References

1. 1.
   Acebes P, Malo J E and Traba J. 2013. Trade-offs between food availability and predation risk in desert environments: the case of polygynous monomorphic guanaco (Lama guanicoe). Journal of Arid Environments, 97: 136-142
2. 2.
   Akkaynak D, Allen J J, Mäthger L M, Chiao C C and Hanlon R T. 2013. Quantification of cuttlefish (Sepia officinalis) camouflage: a study of color and luminance using in situ spectrometry. Journal of Comparative Physiology A, 199(3): 211-225
3. 3.
   Albornoz R I, Giri K, Hannah M C and Wales W J. 2022. An improved approach to automated measurement of body condition score in dairy cows using a three-dimensional camera system. Animals, 12(1): 72
4. 4.
   Al-Naji A, Tao Y T, Smith I and Chahl J. 2019. A pilot study for estimating the cardiopulmonary signals of diverse exotic animals using a digital camera. Sensors, 19(24): 5445
5. 5.
   Bacheler N M, Shertzer K W, Cheshire R T and Macmahan J H. 2019. Tropical storms influence the movement behavior of a demersal oceanic fish species. Scientific Reports, 9: 1481
6. 6.
   Bartlam-Brooks H L A, Beck P S A, Bohrer G and Harris S. 2013. In search of greener pastures: using satellite images to predict the effects of environmental change on zebra migration. Journal of Geophysical Research: Biogeosciences, 118(4): 1427-1437
7. 7.
   Basedow S L, Mckee D, Lefering I, Gislason A, Daase M, Trudnowska E, Egeland E S, Choquet M and Falk-Petersen S. 2019. Remote sensing of zooplankton swarms. Scientific Reports, 9: 686
8. 8.
   Brown C and Byrnes E E. 2022a. Small-scale movement and migration cues of Australian bass (Percalates novemaculeata) in an urbanised river. Marine and Freshwater Research, 73(6): 742-753
9. 9.
   Brown J, Qiao Y L, Clark C, Lomax S, Rafique K and Sukkarieh S. 2022b. Automated aerial animal detection when spatial resolution conditions are varied. Computers and Electronics in Agriculture, 193: 106689
10. 10.
    Brownscombe J W, Shipley O N, Griffin L P, Morley D, Acosta A, Adams A J, Boucek R, Danylchuk A J, Cooke S J and Power M. 2022. Application of telemetry and stable isotope analyses to inform the resource ecology and management of a marine fish. Journal of Applied Ecology, 59(4): 1110-1121
11. 11.
    Cang Y, He H X and Qiao Y L. 2019. An intelligent pig weights estimate method based on deep learning in sow stall environments. IEEE Access, 7: 164867-164875
12. 12.
    Capello M, Robert M, Soria M, Potin G, Itano D, Holland K, Deneubourg J L and Dagorn L. 2015. A methodological framework to estimate the site fidelity of tagged animals using passive acoustic telemetry. PLoS One, 10(8): e0134002
13. 13.
    Caravaggi A, Banks P B, Burton A C, Finlay C M V, Haswell P M, Hayward M W, Rowcliffe M J and Wood M D. 2017. A review of camera trapping for conservation behaviour research. Remote Sensing in Ecology and Conservation, 3(3): 109-122
14. 14.
    Chabot D, Dillon C and Francis C M. 2018. An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery. Avian Conservation and Ecology, 13(1): 15
15. 15.
    Chen A, Jacob M, Shoshani G and Charter M. 2023. Using computer vision, image analysis and UAVs for the automatic recognition and counting of common cranes (Grus grus). Journal of Environmental Management, 328: 116948
16. 16.
    Chen B A, Feng Q L, Niu B W, Yan F Q, Gao B B, Yang J Y, Gong J H and Liu J T. 2022. Multi-modal fusion of satellite and street-view images for urban village classification based on a dual-branch deep neural network. International Journal of Applied Earth Observation and Geoinformation, 109: 102794
17. 17.
    Chen Y, Zhang Y W, Lei J H and Li L. 2022. Automatic extraction method for gait parameters of quadruped walking based on computer vision. Laser and Optoelectronics Progress, 59(8): 0815006
18. 18.
    Cihan P, Saygili A, Ozmen N E and Akyuzlu M. 2023. Identification and recognition of animals from biometric markers using computer vision approaches: a review. Kafkas Universitesi Veteriner Fakultesi Dergisi, 29(6): 581-593
19. 19.
    Corcoran E, Winsen M, Sudholz A and Hamilton G. 2021. Automated detection of wildlife using drones: synthesis, opportunities and constraints. Methods in Ecology and Evolution, 12(6): 1103-1114
20. 20.
    Corrêa A A, Quoos J H, Barreto A S, Groch K R and Eichler P P B. 2022. Use of satellite imagery to identify southern right whales (Eubalaena australis) on a Southwest Atlantic Ocean breeding ground. Marine Mammal Science, 38(1): 87-101
21. 21.
    Cubaynes H C, Fretwell P T, Bamford C, Gerrish L and Jackson J A. 2019. Whales from space: four mysticete species described using new VHR satellite imagery. Marine Mammal Science, 35(2): 466-491
22. 22.
    Cui K, Hu C, Wang R, Li S W, Wu D L and Ma S Q. 2019. Extracting vertical distribution of aerial migratory animals using weather radar//2019 International Applied Computational Electromagnetics Society Symposium - China (ACES). Nanjing: IEEE: 1-2
23. 23.
    Dearborn K D and Danby R K. 2022. Remotely sensed trends in vegetation productivity and phenology during population decline of the Bathurst caribou (Rangifer tarandus groenlandicus) herd. Arctic Science, 8(1): 228-251
24. 24.
    Dhellemmes F, Aspillaga E, Rittweg T, Alós J, Möller P and Arlinghaus R. 2023. Body size scaling of space use in coastal pike (Esox lucius) in brackish lagoons of the southern Baltic Sea. Fisheries Research, 260: 106560
25. 25.
    Doherty P D, Baxter J M, Gell F R, Godley B J, Graham R T, Hall G, Hall J, Hawkes L A, Henderson S M, Johnson L, Speedie C and Witt M J. 2017. Long-term satellite tracking reveals variable seasonal migration strategies of basking sharks in the north-east Atlantic. Scientific Reports, 7: 42837
26. 26.
    Double M C, Andrews-Goff V, Jenner K C S, Jenner M N, Laverick S M, Branch T A and Gales N J. 2014. Migratory movements of pygmy blue whales (Balaenoptera musculus brevicauda) between Australia and Indonesia as revealed by satellite telemetry. PLoS One, 9(4): e93578
27. 27.
    Du A, Guo H, Lu J, Su Y, Ma Q, Ruchay A, Marinello F and Pezzuolo A. 2022. Automatic livestock body measurement based on keypoint detection with multiple depth cameras. Computers and Electronics in Agriculture, 198: 107059
28. 28.
    Duporge I, Isupova O, Reece S, Macdonald D W and Wang T J. 2021. Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes. Remote Sensing in Ecology and Conservation, 7(3): 369-381
29. 29.
    Evans D R, Carthy R R and Ceriani S A. 2019. Migration routes, foraging behavior, and site fidelity of loggerhead sea turtles (Caretta caretta) satellite tracked from a globally important rookery. Marine Biology, 166(10): 134
30. 30.
    Garcia-Quintas A, Roy A, Barbraud C, Demarcq H, Denis D and Bertrand S L. 2023. Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding. Ecology and Evolution, 13(9): e10549
31. 31.
    Gerber K M, Mather M E, Smith J M and Peterson Z J. 2019. Multiple metrics provide context for the distribution of a highly mobile fish predator, the blue catfish. Ecology of Freshwater Fish, 28(1): 141-155
32. 32.
    Gibb R, Shoji A, Fayet A L, Perrins C M, Guilford T and Freeman R. 2017. Remotely sensed wind speed predicts soaring behaviour in a wide-ranging pelagic seabird. Journal of the Royal Society Interface, 14(132): 20170262
33. 33.
    Gomez Villa A, Salazar A and Vargas F. 2017. Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks. Ecological Informatics, 41: 24-32
34. 34.
    Guo H, Ma Q, Zhang S L, Su W, Zhu D H and Gao Y B. 2014. Prototype system of shape measurements of animal based on 3D reconstruction. Transactions of the Chinese Society for Agricultural Machinery, 45(5): 227-232, 246
35. 35.
    Guo X J and Shao Q Q. 2023. Population of kiangs and spatio-temporal variation of its habitat in Sanjiangyuan National Park based on unmanned aerial vehicle remote sensing. Acta Ecologica Sinica, 43(19): 7886-7895
36. 36.
    Guo X J, Shao Q Q, Yang F, Li Y Z, Wang Y C and Wang D L. 2019. Using UAV remote sensing for a population census of blue sheep (Pseudois nayaur) in Maduo county, source region of the Yellow River. Journal of Natural Resources, 34(5): 1054-1065
37. 37.
    Gurel N Z, Jeong H K, Kloefkorn H, Hochman S and Iran O T. 2018. Unobtrusive heartbeat detection from mice using sensors embedded in the nest//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE: 1604-1607
38. 38.
    Hays G C and Scott R. 2013. Global patterns for upper ceilings on migration distance in sea turtles and comparisons with fish, birds and mammals. Functional Ecology, 27(3): 748-756
39. 39.
    He A, Li X B, Wu X M, Su C Y, Chen J, Xu S and Guo X B. 2024. ALSS-YOLO: an adaptive lightweight channel split and shuffling network for TIR wildlife detection in UAV imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17: 17308-17326
40. 40.
    He C, Qiao Y L, Mao R, Li M and Wang M L. 2023a. Enhanced LiteHRNet based sheep weight estimation using RGB-D images. Computers and Electronics in Agriculture, 206: 107667
41. 41.
    He H X, Chen C Y, Zhang W W, Wang Z W and Zhang X F. 2023b. Body condition scoring network based on improved YOLOX. Pattern Analysis and Applications, 26(3): 1071-1087
42. 42.
    He H X, Qiao Y L, Li X M, Chen C Y and Zhang X F. 2021. Automatic weight measurement of pigs based on 3D images and regression network. Computers and Electronics in Agriculture, 187: 106299
43. 43.
    He K, Fan C J, Zhong M C, Cao F L, Wang G B and Cao L. 2023c. Evaluation of habitat suitability for Asian elephants in Sipsongpanna under climate change by coupling multi-source remote sensing products with MaxEnt model. Remote Sensing, 15(4): 1047
44. 44.
    Hitelman A, Edan Y, Godo A, Berenstein R, Lepar J and Halachmi I. 2022. Biometric identification of sheep via a machine-vision system. Computers and Electronics in Agriculture, 194: 106713
45. 45.
    Hou Z X, Huang L, Zhang Q and Miao Y S. 2023. Body weight estimation of beef cattle with 3D deep learning model: PointNet++. Computers and Electronics in Agriculture, 213: 108184
46. 46.
    Hua A, Martin K, Shen Y Z, Chen N, Mou C, Sterk M, Reinhard B, Reinhard F F, Lee S, Alibhai S and Jewell Z C. 2022. Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia. PeerJ, 10: e13779
47. 47.
    Hughey L F, Shoemaker K T, Stewart K M, McCauley D J and Cushman J H. 2021. Effects of human-altered landscapes on a reintroduced ungulate: patterns of habitat selection at the rangeland-wildland interface. Biological Conservation, 257: 109086
48. 48.
    Institute of Botany, the Chinese Academy of Sciences. 2020. Method and system for recognizing cattle and sheep based on remote sensing images. China, CN111339912A
49. 49.
    Ivanova S, Prosekov A and Kaledin A. 2022. A survey on monitoring of wild animals during fires using drones. Fire, 5(3): 60
50. 50.
    Ivanova S V, Kessel S T, Espinoza M, McLean M F, O’Neill C, Landry J, Hussey N E, Williams R, Vagle S and Fisk A T. 2020. Shipping alters the movement and behavior of Arctic cod (Boreogadus saida), a keystone fish in Arctic marine ecosystems. Ecological Applications, 30(3): e02050
51. 51.
    Jain A D, Ignisca A, Yi D H, Ratilal P and Makris N C. 2014. Feasibility of Ocean Acoustic Waveguide Remote Sensing (OAWRS) of Atlantic cod with seafloor scattering limitations. Remote Sensing, 6(1): 180-208
52. 52.
    Joshi A R, Dinerstein E, Wikramanayake E, Anderson M L, Olson D, Jones B S, Seidensticker J, Lumpkin S, Hansen M C, Sizer N C, Davis C L, Palminteri S and Hahn N R. 2016. Tracking changes and preventing loss in critical tiger habitat. Science Advances, 2(4): e1501675
53. 53.
    Kalan A K, Piel A K, Mundry R, Wittig R M, Boesch C and Kühl H S. 2016. Passive acoustic monitoring reveals group ranging and territory use: a case study of wild chimpanzees (Pan troglodytes). Frontiers in Zoology, 13(1): 34
54. 54.
    Kearney S P, Porensky L M, Augustine D J and Pellatz D W. 2023. Toward broad-scale mapping and characterization of prairie dog colonies from airborne imagery using deep learning. Ecological Indicators, 154: 110684
55. 55.
    Kizenga H J, Jebri F, Shaghude Y, Raitsos D E, Srokosz M, Jacobs Z L, Nencioli F, Shalli M, Kyewalyanga M S and Popova E. 2021. Variability of mackerel fish catch and remotely-sensed biophysical controls in the eastern Pemba Channel. Ocean and Coastal Management, 207: 105593
56. 56.
    Kongsro J. 2014. Estimation of pig weight using a Microsoft Kinect prototype imaging system. Computers and Electronics in Agriculture, 109: 32-35
57. 57.
    Kusakunniran W, Wiratsudakul A, Chuachan U, Kanchanapreechakorn S and Imaromkul T. 2018. Automatic cattle identification based on fusion of texture features extracted from muzzle images//2018 IEEE International Conference on Industrial Technology (ICIT). Lyon: IEEE: 1484-1489
58. 58.
    Leblanc G, Francis C M, Soffer R, Kalacska M and de Gea J. 2016. Spectral reflectance of polar bear and other large arctic mammal pelts; potential applications to remote sensing surveys. Remote Sensing, 8(4): 273
59. 59.
    Le Cozler Y, Allain C, Caillot A, Delouard J M, Delattre L, Luginbuhl T and Faverdin P. 2019. High-precision scanning system for complete 3D cow body shape imaging and analysis of morphological traits. Computers and Electronics in Agriculture, 157: 447-453
60. 60.
    Lee Y C, Syakura A, Khalil M A, Wu C H, Ding Y F and Wang C W. 2021. A real-time camera-based adaptive breathing monitoring system. Medical and Biological Engineering and Computing, 59(6): 1285-1298
61. 61.
    Li D R and Li M. 2014. Research advance and application prospect of unmanned aerial vehicle remote sensing system. Geomatics and Information Science of Wuhan University, 39(5): 505-513, 540
62. 62.
    Li G, Chen X J, Lei L and Guan W J. 2014. Distribution of hotspots of chub mackerel based on remote-sensing data in coastal waters of China. International Journal of Remote Sensing, 35(11/12): 4399-4421
63. 63.
    Li G, Sun W L, Zhang H and Gao C Y. 2014. Balance between actual number of livestock and livestock carrying capacity of grassland after added forage of straw based on remote sensing in Tibetan Plateau. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 30(17): 200-211
64. 64.
    Li J Y, Qian F W, Zhang Y, Zhao L N, Deng W Q and Ma K M. 2023a. Identifying seasonal differences in migration characteristics of Oriental white stork (Ciconia boyciana) through satellite tracking and remote sensing. Ecological Indicators, 146: 109760
65. 65.
    Li J Y, Wang C Y, Pan W W, Du H, Zhang H, Wu J M and Wei Q W. 2021. Migration and distribution of adult hatchery reared Yangtze sturgeons (Acipenser dabryanus) after releasing in the upper Yangtze River and its implications for stock enhancement. Journal of Applied Ichthyology, 37(1): 3-11
66. 66.
    Li X P, Xiang Y Y and Li S Q. 2023b. Combining convolutional and vision transformer structures for sheep face recognition. Computers and Electronics in Agriculture, 205: 107651
67. 67.
    Li Z, Mao T T, Liu T H and Teng G H. 2015. Comparison and optimization of pig mass estimation models based on machine vision. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 31(2): 155-161
68. 68.
    Liu D. 2020. Key Technology of Computer Vision in Precision Livestock Farming. Yangling: Northwest A and F University
69. 69.
    Liu L L, Zhao Y J, Wang Q C, Cui G F, Yang N, Zheng C Y and Liu D. 2018. Preliminary investigation of wildlife using camera traps along tourism routes in Beijing Baihua Mountain National Nature Reserve. Acta Ecologica Sinica, 38(23): 8324-8335
70. 70.
    Liu T H. 2014. Study of Pig’s Body Size Parameter Extraction Algorithm Optimization and Three-dimensional Reconstruction Based-on Binocular Stereo Vision. Beijing: China Agricultural University (刘同海. 2014. 基于双目视觉的猪体体尺参数提取算法优化及三维重构. 北京: 中国农业大学)
71. 71.
    Liu X H, Devred E, Johnson C L, Keith D and Sameoto J A. 2021. Using satellite remote sensing to improve the prediction of scallop condition in their natural environment: case study for Georges Bank, Canada. Remote Sensing of Environment, 254: 112251
72. 72.
    Lopez J, Schoonmaker J and Saggese S. 2014. Automated detection of marine animals using multispectral imaging//2014 Oceans. St. John’s: IEEE: 1-6
73. 73.
    Łopucki R, Klich D and Kociuba P. 2022. Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques. Scientific Reports, 12: 8264
74. 74.
    Lu H X, Zhang J L, Yuan X F, Lv J H, Zeng Z W, Guo H and Ruchay A. 2025. Automatic coarse-to-fine method for cattle body measurement based on improved GCN and 3D parametric model. Computers and Electronics in Agriculture, 231: 110017
75. 75.
    Lubitz N, Daly R, Filmalter J D, Sheaves M, Cowley P D, Naesje T F and Barnett A. 2023. Context drives movement patterns in a mobile marine predator. Movement Ecology, 11(1): 28
76. 76.
    Luo X Y, Hu Y H, Gao Z C, Guo H and Su Y. 2023. Automated measurement of livestock body based on pose normalisation using statistical shape model. Biosystems Engineering, 227: 36-51
77. 77.
    Ma W H, Qi X Y, Sun Y, Gao R H, Ding L Y, Wang R, Peng C, Zhang J, Wu J W, Xu Z K, Li M Y, Zhao H Y, Huang S D and Li Q F. 2024a. Computer vision-based measurement techniques for livestock body dimension and weight: a review. Agriculture-Basel, 14(2): 306
78. 78.
    Ma Y W, Tan M Y, Liu X Y, Zhang Y J, Xu Z C, Sun W Q, Ge J P and Feng L M. 2025. Deep learning for Amur tiger re-identification in camera traps: a tool assisting population monitoring and spatio-temporal analysis. Ecological Indicators, 171: 113227
79. 79.
    Ma Y Y, Liang F L, Wang P F, Yin Y, Zhang Y and Wang J Q. 2019. Research on Identifying different life states based on the changes of vital signs of rabbit under water and food deprivation by UWB radar measurement//2019 Photonics and Electromagnetics Research Symposium. Xiamen: IEEE: 397-403
80. 80.
    Ma Z B, Dong Y Q, Xia Y, Xu D L, Xu F and Chen F X. 2024b. Wildlife real-time detection in complex forest scenes based on YOLOv5s deep learning network. Remote Sensing, 16(8): 1350
81. 81.
    Mahoney P J, Liston G E, Lapoint S, Gurarie E, Mangipane B, Wells A G, Brinkman T J, Eitel J U H, Hebblewhite M, Nolin A W, Boelman N and Prugh L R. 2018. Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties. Ecological Applications, 28(7): 1715-1729
82. 82.
    Makris N C, Godø O R, Yi D H, Macaulay G J, Jain A D, Cho B, Gong Z, Jech J M and Ratilal P. 2019. Instantaneous areal population density of entire Atlantic cod and herring spawning groups and group size distribution relative to total spawning population. Fish and Fisheries, 20(2): 201-213
83. 83.
    Marston C G, Wilkinson D M, Sponheimer M, Codron D, Codron J and O’Regan H J. 2020. ‘Remote’ behavioural ecology: do megaherbivores consume vegetation in proportion to its presence in the landscape? PeerJ, 8: e8622
84. 84.
    Matley J K, Klinard N V, Larocque S M, Mclean M F, Brownscombe J W, Raby G D, Nguyen V M and Martins A P B. 2023. Making the most of aquatic animal tracking: a review of complementary methods to bolster acoustic telemetry. Reviews in Fish Biology and Fisheries, 33(1): 35-54
85. 85.
    Mawer R, Bruneel S P, Pauwels I S, Elings J, Pickholtz E, Pickholtz R, Schneider M, Coeck J and Goethals P L M. 2023. Individual variation in the habitat selection of upstream migrating fish near a barrier. Movement Ecology, 11(1): 49
86. 86.
    Miller P I, Scales K L, Ingram S N, Southall E J and Sims D W. 2015. Basking sharks and oceanographic fronts: quantifying associations in the north-east Atlantic. Functional Ecology, 29(8): 1099-1109
87. 87.
    Mitamura H, Wada T, Takagi J, Noda T, Hori T, Takasaki K, Kawata G and Arai N. 2022. Acoustic zone monitoring to quantify fine-scale movements of aquatic animals in a narrow water body. Environmental Biology of Fishes, 105(12): 1919-1931
88. 88.
    Moreira F S, Regos A, Gonçalves J F, Rodrigues T M, Verde A, Pagès M, Pérez J A, Meunier B, Lepetit J P, Honrado J P and Gonçalves D. 2022. Combining citizen science data and satellite descriptors of ecosystem functioning to monitor the abundance of a migratory bird during the non-breeding season. Remote Sensing, 14(3): 463
89. 89.
    Mutlu K, Rabell J E, Del Olmo P M and Haesler S. 2018. IR thermography-based monitoring of respiration phase without image segmentation. Journal of Neuroscience Methods, 301: 1-8
90. 90.
    Na X D, Zang S Y, Zhang Y H and Li W L. 2015. Assessing breeding habitat suitability for the endangered red-crowned crane (Grus japonensis) based on multi-source remote sensing data. Wetlands, 35(5): 955-967
91. 91.
    Nakayama S, Doering-Arjes P, Linzmaier S, Briege J, Klefoth T, Pieterek T and Arlinghaus R. 2018. Fine-scale movement ecology of a freshwater top predator, Eurasian perch (Perca fluviatilis), in response to the abiotic environment over the course of a year. Ecology of Freshwater Fish, 27(3): 798-812
92. 92.
    Newcombe P B, Nilsson C, Lin T Y, Winner K, Bernstein G, Maji S, Sheldon D, Farnsworth A and Horton K G. 2019. Migratory flight on the Pacific Flyway: strategies and tendencies of wind drift compensation. Biology Letters, 15(9): 20190383
93. 93.
    Norouzzadeh M S, Nguyen A, Kosmala M, Swanson A, Palmer M S, Packer C and Clune J. 2018. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences of the United States of America, 115(25): E5716-E5725
94. 94.
    O’Connor J, Morrongiello J, Ayres R, Amtstaetter F, Koster W, Kitchingman A, Cowell T, Bowler M and Hale R. 2023. Understanding movement and habitat-use to guide reintroductions and habitat rehabilitation for a nonmigratory freshwater fish. Restoration Ecology, 31(5): e13869
95. 95.
    Oishi Y, Oguma H, Tamura A, Nakamura R and Matsunaga T. 2018. Animal detection using thermal images and its required observation conditions. Remote Sensing, 10(7): 1050
96. 96.
    Okura F, Ikuma S, Makihara Y, Muramatsu D, Nakada K and Yagi Y. 2019. RGB-D video-based individual identification of dairy cows using gait and texture analyses. Computers and Electronics in Agriculture, 165: 104944
97. 97.
    Olsoy P J, Shipley L A, Rachlow J L, Forbey J S, Glenn N F, Burgess M A and Thornton D H. 2018. Unmanned aerial systems measure structural habitat features for wildlife across multiple scales. Methods in Ecology and Evolution, 9(3): 594-604
98. 98.
    Osman D, Fruhwirth G, Rhode K and Noh Y. 2018. Development of a respiration monitor based on fibre optics and CCD camera for small animal preclinical imaging//2018 IEEE SENSORS. New Delhi: IEEE: 1-4
99. 99.
    Pallottino F, Steri R, Menesatti P, Antonucci F, Costa C, Figorilli S and Catillo G. 2015. Comparison between manual and stereovision body traits measurements of Lipizzan horses. Computers and Electronics in Agriculture, 118: 408-413
100. 100.
     Papastamatiou Y P, Meyer C G, Carvalho F, Dale J J, Hutchinson M R and Holland K N. 2013. Telemetry and random-walk models reveal complex patterns of partial migration in a large marine predator. Ecology, 94(11): 2595-2606
101. 101.
     Parente L, Ehrmann S, Hengl T, Fritz S, Bonannella C, Malek Ž, Fischer C, Perez K, Stanimirova R, Meyer C, Wisser D, Cinardi G and Sloat L. 2025. Global distribution of cattle, goats, sheep and horses at 1-km resolution for 2000—2022 based on sub-national census data and spatiotemporal Machine Learning. SquareResearch,PREPRINT(Version1) [DOI: 10.21203/rs.3.rs-6201916/v1]
102. 102.
     Peng J B, Wang D L, Liao X H, Shao Q Q, Sun Z G, Yue H Y and Ye H P. 2020. Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau. ISPRS Journal of Photogrammetry and Remote Sensing, 169: 364-376
103. 103.
     Pillans R D, Rochester W, Babcock R C, Thomson D P, Haywood M D E and Vanderklift M A. 2021. Long-term acoustic monitoring reveals site fidelity, reproductive migrations, and sex specific differences in habitat use and migratory timing in a large coastal shark (Negaprion acutidens). Frontiers in Marine Science, 8: 616633
104. 104.
     Ponsioen L, Kapralova K H, Holm F and Hennig B D. 2023. Remote sensing of salmonid spawning sites in freshwater ecosystems: the potential of low-cost UAV data. PLoS One, 18(8): e0290736
105. 105.
     Prompalit S, Fruhwirth G, Rhode K and Noh Y. 2018. Development of non-contact device based on optical fibre technology for detecting respiration of small animal in imaging instruments//2018 IEEE SENSORS. New Delhi: IEEE: 1-4
106. 106.
     Qi J D, Ma Z T and Zheng S Z. 2024. Wildlife image screening for infrared cameras based on YOLOv7. Journal of Beijing Forestry University, 46(2): 143-154
107. 107.
     Qiao Y L, Su D B L G, Kong H, Sukkarieh S, Lomax S and Clark C. 2020. BiLSTM-based Individual Cattle Identification for Automated Precision Livestock Farming//2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). Hong Kong, China: IEEE: 967-972
108. 108.
     Recio M R, Mathieu R, Hall G B, Moore A B and Seddon P J. 2013. Landscape resource mapping for wildlife research using very high resolution satellite imagery. Methods in Ecology and Evolution, 4(10): 982-992
109. 109.
     Remelgado R, Leutner B, Safi K, Sonnenschein R, Kuebert C and Wegmann M. 2018. Linking animal movement and remote sensing-mapping resource suitability from a remote sensing perspective. Remote Sensing in Ecology and Conservation, 4(3): 211-224
110. 110.
     Rezaei B, Huang X F, Yee J R and Ostadabbas S. 2017. Long-term non-contact tracking of caged rodents//2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New Orleans: IEEE: 1952-1956
111. 111.
     Rickbeil G J M, Coops N C, Berman E E, Mcclelland C J R, Bolton D K and Stenhouse G B. 2020. Changing spring snow cover dynamics and early season forage availability affect the behavior of a large carnivore. Global Change Biology, 26(11): 6266-6275
112. 112.
     Robb B, Huang Q Y, Sexton J O, Stoner D and Leimgruber P. 2019. Environmental differences between migratory and resident ungulates-predicting movement strategies in rocky mountain mule deer (Odocoileus hemionus) with remotely sensed plant phenology, snow, and land cover. Remote Sensing, 11(17): 1980
113. 113.
     Robinson C J, Gómez-Gutiérrez J, Markaida U and Gilly W F. 2016. Prolonged decline of jumbo squid (Dosidicus gigas) landings in the Gulf of California is associated with chronically low wind stress and decreased chlorophyll a after El Niño 2009-2010. Fisheries Research, 173: 128-138
114. 114.
     Rodríguez Alvarez J, Arroqui M, Mangudo P, Toloza J, Jatip D, Rodríguez J M, Teyseyre A, Sanz C, Zunino A, Machado C and Mateos C. 2018. Body condition estimation on cows from depth images using Convolutional Neural Networks. Computers and Electronics in Agriculture, 155: 12-22
115. 115.
     Santos C D, Campos L F A S and Efe M A. 2019. Foraging habitat choice of White-tailed Tropicbirds revealed by fine-scale GPS tracking and remote sensing. PeerJ, 7: e6261
116. 116.
     Scales K L, Miller P I, Embling C B, Ingram S N, Pirotta E and Votier S C. 2014. Mesoscale fronts as foraging habitats: composite front mapping reveals oceanographic drivers of habitat use for a pelagic seabird. Journal of the Royal Society Interface, 11(100): 20140679
117. 117.
     Schad L and Fischer J. 2023. Opportunities and risks in the use of drones for studying animal behaviour. Methods in Ecology and Evolution, 14(8): 1864-1872
118. 118.
     Schneider S, Taylor G W, Linquist S and Kremer S C. 2019. Past, present and future approaches using computer vision for animal re-identification from camera trap data. Methods in Ecology and Evolution, 10(4): 461-470
119. 119.
     Schütz A K, Louton H, Fischer M, Probst C, Gethmann J M, Conraths F J and Homeier-Bachmann T. 2024. Automated detection and counting of wild boar in camera trap images. Animals, 14(10): 1408
120. 120.
     Scott M E, Heupel M R, Simpfendorfer C A, Matley J K and Pratchett M S. 2019. Latitudinal and seasonal variation in space use by a large, predatory reef fish, Plectropomus leopardus. Functional Ecology, 33(4): 670-680
121. 121.
     Sha J C M, Kaneko A, Suda Hashimoto N, He T M, Take M, Peng Z and Hanya G. 2017. Estimating activity of Japanese macaques (Macaca fuscata) using accelerometers. American Journal of Primatology, 79(10): e22694
122. 122.
     Shamoun-Baranes J, Farnsworth A, Aelterman B, Alves J A, Azijn K, Bernstein G, Branco S, Desmet P, Dokter A M, Horton K, Kelling S, Kelly J F, Leijnse H, Rong J J, Sheldon D, Van den Broeck W, Van den Meersche J K, Van Doren B M and van Gasteren H. 2016. Innovative visualizations shed light on avian nocturnal migration. PLoS One, 11(8): e0160106
123. 123.
     Shao Q Q, Guo X J, Li Y Z, Wang Y C, Wang D L, Liu J Y, Fan J W and Yang F. 2018. Using UAV remote sensing to analyze the population and distribution of large wild herbivores. Journal of Remote Sensing (in Chinese), 22(3): 497-507
124. 124.
     Shaw R L, Curtis T H, Metzger G, McCallister M P, Newton A, Fischer G C and Ajemian M J. 2021. Three-dimensional movements and habitat selection of young white sharks (Carcharodon carcharias) across a temperate continental shelf ecosystem. Frontiers in Marine Science, 8: 643831
125. 125.
     Shi W, Dai B S, Shen W Z, Sun Y K, Zhao K X and Zhang Y G. 2023. Automatic estimation of dairy cow body condition score based on attention-guided 3D point cloud feature extraction. Computers and Electronics in Agriculture, 206: 107666
126. 126.
     Song G F. 2019. Research on Animal Facial Recognition Algorithm Based on Deep Learning. Hangzhou: Hangzhou Dianzi University
127. 127.
     Su X H, Zhang J W, Ma Z B, Dong Y Q, Zi J L, Xu N, Zhang H Y, Xu F and Chen F X. 2024. Identification of rare wildlife in the field environment based on the improved YOLOv5 model. Remote Sensing, 16(9): 1535
128. 128.
     Sun Y K, Huo P J, Wang Y J, Cui Z Q, Li Y, Dai B S, Li R Z and Zhang Y G. 2019. Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score. Journal of Dairy Science, 102(11): 10140-10151
129. 129.
     Sutton L J, Ibañez J C, Salvador D I, Taraya R L, Opiso G S, Senarillos T L P and McClure C J W. 2023. Space-time home-range estimates and resource selection for the Critically Endangered Philippine Eagle on Mindanao. IBIS, 166(1): 156-170
130. 130.
     Taylor R B, Mather M E, Smith J M and Boles K M. 2021. Can identifying discrete behavioral groups with individual-based acoustic telemetry advance the understanding of fish distribution patterns?. Frontiers in Marine Science, 8: 723025
131. 131.
     Tazen M, Sasaoka N and Okamoto Y. 2023. Non-contact heart rate measurement based on adaptive notch filter and elimination of respiration harmonics. IEEE Access, 11: 46107-46119
132. 132.
     Tremblay Y, Thiebault A, Mullers R and Pistorius P. 2014. Bird-borne video-cameras show that seabird movement patterns relate to previously unrevealed proximate environment, not prey. PLoS One, 9(2): e88424
133. 133.
     Tsujii K, Otsuki M, Akamatsu T, Matsuo I, Amakasu K, Kitamura M, Kikuchi T, Miyashita K and Mitani Y. 2016. The migration of fin whales into the southern Chukchi Sea as monitored with passive acoustics. ICES Journal of Marine Science, 73(8): 2085-2092
134. 134.
     Vayghan A H and Lee M A. 2022. Hotspot habitat modeling of skipjack tuna (Katsuwonus pelamis) in the Indian Ocean by using multisatellite remote sensing. Turkish Journal of Fisheries and Aquatic Sciences, 22(9): TRJFAS19107
135. 135.
     Villegas-Ríos D, Réale D, Freitas C, Moland E and Olsen E M. 2017. Individual level consistency and correlations of fish spatial behaviour assessed from aquatic animal telemetry. Animal Behaviour, 124: 83-94
136. 136.
     Wang D L, Shao Q Q and Yue H Y. 2019a. Surveying wild animals from satellites, manned aircraft and Unmanned Aerial Systems (UASs): a review. Remote Sensing, 11(11): 1308
137. 137.
     Wang F. 2022. Research on Cattle Muzzle Recognition Method Based on Machine Vision. Baotou: Inner Mongolia University of Science and Technology
138. 138.
     Wang P F, Ma Y Y, Liang F L, Zhang Y, Yu X, Li Z, An Q, Lv H and Wang J Q. 2020. Non-contact vital signs monitoring of dog and cat using a UWB radar. Animals, 10(2): 205
139. 139.
     Wang P F, Zhang Y, Ma Y Y, Liang F L, An Q, Xue H J, Yu X, Lv H and Wang J Q. 2019b. Method for distinguishing humans and animals in vital signs monitoring using IR-UWB radar. International Journal of Environmental Research and Public Health, 16(22): 4462
140. 140.
     Wang S W, Wang D L, Ling C X, Zhang J, Jin Y and Liu S G. 2023. Population and spatial distribution of moose in Nanwenghe National Nature Reserve, Heilongjiang Province based on Unmanned Aerial Vehicle (UAV) remote sensing. Chinese Journal of Wildlife, 44(3): 486-493
141. 141.
     Wang X L, Xu J Q, Zhang S F, Li H and Li J. 2024. A preliminary survey of mammals and birds diversity based on camera trapping in Qimantag Mountain of Altun Mountain National Nature Reserve, Xinjiang. Arid Land Geography, 47(10): 1662-1673
142. 142.
     Wang Y F, Xu X S, Wang Z, Li R, Hua Z X and Song H B. 2023. ShuffleNet-Triplet: a lightweight RE-identification network for dairy cows in natural scenes. Computers and Electronics in Agriculture, 205: 107632
143. 143.
     Wang Z Y, Shadpour S, Chan E, Rotondo V, Wood K M and Tulpan D. 2021. ASAS-NANP SYMPOSIUM: applications of machine learning for livestock body weight prediction from digital images. Journal of Animal Science, 99(2): skab022
144. 144.
     Weinz A A, Matley J K, Klinard N V, Fisk A T and Colborne S F. 2020. Identification of predation events in wild fish using novel acoustic transmitters. Animal Biotelemetry, 8(1): 28
145. 145.
     Wongsriworaphon A, Arnonkijpanich B and Pathumnakul S. 2015. An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture, 115: 26-33
146. 146.
     Wu Z J, Zhang C, Gu X W, Duporge I, Hughey L F, Stabach J A, Skidmore A K, Hopcraft J G C, Lee S J, Atkinson P M, McCauley D J, Lamprey R, Ngene S and Wang T J. 2023. Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. Nature Communications, 14(1): 3072
147. 147.
     Xiao R L, Gao J X, Liu A J, Hou P, Zhang W G, Yang Y, Li Y B, Fu Z, Jin C P, Yang X, Zheng S H and Yin S J. 2023. Remote sensing monitoring method of livestock in grassland based on multi-scale features and multi-models fusion. National Remote Sensing Bulletin, 27(10): 2383-2394
148. 148.
     Xu D M, Wu J L, Zhu Z C, Wu Y Q, Zhou R F, Ye Z L and Wu Y G. 2023. Effects of different degree of human disturbance on forest wildlife habitats based on camera trap data. Chinese Journal of Zoology, 58(3): 357-365
149. 149.
     Xu Z Y, Wang T J, Skidmore A K and Lamprey R. 2024. A review of deep learning techniques for detecting animals in aerial and satellite images. International Journal of Applied Earth Observation and Geoinformation, 128: 103732
150. 150.
     Xue Y F, Wang T J and Skidmore A K. 2017. Automatic counting of large mammals from very high resolution panchromatic satellite imagery. Remote Sensing, 9(9): 878
151. 151.
     Yi D H, Gong Z, Jech J M, Ratilal P and Makris N C. 2018. Instantaneous 3D continental-shelf scale imaging of oceanic fish by multi-spectral resonance sensing reveals group behavior during spawning migration. Remote Sensing, 10(1): 108
152. 152.
     Yoshino K, Takahashi A, Adachi T, Costa D P, Robinson P W, Peterson S H, Hückstädt L A, Holser R R and Naito Y. 2020. Acceleration-triggered animal-borne videos show a dominance of fish in the diet of female northern elephant seals. Journal of Experimental Biology, 223: jeb212936
153. 153.
     Youngentob K N, Yoon H J, Stein J, Lindenmayer D B and Held A A. 2015. Where the wild things are: using remotely sensed forest productivity to assess arboreal marsupial species richness and abundance. Diversity and Distributions, 21(8): 977-990
154. 154.
     Youssef A, Viazzi S, Exadaktylos V and Berckmans D. 2014. Non-contact, motion-tolerant measurements of chicken (Gallus gallus) embryo heart rate (HR) using video imaging and signal processing. Biosystems Engineering, 125: 9-16
155. 155.
     Yu P B, Li Y D, Xu B, Wei J, Li S, Dong J H, Qu J H, Xu J, Huang Z Y X, Ma C F, Yang J, Zhang G G, Chen B, Huang S Q, Shi C M, Gao H W, Liu F, Tian H Y, Stenseth N C, Xu B and Wang J J. 2017. Using satellite data for the characterization of local animal reservoir populations of Hantaan virus on the Weihe Plain, China. Remote Sensing, 9(10): 1076
156. 156.
     Yu W, Chen X J, Yi Q and Chen Y. 2016. Influence of oceanic climate variability on stock level of western winter-spring cohort of Ommastrephes bartramii in the Northwest Pacific Ocean. International Journal of Remote Sensing, 37(17): 3974-3994
157. 157.
     Zainuddin M, Safruddin S, Farhum A, Budimawan B, Hidayat R, Selamat M B, Wiyono E S, Ridwan M, Syamsuddin M and Ihsan Y N. 2023. Satellite-based ocean color and thermal signatures defining habitat hotspots and the movement pattern for commercial skipjack tuna in Indonesia fisheries management area 713, western tropical Pacific. Remote Sensing, 15(5): 1268
158. 158.
     Zhang H M, Li J H, Zhou Y C, Wang X Z and Yan B P. 2013. Using a time series of satellite imagery to study the wild birds’ migration//2013 Fourth International Conference on Networking and Distributed Computing. Los Angeles: IEEE: 46-50
159. 159.
     Zhang J L, Lei J, Wu J H, Lu H X, Guo H, Pezzuolo A, Kolpakov V and Ruchay A. 2023a. Automatic method for quantitatively analyzing the body condition of livestock from 3D shape. Computers and Electronics in Agriculture, 214: 108307
160. 160.
     Zhang Y L and Cai Z C. 2023b. CE-RetinaNet: a channel enhancement method for infrared wildlife detection in UAV images. IEEE Transactions on Geoscience and Remote Sensing, 61: 4104012
161. 161.
     Zhao J M, Zhao Z X and Li Q. 2015. A Kinect-Based Morphometric Measurement Method for Sheep. Jiangsu Agricultural Sciences, 43(11): 495-499
162. 162.
     Zhao K X. 2017. Dairy Cattle’s Information Perception and Behavior Analysis based on Machine Vision. Yangling: Northwest A and F University
163. 163.
     Zhao K X, Zhang M, Shen W Z, Liu X H, Ji J T, Dai B S and Zhang R H. 2023. Automatic body condition scoring for dairy cows based on efficient net and convex hull features of point clouds. Computers and Electronics in Agriculture, 205: 107588
164. 164.
     Zhao P, Liu S M, Zhou Y, Lynch T, Lu W H, Zhang T and Yang H S. 2021. Estimating animal population size with very high-resolution satellite imagery. Conservation Biology, 35(1): 316-324
165. 165.
     Zhao Y L, Zeng F G, Jia N, Zhu J, Wang H F and Li B. 2023. Rapid measurements of pig body size based on DeepLabCut algorithm. Transactions of the Chinese Society for Agricultural Machinery, 54(2): 249-255, 292
166. 166.
     Zheng X, Owen M A, Nie Y, Hu Y, Swaisgood R R, Yan L and Wei F. 2016. Individual identification of wild giant pandas from camera trap photos - a systematic and hierarchical approach. Journal of Zoology, 300(4): 247-256
167. 167.
     Zong S, Brantschen J, Zhang X W, Albouy C, Valentini A, Zhang H, Altermatt F and Pellissier L. 2023. Combining environmental DNA with remote sensing variables to map fish species distributions along a large river. Remote Sensing in Ecology and Conservation, 10(2): 220-235