![]() ![]() For example, measuring the height and volume of an individual tree or shrub requires accurate minimum elevation (bare ground), apical leader, and crown diameter measurements. Recent advances in sensor and platform technology appear to bring these resolutions within reach, but better understanding of detection bias and accuracy are needed, as well as examining the potential for combining multiple sources in a data-fusion approach that builds on strengths of one system to avoid the weakness of the other. For instance, landscape-scale models of primary production and carbon-uptake should be more accurate when the vegetation abundance is frequently represented at a fine, plant-scale spatial resolution. Therefore, characterization of both spatial and temporal patterns of vegetation abundance requires both high spatial resolution and temporal repetition over short periods of time. 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).ĭryland ecosystems, characterized by sparse or patchy vegetation and long periods of senescence, are punctuated by short periods of rapid growth following seasonal precipitation events. Conventional ecological research studies require tens to hundreds of small quadrats or plots between 1 and 100 meters square (m 2) for enough observations to ensure statistically significant p-values for testing hypotheses ( Huenneke et al., 2001 Kachamba et al., 2017). 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. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. ![]() Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. 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. 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. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. 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. 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. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. 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. Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. 4USDA Agricultural Research Service, Southwest Watershed Research Center, Tucson, AZ, United States.3Informatics and Computing Program, Northern Arizona University, Flagstaff, AZ, United States.2School of Natural Resource and Environment, University of Arizona, Tucson, AZ, United States.1BIO5 Institute, University of Arizona, Tucson, AZ, United States.Nichols 4, Philip Heilman 4 and Jason McVay 3 ![]()
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