Use of airborne lidar data to improve plant species richness and diversity monitoring in lowland and mountain forests

Abstract

We explored the potential of airborne laser scanner (ALS) data to improve Bayesian models linking biodiversity indicators of the understory vegetation to environmental factors. Biodiversity was studied at plot level and models were built to investigate species abundance for the most abundant plants found on each study site, and for ecological group richness based on light preference. The usual abiotic explanatory factors related to climate, topography and soil properties were used in the models. ALS data, available for two contrasting study sites, were used to provide biotic factors related to forest structure, which was assumed to be a key driver of understory biodiversity. Several ALS variables were found to have significant effects on biodiversity indicators. However, the responses of biodiversity indicators to forest structure variables, as revealed by the Bayesian model outputs, were shown to be dependent on the abiotic environmental conditions characterizing the study areas. Lower responses were observed on the lowland site than on the mountainous site. In the latter, shade-tolerant and heliophilous species richness was impacted by vegetation structure indicators linked to light penetration through the canopy. However, to reveal the full effects of forest structure on biodiversity indicators, forest structure would need to be measured over much wider areas than the plot we assessed. It seems obvious that the forest structure surrounding the field plots can impact biodiversity indicators measured at plot level. Various scales were found to be relevant depending on: the biodiversity indicators that were modelled, and the ALS variable. Finally, our results underline the utility of lidar data in abundance and richness models to characterize forest structure with variables that are difficult to measure in the field, either due to their nature or to the size of the area they relate to.

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

“…When the raster resolution increased, image pixels were divided into several parts, and the p value increased. In addition, the segmentation distance of the LiDAR point cloud also affects the p value [27]. Next, we will use more advanced LiDAR sensors to further improve the LiDAR image resolution and obtain a more accurate LiDAR-derived grassland AGB perdition map.…”

“…Owing to their low weight and cost, UAV LiDAR has been widely used in vegetation monitoring. Xiangqian Wu et al [27] used a Velodyne LiDAR sensor to detect individual trees and estimate forests in China. Wang et al [28] used Velodyne's HDL-32E UAV LiDAR system to study the Hulun Buir grassland ecosystem.…”

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

“…Very-high-spatial-resolution multispectral data from an Unmanned Aerial Vehicle (UAV) platform have also been explored for richness and diversity monitoring [25]. Some other studies, albeit few, have also used active sensors extensively for forest tree diversity measurement as in the study of Bouvier et al [26], where airborne laser scanner (ALS) data has been assessed in a mixed forest area in France, for richness and diversity monitoring.…”

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

“…In forestry, the main use is for the prediction of forest volume or other forest characteristics [6], while in the ecology community many studies are also related to animal habitat assessment [7][8][9]. ALS data are also widely used for the prediction of species diversity indices, like the Shannon and Simpson species diversity indices [10].…”