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

This article reviews studies regarding wild animal surveys based on multiple platforms, including satellites, manned aircraft, and unmanned aircraft systems (UASs), and focuses on the data used, animal detection methods, and their accuracies. We also discuss the advantages and limitations of each type of remote sensing data and highlight some new research opportunities and challenges. Submeter very-high-resolution (VHR) spaceborne imagery has potential in modeling the population dynamics of large (>0.6 m) wild animals at large spatial and temporal scales, but has difficulty discerning small (<0.6 m) animals at the species level, although high-resolution commercial satellites, such as WorldView-3 and -4, have been able to collect images with a ground resolution of up to 0.31 m in panchromatic mode. This situation will not change unless the satellite image resolution is greatly improved in the future. Manned aerial surveys have long been employed to capture the centimeter-scale images required for animal censuses over large areas. However, such aerial surveys are costly to implement in small areas and can cause significant disturbances to wild animals because of their noise. In contrast, UAS surveys are seen as a safe, convenient and less expensive alternative to ground-based and conventional manned aerial surveys, but most UASs can cover only small areas. The proposed use of UAS imagery in combination with VHR satellite imagery would produce critical population data for large wild animal species and colonies over large areas. The development of software systems for automatically producing image mosaics and recognizing wild animals will further improve survey efficiency.

[1]  Andrew M. Wilson,et al.  The feasibility of counting songbirds using unmanned aerial vehicles , 2017, The Auk.

[2]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Zheng Yang,et al.  Spotting East African Mammals in Open Savannah from Space , 2014, PloS one.

[4]  A. Andriolo,et al.  The first aerial survey to estimate abundance of humpback whales (Megaptera novaeangliae) in the breeding ground off Brazil (Breeding Stock A) , 2023, J. Cetacean Res. Manage..

[5]  S. Mduma,et al.  Assessment of Effectiveness of Protection Strategies in Tanzania Based on a Decade of Survey Data for Large Herbivores , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[6]  Mathew R. Schwaller,et al.  Continental-scale mapping of Adélie penguin colonies from Landsat imagery , 2013 .

[7]  Devis Tuia,et al.  Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning , 2018, Remote Sensing of Environment.

[8]  Dominique Chabot,et al.  An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery , 2018 .

[9]  Karen Anderson,et al.  A UK Civil Aviation Authority (CAA)-approved operations manual for safe deployment of lightweight drones in research , 2017 .

[10]  Peter Christiansen,et al.  Automated Detection and Recognition of Wildlife Using Thermal Cameras , 2014, Sensors.

[11]  Robert Fitch,et al.  Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System , 2015, Robotics: Science and Systems.

[12]  Xiao Xiang Zhu,et al.  Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.

[13]  Curt Schurgers,et al.  Radio receiver design for Unmanned Aerial wildlife tracking , 2017, 2017 International Conference on Computing, Networking and Communications (ICNC).

[14]  K. Houtan,et al.  Five Decades of Marine Megafauna Surveys from Micronesia , 2016, Front. Mar. Sci..

[15]  Johann Mourier,et al.  Using unmanned aerial vehicles (UAVs) to investigate shark and ray densities in a shallow coral lagoon , 2016 .

[16]  P. Trathan,et al.  Penguins from space: faecal stains reveal the location of emperor penguin colonies , 2009 .

[17]  J. H. Knapen,et al.  Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems , 2017, 1701.01611.

[18]  R. Weibel,et al.  Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity , 2018, Landscape Ecology.

[19]  Douglas J. Krause,et al.  A small unmanned aerial system for estimating abundance and size of Antarctic predators , 2015, Polar Biology.

[20]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

[21]  U. Franke,et al.  Aerial ungulate surveys with a combination of infrared and high–resolution natural colour images , 2012 .

[22]  Paul A. Iaizzo,et al.  Bears Show a Physiological but Limited Behavioral Response to Unmanned Aerial Vehicles , 2015, Current Biology.

[23]  J. Rotella,et al.  Satellite imagery can be used to detect variation in abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica , 2011, Polar Biology.

[24]  DOUGLAS M. BURN,et al.  Application of Airborne Thermal Imagery to Surveys of Pacific Walrus , 2006 .

[25]  M. Mulero-Pázmány,et al.  Remotely Piloted Aircraft Systems as a Rhinoceros Anti-Poaching Tool in Africa , 2014, PloS one.

[26]  Josiane Zerubia,et al.  An automatic counter for aerial images of aggregations of large birds , 2011 .

[27]  Cheng-Chien Liu,et al.  Supporting the annual international black-faced spoonbill census with a low-cost unmanned aerial vehicle , 2015, Ecol. Informatics.

[28]  Michael C. Hatfield,et al.  Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology , 2016 .

[29]  Charles M. Francis,et al.  The motus wildlife tracking system: A collaborative research network to enhance the understanding of wildlife movement , 2017 .

[30]  S. Kessel,et al.  Aerial Survey as a Tool to Estimate Abundance and Describe Distribution of a Carcharhinid Species, the Lemon Shark, Negaprion brevirostris , 2013 .

[31]  S. Gomáriz,et al.  Fine-scale bird monitoring from light unmanned aircraft systems , 2012 .

[32]  Graeme Caughley,et al.  Experiments in Aerial Survey , 1976 .

[33]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[34]  GEORGE PIERCE JONES,et al.  An Assessment of Small Unmanned Aerial Vehicles for Wildlife Research , 2006 .

[35]  George Leblanc,et al.  Spectral Reflectance of Polar Bear and Other Large Arctic Mammal Pelts; Potential Applications to Remote Sensing Surveys , 2016, Remote. Sens..

[36]  Peter T. Fretwell,et al.  Using super-high resolution satellite imagery to census threatened albatrosses , 2017 .

[37]  A. C. Seymour,et al.  Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery , 2017, Scientific Reports.

[38]  A. Hart,et al.  Empirically testing the effectiveness of thermal imaging as a tool for identification of large mammals in the African bushveldt , 2018 .

[39]  Margarita Mulero-Pázmány,et al.  Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review , 2017, PloS one.

[40]  Gerald L. Kooyman,et al.  Correction: An Emperor Penguin Population Estimate: The First Global, Synoptic Survey of a Species from Space , 2012, PLoS ONE.

[41]  N. Pettorelli,et al.  Satellite remote sensing for applied ecologists: opportunities and challenges , 2014 .

[42]  Huadong Guo,et al.  Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks , 2014 .

[43]  C. Sandbrook.  The social implications of using drones for biodiversity conservation , 2015, Ambio.

[44]  Mirjana Bevanda,et al.  Bridging disciplines with training in remote sensing for animal movement: an attendee perspective , 2017 .

[45]  Mathew R. Schwaller,et al.  A remote sensing analysis of Adelie penguin rookeries , 1989 .

[46]  Miguel A. Olivares-Méndez,et al.  Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers , 2015, Sensors.

[47]  Frédéric Baret,et al.  Ear density estimation from high resolution RGB imagery using deep learning technique , 2019, Agricultural and Forest Meteorology.

[48]  D. Bird,et al.  Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?1 , 2015 .

[49]  Seth P. Stapleton,et al.  Aerial surveys suggest long-term stability in the seasonally ice-free Foxe Basin (Nunavut) polar bear population , 2016 .

[50]  Steven M. De Jong,et al.  Detecting plague-host abundance from space: Using a spectral vegetation index to identify occupancy of great gerbil burrows , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[51]  Julie Linchant,et al.  Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges , 2015 .

[52]  S. T. Buckland,et al.  Estimating Animal Abundance , 2002 .

[53]  M. Larue,et al.  Feasibility of using high‐resolution satellite imagery to assess vertebrate wildlife populations , 2017, Conservation biology : the journal of the Society for Conservation Biology.

[54]  Arthur P. Cracknell,et al.  UAVs: regulations and law enforcement , 2017 .

[55]  M. Betts,et al.  Uneven Rates of Landscape Change as a Source of Bias in Roadside Wildlife Surveys , 2007 .

[56]  H. Marsh,et al.  An Experimental Evaluation of Dugong and Sea Turtle Aerial Survey Techniques , 1989 .

[57]  M. O'Donnell,et al.  Radio tracking detects behavioral thermoregulation at a snail's pace , 2018 .

[58]  D. Bird,et al.  A low-cost technique for radio-tracking wildlife using a small standard unmanned aerial vehicle , 2017 .

[59]  A. Hodgson,et al.  Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study , 2013, PloS one.

[60]  J. Dowdeswell,et al.  Mapping seabird nesting habitats in Franz Josef Land, Russian High Arctic, using digital Landsat Thematic Mapper imagery , 1998 .

[61]  Aerial strip surveys of polar bears in the Barents Sea , 1990 .

[62]  Andrew K. Skidmore,et al.  Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery , 2017, Remote. Sens..

[63]  W. Turner.  Sensing biodiversity , 2014, Science.

[64]  J. Leeuw,et al.  Population trends of large non‐migratory wild herbivores and livestock in the Masai Mara ecosystem, Kenya, between 1977 and 1997 , 2000 .

[65]  W. Bean,et al.  Environmental Reviews and Case Studies: Applications of Unmanned Aircraft Systems (UAS) for Waterbird Surveys , 2015 .

[66]  H. Reeves,et al.  Flyways: Pioneering Waterfowl Management in North America , 2017 .

[67]  Jarrod C Hodgson,et al.  Precision wildlife monitoring using unmanned aerial vehicles , 2016, Scientific Reports.

[68]  Sandra Johnson,et al.  Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation , 2016, Sensors.

[69]  W. F. Porter,et al.  Use of modern infrared thermography for wildlife population surveys , 1995 .

[70]  Pierre Jouventin,et al.  Satellite remote sensing in monitoring change of seabirds: use of Spot Image in king penguin population increase at Ile aux Cochons, Crozet Archipelago , 1995, Polar Biology.

[71]  S. Wich,et al.  Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation , 2012 .

[72]  Marius Gilbert,et al.  How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals , 2018 .

[73]  N. G. Platonov,et al.  The possibility of using high resolution satellite images for detection of marine mammals , 2013, Biology Bulletin.

[74]  Nicolas Lecomte,et al.  Polar Bears from Space: Assessing Satellite Imagery as a Tool to Track Arctic Wildlife , 2014, PloS one.

[75]  Philippe Bouché,et al.  Unmanned Aerial Survey of Elephants , 2013, PloS one.

[76]  Anna Zmarz,et al.  Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests - preliminary results , 2018 .

[77]  R. Abileah,et al.  Use of high resolution space imagery to monitor the abundance, distribution, and migration patterns of marine mammal populations , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[78]  Geoff Groom,et al.  Remote sensing image data and automated analysis to describe marine bird distributions and abundances , 2013, Ecol. Informatics.

[79]  Clive R. McMahon,et al.  Satellites, the All-Seeing Eyes in the Sky: Counting Elephant Seals from Space , 2014, PloS one.

[80]  Antoine M. Dujon,et al.  Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales , 2016 .

[81]  Ohseok Kwon,et al.  Monitoring butterflies with an unmanned aerial vehicle: current possibilities and future potentials , 2017 .

[82]  Martin Israel.  A UAV-BASED ROE DEER FAWN DETECTION SYSTEM , 2012 .

[83]  Peter T. Fretwell,et al.  Whales from space: Four mysticete species described using new VHR satellite imagery , 2018, Marine Mammal Science.

[84]  Margaret Kosmala,et al.  Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning , 2017, Proceedings of the National Academy of Sciences.

[85]  Amon Murwira,et al.  Cotton fields drive elephant habitat fragmentation in the Mid Zambezi Valley, Zimbabwe , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[86]  Gerald L. Kooyman,et al.  An Emperor Penguin Population Estimate: The First Global, Synoptic Survey of a Species from Space , 2012, PloS one.

[87]  William A. Link,et al.  The North American Breeding Bird Survey 1966–2011: Summary Analysis and Species Accounts , 2013 .

[88]  Kevin C Elliott,et al.  Using Drones to Study Human Beings: Ethical and Regulatory Issues , 2019, Sci. Eng. Ethics.

[89]  Michele Volpi,et al.  Detecting animals in African Savanna with UAVs and the crowds , 2017, ArXiv.

[90]  David Peel,et al.  Unmanned aerial vehicles for surveying marine fauna: assessing detection probability. , 2017, Ecological applications : a publication of the Ecological Society of America.

[91]  Paul G. McDonald,et al.  Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: disturbance effects and species recognition , 2016, PeerJ.

[92]  Nicholas C. Coops,et al.  Estimating moose (Alces alces) occurrence and abundance from remotely derived environmental indicators , 2014 .

[93]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[94]  Peter T. Fretwell,et al.  Whales from Space: Counting Southern Right Whales by Satellite , 2014, PloS one.

[95]  C. Margules,et al.  Wombats detected from space , 1980 .

[96]  L. P. Koh,et al.  Best practice for minimising unmanned aerial vehicle disturbance to wildlife in biological field research , 2016, Current Biology.

[97]  V Dayal,et al.  Report of the scientific committee. , 2000, Journal of the Indian Medical Association.

[98]  David Moyer,et al.  Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts , 2016, PloS one.

[99]  David Grémillet,et al.  Approaching birds with drones: first experiments and ethical guidelines , 2015, Biology Letters.

[100]  G. Witmer.  Wildlife population monitoring: some practical considerations , 2005 .

[101]  Lorenzo Fiori,et al.  The Use of Unmanned Aerial Systems in Marine Mammal Research , 2017, Remote. Sens..

[102]  E. O. Garton,et al.  Double-observer evaluation of pronghorn aerial line-transect surveys , 2016, Wildlife Research.

[103]  Measuring the influence of unmanned aerial vehicles on Adélie penguins , 2016, Polar Biology.