Abstract
Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft.
Highlights
Measurement and monitoring of ecological processes are limited by what Levin (1992) termed the ‘problem of pattern and scale’ where linking observations across cells, leaves, plants, community, and ecosystem require exponential amounts of information be transferred between fine and broad spatial scale, short and long temporal scale
We suggest the combination of small unmanned aerial systems (sUAS) structure from motion (SfM) and either manned or sUAS aerial lidar as providing the best solution at large spatial scales amongst the platforms studied
Manned aerial lidar provides spatial resolutions of 0.25–1.0 m2, and sUAS Velodyne lidar spatial resolution of 0.1–0.25 m2 which is slightly coarser than the terrestrial lidar or sUAS SfM
Summary
Measurement and monitoring of ecological processes are limited by what Levin (1992) termed the ‘problem of pattern and scale’ where linking observations across cells, leaves, plants, community, and ecosystem require exponential amounts of information be transferred between fine and broad spatial scale, short and long temporal scale. Conventional ecological research studies require tens to hundreds of small quadrats or plots between 1 and 100 meters square (m2) for enough observations to ensure statistically significant p-values for testing hypotheses (Huenneke et al, 2001; Kachamba et al, 2017) This change in scale from individual plot to ecosystem inventory is perhaps the most important concept of remote sensing in the life sciences (Woodcock and Strahler, 1987; Turner, 1989; Turner et al, 1989). Measuring the height and volume of an individual tree or shrub requires accurate minimum elevation (bare ground), apical leader, and crown diameter measurements Given these requirements, the data may become unusable if bare ground is obstructed or incompletely illuminated. Multiple observations from different platforms may be required to generate a better estimate
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