Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

Abstract

Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.

Old Man Saltbush mortality following fire challenges the resilience of post‐mine rehabilitation in central Queensland, Australia

“…Additionally, Acacia and Eucalyptus canopies were highly variable in the imagerysometimes represented by cloudy/convoluted circles and other times mostly absent, and only noticeable because of the adjoining shadow. This is primarily an error associated with the point cloud coverage of each canopy during the image capture and Pix4D processing and to some extent can be improved through multiple drone flights at different solar elevations (Fletcher & Mather 2020). Other improvements could be made by combining structural data such as LiDAR or canopy height models derived from the drone point clouds to improve the classification (Dash et al 2019;Xu et al 2020).…”

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

“…Several flight planning apps and processing software packages were used throughout the reviewed studies. These include mission planning apps such as Pix4DCapture [62,63], Map Pilot App [64,65], Litchi [58,66], UgCS [11], and Autopilot for DJI [67]. Most researchers used AgiSoft PhotoScan processing software for SfM and orthomosaic stitching [11,25,61,65,68], as well as Pix4DMapper, ArcGIS, QGIS, eCognition, R, Python, MAT-LAB, Lastools, and LiDAR360.…”

“…Others linked targeted UAV data with satellite imagery to map biomass, including combining NDVI from multispectral UAV imagery and Landsat imagery [33], UAV-LiDAR and Sentinel-2 imagery [86], and RGB UAV imagery with Sentinel-1 and Sentinel-2 imagery [62]. Two studies [54,67] used SfM with RGB imagery to capture detailed models of riparian vegetation in order to reconstruct physical models of structure and shading properties, while another riverine study [23] used RGB and multispectral orthophotos with an OBIA approach. They first mapped spectrally similar riparian objects and then applied in situ carbon stocks estimations to the objects to estimate the entire riparian forest carbon reservoir.…”

“…On the field operations side, a critical precursor of such efficiency lies in navigating the tradeoff among spatial coverage and timing of operations and informative content of the data. For a given camera field of view, reducing flight altitude can increase the flight time of a study site, but also spatial resolution [34,65,67,69,115,133]. Similarly, expanding the overlap among images at a given altitude may improve the quality of photogrammetric products such as DSMs [57,95], but escalate both the time of data collection and postprocessing computational workload [13,65].…”

“…This means that data analyses, especially computer-based routines, may not always require the highest "achievable" spatial resolution and may, in fact, benefit from some degree of data aggregation and filtering to reduce noise [14,96]. Interestingly, several studies used MMUs substantially exceeding the original ground sampling distance of their UAV data (e.g., [14,35,56,67,75,87]), often in order to match the coarser scales of other relevant datasets. Rather than simply representing local spectral variation, texture measures can be strategically used to accentuate certain forms of heterogeneity most relevant to class differences, including more sophisticated filters that can be more easily computed with modern tools [68].…”