Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Abstract

Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.

A New Individual Tree Crown Delineation Method for High Resolution Multispectral Imagery

“…The measurement of some forest parameters, such as crown area [5], stem diameter [6], tree height, tree growth, and crown closure, is performed at individual tree level and requires information about individual trees. The accuracy of ITC delineation is not only related to the accuracy of the subsequent species identification, gap analysis, and estimation of stand-level characteristics such as above-ground biomass and forest carbon [7], but also highly relevant to a variety of forest-related activities, such as silviculture treatment, biodiversity assessment [8], saw timber volume, and bioenergy supply [9].…”

Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

“…Remote sensing images and aerial photogrammetry promptly obtain forest surface data over a large area. However, these two methods have a limitation because the accuracy of the terrestrial data under the tree crown is compromised [10][11][12][13]. 3D laser scanning technology can efficiently collect high precision 3D data of a forest's vertical structure.…”

Introduction to geoinformation science

“…Airborne laser scanning (ALS) started the three dimensional revolution in remote sensing in environmental viewpoint, followed by re-invention of the image-matching derived photogrammetric point clouds that can both be used to characterize terrain, vegetation but also urban areas with unprecedented level of detail [9,10] . Miniaturization of sensors and GNSS technology has enabled use of drones for small-scale environmental mapping and monitoring [11][12][13] . Geospatial data collected by these varying sensor technologies easily become geospatial big data and one of the current challenges is how to efficiently analyse geospatial big data and how to handle issues related to data quality when data from different sources are fused in analyses.…”