Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Date and Origin of Studies
3.2. Terminology
3.3. Platform and Sensors
3.4. Classification and Processing of Captured Data
4. Discussion
4.1. Regional Bias in UAV Application
4.2. A Need to Realise the Full Potential of UAV Platforms and Sensors in Monitoring Ecological Recovery
4.3. Analysis and Reporting of Restoration Monitoring Data
4.4. Regulatory and Community Expectations of Restoration Monitoring
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Shaw, I.G.R. Predator Empire: The Geopolitics of US Drone Warfare. Geopolitics 2013, 18, 536–559. [Google Scholar] [CrossRef]
- Schmidt, H. From a bird’s eye perspective: Aerial drone photography and political protest. A case study of the Bulgarian #resign movement 2013. Digit. Icons Stud. Russ. Eur. Cent. Eur. New Med. 2015, 13, 1–27. [Google Scholar]
- Hugenholtz, C.H.; Whitehead, K.; Brown, O.W.; Barchyn, T.E.; Moorman, B.J.; LeClair, A.; Riddell, K.; Hamilton, T. Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model. Geomorphology 2013, 194, 16–24. [Google Scholar] [CrossRef]
- Watts, A.C.; Ambrosia, V.G.; Hinkley, E.A. Unmanned Aircraft Systems in Remote Sens. and Scientific Research: Classification and Considerations of Use. Remote Sens. 2012, 4, 1671–1692. [Google Scholar] [CrossRef]
- Warwick, G. AUVSI—Precision Agriculture will Lead Civil UAS. 2014. Available online: http://aviationweek.com/blog/auvsi-precision-agriculture-will-lead-civil-uas (accessed on 15 March 2019).
- Berni, J.; Zarco-Tejada, P.J.; Suarez, L.; Fereres, E. Thermal and Narrowband Multispectral Remote Sens. for Vegetation Monitoring from an Unmanned Aerial Vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722–738. [Google Scholar] [CrossRef]
- Hunt, E.R.; Hively, W.D.; Fujikawa, S.; Linden, D.; Daughtry, C.S.; McCarty, G. Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring. Remote Sens. 2010, 2, 290–305. [Google Scholar] [CrossRef]
- Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693–712. [Google Scholar] [CrossRef]
- Sankaran, S.; Khot, L.R.; Espinoza, C.Z.; Jarolmasjed, S.; Sathuvalli, V.R.; Vandemark, G.J.; Miklas, P.N.; Carter, A.H.; Pumphrey, M.O.; Knowles, N.R.; et al. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. Eur. J. Agron. 2015, 70, 112–123. [Google Scholar] [CrossRef]
- Langford, J.S. New aircraft platforms for earth system science: an opportunity for the 1990s. In Proceedings of the 17th Congress of the International Council of the Aeronautical Sciences, Sweden, Stockholm, 9–14 September 1990. [Google Scholar]
- Conniff, R. Drones are Ready for Takeoff. 2012. Available online: http://www.smithsonianmag.com/science-nature/drones-are-ready-for-takeoff-160062162/ (accessed on 17 March 2019).
- Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogram. Remote Sens. 2014, 92, 79–97. [Google Scholar] [CrossRef]
- Pádua, L.; Vanko, J.; Hruška, J.; Adão, T.; Sousa, J.J.; Peres, E.; Morais, R. UAS, sensors, and data processing in agroforestry: A review towards practical applications. Int. J. Remote Sens. 2017, 38, 2349–2391. [Google Scholar]
- Torresan, C.; Berton, A.; Carotenuto, F.; Di Gennaro, S.F.; Gioli, B.; Matese, A.; Miglietta, F.; Vagnoli, C.; Zaldei, A.; Wallace, L. Forestry applications of UAVs in Europe: A review. Int. J. Remote Sens. 2017, 38, 2427–2447. [Google Scholar] [CrossRef]
- Dudek, M.; Tomczyk, P.; Wygonik, P.; Korkosz, M.; Bogusz, P.; Lis, B. Hybrid fuel cell—Battery system as a main power unit for small unmanned aerial vehicles (UAV). Int. J. Electrochem. Sci. 2013, 8, 8442–8463. [Google Scholar]
- Bennett, A.; Preston, V.; Woo, J.; Chandra, S.; Diggins, D.; Chapman, R.; Wang, Z.; Rush, M.; Lye, L.; Tieu, M.; et al. Autonomous vehicles for remote sample collection in difficult conditions: Enabling remote sample collection by marine biologists. In Proceedings of the IEEE International Conference on Technologies for Practical Robot Applications, Woburn, MA, USA, 11–12 May 2015. [Google Scholar]
- Cruzan, M.B.; Weinstein, B.G.; Grasty, M.R.; Kohrn, B.F.; Hendrickson, E.C.; Arredondo, T.M.; Thompson, P.G. Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology. Appl. Plant. Sci. 2016, 4, 160004. [Google Scholar] [CrossRef]
- Baena, S.; Moat, J.; Whaley, O.; Boyd, D.S. Identifying species from the air: UAVs and the very high resolution challenge for plant conservation. PLoS ONE 2017, 12, e0188714. [Google Scholar] [CrossRef] [PubMed]
- Suduwella, C.; Amarasinghe, A.; Niroshan, L.; Elvitigala, C.; De Zoysa, K.; Keppetiyagama, C. Identifying Mosquito Breeding Sites via Drone Images. In Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications—DroNet ’17, Niagara Falls, NY, USA, 23 June 2017; pp. 27–30. [Google Scholar]
- Knoth, C.; Klein, B.; Prinz, T.; Kleinebecker, T. Unmanned aerial vehicles as innovative remote sensing platforms for high-resolution infrared imagery to support restoration monitoring in cut-over bogs. Appl. Veg. Sci. 2013, 16, 509–517. [Google Scholar] [CrossRef]
- Anderson, K.; Gaston, K.J. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 2013, 11, 138–146. [Google Scholar] [CrossRef]
- Ludwig, J.A.; Hindley, N.; Barnett, G. Indicators for monitoring minesite rehabilitation: Trends on waste-rock dumps, northern Australia. Ecol. Indic. 2003, 3, 143–153. [Google Scholar] [CrossRef]
- Ishihama, F.; Watabe, Y.; Oguma, H.; Moody, A. Validation of a high-resolution, remotely operated aerial remote-sensing system for the identification of herbaceous plant species. Appl. Veg. Sci. 2012, 15, 383–389. [Google Scholar] [CrossRef]
- Woodget, A.S.; Austrums, R.; Maddock, I.P.; Habit, E. Drones and digital photogrammetry: From classifications to continuums for monitoring river habitat and hydromorphology. Wiley Interdiscip. Rev. Water 2017, 4, e1222. [Google Scholar] [CrossRef]
- Cao, J.; Leng, W.; Liu, K.; Liu, L.; He, Z.; Zhu, Y. Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models. Remote Sens. 2018, 10, 89. [Google Scholar] [CrossRef]
- Zmarz, A.; Korczak-Abshire, M.; Storvold, R.; Rodzewicz, M.; Kędzierska, I. Indicator Species Population Monitoring in Antarctica with Uav. ISPRS Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 2015, 40, 189–193. [Google Scholar] [CrossRef]
- D’Oleire-Oltmanns, S.; Marzolff, I.; Peter, K.; Ries, J. Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco. Remote Sens. 2012, 4, 3390–3416. [Google Scholar] [CrossRef]
- Vasuki, Y.; Holden, E.-J.; Kovesi, P.; Micklethwaite, S. Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach. Comput. Geosci. 2014, 69, 22–32. [Google Scholar] [CrossRef]
- Takayama, L.; Ju, W.; Nass, C. Beyond dirty, dangerous and dull: what everyday people think robots should do. In Proceedings of the 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), Amsterdam, The Netherlands, 12–15 March 2008; pp. 25–32. [Google Scholar]
- Cross, A.T.; Young, R.; Nevill, P.; McDonald, T.; Prach, K.; Aronson, J.; Wardell-Johnson, G.W.; Dixon, K.W. Appropriate aspirations for effective post-mining restoration and rehabilitation: A response to Kaz’mierczak et al. Environ. Earth Sci. 2018, 77, 256. [Google Scholar] [CrossRef]
- Clewell, A.; Aronson, J.; Winterhalder, K. The SER International Primer on Ecological Restoration; Science & Policy Working Group: Washington, DC, USA, 2004. [Google Scholar]
- McDonald, T.; Gann, G.D.; Jonson, J.; Dixon, K.W. International Standards for the Practice of Ecological Restoration—Including Principles and Key Concepts; Society for Ecological Restoration: Washington, DC, USA, 2016. [Google Scholar]
- Huang, Y.; Thomson, S.J.; Hoffman, W.C.; Lan, Y.; Fritz, B.K. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. Int. J. Agric. Biol. Eng. 2013, 6, 1–10. [Google Scholar] [CrossRef]
- Honkavaara, E.; Saari, H.; Kaivosoja, J.; Pölönen, I.; Hakala, T.; Litkey, P.; Mäkynen, J.; Pesonen, L. Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sens. 2013, 5, 5006–5039. [Google Scholar] [CrossRef]
- Shahbazi, M.; Théau, J.; Ménard, P. Recent applications of unmanned aerial imagery in natural resource management. GISci. Remote Sens. 2014, 51, 339–365. [Google Scholar] [CrossRef]
- Tripicchio, P.; Satler, M.; Dabisias, G.; Ruffaldi, E.; Avizzano, C.A. Towards Smart Farming and Sustainable Agriculture with Drones. In Proceedings of the 2015 International Conference on Intelligent Environments, Prague, Czech Republic, 15–17 July 2015; pp. 140–143. [Google Scholar]
- Tang, L.; Shao, G. Drone remote sensing for forestry research and practices. J. For. Res. 2015, 26, 791–797. [Google Scholar] [CrossRef]
- Lehmann, J.; Nieberding, F.; Prinz, T.; Knoth, C. Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels. Forests 2015, 6, 594–612. [Google Scholar] [CrossRef]
- Paneque-Gálvez, J.; McCall, M.; Napoletano, B.; Wich, S.; Koh, L. Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas. Forests 2014, 5, 1481–1507. [Google Scholar] [CrossRef]
- Koh, L.P.; Wich, S.A. Dawn of drone ecology: Low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 2012, 5, 121–132. [Google Scholar] [CrossRef]
- Van Gemert, J.C.; Verschoor, C.R.; Mettes, P.; Epema, K.; Koh, L.P.; Wich, S. Nature Conservation Drones for Automatic Localization and Counting of Animals; Springer International Publishing: Cham, Switzerland, 2015; pp. 255–270. [Google Scholar]
- Linchant, J.; Lisein, J.; Semeki, J.; Lejeune, P.; Vermeulen, C. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mamm. Rev. 2015, 45, 239–252. [Google Scholar] [CrossRef]
- Arnon, A.I.; Ungar, E.D.; Svoray, T.; Shachak, M.; Blankman, J.; Perevolotsky, A. The Application of Remote Sens. to Study Shrub-Herbaceous Relations at a High Spatial Resolution. Israel J. Plant Sci. 2007, 55, 73–82. [Google Scholar] [CrossRef]
- Chen, S.; McDermid, G.; Castilla, G.; Linke, J. Measuring Vegetation Height in Linear Disturbances in the Boreal Forest with UAV Photogrammetry. Remote Sens. 2017, 9, 1257. [Google Scholar] [CrossRef]
- Panagiotidis, D.; Abdollahnejad, A.; Surový, P.; Chiteculo, V. Determining tree height and crown diameter from high-resolution UAV imagery. Int. J. Remote Sens. 2017, 38, 2392–2410. [Google Scholar] [CrossRef]
- Laliberte, A.S.; Rango, A.; Fredrickson, E.L. Unmanned aerial vehicles for rangeland mapping and monitoring: a comparison of two systems. In Proceedings of the American Society for Photogrammetry and Remote Sensing Annual Conference, Tampa, FL, USA, 7–11 May 2007. [Google Scholar]
- Laliberte, A.S.; Rango, A. Incorporation of texture, intensity, hue, and saturation for rangeland monitoring with unmanned aircraft imagery. In GEOBIA Proceedings; Hay, G.J., Blaschke, T., Marceau, D., Eds.; GEOBIA/ISPRS: Calgary, AB, Canada, 2008. [Google Scholar]
- Hird, J.; Montaghi, A.; McDermid, G.; Kariyeva, J.; Moorman, B.; Nielsen, S.; McIntosh, A. Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites. Remote Sens. 2017, 9, 413. [Google Scholar] [CrossRef]
- Waite, C.E.; van der Heijden, G.M.F.; Field, R.; Boyd, D.S.; Magrach, A. A view from above: Unmanned aerial vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies. J. Appl. Ecol. 2019. [Google Scholar] [CrossRef]
- Turner, D.; Lucieer, A.; Watson, C. An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds. Remote Sens. 2012, 4, 1392–1410. [Google Scholar] [CrossRef]
- Dandois, J.P.; Ellis, E.C. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 2013, 136, 259–276. [Google Scholar] [CrossRef]
- Zahawi, R.A.; Dandois, J.P.; Holl, K.D.; Nadwodny, D.; Reid, J.L.; Ellis, E.C. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biol. Conserv. 2015, 186, 287–295. [Google Scholar] [CrossRef]
- Tahar, K.; Ahmad, A.; Akib, W.; Mohd, W. A New Approach on Production of Slope Map Using Autonomous Unmanned Aerial Vehicle. Int. J. Phys. Sci. 2012, 7, 5678–5686. [Google Scholar]
- Rauhala, A.; Tuomela, A.; Davids, C.; Rossi, P. UAV Remote Sensing Surveillance of a Mine Tailings Impoundment in Sub-Arctic Conditions. Remote Sens. 2017, 9, 1318. [Google Scholar] [CrossRef]
- Cross, S.L.; Tomlinson, S.; Craig, M.D.; Dixon, K.W.; Bateman, P.W. Overlooked and undervalued: The neglected role of fauna and a global bias in ecological restoration assessments. Pac. Conserv. Biol. 2019. [Google Scholar] [CrossRef]
- Wich, S.; Dellatore, D.; Houghton, M.; Ardi, R.; Koh, L.P. A preliminary assessment of using conservation drones for Sumatran orang-utan (Pongo abelii) distribution and density. J. Unmanned Veh. Syst. 2015, 4, 45–52. [Google Scholar] [CrossRef]
- McClelland, G.T.W.; Bond, A.L.; Sardana, A.; Glass, T. Rapid population estimate of a surface-nesting seabird on a remote island using a low-cost unmanned aerial vehicle. Mar. Ornithol. 2016, 44, 215–220. [Google Scholar]
- Ratcliffe, N.; Guihen, D.; Robst, J.; Crofts, S.; Stanworth, A.; Enderlein, P. A protocol for the aerial survey of penguin colonies using UAVs. J. Unmanned Veh. Syst. 2015, 3, 95–101. [Google Scholar] [CrossRef]
- Koski, W.R.; Gamage, G.; Davis, A.R.; Mathews, T.; LeBlanc, B.; Ferguson, S.H. Evaluation of UAS for photographic re-identification of bowhead whales, Balaena mysticetus. J. Unmanned Veh. Syst. 2015, 3, 22–29. [Google Scholar] [CrossRef]
- Durban, J.W.; Fearnbach, H.; Barrett-Lennard, L.G.; Perryman, W.L.; Leroi, D.J. Photogrammetry of killer whales using a small hexacopter launched at sea. J. Unmanned Veh. Syst. 2015, 3, 131–135. [Google Scholar] [CrossRef]
- Schofield, G.; Katselidis, K.A.; Lilley, M.K.S.; Reina, R.D.; Hays, G.C.; Gremillet, D. Detecting elusive aspects of wildlife ecology using drones: New insights on the mating dynamics and operational sex ratios of sea turtles. Funct. Ecol. 2017, 31, 2310–2319. [Google Scholar] [CrossRef]
- Martin, J.; Edwards, H.H.; Burgess, M.A.; Percival, H.F.; Fagan, D.E.; Gardner, B.E.; Ortega-Ortiz, J.G.; Ifju, P.G.; Evers, B.S.; Rambo, T.J. Estimating distribution of hidden objects with drones: From tennis balls to manatees. PLoS ONE 2012, 7, e38882. [Google Scholar] [CrossRef]
- Hodgson, J.C.; Mott, R.; Baylis, S.M.; Pham, T.T.; Wotherspoon, S.; Kilpatrick, A.D.; Raja Segaran, R.; Reid, I.; Terauds, A.; Koh, L.P.; et al. Drones count wildlife more accurately and precisely than humans. Methods Ecol. Evol. 2018, 9, 1160–1167. [Google Scholar] [CrossRef]
- Weimerskirch, H.; Prudor, A.; Schull, Q. Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses. Polar Biol. 2017, 41, 259–266. [Google Scholar] [CrossRef]
- Vas, E.; Lescroel, A.; Duriez, O.; Boguszewski, G.; Gremillet, D. Approaching birds with drones: First experiments and ethical guidelines. Biol. Lett. 2015, 11, 20140754. [Google Scholar] [CrossRef]
- Ditmer, M.A.; Vincent, J.B.; Werden, L.K.; Tanner, J.C.; Laske, T.G.; Iaizzo, P.A.; Garshelis, D.L.; Fieberg, J.R. Bears Show a Physiological but Limited Behavioral Response to Unmanned Aerial Vehicles. Curr. Biol. 2015, 25, 2278–2283. [Google Scholar] [CrossRef]
- Lyons, M.; Brandis, K.; Callaghan, C.; McCann, J.; Mills, C.; Ryall, S.; Kingsford, R. Bird interactions with drones, from individuals to large colonies. Aust. Field Ornithol. 2018, 35, 51–56. [Google Scholar] [CrossRef]
- Borrelle, S.; Fletcher, A. Will drones reduce investigator disturbance to surface-nesting seabirds? Mar. Ornithol. 2017, 45, 89–94. [Google Scholar]
- Richardson, A.J.; Menges, R.M.; Nixon, P.R. Distinguishing weed from crop plants using video remote sensing. Photogram. Eng. Remote Sens. 1985, 51, 1785–1790. [Google Scholar]
- Lacar, F.M.; Lewis, M.M.; Grierson, I.T. Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia. In Proceedings of the Scanning the Present and Resolving the Future, IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, NSW, Australia, 9–13 July 2001; Volume 6, pp. 2875–2877. [Google Scholar] [CrossRef]
- Ishida, T.; Kurihara, J.; Viray, F.A.; Namuco, S.B.; Paringit, E.C.; Perez, G.J.; Takahashi, Y.; Marciano, J.J. A novel approach for vegetation classification using UAV-based hyperspectral imaging. Comput. Electron. Agric. 2018, 144, 80–85. [Google Scholar] [CrossRef]
- Zarco-Tejada, P.J.; Guillén-Climent, M.L.; Hernández-Clemente, R.; Catalina, A.; González, M.R.; Martín, P. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric. For. Meteorol. 2013, 171–172, 281–294. [Google Scholar] [CrossRef]
- Zarco-Tejada, P.J.; González-Dugo, V.; Berni, J.A.J. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 2012, 117, 322–337. [Google Scholar] [CrossRef]
- Yue, J.; Lei, T.; Li, C.; Zhu, J. The Application of Unmanned Aerial Vehicle Remote Sens. in Quickly Monitoring Crop Pests. Intell. Autom. Soft Comput. 2012, 18, 1043–1052. [Google Scholar] [CrossRef]
- Calderón, R.; Navas-Cortés, J.A.; Lucena, C.; Zarco-Tejada, P.J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ. 2013, 139, 231–245. [Google Scholar] [CrossRef]
- Iizuka, K.; Watanabe, K.; Kato, T.; Putri, N.; Silsigia, S.; Kameoka, T.; Kozan, O. Visualizing the Spatiotemporal Trends of Thermal Characteristics in a Peatland Plantation Forest in Indonesia: Pilot Test Using Unmanned Aerial Systems (UASs). Remote Sens. 2018, 10. [Google Scholar] [CrossRef]
- Sankey, T.; Donager, J.; McVay, J.; Sankey, J.B. UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens. Environ. 2017, 195, 30–43. [Google Scholar] [CrossRef]
- Chrétien, L.-P.; Théau, J.; Ménard, P. Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system. Wildl. Soc. Bull. 2016, 40, 181–191. [Google Scholar] [CrossRef]
- Gonzalez, L.F.; Montes, G.A.; Puig, E.; Johnson, S.; Mengersen, K.; Gaston, K.J. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors 2016, 16, 97. [Google Scholar] [CrossRef]
- Mulero-Pazmany, M.; Stolper, R.; van Essen, L.D.; Negro, J.J.; Sassen, T. Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS ONE 2014, 9, e83873. [Google Scholar] [CrossRef]
- Hoffmann, H.; Jensen, R.; Thomsen, A.; Nieto, H.; Rasmussen, J.; Friborg, T. Crop water stress maps for an entire growing season from visible and thermal UAV imagery. Biogeosciences 2016, 13, 6545–6563. [Google Scholar] [CrossRef]
- Lamb, A.D. Earth observation technology applied to mining-related environmental issues. Min. Technol. 2013, 109, 153–156. [Google Scholar] [CrossRef]
- Benz, U.C.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogram. Remote Sens. 2004, 58, 239–258. [Google Scholar] [CrossRef]
- Michez, A.; Piégay, H.; Jonathan, L.; Claessens, H.; Lejeune, P. Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery. Int. J. Appl. Earth Observ. Geoinf. 2016, 44, 88–94. [Google Scholar] [CrossRef]
- Ye, X.; Sakai, K.; Asada, S.-I.; Sasao, A. Use of airborne multispectral imagery to discriminate and map weed infestations in a citrus orchard. Weed Biol. Manag. 2007, 7, 23–30. [Google Scholar] [CrossRef]
- Pena, J.M.; Torres-Sanchez, J.; de Castro, A.I.; Kelly, M.; Lopez-Granados, F. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE 2013, 8, e77151. [Google Scholar] [CrossRef] [PubMed]
- Whiteside, T.G.; Bartolo, R.E. A robust object-based woody cover extraction technique for monitoring mine site revegetation at scale in the monsoonal tropics using multispectral RPAS imagery from different sensors. Int. J. Appl. Earth Observ. Geoinf. 2018, 73, 300–312. [Google Scholar] [CrossRef]
- Zhao, H.; Fang, X.; Ding, H.; Josef, S.; Xiong, L.; Na, J.; Tang, G. Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis. ISPRS Int. J. Geo-Inf. 2017, 6, 157. [Google Scholar] [CrossRef]
- Johansen, K.; Erskine, P.D.; McCabe, M.F. Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines. J. Clean. Prod. 2019, 209, 819–833. [Google Scholar] [CrossRef]
- Ventura, D.; Bruno, M.; Jona Lasinio, G.; Belluscio, A.; Ardizzone, G. A low-cost drone based application for identifying and mapping of coastal fish nursery grounds. Estuar. Coast. Shelf Sci. 2016, 171, 85–98. [Google Scholar] [CrossRef]
- Wundram, D.; Löffler, J. High-resolution spatial analysis of mountain landscapes using a low-altitude remote sensing approach. Int. J. Remote Sens. 2008, 29, 961–974. [Google Scholar] [CrossRef]
- Green, K.; Congalton, R. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; CRC Press: Boca Raton, FL, USA, 2008. [Google Scholar]
- Ammour, N.; Alhichri, H.; Bazi, Y.; Benjdira, B.; Alajlan, N.; Zuair, M. Deep Learning Approach for Car Detection in UAV Imagery. Remote Sens. 2017, 9, 312. [Google Scholar] [CrossRef]
- Bryson, M.; Reid, A.; Ramos, F.; Sukkarieh, S. Airborne vision-based mapping and classification of large farmland environments. J. Field Robot. 2010, 27, 632–655. [Google Scholar] [CrossRef]
- Bryson, M.; Reid, A.; Hung, C.; Ramos, F.T.; Sukkarieh, S. Cost-Effective Mapping Using Unmanned Aerial Vehicles in Ecology Monitoring Applications. In Experimental Robotics; Springer: Berlin/Heidelberg, Germany, 2014; pp. 509–523. [Google Scholar]
- Flynn, K.; Chapra, S. Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle. Remote Sens. 2014, 6, 12815–12836. [Google Scholar] [CrossRef]
- Reis, B.P.; Martins, S.V.; Fernandes Filho, E.I.; Sarcinelli, T.S.; Gleriani, J.M.; Leite, H.G.; Halassy, M. Forest restoration monitoring through digital processing of high resolution images. Ecol. Eng. 2019, 127, 178–186. [Google Scholar] [CrossRef]
- Padro, J.C.; Carabassa, V.; Balague, J.; Brotons, L.; Alcaniz, J.M.; Pons, X. Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery. Sci. Total Environ. 2019, 657, 1602–1614. [Google Scholar] [CrossRef]
- Resop, J.P.; Lehmann, L.; Hession, W.C. Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar. Drones 2019, 3, 35–49. [Google Scholar] [CrossRef]
- Felderhof, L.; Gillieson, D. Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops. Can. J. Remote Sens. 2014, 37, 376–386. [Google Scholar] [CrossRef]
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Buters, T.M.; Bateman, P.W.; Robinson, T.; Belton, D.; Dixon, K.W.; Cross, A.T. Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration. Remote Sens. 2019, 11, 1180. https://doi.org/10.3390/rs11101180
Buters TM, Bateman PW, Robinson T, Belton D, Dixon KW, Cross AT. Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration. Remote Sensing. 2019; 11(10):1180. https://doi.org/10.3390/rs11101180
Chicago/Turabian StyleButers, Todd M., Philip W. Bateman, Todd Robinson, David Belton, Kingsley W. Dixon, and Adam T. Cross. 2019. "Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration" Remote Sensing 11, no. 10: 1180. https://doi.org/10.3390/rs11101180
APA StyleButers, T. M., Bateman, P. W., Robinson, T., Belton, D., Dixon, K. W., & Cross, A. T. (2019). Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration. Remote Sensing, 11(10), 1180. https://doi.org/10.3390/rs11101180