Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review
DOI:
https://doi.org/10.2478/eje-2018-0012Keywords:
Plant conservation, wildlife monitoring, UAS, Drone, ecology, aerial survey, Remote sensingAbstract
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.
References
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.
Downloads
Published
Issue
Section
License
Copyright (c) 2018 Maciej M Nowak, Katarzyna Dziob, Pawel Bogawski

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors retain copyright in their articles.
Articles in the European Journal of Ecology published 2020 and after are made available under a Creative Commons Attribution 4.0 license.
Articles in the European Journal of Ecology published 2015-2019 are made available under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 license.