Spectral Reflectance of Polar Bear and Other Large Arctic Mammal Pelts; Potential Applications to Remote Sensing Surveys

Abstract

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 (<10%), increased through the visual and near infrared, peaking at 40%-60% between 1100 and 1400 nm and then gradually dropped, though remaining above 20% until at least 1800 nm. In contrast, reflectance of snow was very high in the UV range (>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.

Function and underlying mechanisms of seasonal colour moulting in mammals and birds: what keeps them changing in a warming world?

“…The observation that white arctic and SCC animals appear black under ultra‐violet (UV) light (Reynolds & Lavigne, ; Leblanc et al, ) led to a premise that they would be warmer because the white hairs act as optical fibres that transmit UV heat to the skin below (Grojean, Sousa, & Henry, ). This idea has been rejected based on the evidence that low UV reflection of the fur is instead due to high absorption by the hair protein keratin (Bohren & Sardie, ; Koon, ), and because UV represents only about 1% of the solar spectrum.…”

Surveying Wild Animals from Satellites, Manned Aircraft and Unmanned Aerial Systems (UASs): A Review

“…Other efforts include assessing the possibility of detecting marine mammals (polar bears, walruses, and bowhead whales) in GeoEye-1 images [31], monitoring marine mammals (e.g., humpback whales) using IKONOS imagery [68], manually detecting, describing, and counting four different mysticete species (fin whales in the Ligurian Sea, humpback whales off Hawaii, southern right whales off Península Valdés, and gray whales in Laguna San Ignacio) in WorldView-3 imagery [34], manually recognizing elephant seals (Mirounga leonina) in GeoEye-1 images of Macquarie Island in the southern Pacific Ocean [69], and monitoring the population changes of Weddell seals along the Victoria Land coast based on an analysis of high-resolution satellite images captured by the DigitalGlobe and GeoEye platforms [89]. Leblanc et al [90] presented a study that analyzed the potential to detect and differentiate large arctic mammals using spaceborne optical satellites (e.g., Pleiades and WorldView-2 and 3) by using a ground-based portable ASD FieldSpec®3 spectroradiometer to measure and analyze the spectral reflectance (within the 350-2500 nm range) of snow and several arctic mammal pelts (polar bear, caribou, muskox, and ringed, harp and bearded seals) under winter conditions. Data fusion of multiresolution spaceborne imagery was also used to produce large-scale and accurate wild animal population data.…”

Biodiversity Monitoring in Changing Tropical Forests: A Review of Approaches and New Opportunities

“…The application of remote sensing has been limited to deriving indirect indicators of biodiversity; mostly through using coarse to high spatial resolution satellite images for habitat analysis. However, advances in the field are bringing opportunities to develop direct indicators, e.g., using very high spatial resolution satellite images to identify large trees and animals [70,71], using hyper spectral sensors to ascertain vegetation biochemistry [72], and using LiDAR sensors to map the three-dimensional vegetation structure [73,74]. Moreover, such scale-related limitations can be overcome by coupling remote sensing with in situ sensors and DNA barcoding [19].…”