Unmanned Aerial Vehicles (UAVs) in environmental biology: a review

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Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future.

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  • Anderson K. Gaston K.J. (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11(3): 138-146.

  • Baena S. Boyd D.S. Moat J. (2018) UAVs in pursuit of plant conservation – real World experiences. Ecological Informatics 47: 2-9.

  • Bagaram M.B. Giuliarelli D. Chirici G. Giannetti F. Barbati A. (2018) UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates? Remote Sensing 10(9): 1397.

  • Ballari D. Orellana D. Acosta E. Espinoza A. Morocho V. (2016) UAV monitoring for environmental management in Galapagos Islands. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences Volume XLI-B1 2016 XXIII ISPRS Congress 12-19 July 2016 Prague Czech Republic.

  • Baluja J. Diago M.P. Balda P. Zorer R. Meggio F. Morales F. Tardaguila J. (2012) Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science 30(6): 511-522.

  • Barasona J.A. Mulero-Pázmány M. Acevedo P. Negro J.J. Torres M.J. Gortázar C. Vicente J. (2014) Unmanned Aircraft Systems for Studying Spatial Abundance of Ungulates: Relevance to Spatial Epidemiology. PLoS ONE 9(12): e115608.

  • Bernardes S. Madden M. Jordan T. Knight A. Aragon A. (2017) Integrating UAV and orbital remote sensing for spatiotemporal assessment of coastal vegetation health following hurricane events. American Geophysical Union Fall Meeting 2017 abstract #NH23E-2801.

  • Bevan E. Wibbels T. Najera B.M.Z. Martinez M.A.C. Martinez L.A.S. Martinez F.I. Cuevas J.M. Anderson T. Bonka A. Hernandez M.H. Pena L.J. Burchfield P.M. (2015) Unmanned Aerial Vehicles (UAVs) for Monitoring Sea Turtles in Near-Shore Waters. Marine Turtle Newsletter 145: 19-22.

  • Bevan E. Wibbels T. Navarro E. Rosas M. Sarti L. Illescas F. Montano J. Peña L.J. Burchfield P.M. (2016) Using Unmanned Aerial Vehicle (UAV) Technology for Locating Identifying and Monitoring Courtship and Mating Behavior in the Green Turtle (Chelonia mydas). Herpetological Review 47(1): 27–32.

  • Boon M.A. Tesfamichael S. (2017) Determination of the present vegetation state of a wetland with UAV RGB imagery. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences Volume XLII-3/W2: 37-41. 37th International Symposium on Remote Sensing of Environment 8-12 May 2017 Tshwane South Africa.

  • Będkowski K. Stereńczak K. (2012) An outline of a quasi-object-based analysis of multispectral aerial images and its use to determine species composition of forest stands. Annals of Geomatics 10(5): 19-26.

  • Będkowski K. Stereńczak K. (2013) Sessile oak (Quercus petraea (Mattuschka) Liebl.) trees variability according to an analysis of multispectral images taken from UAV – first results. Ecological Questions 17: 25-33.

  • Chabot D. Bird D.M. (2013) Small unmanned aircraft: precise and convenient new tools for surveying wetlands. Journal of Unmanned Vehicle Systems 01(01): 15-24.

  • Chabot D. Carignan V. Bird D.M. (2014) Measuring Habitat Quality for Least Bitterns in a Created Wetland with Use of a Small Unmanned Aircraft. Wetlands 34: 527-533.

  • Chabot D. Bird D.M. (2015a) Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in? Journal of Unmanned Vehicle Systems 3: 137-155.

  • Chabot D. Craik S.R. Bird D.M. (2015b) Population Census of a Large Common Tern Colony with a Small Unmanned Aircraft. PLoS ONE 10(4): e0122588.

  • Chabot D. Dillon C. Shemrock A. Weissflog N. Sager E.P.S. (2018) An Object-Based Image Analysis Workflow for Monitoring Shallow- Water Aquatic Vegetation in Multispectral Drone Imagery. ISPRS International Journal of Geo-Information 7(8): 294.

  • Chianucci F. Disperati L. Guzzi D. Bianchini D. Nardino V. Lastri C. Rindinella A. Corona P. (2017) Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV. International Journal of Applied Earth Observation and Geoinformation 47: 60-68.

  • Colomina I. Molina P. (2014) Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 92: 79-97.

  • Cruz H. Eckert M. Meneses J. Martínez J.-F. (2016) Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs). Sensors 16(6): 893.

  • Cruzan M.B. Weinstein B.G. Grasty M.R. Kohrn B.F. Hendrickson E.C. Arredondo T.M. Thompson P.G. (2016) Small unmanned aerial vehicles (micro-UAVs drones) in plant ecology. Applications in Plant Sciences 4(9): 1600041.

  • Cunliffe A.M. Brazier R.E. Anderson K. (2016) Ultra-fine grain landscape- scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment 183: 129-143.

  • Dandois J.P. Ellis E.C. (2013) High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sensing of Environment 136: 259-276.

  • Delord K. Roudaut G. Guinet C. Barbraud C. Bertrand S. Weimerskirch H. (2015) Kite aerial photography: a low-cost method for monitoring seabird colonies. Journal of Field Ornithology 86:173-179.

  • de Sá N.C. Castro P. Carvalho S. Marchante E. López-Núñez F.A. Marchante H. (2018) Mapping the Flowering of an Invasive Plant Using Unmanned Aerial Vehicles: Is There Potential for Biocontrol Monitoring? Frontiers in Plant Science 9: 293.

  • Di Gennaro S.F. Battiston E. Di Marco S. Facini O. Matese A. Nocentini M. Palliotti A. Mugnai L. (2016) Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathologia Mediterranea 55(2): 262−275.

  • Dijkstra K. van de Loosdrecht J. Schomaker L. Wiering M. (2017) Hyper-spectral frequency selection for the classification of vegetation diseases. European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning (2017 ed. pp. 483-488). Bruges (Belgium): ESANN.

  • Durban J.W. Fearnbach H. Barrett-Lennard L.G. Perryman W.L. LeRoi D.J. (2015) Photogrammetry of killer whales using a small hexacopter launched at sea. Journal of Unmanned Vehicle Systems 3: 131-135.

  • ESA (2015) Sentinel-2 User Handbook. European Space Agency.

  • Evans L.J. Jones T.H. Pang K. Evans M.N. Saimin S. Goossens B. (2015) Use of drone technology as a tool for behavioral research: A case study of crocodilian nesting. Herpetological Conservation and Biology 10(1): 90-98.

  • Ezat M.A. Fritsch C.J. Downs C.T. (2018) Use of an unmanned aerial vehicle (drone) to survey Nile crocodile populations: A case study at Lake Nyamithi Ndumo game reserve South Africa. Biological Conservation 223: 76-81.

  • Fernández-Guisuraga J.M. Sanz-Ablanedo E. Suárez-Seoane S. Calvo L. (2018) Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges. Sensors 18(2): 586.

  • Ferguson M.C. Angliss R.P. Kennedy A. Lynch B. Willoughby A. Helker V. Brower A.A. Clarke J.T. (2018) Performance of manned and unmanned aerial surveys to collect visual data and imagery for estimating arctic cetacean density and associated uncertainty. Journal of Unmanned Vehicle Systems 6: 128-154.

  • Flamm R.O. Owen E.C. Owen C.F. Wells R.S. Nowacek D. (2000) Aerial videogrammetry from a tethered airship to assess manatee lifestage structure. Marine Mammal Science 16: 617-630.

  • Fraser R.H. van der Sluijs J. Hall R.J. (2017) Calibrating Satellite-Based Indices of Burn Severity from UAV-Derived Metrics of a Burned Boreal Forest in NWT Canada. Remote Sensing 9(3): 279.

  • Fraser B.T. Congalton R.G. (2018) Issues in Unmanned Aerial Systems (UAS) Data Collection of Complex Forest Environments. Remote Sensing 10(6): 908.

  • Fu Y. Kinniry M. Kloepper L.N. (2018) The Chirocopter: A UAV for recording sound and video of bats at altitude. Methods in Ecology and Evolution 9: 1531-1535.

  • Gago J. Douthe C. Coopman R.E. Gallego P.P. Ribas-Carbo M. Flexas J. Escalona J. Medrano H. (2015) UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management 153: 9-19.

  • Garcia-Ruiz F. Sankaran S. Maja J.M. Lee W.S. Rasmussen J. Ehsani R. (2013) Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics in Agriculture 91: 106-115.

  • Getzin S. Wiegand K. Schoning I. (2012) Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods in Ecology and Evolution 3(2): 397-404.

  • Getzin S. Nuske R.S. Wiegand K. (2014) Using Unmanned Aerial Vehicles (UAV) to Quantify Spatial Gap Patterns in Forests. Remote Sensing 6(8): 6988-7004.

  • Goebel M.E. Perryman W.L. Hinke J.T. Krause D.J. Hann N.A. Gardner S. LeRoi D.J. (2015) A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biology 38(6): 619-630.

  • Gooday O.J. Key N. Goldstien S. Zawar-Reza P. (2018) An assessment of thermal-image acquisition with an unmanned aerial vehicle (UAV) for direct counts of coastal marine mammals ashore. Journal of Unmanned Vehicle Systems 6: 100-108.

  • Gray P.C. Ridge J.T. Poulin S.K. Seymour A.C. Schwantes A.M. Swenson J.J. Johnston D.W. (2018) Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments. Remote Sensing 10(8): 1257.

  • Grenzdörffer G.J. (2013) UAS-based automatic bird count of a common gull colony. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences XL-1/W2 169-174 UAV-g2013 4 – 6 September 2013 Rostock Germany.

  • Hardin P.J. Jackson M.W. (2005) An unmanned aerial vehicle for rangeland photography. Rangeland Ecology Management 58: 439-442.

  • Hodgson A. Kelly N. Peel D. (2013) Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study. PLoS ONE 8(11): e 79556.

  • Hodgson J.C. Baylis S.M. Mott R. Herrod A. Clarke R.H. (2016) Precision wildlife monitoring using unmanned aerial vehicles. Scientific Reports 6: 22574.

  • Hung C. Xu Z. Sukkarieh S. (2014) Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV. Remote Sensing 6(12): 12037-12054.

  • Hunt E.R. Jr. Rondon S.I. (2017) Detection of potato beetle damage using remote sensing from small unmanned aircraft systems. Journal of Applied Remote Sensing 11(2): 026013.

  • Husson E. Hagner O. Ecke F. (2013) Unmanned aircraft systems help to map aquatic vegetation. Applied Vegetation Science 17(3): 567-577.

  • Husson E. Ecke F. Reese H. (2016) Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images. Remote Sensing 8(9): 724.

  • Inoue T. Nagai S. Yamashita S. Fadaei H. Ishii R. Okabe K. Taki H. Honda Y. Kajiwara K. Suzuki R. (2014) Unmanned Aerial Survey of Fallen Trees in a Deciduous Broadleaved Forest in Eastern Japan. PLoS ONE 9(10): e109881.

  • Israel M. (2011) A UAV-based roe deer fawn detection system. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences Vol. XXXVIII-1/C22 UAV-g 2011 Conference on Unmanned Aerial Vehicle in Geomatics Zurich Switzerland

  • Jaakkola A. Hyyppä J. Kukko A. Yu X. Kaartinen H. Lehtomäki M. Lin Y. (2010) A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS Journal of Photogrammetry and Remote Sensing 65: 514-522.

  • Jones IV G.P. Pearlstine L.G. Percival H.F. (2006) An Assessment of Small Unmanned Aerial Vehicles for Wildlife Research. Wildlife Society Bulletin 34(3): 750-758.

  • Johnston D.W. Dale J. Murray K.T. Josephson E. Newton E. Wood S. (2017) Comparing occupied and unoccupied aircraft surveys of wildlife populations: assessing the gray seal (Halichoerus grypus) breeding colony on Muskeget Island USA. Journal of Unmanned Vehicle Systems 5: 178-191.

  • Junda J. Greene E. Bird D.M. (2015) Proper flight technique for using a small rotary-winged drone aircraft to safely quickly and accurately survey raptor nests. Journal of Unmanned Vehicle Systems 3: 222-236.

  • Jung S. Cho H. Kim D. Kim K. Han J.-I. Myung H. (2017) Development of Algal Bloom Removal System Using Unmanned Aerial Vehicle and Surface Vehicle. IEEE Access 5: 22166-22176.

  • Kellenberger B. Marcos D. Tuia D. (2018) Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning. Remote Sensing of Environment 216: 139-153.

  • Kislik C. Dronova I. Kelly M. (2018) UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities. Drones 2(4): 35.

  • Krause D.J. Hinke J.T. Perryman W.L. Goebel M.E. LeRoi D.J. (2017) An accurate and adaptable photogrammetric approach for estimating the mass and body condition of pinnipeds using an unmanned aerial system. PLoS ONE 12(11): e0187465.

  • Lambert J.P.T. Hicks H.L. Childs D.Z. Freckleton R.P. (2017) Evaluating the potential of Unmanned Aerial Systems for mapping weeds at field scales: a case study with Alopecurus myosuroides. Weed Research 58(1): 35-45.

  • Lehmann J.R.K. Prinz T. Ziller S.R. Thiele J. Heringer G. Meira-Neto J.A.A. Buttschardt T.K. (2017) Open-Source Processing and Analysis of Aerial Imagery Acquired with a Low-Cost Unmanned Aerial System to Support Invasive Plant Management. Frontiers in Plant Science 5: 44.

  • Lin B. Ross S.D. Prussin II A.J. Schmale III D.G. (2014) Seasonal associations and atmospheric transport distances of fungi in the genus Fusarium collected with unmanned aerial vehicles and ground-based sampling devices. Atmospheric Environment 94: 385-391.

  • Lin Y. Jiang M. Yao Y. Zhang L. Lin J. (2015) Use of UAV oblique imaging for the detection of individual trees in residential environments. Urban Forestry & Urban Greening 14(2): 404-412.

  • Lu B. He Y. (2017) Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS Journal of Photogrammetry and Remote Sensing 128: 73-85.

  • Lucieer A. Turner D. King D. H. Robinson S. A. (2014) Using an Unmanned Aerial Vehicle (UAV) to capture micro-topography of Antarctic moss beds. International Journal of Applied Earth Observation and Geoinformation 27 (Part A): 53-62.

  • Martin J. Edwards H.H. Burgess M.A. Percival H.F. Fagan D.E. Gardner B.E. et al. (2012) Estimating Distribution of Hidden Objects with Drones: From Tennis Balls to Manatees. PLoS ONE 7(6): e38882.

  • McKenna P. Erskine P.D. Lechner A.M. Phinn S. (2017) Measuring fire severity using UAV imagery in semi-arid central Queensland Australia. International Journal of Remote Sensing 38(14): 4244-4264.

  • Meneses N.C. Baier S. Reidelstürz P. Geist J. Schneider T. (2018) Modelling heights of sparse aquatic reed (Phragmites australis) using Structure from Motion point clouds derived from Rotary- and Fixed-Wing Unmanned Aerial Vehicle (UAV) data. Limnologica 72: 10-21.

  • Messinger M. Asner G.P. Silman M. (2016) Rapid Assessments of Amazon Forest Structure and Biomass Using Small Unmanned Aerial Systems. Remote Sensing 8(8): 615.

  • Michez A. Piégay H. Jonathan L. Claessens H. Lejeune P. (2016a) Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery. International Journal of Applied Earth Observation and Geoinformation 44: 88-94.

  • Michez A. Piégay H. Lisein J. Claessens H. Lejeune P. (2016b) Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environ Monitoring and Assessment 188: 146.

  • Micheletti N. Chandler J.H. Lane S.N. (2015) Structure from Motion (SfM) Photogrammetry. Chap. 2 Sec. 2.2 In: Cook S.J. Clarke L.E. & Nield J.M. (Eds.) Geomorphological Techniques (Online Edition). British Society for Geomorphology London.

  • Moreland E.E. Cameron M.F. Angliss R.P. Boveng P.L. (2015) Evaluation of a ship-based unoccupied aircraft system (UAS) for surveys of spotted and ribbon seals in the Bering Sea pack ice. Journal of Unmanned Vehicle Systems 3: 114-122.

  • Mulero-Pázmány M. Stolper R. van Essen L.D. Negro J.J. Sassen T. (2014) Remotely Piloted Aircraft Systems as a Rhinoceros Anti- Poaching Tool in Africa. PLoS ONE 9(1): e83873

  • Müllerová J. Brůna J. Bartaloš T. Dvořák P. Vítková M. Pyšek P. (2017) Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring. Front Plant Sci 8:887

  • Murfitt S.L. Allan B.M. Bellgrove A. Rattray A. Young M.A. Ierodiaconou D. (2017) Applications of unmanned aerial vehicles in intertidal reef monitoring. Scientific Reports 7: 10259.

  • Näsi R. Honkavaara E. Lyytikäinen-Saarenmaa P. Blomqvist M. Litkey P. Hakala T. Viljanen N. Kantola T. Tanhuanpää T. Holopainen M. (2015) Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote Sensing 7(11): 15467-15493.

  • Nyquist J.E. (1997) Unmanned aerial vehicles that even geoscience departments can afford. Geotimes 42: 20-23.

  • Paneque-Gálvez J. Vargas-Ramírez N. Napoletano B.M. Cummings A. (2017) Grassroots Innovation Using Drones for Indigenous Mapping and Monitoring. Land 6(4): 86.

  • Peña J.M. Torres-Sánchez J. de Castro A.I. Kelly M. López-Granados F. (2013) Weed Mapping in Early-Season Maize Fields Using Object- Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PLoS ONE 8(10): e77151.

  • Pirotta V. Smith A. Ostrowski M. Russell D. Jonsen I.D. Grech A. Harcourt R. (2017) An Economical Custom-Built Drone for Assessing Whale Health. Frontiers in Marine Science 4: 425.

  • Potapov E. Utekhina I. McGrady M.J. Rimlinger D. (2013) Steller’s Sea Eagle Monitoring at the Northern Part of the Sea of Okhotsk: Birds People Technologies. Raptors Conservation 27: 46-57.

  • Puliti S. Ørka H.O. Gobakken T. Næsset E. (2015) Inventory of Small Forest Areas Using an Unmanned Aerial System. Remote Sensing 7(8): 9632-9654.

  • Quilter M.C. Anderson V.J. (2001) A proposed method for determining shrub utilization using (LA/LS) imagery. Journal of Range Management 54: 378-381.

  • Radjawali I. Pye O. (2017) Drones for justice: inclusive technology and river-related action research along the Kapuas. Geographica Helvetica 72: 17-27.

  • Ratcliffe N. Guihen D. Robst J. Crofts S. Stanworth A. Enderlein P. (2015) A protocol for the aerial survey of penguin colonies using UAVs. Journal of Unmanned Vehicle Systems 3: 95-101.

  • R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria. URL https://www.R-project.org/.

  • Rey N. Volpi M. Joost S. Tuia D. (2017) Detecting animals in African Savanna with UAVs and the crowds. Remote Sensing of Environment 200: 341-351.

  • Rodríguez A. Negro J.J. Mulero M. Rodríguez C. Hernández-Pliego J. Bustamante J. (2012) The Eye in the Sky: Combined Use of Unmanned Aerial Systems and GPS Data Loggers for Ecological Research and Conservation of Small Birds. PLoS ONE 7(12): e50336.

  • Rosca S. Suomalainen J. Bartholomeus H. Herold M. (2017) Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests. Interface Focus 8(2): 20170038.

  • Rossi C.F. Fritz A. Becker G. (2018) Combining Satellite and UAV Imagery to Delineate Forest Cover and Basal Area after Mixed-Severity Fires. Sustainability 10 (7): 2227.

  • Saadat M.N. Sharif M.M.M. (2017) Unmanned aerial vehicle surveillance system (UAVSS) for forest surveillance and data acquisition. International Conference on Information and Communication Technology Convergence (ICTC) Jeju 2017 pp. 178-183

  • Saarinen N. Vastaranta M. Näsi R. Rosnell T. Hakala T. Honkavaara E. Wulder M.A. Luoma V. Tommaselli A.M.G. Imai N.N. Ribeiro E.A. Guimarães R.B. Holopainen M. Hyyppä J. (2018) Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sensing 10(2): 338.

  • Sandino J. Pegg G. Gonzalez F. Smith G. (2018) Aerial Mapping of Forests Affected by Pathogens Using UAVs Hyperspectral Sensors and Artificial Intelligence. Sensors 18(4): 944.

  • Sankey T. Donager J. McVay J. Sankey J.B. (2017) UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sensing of Environment 195: 30-43.

  • Sardà-Palomera F. Bota G. Viñolo C. Pallarés O. Sazatornil V. Brotons L. Gomáriz S. and Sardà F. (2012) Fine-scale bird monitoring from light unmanned aircraft systems. Ibis 154: 177-183.

  • Schoonmaker J. Wells T. Gilbert G. Podobna Y. Petrosyuk I. Dirbas J. (2008) Spectral detection and monitoring of marine mammals. Proc. SPIE 6946 Airborne Intelligence Surveillance Reconnaissance (ISR) Systems and Applications V 694606.

  • Shang S. Lee Z. Lin G. Hu C. Shi L. Zhang Y. Li X. Wu J. Yan J. (2017) Sensing an intense phytoplankton bloom in the western Taiwan Strait from radiometric measurement on a UAV. Remote Sensing of Environment 198: 85-94.s

  • Stoyanova M. Kandilarov A. Koutev V. Nitcheva O. Dobreva P. (2018) Potential of multispectral imaging technology for assessment coniferous forests bitten by a bark beetle in Central Bulgaria. MATEC Web of Conferences 145 01005.

  • Sykora-Bodie S.T. Bézy V. Johnston D.W. Newton E. Lohmann K.J. (2017) Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments. Scientific Reports 7: 17690.

  • Techy L. Schmale D.G. Woolsey C.A. (2010) Coordinated aerobiological sampling of a plant pathogen in the lower atmosphere using two autonomous unmanned aerial vehicles. Journal of Field Robotics 27: 335–343.

  • Thapa G.J. Thapa K. Thapa R. Jnawali S.R. Wich S.A. Poudyal L.P. Karki S. (2018) Counting crocodiles from the sky: monitoring the critically endangered gharial (Gavialis gangeticus) population with an unmanned aerial vehicle (UAV). Journal of Unmanned Vehicle Systems 6: 71-82.

  • Tremblay J.A. Desrochers A. Aubry Y. Pace P. Bird D.M. (2017) A lowcost technique for radio-tracking wildlife using a small standard unmanned aerial vehicle. Journal of Unmanned Vehicle Systems 5: 102-108.

  • Twilley R.R. Kemp W.M. Staver K.W. Stevenson J.C. Boynton W.R. (1985) Nutrient enrichment of estuarine submersed vascular plant communities. 1. Algal growth and effects on production of plants and associated communities. Marine Ecology Progress Series 23: 179-191.

  • USGS (2018) Landsat 8 (L8) data User Handbook. Version 3.0. U.S. Geological Survey.

  • Ventura D. Bonifazi A. Gravina M.F. Belluscio A. Ardizzone G. (2018) Mapping and Classification of Ecologically Sensitive Marine Habitats Using Unmanned Aerial Vehicle (UAV) Imagery and Object- Based Image Analysis (OBIA). Remote Sensing 10(9): 1331.

  • Vermeulen C. Lejeune P. Lisein J. Sawadogo P. Bouché P. (2013) Unmanned Aerial Survey of Elephants. PLoS ONE 8(2): e54700.

  • Vermote E.F. Roger J.C. Ray J.P. (2015) MODIS Surface Reflectance User’s Guide. MODIS Land Surface Reflectance Science Computing Facility.

  • Wallace L. Lucieer A. Watson C. (2012) Development of a UAV-LiDAR system with application to forest inventory. Remote Sensing 4(6): 1519-1543.

  • Wang H. Zhong G. Yan H. Liu H. Wang Y. Zhang C. (2012) Growth Control of Cyanobacteria by Three Submerged Macrophytes. Environmental Engineering Science 29(6): 420-425.

  • Weissensteiner M.H. Poelstra J.W. Wolf J.B. (2015) Low-budget readyto- fly unmanned aerial vehicles: an effective tool for evaluating the nesting status of canopy-breeding bird species. Journal of Avian Biology 46: 425-430.

  • Wich S. Dellatore D. Houghton M. Ardi R. Koh L.P (2016) A preliminary assessment of using conservation drones for Sumatran orang-utan (Pongo abelii) distribution and density. Journal of Unmanned Vehicle Systems 4: 45-52.

  • Wich S.A. Koh L.P. (2018) Conservation drones : mapping and monitoring biodiversity. Oxford University Press Oxford 144 p.

  • Wickham H. (2009) ggplot2: Elegant Graphics for Data Analysis. Springer- Verlag New York.

  • Xu F. Gao Z. Jiang X. Shang W. Ning J. Song D. Ai J. (2018) A UAV and S2A data-based estimation of the initial biomass of green algae in the South Yellow Sea. Marine Pollution Bulletin 128: 408-414.

  • Yuan C. Zhang Y. Liu Z. (2015) A survey on technologies for automatic forest fire monitoring detection and fighting using unmanned aerial vehicles and remote sensing techniques. Canadian Journal of Forest Research 45(7): 783-792.

  • Zahawi R.A. Dandois J.P. Holl K.D. Nadwodny D. Reid J.L. Ellis E.C. (2015) Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation 186: 287-295.

  • Zarco-Tejada P.J. Diaz-Varela R. Angileri V. Loudjania P. (2014) Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy 55: 89-99.

  • Zhang J. Hu J. Lian J. Fan Z. Ouyang X. Ye W. (2016) Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring. Biological Conservation 198: 60-69.

  • Zhou J. Pavek M.J. Shelton S.C. Holden Z.J. Sankaran S. (2016) Aerial multispectral imaging for crop hail damage assessment in potato. Computers and Electronics in Agriculture 127: 406-412.

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