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

Abstract

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 Remote Sens. 2012, 4 2819 final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R 2 = 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 (R 2 = 0.39 to 0.78), but poor accuracies for the second axis (R 2 ≤ 0.3). LiDAR variables were the best predictors for total species richness across all forest layers (R 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.

Environmental and Human Controls of Ecosystem Functional Diversity in Temperate South America

“…Satellite-derived time-series of the Enhanced Vegetation Index were used to quantify functional diversity at the ecosystem level through the identification of Ecosystem Functional Types, defined here as ecological entities that have similar properties and dynamics of primary production. Numerous studies have successfully evaluated the use of remotely-sensed spectral diversity to estimate species richness [79][80][81][82][83] and species beta-diversity [84,85]. Our study focused not on estimating the diversity of species composition or turn-over, but on assessing the diversity of ecosystem functioning.…”

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

“…Over complex terrain areas, the inclusion of high-resolution topographical and structural variables derived from a LiDAR point cloud may represent a relevant contribution to the estimation of diversity, as demonstrated in several studies [20,41,46,47] including this one. Because of the singular conditions of the complex native forest of the Andes foothills of central Chile, it is expected that more robust results will be obtained by modeling plant diversity using the differentiation of different plant strata (tree, shrub, and herb canopies separately), due to the high capacity of LiDAR to capture the variability of these strata, as shown in other studies [26,57].…”

“…The results of this research did not allow us to confirm the spectral variation hypothesis on a local scale, even though Oldeland et al [56] suggested that the use of hyperspectral information should improve monitoring of species diversity. In this light, recent studies [43,44] have proven that the hyperspectral information from airborne sensors can significantly enhance the predictive power of models not only because of the spectral information but also because of the improvement provided by the spatial resolution [24,26,41,58,59]. Another relevant source of information could be an analysis of seasonal and annual variability that can provide an additional element in estimating biodiversity [24], particularly in deciduous forests like Monte Oscuro.…”

“…Topographical and structural information that an active LiDAR (Light Detection and Ranging) sensor is capable of capturing can be used to provide efficacious variables in studies on different ecosystems [37], especially forests with high vertical complexity [20]. It shows great potential for biodiversity characterization [26]. Within this context, combining LiDAR and hyperspectral data can be useful as they are related to different non-correlated proxies of plant biodiversity [38].…”

“…On the one hand, a number of studies have associated the biodiversity of different sites with the information obtained by passive remote sensors [23][24][25][26][27], correlating biological diversity directly with spectral reflectance values [28], with different spectral vegetation indexes [19,25,[29][30][31], and different types of feature extraction like principal components analysis (PCA) [32] or minimum noise fraction (MNF) [26]. Taking this into account, it is expected that hyperspectral sensors, which have great ability to detect characteristics associated with the biochemical, physiological, and structural spectral variability of the vegetation in the electromagnetic spectrum, will provide valuable information for the evaluation of biodiversity [33,34].…”