Comparing Terrestrial Laser Scanning and UAV Structure from Motion to Assess Top of Canopy Structure in Tropical Forests

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/28th May 2018, The Royal Society Publishing by Sabina Roşca, Juha Suomalainen, Harm Bartholomeus, Martin Herold/ Terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) equipped with digital cameras have attracted much attention from the forestry community as potential tools for forest inventories and forest monitoring. This research fills a knowledge gap about the viability and dissimilarities of using these technologies for measuring the top of canopy structure in tropical forests. In an empirical study with data acquired in a Guyanese tropical forest, we assessed the differences between top of canopy models (TCMs) derived from TLS measurements and from UAV imagery, processed using structure from motion.

Firstly, canopy gaps lead to differences in TCMs derived from TLS and UAVs. UAV TCMs overestimate canopy height in gap areas and often fail to represent smaller gaps altogether. Secondly, it was demonstrated that forest change caused by logging can be detected by both TLS and UAV TCMs, although it is better depicted by the TLS. Thirdly, this research shows that both TLS and UAV TCMs are sensitive to the small variations in sensor positions during data collection.

TCMs rendered from UAV data acquired over the same area at different moments are more similar (RMSE 0.11–0.63 m for tree height, and 0.14–3.05 m for gap areas) than those rendered from TLS data (RMSE 0.21–1.21 m for trees, and 1.02–2.48 m for gaps). This study provides support for a more informed decision for choosing between TLS and UAV TCMs to assess top of canopy in a tropical forest by advancing our understanding on: (i) how these technologies capture the top of the canopy, (ii) why their ability to reproduce the same model varies over repeated surveying sessions and (iii) general considerations such as the area coverage, costs, fieldwork time and processing requirements needed.

Forest ecosystems are an important global resource playing key roles in both the environment and the economy. In the context of sustainable development and climate change mitigation, tropical forests are a major focus for research due to the role they play in the global carbon cycle, and recently, in climate mitigation policies through REDD (reduced emissions from deforestation and degradation) [1]. Remote sensing techniques are increasingly valued by ecologists for the unique perspective they offer to describe ecosystem states and dynamics. They have proven to be successful when it comes to understanding forest structure, from plot-scale measurements using terrestrial laser scanning (TLS), to meso-scale (1–100 km2) using aerial laser scanning and aerial imagery, and up to global-scale perspective from satellite imagery, radar] and light detection and ranging (LiDAR).

In recent years, TLS and airborne laser scanning (ALS) have attracted much attention from the forestry community as rapid and efficient tools for quantifying forest parameters. Although researchers confirm that ALS is an adequate method to estimate canopy height in coniferous and deciduous forests, there are few studies that have evaluated canopy height with ALS in tropical forests. In turn, TLS is capable of acquiring levels of detail far beyond what ALS is capable of.

The high-resolution capability offers many exciting opportunities for vegetation research, and several have explored TLS applications in measuring forest structure and tree parameters. More recently, the usage of small unmanned aerial vehicles (UAVs) has increased dramatically. This has been made possible by advancements in technology such as the availability of accurate and miniature global navigation satellite systems, inertial measurement units, smaller and lighter batteries, and high-quality consumer digital cameras. With the advent of new algorithms, such as the Scale Invariant Feature Transform (SIFT), that can directly georeference and rectify the imagery with only low accuracy camera positions, UAVs also found their use as remote sensing tools. These platforms can be customized and equipped with different sensors. For example, Wallace et al. are using a UAV equipped with laser scanning sensors, while in other studies, UAVs equipped with digital cameras are used.

At present, UAVs equipped with digital consumer cameras are considerably cheaper compared with UAVs equipped with laser scanning sensors. The large sets of overlapping digital photographs, taken from different locations, can be automatically post-processed to geometrically precise three-dimensional point cloud datasets, using computer vision structure from motion (SfM) algorithms. The goal of this study is to provide a comparison between UAV-SfM and TLS in assessing the top of canopy structure, at a study site in the tropical forest of Guyana. We assessed (i) how different features in forest structure affect the quality of the top of canopy modelled with the two technologies, (ii) how accurately forest change is captured by the two technologies and (iii) if, over repeated surveying sessions, the TLS and the UAV-SfM derived point clouds render the same top of canopy model (TCM).

The full research paper you can download from here: Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests

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