Hyperspectral remote sensing provides a wealth of data essential for vegetation studies encompassing a wide range of applications (e.g., species diversity, ecosystem monitoring, etc.). The development and implementation of UAV-based hyperspectral systems have gained popularity over the last few years with novel efforts to demonstrate their operability. Here we describe the design, implementation, testing, and early results of the UAV-μCASI system, which showcases a relatively new hyperspectral sensor suitable for ecological studies. The μCASI (288 spectral bands) was integrated with a custom IMU-GNSS data recorder built in-house and mounted on a commercially available hexacopter platform with a gimbal to maximize system stability and minimize image distortion. We deployed the UAV-μCASI at three sites with different ecological characteristics across Canada: The Mer Bleue peatland, an abandoned agricultural field on Ile Grosbois, and the Cowichan Garry Oak Preserve meadow. We examined the attitude data from the flight controller to better understand airframe motion and the effectiveness of the integrated Differential Real Time Kinematic (RTK) GNSS. We describe important aspects of mission planning and show the effectiveness of a bundling adjustment to reduce boresight errors as well as the integration of a digital surface model for image geocorrection to account for parallax effects at the Mer Bleue test site. Finally, we assessed the quality of the radiometrically and atmospherically corrected imagery from the UAV-μCASI and found a close agreement (<2%) between the image derived reflectance and in-situ measurements. Overall, we found that a flight speed of 2.7 m/s, careful mission planning, and the integration of the bundling adjustment were important system characteristics for optimizing the image quality at an ultra-high spatial resolution (3–5 cm). Furthermore, environmental considerations such as wind speed (<5 m/s) and solar illumination also play a critical role in determining image quality. With the growing popularity of “turnkey” UAV-hyperspectral systems on the market, we demonstrate the basic requirements and technical challenges for these systems to be fully operational.
Abstract. One objective of the Boreal Ecosystem-Atmospheric Study (BOREAS) is to increase our understanding of the nature of canopy spectral bidirectional reflectance in the visible/near-infrared regimes for open canopies typical of boreal forest stands. For such stands, the need to characterize the reflectance of the sunlit and shaded vegetated understory is critical. These variables are subject to temporal variability due to differences in species phenology and foliar display as well as diurnal and seasonal changes in solar illumination through a seasonally varying upper canopy foliar area. To provide for this need, a multiteam field effort was mounted to measure the nadir midday understory reflectance for the flux tower sites during 1994 BOREAS field campaigns between February and October, specifically during the winter focused field campaign (FFC-W), the spring thaw focused field campaign (FFC-T), and the three intensive field campaigns (IFC-1, IFC-2, and IFC-3) between June and September, which sample vegetation phenological change. This was accomplished by measuring at near-solar noon the sunlit and shaded nadir reflectance of the understory along a surveyed leaf area index (LAI) transect line at each flux tower site. Site-to-site comparisons of understory reflectance spectra reveal stand differences that become more significant as the season progresses. Mean midday understory reflectance spectra were observed to be remarkably consistent over the season for young jack pine stands, followed by somewhat increased variability for mature jack pine, and significant seasonal variability for black spruce stands. Derived vegetation indices for understories are generally consistent with extrapolations of previous relationships of canopy spectral vegetation indices (VIs) versus leaf area index to zero LAI. Inclusion of these "zero-LAI" understory-derived indices significantly enhance the correlation in the linear VI-LAI relationships.
Peatlands cover a large area in Canada and globally (12% and 3% of the landmass, respectively). These ecosystems play an important role in climate regulation through the sequestration of carbon dioxide from, and the release of methane to, the atmosphere. Monitoring approaches, required to understand the response of peatlands to climate change at large spatial scales, are challenged by their unique vegetation characteristics, intrinsic hydrological complexity, and rapid changes over short periods of time (e.g., seasonality). In this study, we demonstrate the use of multitemporal, high spatial resolution (1 m 2 ) hyperspectral airborne imagery (Compact Airborne Spectrographic Imager (CASI) and Shortwave Airborne Spectrographic Imager (SASI) sensors) for assessing maximum instantaneous gross photosynthesis (PGmax) in hummocks, and gravimetric water content (GWC) and carbon uptake efficiency in hollows, at the Mer Bleue ombrotrophic bog. We applied empirical models (i.e., in situ data and spectral indices) and we derived spatial and temporal trends for the aforementioned variables. Our findings revealed the distribution of hummocks (51.2%), hollows (12.7%), and tree cover (33.6%), which is the first high spatial resolution map of this nature at Mer Bleue. For hummocks, we found growing season PGmax values between 8 µmol m −2 s −1 and 12 µmol m −2 s −1 were predominant (86.3% of the total area). For hollows, our results revealed, for the first time, the spatial heterogeneity and seasonal trends for gravimetric water content and carbon uptake efficiency for the whole bog.
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