Remote sensing of the environment with small unmanned aircraft systems (Uass), part 2: Scientific and commercial applications

Journal of Unmanned Vehicle Systems

Volumn 2, Issue 3, 2014, Pages 86-102

  Whitehead, Ken ^a   Hugenholtz, Chris H ^a   Myshak, Stephen ^b   Brown, Owen ^b   Leclair, Adam ^a   Tamminga, Aaron ^c   Barchyn, Thomas E ^a   Moorman, Brian ^a   Eaton, Brett ^c  

^a UNIVERSITY OF CALGARY   (Canada)

^b Isis Geomatics   (Canada)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Case studies; Remote sensing; Remote sensing of the environment; Remotely piloted aircraft; UAS; Unmanned aerial vehicles

Indexed keywords

AGRICULTURAL ROBOTS; ANIMALS; CROPS; DECISION MAKING; UNMANNED AERIAL VEHICLES (UAV);

AGRICULTURAL CROPS; COMMERCIAL APPLICATIONS; CONCENTRATED ANIMAL FEEDING OPERATIONS; LANDSCAPE DYNAMICS; PRACTICAL GUIDANCE; REMOTE SENSING APPLICATIONS; REMOTE SENSING PLATFORMS; SMALL UNMANNED AIRCRAFTS;

REMOTE SENSING;

EID: 85101255199     PISSN: None     EISSN: 22913467     Source Type: Journal    
DOI: 10.1139/juvs-2014-0007     Document Type: Article

References (59)

1. Adams River Salmon Society. 2013. 2010 Late Run Sockeye Salmon Preliminary Escapement Estimates. Available from http://www.salmonsociety.com/DFO%20salmon%20count.pdf. Accessed 26 June 2014.
2. Baluja, J., Diago, M.P., Balda, P., Zorer, R., Meggio, F., Morales, F., and Tardaguila, J. 2012. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrig. Sci. 30(6): 511–522. doi: 10.1007/s00271-012-0382-9
3. Bellvert, J., Zarco-Tejada, P.J., Girona, J., and Fereres, E. 2013. Mapping crop water stress index in a Pinot-noir vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture. 15(4): 361– 376. doi: 10.1007/s11119-013-9334-5
4. Bendig, J., Bolten, A., and Bareth, G. 2013. UAV-based Imaging for Multi-Temporal, very high resolution crop surface models to monitor crop growth variability. Photogrammetrie-Fernerkundung-Geoinformation. 2013(6): 551–562
5. Berni, J., Zarco-Tejada, P.J., Suarez, L., and Fereres, E. 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 47(3): 722–738. doi: 10.1109/TGRS.2008.2010457
6. Calderón, R., Navas-Cortés, J.A., Lucena, C., and Zarco-Tejada, P.J. 2013. 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. 139: 231–245. doi: 10.1016/j.rse.2013.07.031
7. Chabot, D., and Bird, D.M. 2012. Evaluation of an off-the-shelf unmanned aircraft system for surveying flocks of geese. Waterbirds. 35(1): 170–174. doi: 10.1675/063.035.0119
8. Corcóles, J.I., Ortega, J.F., Hernández, D., and Moreno, M.A. 2013. Estimation of leaf area index in onion (Allium cepa) using an unmanned aerial vehicle. Biosystems Eng. 115(1): 31–42. doi: 10.1016/j.biosystemseng.2013.02.002
9. Crosetto, M., and Aragues, F.P. 2000. Radargrammetry and SAR interferometry for DEM generation validation and data fusion. Proceedings of the CEOS SAR Workshop, Toulouse, 26–29 October 1999, ESA SP-450. Available from http://icc.cat/index.php/content/download/3860/12916/file/radargrammetry_and_sar_interferommetry.pdf. Accessed 26 June 2014.
10. Duan, S.B., Li, Z.L., Wu, H., Tang, B.H., Ma, L., Zhao, E., and Li, C. 2014. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 26: 12–20. doi: 10.1016/j.jag.2013.05.007
11. Eisenbeiss, H. 2009. UAV photogrammetry. Eidgenössische Technische Hochschule, Zürich.
12. Fonstad, M.A., Dietrich, J.T., Courville, B.C., Jensen, J.L., and Carbonneau, P.E. 2013. Topographic structure from motion: a new development in photogrammetric measurement. Earth Surf. Processes Landforms. 38(4): 421–430. doi: 10.1002/esp.3366
13. Foster, M.A., and Stubbendieck, J. 1980. Effects of the plains pocket gopher (Geomys bursarius) on rangeland. J. Range Manage. 33(1): 74–78. doi: 10.2307/3898233
14. Garcia-Ruiz, F., Sankaran, S., Maja, J.M., Lee, W.S., Rasmussen, J., and Ehsani, R. 2013. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Comput. Electron. Agric. 91(0): 106–115. doi: 10.1016/j.compag.2012.12.002
15. Gersher, S. 2014. Canada’s domestic regulatory framework for RPAS. A call for public deliberation. J. Unmanned Veh. Sys. 2(1): 1–4. doi: 10.1139/juvs-2013-0016
16. Glennie, C.L., Carter, W.E., Shrestha, R.L., and Dietrich, W.E. 2013. Geodetic imaging with airborne LiDAR: the Earth’s surface revealed. Rep. Prog. Phys. 76(8): 086801. PMID: 23828665. doi: 10.1088/0034-4885/76/8/086801
17. Gonzalez-Dugo, V., Zarco-Tejada, P., Nicolás, E., Nortes, P.A., Alarcón, J.J., Intrigliolo, D.S., and Fereres, E. 2013. Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture. 14(6): 660–678. doi: 10.1007/s11119-013-9322-9
18. Grant, W.E., French, N.R., and Folse, L.J. Jr. 1980. Effects of pocket gopher mounds on plant production in shortgrass prairie ecosystems. Southwestern Naturalist. 25(2): 215–224. doi: 10.2307/3671243
19. Haala, N., Cramer, M., Weimer, F., and Trittler, M. 2011. Performance test on UAV-based photogrammetric data collection. Proceedings of the International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences. 38(1/C22): 7–12
20. Hardin, P.J., and Jensen, R.R. 2011. Small-scale unmanned aerial vehicles in environmental remote sensing: challenges and opportunities. GIScience & Remote Sensing. 48(1): 99–111
21. Hobbs, R.J., and Mooney, H.A. 1985. Community and population dynamics of serpentine grassland annuals in relation to gopher disturbance. Oecologia. 67(3): 342–351. doi: 10.1007/BF00384939
22. Hodgson, A., Kelly, N., and Peel, D. 2013. Unmanned aerial vehicles (UAVs) for surveying marine fauna: a dugong case study. PLoS One. 8(11): e79556. PMID: 24223967. doi: 10.1371/journal.pone.0079556
23. Huang, Y., Thomson, S.J., Hoffmann, W.C., Lan, Y., and Fritz, B.K. 2013. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. Int. J. Agric. Biol. Eng. 6(3): 1–10
24. Hugenholtz, C.H., Moorman, B.J., Riddell, K., and Whitehead, K. 2012. Small unmanned aircraft systems for remote sensing and earth science research. Eos, Transactions American Geophysical Union. 93(25): 236, doi: 10.1029/2012EO250005
25. Hugenholtz, C.H., Whitehead, K., Barchyn, T.E., Brown, O.W., Moorman, B.J., LeClair, A., Hamilton, T., and Riddell, K. 2013. Geomor-phological mapping with a small unmanned aircraft system (sUAS): feature detection and accuracy assessment of a photogram-metrically-derived digital terrain model. Geomorphology. 194: 16–24. doi: 10.1016/j.geomorph.2013.03.023
26. Hunt, E.R., Hively, W.D., Fujikawa, S.J., Linden, D.S., Daughtry, C.S.T., and McCarty, G.W. 2010. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens. 2(1): 290–305. doi: 10.3390/rs2010290
27. Huntly, N., and Inouye, R. 1988. Pocket gophers in ecosystems: patterns and mechanisms. BioScience. 38(11): 786–793. doi: 10.2307/1310788
28. Jones, G.P., Pearlstine, L.G., and Percival, H.F. 2006. An assessment of small unmanned aerial vehicles for wildlife research. Wildl. Soc. Bull. 34(3): 750–758. doi: 10.2193/0091-7648(2006)34[750:AAOSUA]2.0.CO;2
29. Knoth, C., Klein, B., Prinz, T., and Kleinebecker, T. 2013. Unmanned aerial vehicles as innovative remote sensing platforms for high-resolution infrared imagery to support restoration monitoring in cut-over bogs. Appl. Veg. Sci. 16(3): 509–517. doi: 10.1111/avsc.12024
30. Laliberte, A.S., Herrick, J.E., Rango, A., and Winters, C. 2010. Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogramm. Eng. Remote Sens. 76(6): 661–672. doi: 10.14358/PERS.76.6.661
31. Laliberte, A.S., and Rango, A. 2011. Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands. GIScience & Remote Sensing. 48(1): 4–23. doi: 10.2747/1548-1603.48.1.4
32. Lin, Y., Hyyppa, J., and Jaakkola, A. 2011. Mini-UAV-borne LIDAR for fine-scale mapping. IEEE Geosci. Remote Sens. Lett. 8(3): 426– 430. doi: 10.1109/LGRS.2010.2079913
33. Lucieer, A., de Jong, S., and Turner, D. 2014. Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Prog. Phys. Geog. 38(1): 97–116. doi: 10.1177/0309133313515293
34. Martinez-De Dios, J.R., and Ollero, A. 2006. Automatic detection of windows thermal heat losses in buildings using UAVs. Automation Congress, 2006. WAC ’06. doi: 10.1109/WAC.2006.375998.
35. Mulero-Pazmany, M., Stolper, R., van Essen, L.D., Negro, J.J., and Sassen, T. 2014. Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS One. 9(1): e83873. PMID: 24416177. doi: 10.1371/journal.pone.0083873
36. Mulla, D.J. 2013. Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosystems Eng. 114(4): 358–371. doi: 10.1016/j.biosystemseng.2012.08.009
37. Niethammer, U., James, M.R., Rothmund, S., Travelletti, J., and Joswig, M. 2012. UAV-based remote sensing of the Super-Sauze landslide: evaluation and results. Eng. Geol. 128: 2–11. doi: 10.1016/j.enggeo.2011.03.012
38. Reichman, O.J., Bendix, J.H., and Seastedt, T.R. 1993. Distinct animal-generated edge effects in a tallgrass prairie community. Ecology. 74(4): 1281–1285. doi: 10.2307/1940496
39. Richens, V.B. 1966. Notes on the digging activity of a northern pocket gopher. J. Mammal. 47(3): 531–533. doi: 10.2307/1377704
40. Rothmund, S., Niethammer, U., Malet, J.P., and Joswig, M. 2013. Landslide surface monitoring based on UAV-and ground-based images and terrestrial laser scanning: accuracy analysis and morphological interpretation. First Break. 31(8): 81–87
41. Sarda-Palomera, F., Bota, G., Vinolo, C., Pallarés, O., Sazatornil, V., Brotons, L., Gomariz, S., and Sarda, F. 2012. Fine-scale bird monitoring from light unmanned aircraft systems. Ibis. 154(1): 177–183. doi: 10.1111/j.1474–919X.2011.01177.x
42. Sauerbier, M., Siegrist, E., Eisenbeiss, H., and Demir, N. 2011. The practical application of UAV-based photogrammetry under economic aspects, International Archives of the Photogrammetry.. Remote Sensing and Spatial Information Sciences. ISPRS Zurich 2011 Workshop, 14–16 September 2011. 38(1/C22): 45–50
43. Spencer, S.R., Cameron, G.N., Eshelman, B.D., Cooper, L.C., and Williams, L.R. 1985. Influence of pocket gopher mounds on a Texas coastal prairie. Oecologia. 66(1): 111–115. doi: 10.1007/BF00378561
44. Stagakis, S., González-Dugo, V., Cid, P., Guillén-Climent, M.L., and Zarco-Tejada, P.J. 2012. Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS J. Photogramm. Remote Sens. 71: 47–61. doi: 10.1016/j.isprsjprs.2012.05.003
45. Tamminga, A., Hugenholtz, C., Eaton, B., and Lapointe, M. 2014. Hyperspatial remote sensing of channel reach morphology and hydraulic fish habitat using an unmanned aerial vehicle (UAV): a first assessment in the context of river research and management. River Res. Appl. doi: 10.1002/rra.2743
46. Transport Canada. 2008. The review and processing of an application for a Special Flight Operations Certificate for the Operation of an Unmanned Air Vehicle (UAV) System. Available from http://www.tc.gc.ca/eng/civilaviation/opssvs/managementservices-referen-cecentre-documents-600623-001-972.htm. Accessed 26 June 2014.
47. Transport Canada. 2012. UAV systems program design working group phase 1 final report. Available from http://www.h-a-c.ca/UAV_ REPORT.pdf. Accessed 26 June 2014.
48. Turner, D., Lucieer, A., and Wallace, L. 2014. Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Trans. Geosci. Remote Sens. 52(5): 2738–2745. doi: 10.1109/TGRS.2013.2265295
49. Turner, D., Lucieer, A., and Watson, C. 2012. 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. 4(5): 1392–1410. doi: 10.3390/rs4051392
50. Turner, G.T., Hansen, R.M., Reid, V.H., Tietjen, H.P., and Ward, A.L. 1973. Pocket gophers and Colorado mountain rangeland. Colorado State University Agriculture Experimental Station Bulletin. 554S: 90p.
51. Urbahs, A., and Jonaite, I. 2013. Features of the use of unmanned aerial vehicles for agriculture applications. Aviation. 17(4): 170–175. doi: 10.3846/16487788.2013.861224
52. Vermeulen, C.D., Lejeune, P., Lisein, J., Sawadogo, P., and Bouché, P. 2013. Unmanned aerial survey of elephants. PLoS One. 8(2): e54700. PMID: 23405088. doi: 10.1371/journal.pone.0054700
53. Wallace, L., Lucieer, A., Watson, C., and Turner, D. 2012. Development of a UAV-LiDAR system with application to forest inventory. Remote Sens. 4(6): 1519–1543. doi: 10.3390/rs4061519
54. Watts, A.C., Ambrosia, V.G., and Hinkley, E.A. 2012. Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sens. 4(6): 1671–1692. doi: 10.3390/rs4061671
55. Westaway, R.M., Lane, S.N., and Hicks, D.M. 2000. The development of an automated correction procedure for digital photogrammetry for the study of wide, shallow, gravel-bed rivers. Earth Surf. Processes Landforms. 25(2): 209–226. doi: 10.1002/(SICI)1096-9837(200002)25:2<209::AID-ESP84>3.0.CO;2-Z
56. Whitehead, K., and Hugenholtz, C.H. 2014. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges. J. Unmanned Veh. Syst. This issue. doi: 10.1139/juvs-2014-0006
57. Whitehead, K., Moorman, B.J., and Hugenholtz, C.H. 2013. Low-cost, on-demand aerial photogrammetry for glaciological measurement. The Cryosphere. 7(6): 1879–1884. doi: 10.5194/tc-7-1879-2013
58. Zarco-Tejada, P.J., Guillén-Climent, M.L., Hernández-Clemente, R., Catalina, A., González, M.R., and Martín, P. 2013. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric. For. Meteorol. 171: 281–294. doi: 10.1016/j.agrformet.2012.12.013
59. Zhang, C., and Kovacs, J.M. 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture. 13(6): 693–712. doi: 10.1007/s11119-012-9274-5