This paper describes a new optomechanical design based on a previously presented do-it-yourself pushbroom hyperspectral imager (HSI) using commercial off-the-shelf (COTS) components. The new design uses larger aperture C-mount at F/2.8 instead of S-mount optics at F/4 to increase the throughput, which allows imaging at lower light levels. This is especially useful for dark surfaces like the deep ocean. The improved throughput is 6.77 higher at the center wavelength of 600 nm, which is shown both by theoretical calculations and experimental data. The measured full width at half maximum (FWHM) at 546.1 nm is 3.69 nm, which is close to the theoretical value of 3.3 nm, and smile and keystone are shown to be reduced in the new design. A method to characterize and remove second order effects using a cut-off filter is also presented and discussed.

Conservation of wildlife depends on precise and unbiased knowledge on the abundance and distribution of species. A challenge is to choose appropriate methods to obtain a sufficiently high detectability and spatial coverage matching the species characteristics and spatiotemporal use of the landscape. In remote areas, such as in the Arctic, monitoring efforts are often resource demanding and there is a need for cheap and precise alternative methods. Here, we compare an UAV pilot-survey to traditional population abundance surveys from ground and helicopter of the non-gregarious Svalbard reindeer to investigate whether small quadcopter UAVs can be an efficient alternative technology. We find that estimates of reindeer abundances from UAV imagery have lower precision and are more time consuming than present abundance surveys when used at management relevant spatial scales. We suggest that more efficient long-range fixed-wing UAVs should be evaluated for the job to increase the sampled area by UAV. In addition, the method will depend on the availability of more efficient post-processing methods including automatic animal object identification with machine learning and analytical methods that account for uncertainties.

<p>Ultra low frequency (ULF) waves can contribute significantly to energy and momentum transfer between the Solar Wind – magnetosphere – ionosphere system, however, the energy deposition by ULF waves is often not taken into account in the global energy budget. A case study of spatial and temporal energy deposition of a Pc5 (2 – 7 mHz) ULF wave during non-sunlit conditions is presented. Datasets from the EISCAT Tromsø VHF radar, magnetometers and DMSP satellites were utilized to estimate the wave characteristics and the height-dependent energy deposition rates. The equipartition of energy into the ionosphere through thermal, ion frictional and/or Joule heating are discussed. The goal of this study is to quantify how much energy is deposited by ULF waves in otherwise quiet conditions.</p>

Conservation of wildlife depends on precise and unbiased knowledge on the abundance and distribution of species. It is challenging to choose appropriate methods to obtain a sufficiently high detectability and spatial coverage matching the species characteristics and spatiotemporal use of the landscape. In remote regions, such as in the Arctic, monitoring efforts are often resource-intensive and there is a need for cheap and precise alternative methods. Here, we compare an uncrewed aerial vehicle (UAV; quadcopter) pilot-survey of the non-gregarious Svalbard reindeer to traditional population abundance surveys from ground and helicopter to investigate whether UAVs can be an efficient alternative technology. We found that the UAV survey underestimated reindeer abundance compared to the traditional abundance surveys when used at management relevant spatial scales. Observer variation in reindeer detection on UAV imagery was influenced by the RGB greenness index and mean blue channel. In future studies, we suggest to test long-range fixed-wing UAVs to increase the sample size of reindeer and area coverage and incorporate detection probability in animal density models from UAV imagery. In addition, we encourage focus on more efficient post-processing techniques, including automatic animal object identification with machine learning and analytical methods that account for uncertainties.

Conservation of wildlife depends on precise and unbiased knowledge on the abundance and distribution of species. It is challenging to choose appropriate methods to obtain a sufficiently high detectability and spatial coverage matching the species characteristics and spatiotemporal use of the landscape. In remote regions, such as in the Arctic, monitoring efforts are often resource-intensive and there is a need for cheap and precise alternative methods. Here, we compare an uncrewed aerial vehicle (UAV; quadcopter) pilot survey of the non-gregarious Svalbard reindeer to traditional population abundance surveys from ground and helicopter to investigate whether UAVs can be an efficient alternative technology. We found that the UAV survey underestimated reindeer abundance compared to the traditional abundance surveys when used at management relevant spatial scales. Observer variation in reindeer detection on UAV imagery was influenced by the RGB greenness index and mean blue channel. In future studies, we suggest testing long-range fixed-wing UAVs to increase the sample size of reindeer and area coverage and incorporate detection probability in animal density models from UAV imagery. In addition, we encourage focus on more efficient post-processing techniques, including automatic animal object identification with machine learning and analytical methods that account for uncertainties.