed for different forest strata. The predictive power of LiDAR (LiD) and hyperspectral (MNF) datasets alone and combined (MNF+LiD) was compared using random forest regression in a ten-fold cross-validation scheme that included feature selection and model tuning. The final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R2 = 0.26 to 0.55) depending on the forest layer. In contrast, community composition of the different layers, obtained by multivariate ordination, could in part be modelled with high accuracies for the first ordination axis (R2 = 0.39 to 0.78), but poor accuracies for the second axis (R2 ≤ 0.3). LiDAR variables were the best predictors for total species richness across all forest layers (R2 LiD = 0.3, R2 MNF = 0.08, R2 MNF+LiD = 0.2), while for community composition across all forest layers both hyperspectral and LiDAR predictors achieved similar performances (R2 LiD = 0.75, R2 MNF = 0.76, R2 MNF+LiD = 0.78). The improvement in R2 was small (≤0.07)—if any—when using both LiDAR and hyperspectral data as compared to using only the best single predictor set. This study shows the high potential of LiDAR and hyperspectral data for plant biodiversity modelling, but also calls for a critical evaluation of the added value of combining both with respect to acquisition costs. View Full-Text
Keywords: minimum noise fraction transformation; Boruta feature selection; Shannon index; NMDS; full-waveform LiDAR; HyMap; herb layer; shrub layer; tree layer minimum noise fraction transformation; Boruta feature selection; Shannon index; NMDS; full-waveform LiDAR; HyMap; herb layer; shrub layer; tree layer
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MDPI and ACS Style

Leutner, B.F.; Reineking, B.; Müller, J.; Bachmann, M.; Beierkuhnlein, C.; Dech, S.; Wegmann, M. Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing. Remote Sens. 2012, 4, 2818-2845. https://doi.org/10.3390/rs4092818

AMA Style

Leutner BF, Reineking B, Müller J, Bachmann M, Beierkuhnlein C, Dech S, Wegmann M. Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing. Remote Sensing. 2012; 4(9):2818-2845. https://doi.org/10.3390/rs4092818

Chicago/Turabian Style

Leutner, Benjamin F., Björn Reineking, Jörg Müller, Martin Bachmann, Carl Beierkuhnlein, Stefan Dech, and Martin Wegmann. 2012. "Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing" Remote Sensing 4, no. 9: 2818-2845. https://doi.org/10.3390/rs4092818

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