Use of an Airborne Lidar System to Model Plant Species Composition and Diversity of Mediterranean Oak Forests

Abstract

Airborne lidar is a remote-sensing tool of increasing importance in ecological and conservation research due to its ability to characterize three-dimensional vegetation structure. If different aspects of plant species diversity and composition can be related to vegetation structure, landscape-level assessments of plant communities may be possible. We examined this possibility for Mediterranean oak forests in southern Portugal, which are rich in biological diversity but also threatened. We compared data from a discrete, first-and-last return lidar data set collected for 31 plots of cork oak (Quercus suber) and Algerian oak (Quercus canariensis) forest with field data to test whether lidar can be used to predict the vertical structure of vegetation, diversity of plant species, and community type. Lidar- and field-measured structural data were significantly correlated (up to r= 0.85). Diversity of forest species was significantly associated with lidar-measured vegetation height (R(2) = 0.50, p < 0.001). Clustering and ordination of the species data pointed to the presence of 2 main forest classes that could be discriminated with an accuracy of 89% on the basis of lidar data. Lidar can be applied widely for mapping of habitat and assessments of habitat condition (e.g., in support of the European Species and Habitats Directive [92/43/EEC]). However, particular attention needs to be paid to issues of survey design: density of lidar points and geospatial accuracy of ground-truthing and its timing relative to acquisition of lidar data.

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

“…Other studies obtained better biodiversity predictions (R 2 > 0.73) under tropical conditions by considering the Shannon index as a dependent variable [43,44]. Under Mediterranean conditions, similar predictions to those obtained in our study were obtained by Simonson et al [45] and Bacaro et al [46] by using the Shannon index and richness, respectively. In another context, Camathias et al [41] obtained lower richness prediction accuracies (R 2 = 0.53) for the whole of Switzerland, which is mainly composed of alpine ecosystems (60%).…”

Improving Species Diversity and Biomass Estimates of Tropical Dry Forests Using Airborne LiDAR

“…However, few studies have explicitly analyzed the relationships between plant species diversity and LiDAR measurements [16,17]. Here, we evaluated the potential of LiDAR to map the spatial distribution of species richness in a tropical dry forest based on two main factors related to diversity.…”

“…Recent studies have found that LiDAR (light detection and ranging) can be a powerful predictor of different vegetation attributes, such as height, basal area, stem density and other vegetation structure parameters [13][14][15]; yet, LiDAR has been used only rarely to estimate plant species diversity [16,17]. This sensor uses laser pulses to directly measure ground and vegetation height, as well as the vertical distribution of intercepted surfaces, making it an ideal tool for mapping vegetation structure with no saturation at high biomass values [18].…”

The Optimal Leaf Biochemical Selection for Mapping Species Diversity Based on Imaging Spectroscopy

“…New airborne remote sensing technologies, especially LiDAR (Light Detection and Ranging), and physical modeling approaches, such as 4-SCALE [49], ACRM (Two-Layer Canopy Reflectance Model) [50], FRT (Forest Reflectance and Transmittance) [51] and PROSPECT-DART (Discrete Anisotropic Radiative Transfer) [52], have been developed. LiDAR is an active remote sensing technology, and its scanners can emit a high-frequency pulse that can penetrate vegetation canopy gaps and, by recording the return time and intensity of backscatter from targets, provide detailed information of both the ground and multiple points within the forest canopy [53][54][55]. LiDAR has shown enormous potential for capturing canopy structural characteristics, especially in the retrieval of leaf area index (LAI) and tree height, as well as the isolation of individual tree crowns [53,[56][57][58].…”