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.

The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.

Peatlands play a fundamental role in climate regulation through their long-term accumulation of atmospheric carbon. Despite their resilience, peatlands are vulnerable to climate change. Remote sensing offers the opportunity to better understand these ecosystems at large spatial scales through time. In this study, we estimated water table depth from a 6-year time sequence of airborne shortwave infrared (SWIR) hyperspectral imagery. We found that the narrowband index NDWI₁₂₄₀ is a strong predictor of water table position. However, we illustrate the importance of considering peatland anisotropy on SWIR imagery from the summer months when the vascular plants are in full foliage, as not all illumination conditions are suitable for retrieving water table position. We also model net ecosystem exchange (NEE) from 10 years of Landsat TM5 imagery and from 4 years of Landsat OLI 8 imagery. Our results show the transferability of the model between imagery from sensors with similar spectral and radiometric properties such as Landsat 8 and Sentinel-2. NEE modeled from airborne hyperspectral imagery more closely correlated to eddy covariance tower measurements than did models based on satellite images. With fine spectral, spatial and radiometric resolutions, new generation satellite imagery and airborne hyperspectral imagery allow for monitoring the response of peatlands to both allogenic and autogenic factors.

Spectral reflectance within the 350–2500 nm range was measured for 17 pelts of arctic mammals (polar bear, caribou, muskox, and ringed, harp and bearded seals) in relation to snow. Reflectance of all pelts was very low at the ultraviolet (UV) end of the spectrum (90%), gradually dropped to near zero at 1500 nm, and then fluctuated between zero and 20% up to 2500 nm. All pelts could be distinguished from clean snow at many wavelengths. The polar bear pelts had higher and more uniform averaged reflectance from about 600–1100 nm than most other pelts, but discrimination was challenging due to variation in pelt color and intensity among individuals within each species. Results suggest promising approaches for using remote sensing tools with a broad spectral range to discriminate polar bears and other mammals from clean snow. Further data from live animals in their natural environment are needed to develop functions to discriminate among species of mammals and to determine whether other environmental elements may have similar reflectance.

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 m²) 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.