Using multi-platform LiDAR to assess vegetation structure for woodland forest fauna

Abstract Vegetation structure can support biodiversity by creating a variety of microclimates and microhabitats that contribute to food and shelter for different species. For this reason, biodiversity and wildlife habitat assessments often require accurate measurements of vegetation structure. Traditional methods for measuring the three-dimensional distribution of vegetation are time-consuming and often limited to small areas or a subset of the landscape. Light detection and ranging (LiDAR) is an alternative method for collecting three dimensional information on vegetation structure and other landscape features across wide areas. For the first time, we used multi-platform LiDAR data from a terrestrial sensor (TLS) and an unmanned aerial vehicle (ULS) to investigate the relationship between vegetation structure and the diversity and abundance of birds, reptiles and amphibians in a critically endangered grassy woodland ecosystem. The first Chapter of this thesis involves TLS and ULS data collection methods, post-processing steps and exploratory data analysis. We calculated a number of variables to characterise the three-dimensional structure of vegetation across four structurally different, one hectare sites (high-tree/high-shrub, high-tree/low-shrub, low-tree/high-shrub, and low-tree/low-shrub) and compared the values of the TLS and ULS derived variables. Generally, TLS outperformed ULS by producing higher volumetric and height diversity indices within our landscape. In the Second Chapter, the relationship between TLS and ULS derived vegetation structural variables and overall bird abundance, species richness and diversity were investigated using mixed effects regression models. Models showed strong significant associations between vegetation structural variables including canopy roughness, vegetation volume, vertical complexity and the abundance of individual species and guilds. The best performing models were for individual bird species and guilds, whereas overall diversity and abundance showed less association to LiDAR-derived vegetation structural metrics. TLS and ULS based models performed similarly when identifying vegetation structural associations with bird communities and individual species. In the Third Chapter, coarse woody debris (CWD) from TLS, ULS and the combination of both datasets (Fusion) was extracted. Several topographic variables were calculated as raster imagery from LiDAR point clouds and Random Forest (RF) machine learning algorithms were then utilised to classify CWD. Noise reduction algorithms were applied to reduce noise from the classified imagery. Digital height model (DHM), surface roughness and topographic position index were important variables in classifying CWD with RF. Classification accuracy varied depending on the amount of ground vegetation cover. The impacts of ground vegetation cover on CWD accuracy in a grassy woodland were quantified and discussed. The Fourth Chapter explores the relationship between LiDAR derived vegetation structural metrics and the presence and abundance of reptiles and amphibians. Our models demonstrate that woodland reptile and amphibian populations were significantly associated with a number of vegetation structural characteristics from the selected variables, the most common of which were mean canopy height, canopy skewedness, vertical complexity, volume of vegetation and CWD. Notable relationships between herpetofauna population data and vegetation structural metrics are discussed with reference to existing literature on habitat associations for these animals. We also explore reasons why significant associations between LiDAR derived vegetation structural metrics and animal population data were not consistent across sensors and suggest directions for future research

ANALYSIS OF MULTITEMPORAL AERIAL IMAGES FOR FENYŐFŐ FOREST CHANGE DETECTION

This study evaluated the use of 40 cm spatial resolution aerial images for individual tree crown delineation, forest type classification, health estimation and clear-cut area detection in Fenyőfő forest reserves in 2012 and 2015 years. Region growing algorithm was used for segmentation of individual tree crowns. Forest type (coniferous/deciduous trees) were distinguished based on the orthomosaic images and segments. Research also investigated the height of individual trees, clear-cut areas and cut crowns between 2012 and 2015 years using Canopy Height Models. Results of the research were examined based on the field measurement data. According to our results, we achieved 75.2% accuracy in individual tree crown delineation. Heights of tree crowns have been calculated with 88.5% accuracy. This study had promising result in clear cut area and individual cut crown detection. Overall accuracy of classification was 77.2%, analysis showed that coniferous tree type classification was very accurate, but deciduous tree classification had a lot of omission errors. Based on the results and analysis, general information about forest health conditions has been presented. Finally, strengths and limitations of the research were discussed and recommendations were given for further research

Using multiplatform LiDAR to identify relationships between vegetation structure and the abundance and diversity of woodland reptiles and amphibians

Remotely sensed measures of vegetation structure have been shown to explain patterns in the occurrence and diversity of several animal taxa, including birds, mammals, and invertebrates. However, very little research in this area has focused on reptiles and amphibians (herpetofauna). Moreover, most remote sensing studies on animal–habitat associations have relied on airborne or satellite data that provide coverage over relatively large areas but may not have the resolution or viewing angle necessary to measure vegetation features at scales that are meaningful to herpetofauna. Here, we combined terrestrial laser scanning (TLS), unmanned aerial vehicle laser scanning (ULS), and fused (FLS) data to provide the first test of whether vegetation structural attributes can help explain variation in herpetofauna abundance, species richness, and diversity across a woodland landscape. We identified relationships between the abundance and diversity of herpetofauna and several vegetation metrics, including canopy height, skewedness, vertical complexity, volume of vegetation, and coarse woody debris. These relationships varied across species, groups, and sensors. ULS models tended to perform as well or better than TLS or FLS models based on the methods we used in this study. In open woodland landscapes, ULS data may have some benefits over TLS data for modeling relationships between herpetofauna and vegetation structure, which we discuss. However, for some species, only TLS data identified significant predictor variables among the LiDAR-derived structural metrics. While the overall predictive power of models was relatively low (i.e., at most R2 = 0.32 for ULS overall abundance and R2 = 0.32 for abundance at the individual species level [three-toed skink (Chalcides striatus)]), the ability to identify relationships between specific LiDAR structural metrics and the abundance and diversity of herpetofauna could be useful for understanding their habitat associations and managing reptile and amphibian populations.