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

Wang, Dongliang; Shao, Quanqin; Yue, Huanyin

doi:10.3390/rs11111308

Cited by 108 publications

(87 citation statements)

References 102 publications

(395 reference statements)

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“…Costs may also come down with the use of UAVs for imaging surveys. While great progress has been made in their use for wildlife surveys, endurance limitations restrict their use to small areas (Wang et al 2019). In West Africa, for example, a UAV was successfully used for an elephant SRF of a small reserve of 940 km 2 , but limitations on range and reliability meant that the exercise took many days to complete (Vermeulen et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Cameras replace human observers in multi‐species aerial counts in Murchison Falls, Uganda

Ochanda¹,

Brett

Tumwesigye

et al. 2020

Remote Sens Ecol Conserv

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Wildlife counts in Africa and elsewhere are often implemented using light aircraft with 'rear-seat-observer' (RSO) counting crews. Previous research has indicated that RSOs often fail to detect animals, and that population estimates are therefore biased. We conducted aerial wildlife surveys in Murchison Falls Protected Area, Uganda, in which we replaced RSOs with high-definition 'oblique camera count' (OCC) systems. The survey area comprises forests, woodlands and grasslands. Four counts were conducted in 2015-2016 using a systematic-reconnaissance-flight (SRF) strip-transect design. Camera inclination angles, focal lengths, altitude and frame interval were calibrated to provide imaged strips of known sample size on the left and right sides of the aircraft. Using digital cameras, 24 000 high-definition images were acquired for each count, which were visually interpreted by four airphoto interpreters. We used the standard Jolly II SRF analysis to derive population estimates. Our OCC estimates of the antelopeshartebeest, Uganda kob, waterbuck and oribiwere, respectively, 25%, 103%, 97% and 2100% higher than in the most recent RSO count conducted in 2014. The OCC surveys doubled the 2014 RSO estimate of 58 000 Uganda kob to over 118 000. Population size estimates of elephants and giraffes did not differ significantly. Although all four OCC buffalo estimates were higher than the RSO estimatesin one count by 60%these differences were not significant due to the clumped distribution and high variation in herd sizes, resulting in imprecise estimation by sampling. We conclude that RSO wildlife counts in Murchison have been effective in enumerating elephants and giraffe, but that many smaller species have not been well detected. We emphasize the importance of 60 years of RSO-based surveys across Africa, but suggest that new imaging technologies are embraced to improve accuracy.

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Section: Discussionmentioning

confidence: 99%

Cameras replace human observers in multi‐species aerial counts in Murchison Falls, Uganda

Ochanda¹,

Brett

Tumwesigye

et al. 2020

Remote Sens Ecol Conserv

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“…Burke et al [ 42 ] focused on the optimization of RPAS-based thermal imaging strategies, considering the thermal effects of ground and atmosphere, occlusion by vegetation, and effective flight height for accurate image acquisition and identification of animals of interest. Wang et al [ 43 ] reviewed previous wild animal survey studies using satellites, manned aircraft, and RPAS and focused on the data specifications, animal detection methods, and detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Remotely Piloted Aircraft System (RPAS)-Based Wildlife Detection: A Review and Case Studies in Maritime Antarctica

Hyun

Park

Lee

2020

Animals

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In wildlife biology, it is important to conduct efficient observations and quantitative monitoring of wild animals. Conventional wildlife monitoring mainly relies on direct field observations by the naked eyes or through binoculars, on-site image acquisition at fixed spots, and sampling or capturing under severe areal constraints. Recently, remotely piloted aircraft systems (RPAS), also called drones or unmanned aerial vehicles (UAV), were successfully applied to detect wildlife with imaging sensors, such as RGB and thermal-imaging sensors, with superior detection capabilities to those of human observation. Here, we review studies with RPAS which has been increasingly used in wildlife detection and explain how an RPAS-based high-resolution RGB image can be applied to wild animal studies from the perspective of individual detection and population surveys as well as behavioral studies. The applicability of thermal-imaging sensors was also assessed with further information extractable from image analyses. In addition, RPAS-based case studies of acquisition of high-resolution RGB images for the purpose of detecting southern elephant seals (Mirounga leonina) and shape property extraction using thermal-imaging sensor in King George Island, maritime Antarctica is presented as applications in an extreme environment. The case studies suggest that currently available cost-effective small-sized RPAS, which are capable of flexible operation and mounting miniaturized imaging sensors, and are easily maneuverable even from an inflatable boat, can be an effective and supportive technique for both the visual interpretation and quantitative analysis of wild animals in low-accessible extreme or maritime environments.

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“…Satellite sensors provide a broad‐scale, flexible, economical, and noninvasive method to assess populations in their natural environment and are increasingly being used in the study and management of wildlife (Wang et al. 2019).…”

Section: Discussionmentioning

confidence: 99%

Estimating animal population size with very high‐resolution satellite imagery

Zhao

Shuming

Zhou

et al. 2020

Conservation Biology

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Very high‐resolution (VHR) satellite sensors can be used to estimate the size of animal populations, a critical factor in wildlife management, and acquire animal spatial distributions in an economical, easy, and precise way. We developed a method for satellite population size estimation that includes a noninvasive photogrammetry, from which the animal's average orthographic area is calculated, and an imagery interpretation method that estimates population size based on the ratio of an observed animal population area to the average individual area. As a proof of concept, we used this method to estimate the population size of Whooper Swans (Cygnus cygnus) in a national nature reserve in China. To reduce errors, the reserve was subdivided into regions of interest based on locations of Whooper Swan and background brightness. Estimates from the satellite pixels were compared with manual counts made over 2 years, at 3 locations, and in 3 land‐cover types. Our results showed 1124 Whooper Swans occupied a national nature reserve on 15 February 2013, and the average percent error was 3.16% (SE = 1.37). These results demonstrate that our method produced robust data for population size estimation that were indistinguishable from manual count data. Our method may be used generally to estimate population sizes of visible and gregarious animals that exhibit high contrast relative to their environments and may inform estimations of populations in complex backgrounds.

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

Cited by 108 publications

References 102 publications

Cameras replace human observers in multi‐species aerial counts in Murchison Falls, Uganda

Cameras replace human observers in multi‐species aerial counts in Murchison Falls, Uganda

Remotely Piloted Aircraft System (RPAS)-Based Wildlife Detection: A Review and Case Studies in Maritime Antarctica

Estimating animal population size with very high‐resolution satellite imagery

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