{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:25:25Z","timestamp":1779359125532,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T00:00:00Z","timestamp":1705708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Seaver Institute"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral\u2014rather than spatial\u2014basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed.<\/jats:p>","DOI":"10.3390\/rs16020406","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T06:49:31Z","timestamp":1705906171000},"page":"406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automated Hyperspectral Feature Selection and Classification of Wildlife Using Uncrewed Aerial Vehicles"],"prefix":"10.3390","volume":"16","author":[{"given":"Daniel","family":"McCraine","sequence":"first","affiliation":[{"name":"Geosystems Research Institute, Mississippi State University, Starkville, MS 39759, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8443-883X","authenticated-orcid":false,"given":"Sathishkumar","family":"Samiappan","sequence":"additional","affiliation":[{"name":"Geosystems Research Institute, Mississippi State University, Starkville, MS 39759, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leon","family":"Kohler","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Mississippi State University, Mississippi State, MS 39762, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timo","family":"Sullivan","sequence":"additional","affiliation":[{"name":"Island Conservation, Santa Cruz, CA 95060, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5885-6790","authenticated-orcid":false,"given":"David J.","family":"Will","sequence":"additional","affiliation":[{"name":"Island Conservation, Santa Cruz, CA 95060, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1093\/biosci\/biv031","article-title":"The Importance of Islands for the Protection of Biological and Linguistic Diversity","volume":"65","author":"Tershy","year":"2015","journal-title":"BioScience"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13391","DOI":"10.1038\/s41598-022-14982-5","article-title":"The global contribution of invasive vertebrate eradication as a key island restoration tool","volume":"12","author":"Spatz","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4033","DOI":"10.1073\/pnas.1521179113","article-title":"Invasive mammal eradication on islands results in substantial conservation gains","volume":"113","author":"Jones","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kappes, P.J., Benkwitt, C.E., Spatz, D.R., Wolf, C.A., Will, D.J., and Holmes, N.D. (2021). Do Invasive Mammal Eradications from Islands Support Climate Change Adaptation and Mitigation?. Climate, 9.","DOI":"10.3390\/cli9120172"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1017\/S0376892920000211","article-title":"Invasive vertebrate eradications on islands as a tool for implementing global Sustainable Development Goals","volume":"47","author":"Zilliacus","year":"2020","journal-title":"Environ. Conserv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e2122354119","DOI":"10.1073\/pnas.2122354119","article-title":"Harnessing island\u2013ocean connections to maximize marine benefits of island conservation","volume":"119","author":"Sandin","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rodrigues, A.S.L., Brooks, T.M., Butchart, S.H.M., Chanson, J., Cox, N., Hoffmann, M., and Stuart, S.N. (2014). Spatially Explicit Trends in the Global Conservation Status of Vertebrates. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0113934"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/biosci\/biad018","article-title":"Technological innovations enhance invasive species management in the anthropocene","volume":"73","author":"Fricke","year":"2023","journal-title":"BioScience"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.biocon.2014.10.016","article-title":"The next generation of rodent eradications: Innovative technologies and tools to improve species specificity and increase their feasibility on islands","volume":"185","author":"Campbell","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s10530-019-02146-y","article-title":"Technology innovation: Advancing capacities for the early detection of and rapid response to invasive species","volume":"22","author":"Martinez","year":"2019","journal-title":"Biol. Invasions"},{"key":"ref_11","first-page":"46","article-title":"Review of feral cat eradications on islands","volume":"37","author":"Campbell","year":"2011","journal-title":"Isl. Invasives Erad. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Carrion, V., Donlan, C.J., Campbell, K.J., Lavoie, C., and Cruz, F. (2011). Archipelago-Wide Island Restoration in the Gal\u00e1pagos Islands: Reducing Costs of Invasive Mammal Eradication Programs and Reinvasion Risk. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0018835"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1007\/s10530-017-1490-5","article-title":"Bio-economic optimisation of surveillance to confirm broadscale eradications of invasive pests and diseases","volume":"19","author":"Anderson","year":"2017","journal-title":"Biol. Invasions"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3773","DOI":"10.1007\/s10530-023-03133-0","article-title":"A review of methods for detecting rats at low densities, with implications for surveillance","volume":"25","author":"Davis","year":"2023","journal-title":"Biol. Invasions"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10385","DOI":"10.1038\/s41598-023-37295-7","article-title":"Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys","volume":"13","author":"Krishnan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.1002\/ece3.6147","article-title":"Three critical factors affecting automated image species recognition performance for camera traps","volume":"10","author":"Schneider","year":"2020","journal-title":"Ecol. Evol."},{"key":"ref_17","first-page":"459","article-title":"An evaluation of platforms for processing camera-trap data using artificial intelligence","volume":"14","author":"McShea","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1111\/j.1365-2664.2007.01307.x","article-title":"Indicators of ecological change: New tools for managing populations of large herbivores: Ecological indicators for large herbivore management","volume":"44","author":"Morellet","year":"2007","journal-title":"J. Appl. Ecol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13750-022-00294-8","article-title":"Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: A systematic map","volume":"12","author":"Elmore","year":"2023","journal-title":"Environ. Evid."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Joyce, K.E., Anderson, K., and Bartolo, R.E. (2021). Of Course We Fly Unmanned\u2014We\u2019re Women!. Drones, 5.","DOI":"10.3390\/drones5010021"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1038\/s41598-023-28240-9","article-title":"Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys","volume":"13","author":"Lenzi","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-Torres, M., Silva, C.P., Riquelme, C., Estay, S.A., and Soto-Gamboa, M. (2023). Automatic Recognition of Black-Necked Swan (Cygnus melancoryphus) from Drone Imagery. Drones, 7.","DOI":"10.3390\/drones7020071"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, M., Elmore, J.A., Samiappan, S., Evans, K.O., Pfeiffer, M.B., Blackwell, B.F., and Iglay, R.B. (2021). Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks. Sensors, 21.","DOI":"10.3390\/s21175697"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TGRS.2007.912448","article-title":"Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics","volume":"46","author":"Thomas","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"112","article-title":"Multi- and hyperspectral geologic remote sensing: A review","volume":"14","author":"Hecker","year":"2012","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1080\/01431160902926681","article-title":"A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment","volume":"31","author":"Ghiyamat","year":"2010","journal-title":"Int. J. Remote. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Matese, A., Czarnecki, J., Samiappan, S., and Moorhead, J. (2023). Are unmanned aerial vehicle based hyperspectral imaging and machine learning advancing crop science?. Trends Plant Sci.","DOI":"10.1016\/j.tplants.2023.09.001"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1117\/12.451620","article-title":"Implementation of a Multiscale Bayesian Classification Approach for Hyperspectral Terrain Categorization","volume":"4816","author":"Murphy","year":"2022","journal-title":"Proc. SPIE"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Camps-Valls, G., G\u00f3mez-Chova, L., Calpe-Maravilla, J., Soria-Olivas, E., Mart\u00edn-Guerrero, J.D., and Moreno, J. (2003, January 4\u20136). Support Vector Machines for Crop Classification Using Hyperspectral Data. Proceedings of the 1st Pattern Recognition and Image Analysis, Puerto de Andratx, Mallorca, Spain.","DOI":"10.1007\/978-3-540-44871-6_16"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/S0168-1699(03)00020-6","article-title":"Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn","volume":"39","author":"Goel","year":"2003","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"16157","DOI":"10.1038\/s41598-021-95713-0","article-title":"Hyperspectral data as a biodiversity screening tool can differentiate among diverse Neotropical fishes","volume":"11","author":"Kolmann","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s12665-023-10761-1","article-title":"A novel hyperspectral remote sensing tool for detecting and analyzing human materials in the environment: A geoenvironmental approach to aid in emergency response","volume":"82","author":"Krekeler","year":"2023","journal-title":"Environ. Earth Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.biosystemseng.2009.01.005","article-title":"A first assessment of the use of high spatial resolution hyperspectral imagery in discriminating among animal species, and between animals and their surroundings","volume":"102","author":"Bortolot","year":"2009","journal-title":"Biosyst. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"597","DOI":"10.2747\/1548-1603.49.4.597","article-title":"Spectral Characteristics of Domestic and Wild Mammals","volume":"49","author":"Terletzky","year":"2012","journal-title":"GIScience Remote Sens."},{"key":"ref_36","first-page":"56","article-title":"Spectral analysis reveals limited potential for enhanced-wavelength detection of invasive snakes","volume":"44","author":"Siers","year":"2013","journal-title":"Herpetol. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.biocon.2019.06.022","article-title":"Measuring the spectral signature of polar bears from a drone to improve their detection from space","volume":"237","author":"Chabot","year":"2019","journal-title":"Biol. Conserv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"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 Sens., 8.","DOI":"10.3390\/rs8040273"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Aslett, Z., and Garza, L. (2021, January 24\u201326). Characterization of Domestic Livestock and Associated Agricultural Facilities using NASA\/JPL AVIRIS-NG Imaging Spectroscopy Data. Proceedings of the 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS52202.2021.9483976"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Agrawal, N., and Verma, K. (2020, January 3\u20135). Dimensionality Reduction on Hyperspectral Data Set. Proceedings of the 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T), Raipur, India.","DOI":"10.1109\/ICPC2T48082.2020.9071461"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4817234","DOI":"10.1155\/2020\/4817234","article-title":"Overview of Hyperspectral Image Classification","volume":"2020","author":"Lv","year":"2020","journal-title":"J. Sens."},{"key":"ref_42","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, Wiley. [2nd ed.]."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","article-title":"Limitations of principal components analysis for hyperspectral target recognition","volume":"5","author":"Prasad","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/0167-8655(94)90127-9","article-title":"Floating search methods in feature selection","volume":"15","author":"Pudil","year":"1994","journal-title":"Pattern Recognit. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","article-title":"Classification assessment methods","volume":"17","author":"Tharwat","year":"2018","journal-title":"Appl. Comput. Inform."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Colefax, A.P., Walsh, A.J., Purcell, C.R., and Butcher, P. (2023). Utility of Spectral Filtering to Improve the Reliability of Marine Fauna Detections from Drone-Based Monitoring. Sensors, 23.","DOI":"10.3390\/s23229193"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/406\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:46:23Z","timestamp":1760103983000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/406"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,20]]},"references-count":48,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020406"],"URL":"https:\/\/doi.org\/10.3390\/rs16020406","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,20]]}}}