Multi-temporal analysis of forestry and coastal environments using UASs

Remote Sensing

Volumn 10, Issue 1, 2018, Pages

  Pádua, Luís ^a   Hruška, Jonáš ^a   Bessa, José ^a   Adão, Telmo ^a,b   Martins, Luís M ^a,c   Gonçalves, José A ^d,e   Peres, Emanuel ^a,b   Sousa, António M R ^a,b   Castro, João P ^f   Sousa, Joaquim J ^a,b  

^a UNIVERSITY OF TRÁS OS MONTES AND ALTO DOURO   (Portugal)

^b UNIVERSITY OF PORTO   (Portugal)

^c UNIVERSITY OF TRÁS OS MONTES AND ALTO DOURO   (Portugal)

^d UNIVERSITY OF PORTO   (Portugal)

^e UNIVERSITY OF PORTO   (Portugal)

^f INSTITUTO POLITÉCNICO DE BRAGANÇA   (Portugal)

Author keywords

Chestnut trees; Coastal environments; Multi temporal data analysis; Unmanned aerial systems

Indexed keywords

AERIAL PHOTOGRAPHY; ARTIFICIAL INTELLIGENCE; COASTAL ZONES; DECISION SUPPORT SYSTEMS; FORESTRY; FRUITS; IMAGE ENHANCEMENT; ROCK MECHANICS; TIMBER; UNMANNED AERIAL VEHICLES (UAV);

CHESTNUT TREES; COASTAL ENVIRONMENTS; HIGH RESOLUTION IMAGERY; MULTI-TEMPORAL ANALYSIS; MULTI-TEMPORAL DATA ANALYSIS; REMOTE SENSING APPLICATIONS; REMOTE SENSING TECHNOLOGY; UNMANNED AERIAL SYSTEMS;

REMOTE SENSING;

EID: 85040677440     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10010024     Document Type: Article

References (91)

1. Ermacora, G.; Toma, A.; Rosa, S.; Bona, B.; Chiaberge, M.; Silvagni, M.; Gaspardone, M.; Antonini, R. A Cloud Based Service for Management and Planning of Autonomous UAV Missions in Smart City Scenarios. In Proceedings of the Modelling and Simulation for Autonomous Systems: First InternationalWorkshop (MESAS), Rome, Italy, 5-6 May 2014; Hodicky, J., Ed.; Revised Selected Papers; Springer International Publishing: Cham, Switzerland, 2014; pp. 20-26
2. Oleire-Oltmanns, S.; Marzolff, I.; Peter, D.K.; Ries, B.J. Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco. Remote Sens. 2012, 4, 3390-3416
3. Jenkins, D.; Vasigh, B. The Economic Impact of Unmanned Aircraft Systems Integration in the United States. Available online: http://www.auvsi.org/auvsiresources/economicreport (accessed on 23 December 2017)
4. 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
5. Matese, A.; Toscano, P.; Di Gennaro, S.F.; Genesio, L.; Vaccari, F.P.; Primicerio, J.; Belli, C.; Zaldei, A.; Bianconi, R.; Gioli, B. Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens. 2015, 7, 2971-2990
6. Atzberger, C. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sens. 2013, 5, 949-981
7. Mirijovský, J.; Langhammer, J. Multitemporal Monitoring of the Morphodynamics of a Mid-Mountain Stream Using UAS Photogrammetry. Remote Sens. 2015, 7, 8586-8609
8. Tanteri, L.; Rossi, G.; Tofani, V.; Vannocci, P.; Moretti, S.; Casagli, N. Multitemporal UAV Survey for Mass Movement Detection and Monitoring. In Advancing Culture of Living with Landslides: Volume 2 Advances in Landslide Science; Mikos, M., Tiwari, B., Yin, Y., Sassa, K., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 153-161
9. Jomaa, I.; Auda, Y.; Abi Saleh, B.; Hamzé, M.; Safi, S. Landscape spatial dynamics over 38 years under natural and anthropogenic pressures in Mount Lebanon. Landsc. Urban Plan. 2008, 87, 67-75
10. Niethammer, U.; James, M.R.; Rothmund, S.; Travelletti, J.; Joswig, M. UAV-based Remote Sensing of the Super-Sauze Landslide: Evaluation and Results. Eng. Geol. 2012, 128, 2-11
11. Cho, J.; Lim, G.; Biobaku, T.; Kim, S.; Parsaei, H. Safety and Security Management with Unmanned Aerial Vehicle (UAV) in Oil and Gas Industry. Procedia Manuf. 2015, 3, 1343-1349
12. Dooly, G.; Omerdic, E.; Coleman, J.; Miller, L.; Kaknjo, A.; Hayes, J.; Braga, J.; Ferreira, F.; Conlon, H.; Barry, H.; et al. Unmanned Vehicles for Maritime Spill Response Case Study: Exercise Cathach. Mar. Pollut. Bull. 2016, 110, 528-538
13. Funaki, M.; Higashino, S.I.; Sakanaka, S.; Iwata, N.; Nakamura, N.; Hirasawa, N.; Obara, N.; Kuwabara, M. Small Unmanned Aerial Vehicles for Aeromagnetic Surveys and Their Flights in the South Shetland Islands, Antarctica. Polar Sci. 2014, 8, 342-356
14. Turner, I.L.; Harley, M.D.; Drummond, C.D. UAVs for Coastal Surveying. Coast. Eng. 2016, 114, 19-24
15. Candiago, S.; Remondino, F.; De Giglio, M.; Dubbini, M.; Gattelli, M. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sens. 2015, 7, 4026-4047
16. Lisein, J.; Pierrot-Deseilligny, M.; Bonnet, S.; Lejeune, P. A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery. Forests 2013, 4, 922-944
17. Colomina, I.; Molina, P. Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79-97
18. Jia, Y.; Su, Z.; Shen, W.; Yuan, J.; Xu, Z. UAV Remote Sensing Image Mosaic and Its Application in Agriculture. Int. J. Smart Home 2016, 10, 159-170
19. Prabhakar, M.; Prasad, Y.G.; Rao, M.N. Remote Sensing of Biotic Stress in Crop Plants and Its Applications for Pest Management. In Crop Stress and Its Management: Perspectives and Strategies; Venkateswarlu, B., Shanker, A.K., Shanker, C., Maheswari, M., Eds.; Springer: Dordrecht, The Netherlands, 2012; pp. 517-545
20. De Estatística, I.N. Estatísticas Agrícolas; Instituto Nacional de Estatística Statistics Portugal: Lisbon, Portugal, 2016
21. Chen, J.S.; Li, L.; Wang, J.Y.; Barry, D.A.; Sheng, X.F.; Gu, W.Z.; Zhao, X.; Chen, L. Water resources: Groundwater maintains dune landscape. Nature 2004, 432, 459-460, doi:10.1038/432459a
22. Mancini, F.; Dubbini, M.; Gattelli, M.; Stecchi, F.; Fabbri, S.; Gabbianelli, G. Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments. Remote Sens. 2013, 5, 6880-6898
23. Pettorelli, N.; Laurance, W.F.; O'Brien, T.G.; Wegmann, M.; Nagendra, H.; Turner, W. Satellite remote sensing for applied ecologists: Opportunities and challenges. J. Appl. Ecol. 2014, 51, 839-848
24. Ponti, M.P. Segmentation of Low-Cost Remote Sensing Images Combining Vegetation Indices and Mean Shift. IEEE Geosci. Remote Sens. Lett. 2013, 10, 67-70
25. Zhang, C.; Kovacs, J.M. The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review. Precis. Agric. 2012, 13, 693-712
26. Whitehead, K.; Hugenholtz, C.H. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: A review of progress and challenges. J. Unman. Veh. Syst. 2014, 2, 69-85
27. Fraser, R.H.; Olthof, I.; Lantz, T.C.; Schmitt, C. UAV photogrammetry for mapping vegetation in the low-Arctic. Arct. Sci. 2016, 2, 79-102
28. Gevaert, C.M.; Persello, C.; Sliuzas, R.; Vosselman, G. Informal settlement classification using point-cloud and image-based features from UAV data. ISPRS J. Photogramm. Remote Sens. 2017, 125, 225-236
29. Suomalainen, J.; Anders, N.; Iqbal, S.; Roerink, G.; Franke, J.;Wenting, P.; Hünniger, D.; Bartholomeus, H.; Becker, R.; Kooistra, L. A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles. Remote Sens. 2014, 6, 11013-11030
30. Xie, Y.; Sha, Z.; Yu, M. Remote sensing imagery in vegetation mapping: A review. J. Plant Ecol. 2008, 1, 9-23, doi:10.1093/jpe/rtm005
31. Laliberte, A.S.; Winters, C.; Rango, A. UAS remote sensing missions for rangeland applications. Geocarto Int. 2011, 26, 141-156
32. Puliti, S.; Orka, O.H.; Gobakken, T.; Næsset, E. Inventory of Small Forest Areas Using an Unmanned Aerial System. Remote Sens. 2015, 7, 9632-9654
33. Garcia-Torres, L.; Caballero-Novella, J.J.; Gómez-Candón, D.; De-Castro, A.I. Semi-Automatic Normalization of Multitemporal Remote Images Based on Vegetative Pseudo-Invariant Features. PLoS ONE 2014, 9, e91275
34. Jha, A.R. Theory, Design, and Applications of Unmanned Aerial Vehicles; CRC Press: Boca Raton, FL, USA, 2016
35. Watts, A.C.; Ambrosia, V.G.; Hinkley, E.A. Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use. Remote Sens. 2012, 4, 1671-1692
36. Aber, J.S.; Marzolff, I.; Ries, J. Small-Format Aerial Photography: Principles, Techniques and Geoscience Applications; Elsevier Science: Amsterdam, The Netherlands, 2010
37. Gutiérrez, P.A.; López-Granados, F.; Peña-Barragán, J.M.; Jurado-Expósito, M.; Hervás-Martínez, C. Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Comput. Electron. Agric. 2008, 64, 293-306
38. Lan, Y.; Huang, Y.; Martin, D.E.; Hoffmann, W.C. Development of an Airborne Remote Sensing System for Crop Pest Management: System Integration and Verification. Appl. Eng. Agric. 2009, 25, 607-615
39. Fladeland, M.; Sumich, M.; Lobitz, B.; Kolyer, R.; Herlth, D.; Berthold, R.; McKinnon, D.; Monforton, L.; Brass, J.; Bland, G. The NASA SIERRA science demonstration programme and the role of small-medium unmanned aircraft for earth science investigations. Geocarto Int. 2011, 26, 157-163
40. Primicerio, J.; Di Gennaro, S.F.; Fiorillo, E.; Genesio, L.; Lugato, E.; Matese, A.; Vaccari, F.P. A flexible unmanned aerial vehicle for precision agriculture. Precis. Agric. 2012, 13, 517-523
41. Gonzalez-Dugo, V.; Zarco-Tejada, P.; Nicolás, E.; Nortes, P.A.; Alarcón, J.J.; Intrigliolo, D.S.; Fereres, E. Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precis. Agric. 2013, 14, 660-678
42. Tamminga, A.; Hugenholtz, C.; Eaton, B.; Lapointe, M. 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. 2015, 31, 379-391
43. Turner, D.; Lucieer, A.; de Jong, M.S. Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV). Remote Sens. 2015, 7, 1736-1757
44. Gonçalves, J.A.; Bastos, L.; Pinho, J.L.; Granja, H. Digital Aerial Photography to Monitor Changes in Coastal Areas Based on Direct Georeferencing. In Proceedings of the 5th EARSeL Workshop on Remote Sensing of the Coastal Zone, Prague, Czech Republic, 1-3 June 2011; pp. 1-10
45. Gonçalves, J.A.; Henriques, R. UAV Photogrammetry for Topographic Monitoring of Coastal Areas. ISPRS J. Photogramm. Remote Sens. 2015, 104, 101-111
46. Drummond, C.D.; Harley, M.D.; Turner, I.L.; A Matheen, A.N.; Glamore, W.C. UAV Applications to Coastal Engineering. In Proceedings of the Australasian Coasts & Ports Conference, Auckland, New Zealand, 15-18 September 2015; pp. 267-272
47. Messinger, M.; Silman, M. Unmanned Aerial Vehicles for the Assessment and Monitoring of Environmental Contamination: An Example from Coal Ash Spills. Environ. Pollut. 2016, 218, 889-894
48. Pereira, E.; Bencatel, R.; Correia, J.; Félix, L.; Gonçalves, G.; Morgado, J.; Sousa, J. Unmanned Air Vehicles for Coastal and Environmental Research. J. Coast. Res. 2009, II, 1557-1561
49. Hodgson, A.; Kelly, N.; Peel, D. Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study. PLoS ONE 2013, 8, e79556
50. Rhee, D.S.; Kim, Y.D.; Kang, B.; Kim, D. Applications of Unmanned Aerial Vehicles in Fluvial Remote Sensing: An Overview of Recent Achievements. KSCE J. Civ. Eng. 2017, 1-15, doi:10.1007/s12205-017-1862-5
51. Abdullahi, H.S.; Mahieddine, F.; Sheriff, R.E. Technology Impact on Agricultural Productivity: A Review of Precision Agriculture Using Unmanned Aerial Vehicles. In Proceedings of the Wireless and Satellite Systems: 7th International Conference on WiSATS, Bradford, UK, 6-7 July 2015; Pillai, P., Hu, Y.F., Otung, I., Giambene, G., Eds.; Revised Selected Papers; Springer International Publishing: Cham, Switzerland, 2015; pp. 388-400
52. 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. 2012, 37, 376-386
53. Gómez-Candón, D.; De Castro, A.I.; López-Granados, F. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precis. Agric. 2014, 15, 44-56
54. Chu, T.; Starek, M.J.; Brewer, M.J.; Masiane, T.; Murray, S.C. UAS imaging for automated crop lodging detection: a case study over an experimental maize field. In Proceedings of the SPIE Commercial + Scientific Sensing and Imaging, Anaheim, CA, USA, 8 May 2017; Volume 10218, p. 102180E
55. Wei, L.; Yang, B.; Jiang, J.; Cao, G.;Wu, M. Vegetation filtering algorithm for UAV-borne lidar point clouds: A case study in the middle-lower Yangtze River riparian zone. Int. J. Remote Sens. 2017, 38, 2991-3002
56. Bendig, J.; Bolten, A.; Bareth, G. UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth VariabilityMonitoring des Pflanzenwachstums mit Hilfe multitemporaler und hoch auflösender Oberflächenmodelle von Getreidebeständen auf Basis von Bildern aus UAV-Befliegungen. Photogramm. Fernerkund. Geoinf. 2013, 2013, 551-562
57. Vega, F.A.; Ramírez, F.C.; Saiz, ó.P.; Rosúa, F.O. Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosyst. Eng. 2015, 132, 19-27
58. Castaldi, F.; Pelosi, F.; Pascucci, S.; Casa, R. Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precis. Agric. 2016, 18, 1-19
59. Willkomm, M.; Bolten, A.; Bareth, G. Non-destructive monitoring of rice by hyperspectral in-field spectrometry and UAV-based remote sensing: Case study of field-grown rice in north Rhine-Westphalia, Germany. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, XLI-B1, 1071-1077
60. Du, M.; Noguchi, N. Monitoring of Wheat Growth Status and Mapping of Wheat Yield's within-Field Spatial Variations Using Color Images Acquired from UAV-camera System. Remote Sens. 2017, 9, 289
61. Holman, F.H.; Riche, A.B.; Michalski, A.; Castle, M.; Wooster, M.J.; Hawkesford, M.J. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens. 2016, 8, 1031
62. Ballesteros, R.; Ortega, J.F.; Hernández, D.; Moreno, M.á. Characterization of Vitis vinifera L. canopy using unmanned aerial vehicle-based remote sensing and photogrammetry techniques. Am. J. Enol. Vitic. 2015, 21, doi:10.5344/ajev.2014.14070
63. Long, N.; Millescamps, B.; Guillot, B.; Pouget, F.; Bertin, X. Monitoring the topography of a dynamic tidal inlet using UAV imagery. Remote Sens. 2016, 8, 387
64. Lucieer, A.; Jong, S.M.D.; Turner, D. Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Prog. Phys. Geogr. 2014, 38, 97-116
65. Guerra-Hernández, J.; González-Ferreiro, E.; Monleón, V.J.; Faias, S.P.; Tomé, M.; Díaz-Varela, R.A. Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands. Forests 2017, 8, 300
66. Wang, J.; Ge, Y.; Heuvelink, G.B.M.; Zhou, C.; Brus, D. Effect of the sampling design of ground control points on the geometric correction of remotely sensed imagery. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 91-100
67. Fryskowska, A.; Kedzierski, M.; Grochala, A.; Braula, A. Calibration of low cost RGB and NIR UAV cameras. In Proceedings of the the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, 12-19 July 2016; Volume XLI-B1, pp. 817-821
68. James, M.R.; Robson, S. Mitigating Systematic Error in Topographic Models Derived from UAV and Ground-based Image Networks. Earth Surf. Process. Landf. 2014, 39, 1413-1420
69. Martha, T.R.; Kerle, N.; van Westen, C.J.; Jetten, V.; Vinod Kumar, K. Effect of Sun Elevation Angle on DSMs Derived from Cartosat-1 Data. Photogramm. Eng. Remote Sens. 2010, 76, 429-438
70. Gomes-Laranjo, J.; Dinis, L.T.; Martins, L.; Portela, E.; Pinto, T.; Ara, M.C.; Díaz, I.F.; Majada, J.; Peixoto, F.; Lorenzo, S.P.; et al. Characterization of Chestnut Behavior with Photosynthetic Traits. In Applied Photosynthesis; InTech: Rijeka, Croatia, 2012
71. Santos, C.; Zhebentyayeva, T.; Serrazina, S.; Nelson, C.D.; Costa, R. Development and characterization of EST-SSR markers for mapping reaction to Phytophthora cinnamomi in Castanea spp. Sci. Hortic. 2015, 194, 181-187
72. Robin, C.; Lanz, S.; Soutrenon, A.; Rigling, D. Dominance of natural over released biological control agents of the chestnut blight fungus Cryphonectria parasitica in south-eastern France is associated with fitness-related traits. Biol. Control 2010, 53, 55-61
73. Ambrosini, I.; Gherardi, L.; Viti, M.L.; Maresi, G.; Turchetti, T. Monitoring Diseases of Chestnut Stands by Small Format Aerial Photography. Geocarto Int. 1997, 12, 41-46
74. DRAPN. Plano de Ação Nacional Para o Controlo do Inseto Dryocosmus Kuriphilus YASUMATSU (Vespa das Galhas do Castanheiro); Direção Regional de Agricultura e Pescas do Norte: Mirandela, Portugal, 2014
75. Sartor, C.; Dini, F.; Marinoni, D.T.; Mellano, M.; Beccaro, G.; Alma, A.; Quacchia, A.; Botta, R. Impact of the Asian wasp Dryocosmus kuriphilus (Yasumatsu) on cultivated chestnut: Yield loss and cultivar susceptibility. Sci. Hortic. 2015, 197, 454-460
76. Mozas-Calvache, A.T.; Pérez-García, J.L.; Cardenal-Escarcena, F.J.; Mata-Castro, E.; Delgado-García, J. Method for photogrammetric surveying of archaeological sites with light aerial platforms. J. Archaeol. Sci. 2012, 39, 521-530
77. Xiang, H.; Tian, L. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosyst. Eng. 2011, 108, 174-190
78. Laliberte, A.S.; Herrick, J.E.; Rango, A.; Winters, C. Acquisition, Orthorectification, and Object-based Classification of Unmanned Aerial Vehicle (UAV) Imagery for Rangeland Monitoring. Photogramm. Eng. Remote Sens. 2010, 76, 661-672
79. ICNF. Relatório Final IFN5-FloreStat; ICNF: Lisbon, Portugal, 2010
80. Soares, A. Geoestatística Para as Ciências da Terra e do Ambiente; IST-Instituto Superior Técnico: Lisbon, Portugal, 2000
81. Sousa, A.; Muge, F. Elementos de Geoestatística; IST-Instituto Superior Técnico: Lisbon, Portugal, 1990
82. Dong, X.; Zhou, Y.; Ren, Z.; Zhong, Y. Time-varying Formation Control for Unmanned Aerial Vehicles with Switching Interaction Topologies. Control Eng. Pract. 2016, 46, 26-36
83. Bounous, G.; Conedera, M. Il Castagno: Risorsa Multifunzionale in Italia e nel Mondo, 1st ed.; Edagricole: Bologna, Italy, 2014; from 2002, now updated and enlarged
84. Martins, L.; Castro, J.; Marques, C.; Abreu, C. Assessment of the spread of chestnut ink disease from 1995 to 2005 using aerial photography and geostatistical methods. Int. Chestnut Symp. 2009, 844, 349-354
85. Gehring, E.; Pezzatti, G.B.; Krebs, P.; Mazzoleni, S.; Conedera, M. On the applicability of the pipe model theory on the chestnut tree (Castanea sativa Mill.). Trees 2015, 29, 321-332
86. Martins, L.M.; Lufinha, M.I.; Marques, C.P.; Abreu, C.G. Small format aerial photography to assess chestnut ink disease. For. Snow Landsc. Res. 2001, 73, 357-360
87. Teodoro, A.; Taveira-Pinto, F.; Santos, I. Morphological and statistical analysis of the impact of breakwaters under construction on a sand spit area (Douro River estuary). J. Coast. Conserv. 2014, 18, 177-191
88. Bio, A.; Bastos, L.; Granja, H.; Pinho, L.; Gonçalves, J.; Henriques, R.; Rodrigues, D. Methods for Coastal Monitoring and Erosion Risk Assessment: Two Portuguese Case Studies. J. Integr. Coast. Zone Manag. 2015, 15, 47-63
89. Dandois, P.J.; Olano, M.; Ellis, C.E. Optimal Altitude, Overlap, andWeather Conditions for Computer Vision UAV Estimates of Forest Structure. Remote Sens. 2015, 7, 13895-13920
90. Troen, I.; Lundtang Petersen, E. European Wind Atlas; Riso National Laboratory: Roskilde, Denmark, 1989
91. Rehak, M.; Skaloud, J. Fixed-wing micro aerial vehicle for accurate corridor mapping. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, II-1/W1, 23-31