UAS, sensors, and data processing in agroforestry: a review towards practical applications

References

• Ambrosia, V. G., S. Wegener, T. Zajkowski, D. V. Sullivan, S. Buechel, F. Enomoto, … E. Hinkley. 2011. “The Ikhana Unmanned Airborne System (UAS) Western States Fire Imaging Missions: From Concept to Reality (2006–2010).” Geocarto International 26 (2): 85–101. doi:10.1080/10106049.2010.539302.
   Web of Science ®Google Scholar
• Amon, P., U. Riegl, P. Rieger, and M. Pfennigbauer, & OTHERS. (2015). UAV-Based Laser Scanning to Meet Special Challenges in LiDAR Surveying. Retrieved from http://www.ee.co.za/wp-content/uploads/2015/08/Philipp-Amon-Ursula-Riegl-Peter-Rieger-Martin-Pfenningbauer.pdf
   Google Scholar
• Anderson, K., and K. J. Gaston. 2013. “Lightweight Unmanned Aerial Vehicles will Revolutionize Spatial Ecology.” Frontiers in Ecology and the Environment 11 (3): 138–146. doi:10.1890/120150.
   Web of Science ®Google Scholar
• Austin, R. (2010). Unmanned Aircraft Systems: UAVS Design, Development and Deployment - Reg Austin. Retrieved July 8, 2016, from http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470058196.html
   Google Scholar
• AUVSI Economic Report, 2013. The Economic Impact of Unmanned Aircraft Systems Integration in the United States. Available online at: https://higherlogicdownload.s3.amazonaws.com/AUVSI/958c920a-7f9b-4ad2-9807-f9a4e95d1ef1/UploadedImages/New_Economic%20Report%202013%20Full.pdf (accessed 17 January 2017).
   Google Scholar
• Ballesteros, R., J. F. Ortega, D. Hernández, and M. Á. Moreno. 2015. “Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques.” American Journal of Enology and Viticulture, ajev.2014.14070. 10.5344/ajev.2014.14070.
   Web of Science ®Google Scholar
• Baluja, J., M. P. Diago, P. Balda, R. Zorer, F. Meggio, F. Morales, and J. Tardaguila. 2012. “Assessment of Vineyard Water Status Variability by Thermal And Multispectral Imagery Using an Unmanned Aerial Vehicle (UAV).” Irrigation Science 30 (6): 511–522. doi:10.1007/s00271-012-0382-9.
   Web of Science ®Google Scholar
• Bareth, G., J. Bendig, N. Tilly, D. Hoffmeister, H. Aasen, and A. Bolten. 2016. “A Comparison of UAV- and TLS-derived Plant Height for Crop Monitoring: Using Polygon Grids for the Analysis of Crop Surface Models (CSMs).” Photogrammetrie - Fernerkundung - Geoinformation 2016 (2): 85–94. doi:10.1127/pfg/2016/0289.
   Web of Science ®Google Scholar
• Bellvert, J., and J. Girona. 2012. “The Use of Multispectral and Thermal Images as a Tool for Irrigation Scheduling in Vineyards.” In The Use of Remote Sensing and Geographic Information Systems for Irrigation Management in Southwest Europe», eds M. Erena, A. López-Francos, S. Montesinos, and J. F. Y Berthoumieu, 131–138, Zaragoza: Options Méditerranéennes. Serie B: Studies and Researchs, (67).
   Google Scholar
• Bellvert, J., J. Marsal, J. Girona, V. Gonzalez-Dugo, E. Fereres, S. L. Ustin, and P. J. Zarco-Tejada. 2016. “Airborne Thermal Imagery to Detect the Seasonal Evolution of Crop Water Status in Peach, Nectarine and Saturn Peach Orchards.” Remote Sensing 8 (1): 39. doi:10.3390/rs8010039.
   Web of Science ®Google Scholar
• Bellvert, J., P. J. Zarco-Tejada, J. Girona, and E. Fereres. 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.
   Web of Science ®Google Scholar
• Bendig, J., A. Bolten, S. Bennertz, J. Broscheit, S. Eichfuss, and G. Bareth. 2014. “Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging.” Remote Sensing 6 (11): 10395–10412. doi:10.3390/rs61110395.
   Web of Science ®Google Scholar
• Bendig, J., K. Yu, H. Aasen, A. Bolten, S. Bennertz, J. Broscheit, … G. Bareth. 2015. “Combining UAV-Based Plant Height from Crop Surface Models, Visible, and Near Infrared Vegetation Indices for Biomass Monitoring in Barley.” International Journal of Applied Earth Observation and Geoinformation 39: 79–87. doi:10.1016/j.jag.2015.02.012.
   Web of Science ®Google Scholar
• Bendig, J. V. 2015. Unmanned Aerial Vehicles (UAVs) for Multi-Temporal Crop Surface Modelling. A New Method for Plant Height and Biomass Estimation Based on RGB-Imaging. Cologne: Universität zu Köln. Retrieved from: http://kups.ub.uni-koeln.de/6018/.
   Google Scholar
• Berni, J. A. J., P. J. Zarco-Tejada, L. Suarez, and E. Fereres. 2009. “Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial Vehicle.” IEEE Transactions on Geoscience and Remote Sensing 47 (3): 722–738. doi:10.1109/TGRS.2008.2010457.
   Web of Science ®Google Scholar
• Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
   Web of Science ®Google Scholar
• Bobillet, W., J. P. Da Costa, C. Germain, O. Lavialle, and G. Grenier (2003). Row Detection in High Resolution Remote Sensing Images of Vine Fields. In Precision Agriculture: Papers from the 4th European Conference on Precision Agriculture (pp. 81–87). Berlin, Germany.
   Google Scholar
• Bramley, R. G. V., and R. P. Hamilton. 2007. “Terroir and Precision Viticulture: Are They Compatible?” Journal International Des Sciences de La Vigne et Du Vin 41 (1): 1.
   Google Scholar
• Calderón, R., J. A. Navas-Cortés, C. Lucena, and P. J. Zarco-Tejada. 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 Sensing of Environment 139: 231–245. doi:10.1016/j.rse.2013.07.031.
   Web of Science ®Google Scholar
• Calderón, R., J. A. Navas-Cortés, and P. J. Zarco-Tejada. 2015. “Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas.” Remote Sensing 7 (5): 5584–5610. doi:10.3390/rs70505584.
   Web of Science ®Google Scholar
• Candiago, S., F. Remondino, M. De Giglio, M. Dubbini, and M. Gattelli. 2015. “Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images.” Remote Sensing 7 (4): 4026–4047. doi:10.3390/rs70404026.
   Web of Science ®Google Scholar
• Caturegli, L., M. Corniglia, M. Gaetani, N. Grossi, S. Magni, M. Migliazzi, … M. Volterrani. 2016. “Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses.” PLoS One 11 (6): e0158268. doi:10.1371/journal.pone.0158268.
   PubMed Web of Science ®Google Scholar
• Chisholm, R. A., J. Cui, S. K. Y. Lum, and B. M. Chen. 2013. “UAV LiDAR for Below-Canopy Forest Surveys.” Journal of Unmanned Vehicle Systems 1 (1): 61–68. doi:10.1139/juvs-2013-0017.
   Google Scholar
• Chuvieco, E., M. P. Martín, and A. Palacios. 2002. “Assessment of Different Spectral Indices in the Red-Near-Infrared Spectral Domain for Burned Land Discrimination.” International Journal of Remote Sensing 23 (23): 5103–5110. doi:10.1080/01431160210153129.
   Web of Science ®Google Scholar
• Cohen, Y., V. Alchanatis, M. Meron, Y. Saranga, and J. Tsipris. 2005. “Estimation of Leaf Water Potential by Thermal Imagery and Spatial Analysis.” Journal of Experimental Botany 56 (417): 1843–1852. doi:10.1093/jxb/eri174.
   PubMed Web of Science ®Google Scholar
• Colomina, I., and P. Molina. 2014. “Unmanned Aerial Systems For Photogrammetry and Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 92: 79–97. doi:10.1016/j.isprsjprs.2014.02.013.
   Web of Science ®Google Scholar
• Comba, L., P. Gay, J. Primicerio, and D. Ricauda Aimonino. 2015. “Vineyard Detection from Unmanned Aerial Systems Images.” Computers and Electronics in Agriculture 114: 78–87. doi:10.1016/j.compag.2015.03.011.
   Web of Science ®Google Scholar
• Cracknell, A. P. 2017. “UAVs: Regulations and Law Enforcement.” International Journal of Remote Sensing 38: 3054–3067.
   Web of Science ®Google Scholar
• DroneDeploy (2016). Commercial Drone Industry Trends. DroneDeploy’s Commercial Drone Industry Trends. Retrivied from http://info.dronedeploy.com/commercial-drone-industry-trends/.
   Google Scholar
• Díaz-Varela, R. A., R. De La Rosa, L. León, and P. J. Zarco-Tejada. 2015. “High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials.” Remote Sensing 7 (4): 4213–4232. doi:10.3390/rs70404213.
   Web of Science ®Google Scholar
• Falkowski, M. J., P. E. Gessler, P. Morgan, A. T. Hudak, and A. M. S. Smith.; (2005). Characterizing and Mapping Forest Fire Fuels Using ASTER Imagery And Gradient Modeling. Retrieved from http://www.treesearch.fs.fed.us/pubs/23874 10.1016/j.foreco.2005.06.013
   Google Scholar
• Flener, C., M. Vaaja, A. Jaakkola, A. Krooks, H. Kaartinen, A. Kukko, … P. Alho. 2013. “Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography.” Remote Sensing 5 (12): 6382–6407. doi:10.3390/rs5126382.
   Web of Science ®Google Scholar
• Frankenberger, J. R., C. Huang, and K. Nouwakpo. 2008. “Low-Altitude Digital Photogrammetry Technique to Assess Ephemeral Gully Erosion.” In IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium 4: IV-117-IV-120. doi:10.1109/IGARSS.2008.4779670.
   Google Scholar
• Galiano, S. G. 2012. “Assessment of Vegetation Indexes from Remote Sensing: Theoretical Basis.” Paseo Alfonso XIII 52: 65–75.
   Google Scholar
• Gamon, J. A., J. Peñuelas, and C. B. Field. 1992. “A Narrow-Waveband Spectral Index that Tracks Diurnal Changes In Photosynthetic Efficiency.” Remote Sensing of Environment 41 (1): 35–44. doi:10.1016/0034-4257(92)90059-S.
   Web of Science ®Google Scholar
• Gao, B. 1996. “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58 (3): 257–266. doi:10.1016/S0034-4257(96)00067-3.
   Web of Science ®Google Scholar
• García, M. J. L., and V. Caselles. 1991. “Mapping Burns and Natural Reforestation Using Thematic Mapper Data.” Geocarto International 6 (1): 31–37. doi:10.1080/10106049109354290.
   Google Scholar
• Gatziolis, D., J. F. Lienard, A. Vogs, and N. S. Strigul. 2015. “3D Tree Dimensionality Assessment Using Photogrammetry and Small Unmanned Aerial Vehicles.” PLoS One 10 (9): e0137765. doi:10.1371/journal.pone.0137765.
   PubMed Web of Science ®Google Scholar
• Geipel, J., G. G. Peteinatos, W. Claupein, and R. Gerhards. 2013. “Enhancement of Micro Unmanned Aerial Vehicles for Agricultural Aerial Sensor Systems.” In Precision Agriculture ’13, Ed. J. V. Stafford, 161–167. Wageningen, The Netherlands: Wageningen Academic Publishers. doi:10.3920/978-90-8686-778-3_18.
   Google Scholar
• George, E. A., G. Tiwari, R. N. Yadav, E. Peters, and S. Sadana (2013). UAV Systems for Parameter Identification in Agriculture. In Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS), 2013 IEEE (pp. 270–273). Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6629929
   Google Scholar
• Gertler, J. (2012). US Unmanned Aerial Systems. DTIC Document. Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA566235
   Google Scholar
• Getzin, S., K. Wiegand, and I. Schöning. 2012. “Assessing Biodiversity in Forests Using Very High-Resolution Images and Unmanned Aerial Vehicles.” Methods in Ecology and Evolution 3 (2): 397–404. doi:10.1111/j.2041-210X.2011.00158.x.
   Web of Science ®Google Scholar
• Gitelson, A. A., Y. J. Kaufman, and M. N. Merzlyak. 1996. “Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS.” Remote Sensing of Environment 58 (3): 289–298. doi:10.1016/S0034-4257(96)00072-7.
   Web of Science ®Google Scholar
• Gitelson, A. A., Y. J. Kaufman, R. Stark, and D. Rundquist. 2002. “Novel Algorithms for Remote Estimation of Vegetation Fraction.” Remote Sensing of Environment 80 (1): 76–87. doi:10.1016/S0034-4257(01)00289-9.
   Web of Science ®Google Scholar
• Gnyp, M. L., G. Bareth, F. Li, V. I. S. Lenz-Wiedemann, W. Koppe, Y. Miao, … F. Zhang. 2014. “Development and Implementation of a Multiscale Biomass Model Using Hyperspectral Vegetation Indices for Winter Wheat in the North China Plain.” International Journal of Applied Earth Observation and Geoinformation 33: 232–242. doi:10.1016/j.jag.2014.05.006.
   Web of Science ®Google Scholar
• Gómez-Candón, D., A. I. D. Castro, and F. López-Granados. 2013. “Assessing the Accuracy of Mosaics from Unmanned Aerial Vehicle (UAV) Imagery for Precision Agriculture Purposes in Wheat.” Precision Agriculture 15 (1): 44–56. doi:10.1007/s11119-013-9335-4.
   Web of Science ®Google Scholar
• Gonzalez-Dugo, V., P. Zarco-Tejada, E. Nicolás, P. A. Nortes, J. J. Alarcón, D. S. Intrigliolo, and E. Fereres. 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.
   Web of Science ®Google Scholar
• Grenzdörffer, G. J., A. Engel, and B. Teichert. 2008. “The Photogrammetric Potential of Low-Cost UAVs in Forestry and Agriculture.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 31 (B3): 1207–1214.
   Google Scholar
• Haboudane, D., J. R. Miller, N. Tremblay, P. J. Zarco-Tejada, and L. Dextraze. 2002. “Integrated Narrow-Band Vegetation Indices for Prediction of Crop Chlorophyll Content for Application to Precision Agriculture.” Remote Sensing of Environment 81 (2–3): 416–426. doi:10.1016/S0034-4257(02)00018-4.
   Web of Science ®Google Scholar
• Hague, T., N. D. Tillett, and H. Wheeler. 2006. “Automated Crop and Weed Monitoring in Widely Spaced Cereals.” Precision Agriculture 7 (1): 21–32. doi:10.1007/s11119-005-6787-1.
   Web of Science ®Google Scholar
• Hardin, P. J., and R. R. Jensen. 2011. “Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities.” GIScience & Remote Sensing 48 (1): 99–111. doi:10.2747/1548-1603.48.1.99.
   Web of Science ®Google Scholar
• Herwitz, S. R., L. F. Johnson, S. E. Dunagan, R. G. Higgins, D. V. Sullivan, J. Zheng, … J. A. Brass. 2004. “Imaging from an Unmanned Aerial Vehicle: Agricultural Surveillance and Decision Support.” Computers and Electronics in Agriculture 44 (1): 49–61. doi:10.1016/j.compag.2004.02.006.
   Web of Science ®Google Scholar
• Holden, Z. A., P. Morgan, A. M. S. Smith, M. G. Rollins, and P. E. Gessler; (2005). Evaluation of Novel Thermally Enhanced Spectral Indices for Mapping Fire Perimeters and Comparisons with Fire Atlas Data. Retrieved from http://www.treesearch.fs.fed.us/pubs/24608 10.1080/01431160500239008
   Google Scholar
• Honkavaara, E., J. Kaivosoja, J. Mäkynen, I. Pellikka, L. Pesonen, H. Saari, ... T. Rosnell. 2012. “Hyperspectral Reflectance Signatures and Point Clouds for Precision Agriculture by Light Weight UAV Imaging System.” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences I-7: 353–358. doi:10.5194/isprsannals-i-7-353-2012.
   Google Scholar
• Honkavaara, E., H. Saari, J. Kaivosoja, I. Pölönen, T. Hakala, P. Litkey, … L. Pesonen. 2013. “Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture.” Remote Sensing 5 (10): 5006–5039. doi:10.3390/rs5105006.
   Web of Science ®Google Scholar
• Huete, A. R. 1988. “A Soil-Adjusted Vegetation Index (SAVI).” Remote Sensing of Environment 25 (3): 295–309. doi:10.1016/0034-4257(88)90106-X.
   Web of Science ®Google Scholar
• Idso, S. B., R. D. Jackson, P. J. Pinter, R. J. Reginato, and J. L. Hatfield. 1981. “Normalizing the Stress-Degree-Day Parameter for Environmental Variability.” Agricultural Meteorology 24: 45–55. doi:10.1016/0002-1571(81)90032-7.
   Google Scholar
• Israel, M. 2011. “A UAV-Based Roe Deer Fawn Detection System.” International Archives of Photogrammetry and Remote Sensing 38: 1–5.
   Google Scholar
• Jenkins, D., and B. Vasigh (2013, March). Economic Report of Unmanned Aircraft Systems Integration in the United States. Association for Unmanned Vehicle Systems International. Retrieved from http://www.auvsi.org/auvsiresources/economicreport
   Google Scholar
• Jenks, C. 2010. “Law from above: Unmanned Aerial Systems, Use of Force, and the Law of Armed Conflict.” North Dakota Law Review 85: 649.
   Google Scholar
• Jia, Y., Z. Su, Q. Zhang, Y. Zhang, Y. Gu, and Z. Chen. 2015. “Research on UAV Remote Sensing Image Mosaic Method Based on SIFT.” International Journal of Signal Processing, Image Processing and Pattern Recognition 8 (11): 365–374. doi:10.14257/ijsip.2015.8.11.33.
   Google Scholar
• Jones, H. G. 1999. “Use of Infrared Thermometry for Estimation of Stomatal Conductance as a Possible Aid to Irrigation Scheduling.” Agricultural and Forest Meteorology 95 (3): 139–149. doi:10.1016/S0168-1923(99)00030-1.
   Web of Science ®Google Scholar
• Jordan, C. F. 1969. “Derivation of Leaf-Area Index from Quality of Light on the Forest Floor.” Ecology 50 (4): 663–666. doi:10.2307/1936256.
   Web of Science ®Google Scholar
• Justice, C. O., E. Vermote, J. R. G. Townshend, R. Defries, D. P. Roy, D. K. Hall, … M. J. Barnsley. 1998. “The Moderate Resolution Imaging Spectroradiometer (MODIS): Land Remote Sensing for Global Change Research.” IEEE Transactions on Geoscience and Remote Sensing 36 (4): 1228–1249. doi:10.1109/36.701075.
   Web of Science ®Google Scholar
• Juul, M. (2015, October). Civil Drones in the European Union - Think Tank. Retrieved July 19, 2016, from http://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_BRI(2015)571305
   Google Scholar
• Kalisperakis, I., C. Stentoumis, L. Grammatikopoulos, and K. Karantzalos. 2015. “Leaf Area Index Estimation in Vineyards from UAV Hyperspectral Data, 2D Image Mosaics and 3D Canopy Surface Models.” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4: 299–303. doi:10.5194/isprsarchives-XL-1-W4-299-2015.
   Google Scholar
• Klemas, V. V. 2015. “Coastal and Environmental Remote Sensing from Unmanned Aerial Vehicles: An Overview.” Journal of Coastal Research 1260–1267. doi:10.2112/JCOASTRES-D-15-00005.1.
   Web of Science ®Google Scholar
• Lagüela, S., L. Díaz−Vilariño, D. Roca, and H. Lorenzo. 2015. “Aerial Thermography from Low-Cost UAV for the Generation of Thermographic Digital Terrain Models.” Opto-Electronics Review 23 (1): 78–84. doi:10.1515/oere-2015-0006.
   Web of Science ®Google Scholar
• López-López, M., R. Calderón, V. González-Dugo, P. J. Zarco-Tejada, and E. Fereres. 2016. “Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery.” Remote Sensing 8 (4): 276. doi:10.3390/rs8040276.
   Web of Science ®Google Scholar
• Lukas, V., J. Novák, L. Neudert, I. Svobodova, F. Rodriguez-Moreno, M. Edrees, and J. Kren. 2016. “The Combination of UAV Survey and Landsat Imagery for Monitoring of Crop Vigor in Precision Agriculture.” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8: 953–957. doi:10.5194/isprsarchives-XLI-B8-953-2016.
   Google Scholar
• Malinowski, R., B. Höfle, K. Koenig, G. Groom, W. Schwanghart, and G. Heckrath. 2016. “Local-Scale Flood Mapping on Vegetated Floodplains from Radiometrically Calibrated Airborne LiDAR Data.” ISPRS Journal of Photogrammetry and Remote Sensing 119: 267–279. doi:10.1016/j.isprsjprs.2016.06.009.
   Web of Science ®Google Scholar
• Martín Isabel, M., P. Del, and E. Chuvieco Salinero (1998). Cartografía de grandes incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR. Retrieved from http://dspace.uah.es/dspace/handle/10017/1059
   Google Scholar
• Martín, P., P. J. Zarco-Tejada, M. R. González, and A. Berjón. 2015. “Using Hyperspectral Remote Sensing to Map Grape Quality in “Tempranillo” Vineyards Affected by Iron Deficiency Chlorosis.” VITIS - Journal of Grapevine Research 46 (1): 7.
   Google Scholar
• Matese, A., P. Toscano, S. F. Di Gennaro, L. Genesio, F. P. Vaccari, J. Primicerio, … B. Gioli. 2015. “Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture.” Remote Sensing 7 (3): 2971–2990. doi:10.3390/rs70302971.
   Web of Science ®Google Scholar
• Mathews, A. J. 2015. “A Practical Uav Remote Sensing Methodology To Generate Multispectral Orthophotos For Vineyards: Estimation of Spectral Reflectance Using Compact Digital Cameras.” International Journal of Applied Geospatial Research 6(4): 65–87. doi:10.4018/ijagr.2015100104.
   Google Scholar
• Mathews, A. J., and J. L. R. Jensen. 2013. “Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud.” Remote Sensing 5 (5): 2164–2183. doi:10.3390/rs5052164.
   Web of Science ®Google Scholar
• Mejias, L., J. Lai, and T. Bruggemann. 2015. Sensors for Missions. In Handbook of Unmanned Aerial Vehicles (pp. 385–399), Eds. K. P. Valavanis and G. J. Vachtsevanos, Dordrecht, The Netherlands: Springer. Retrieved from: http://link.springer.com/10.1007/978-90-481-9707-1_6.
   Google Scholar
• Merino, L., F. Caballero, J. R. Martínez-De-Dios, I. Maza, and A. Ollero. 2011. “An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement.” Journal of Intelligent & Robotic Systems 65 (1–4): 533–548. doi:10.1007/s10846-011-9560-x.
   Web of Science ®Google Scholar
• Meyer, G. E., and J. C. Neto. 2008. “Verification of Color Vegetation Indices for Automated Crop Imaging Applications.” Computers and Electronics in Agriculture 63 (2): 282–293. doi:10.1016/j.compag.2008.03.009.
   Web of Science ®Google Scholar
• Motohka, T., K. N. Nasahara, H. Oguma, and S. Tsuchida. 2010. “Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology.” Remote Sensing 2 (10): 2369–2387. doi:10.3390/rs2102369.
   Web of Science ®Google Scholar
• Muchiri, N., and S. Kimathi. 2016. “A Review of Applications and Potential Applications of UAV.” Proceedings of Sustainable Research and Innovation Conference 0 (0): 280–283.
   Google Scholar
• Navia, J., I. Mondragon, D. Patino, and J. Colorado (2016). Multispectral Mapping in Agriculture: Terrain Mosaic Using an Autonomous Quadcopter UAV. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1351–1358). Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7502606
   Google Scholar
• Nebiker, S., A. Annen, M. Scherrer, and D. Oesch. 2008. “A Light-Weight Multispectral Sensor for Micro UAV—Opportunities for Very High Resolution Airborne Remote Sensing.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37(B1): 1193–1199.
   Google Scholar
• Nebiker, S., N. Lack, M. Abächerli, and S. Läderach (2016). Light-Weight Multispectral UAV Sensors and Their Capabilities for Predicting Grain Yield and Detecting Plant Diseases. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 963–970.
   Google Scholar
• Neto, J. C. (2004). A Combined Statistical-Soft Computing Approach for Classification and Mapping Weed Species in Minimum -Tillage Systems. ResearchGate. Retrieved from https://www.researchgate.net/publication/280148076_A_combined_statistical-soft_computing_approach_for_classification_and_mapping_weed_species_in_minimum_-tillage_systems
   Google Scholar
• Nex, F., and F. Remondino. 2013. “UAV for 3D Mapping Applications: A Review.” Applied Geomatics 6 (1): 1–15. doi:10.1007/s12518-013-0120-x.
   Google Scholar
• Nolan, A. P., S. Park, M. O’connell, S. Fuentes, D. Ryu, and H. Chung (2015). Automated Detection and Segmentation of Vine Rows Using High Resolution UAS Imagery in a Commercial Vineyard. Presented at the 21st International Congress on Modelling and Simulation, Gold Coast, Australia. Retrieved from https://www.researchgate.net/profile/Mark_Oconnell/publication/284206199_Automated_detection_and_segmentation_of_vine_rows_using_high_resolution_UAS_imagery_in_a_commercial_vineyard/links/566e1c9f08ae1a797e405f39.pdf
   Google Scholar
• Otsu, N. 1979. “A Threshold Selection Method from Gray-Level Histograms.” IEEE Transactions on Systems, Man, and Cybernetics 9 (1): 62–66. doi:10.1109/TSMC.1979.4310076.
   Web of Science ®Google Scholar
• Pajares, G. 2015. “Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs).” Photogrammetric Engineering & Remote Sensing 81 (4): 281–329. doi:10.14358/PERS.81.4.281.
   Web of Science ®Google Scholar
• Pappalardo, J. (2015). Unmanned Aircraft “Roadmap” Reflects Changing Priorities. Retrieved July 7, 2016, from http://www.nationaldefensemagazine.org/archive/2005/April/Pages/Unmanned_Aircraft5815.aspx
   Google Scholar
• Park, S., A. Nolan, D. Ryu, S. Fuentes, E. Hernandez, H. Chung, and M. O’connell (2015). Estimation of Crop Water Stress in a Nectarine Orchard Using High-Resolution Imagery from Unmanned Aerial Vehicle (UAV). In International Congress on Modelling and Simulation (MODSIM)(Tony Weber and Malcolm McPhee 29 November 2015 to 04 December 2015) (pp. 1413–1419). Modelling and Simulation Society of Australia and New Zealand. Retrieved from https://www.researchgate.net/profile/Sigfredo_Fuentes/publication/298070136_park/links/56e5daa008ae68afa112b648.pdf
   Google Scholar
• Peña, J. M., J. Torres-Sánchez, A. I. Castro, M. De, Kelly, and F. López-Granados. 2013. “Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images.” PLoS One 8 (10): e77151. doi:10.1371/journal.pone.0077151.
   PubMed Web of Science ®Google Scholar
• Perez, A. J., F. Lopez, J. V. Benlloch, and S. Christensen. 2000. Colour and Shape Analysis Techniques for Weed Detection in Cereal Fields. In Computers and Electronics in Agriculture 25(3): 197–212. Elsevier. Retrieved from: http://cat.inist.fr/?aModele=afficheN&cpsidt=1386838.
   Google Scholar
• Pölönen, I., H. Saari, J. Kaivosoja, E. Honkavaara, and L. Pesonen (2013). Hyperspectral Imaging Based Biomass and Nitrogen Content Estimations from Light-Weight UAV. In SPIE Remote Sensing (p. 88870J–88870J). International Society for Optics and Photonics. Retrieved from http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1757263
   Google Scholar
• Poulton, C. V., and M. R. Watts. (2016). MIT and DARPA Pack Lidar Sensor Onto Single Chip. Retrieved August 4, 2016, from http://spectrum.ieee.org/tech-talk/semiconductors/optoelectronics/mit-lidar-on-a-chip
   Google Scholar
• Primicerio, J., S. F. Di Gennaro, E. Fiorillo, L. Genesio, E. Lugato, A. Matese, and F. P. Vaccari. 2012. “A Flexible Unmanned Aerial Vehicle for Precision Agriculture.” Precision Agriculture 13 (4): 517–523. doi:10.1007/s11119-012-9257-6.
   Web of Science ®Google Scholar
• Qi, J., A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian. 1994. “A Modified Soil Adjusted Vegetation Index.” Remote Sensing of Environment 48 (2): 119–126. doi:10.1016/0034-4257(94)90134-1.
   Web of Science ®Google Scholar
• Quater, P. B., F. Grimaccia, S. Leva, M. Mussetta, and M. Aghaei. 2014. “Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants.” IEEE Journal of Photovoltaics 4 (4): 1107–1113. doi:10.1109/JPHOTOV.2014.2323714.
   Web of Science ®Google Scholar
• Quiroz, R. (2015). Remote Sensing As a Monitoring Tool for Cropping Area Determination in Smallholder Agriculture in Tanzania and Uganda. Retrieved from https://cgspace.cgiar.org/handle/10568/69110
   Google Scholar
• Ramasamy, S., R. Sabatini, A. Gardi, and J. Liu. 2016. “LIDAR Obstacle Warning and Avoidance System for Unmanned Aerial Vehicle Sense-and-Avoid.”  Aerospace Science and Technology 55: 344–358. doi:10.1016/j.ast.2016.05.020. Retrieved from: http://www.sciencedirect.com/science/article/pii/S1270963816301900.
   Google Scholar
• Richards, J. A., and X. Jia. 2006. Remote Sensing Digital Image Analysis: An Introduction. 4th ed. Berlin: Springer.
   Google Scholar
• Riggs, G. A., D. K. Hall, and V. V. Salomonson. 1994. “A Snow Index for the Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer.”  In Geoscience and Remote Sensing Symposium, 1994. IGARSS ’94. International Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation 4:1942–1944. doi:10.1109/IGARSS.1994.399618.
   Google Scholar
• Rokhmana, C. A. 2015. “The Potential of UAV-based Remote Sensing for Supporting Precision Agriculture in Indonesia.” Procedia Environmental Sciences 24: 245–253. doi:10.1016/j.proenv.2015.03.032.
   Google Scholar
• Rondeaux, G., M. Steven, and F. Baret. 1996. “Optimization of Soil-Adjusted Vegetation Indices.” Remote Sensing of Environment 55 (2): 95–107. doi:10.1016/0034-4257(95)00186-7.
   Web of Science ®Google Scholar
• Rouse, J. W. JR, R. H. Haas, J. A. Schell, and D. W. Deering. 1974. “Monitoring Vegetation Systems in the Great Plains with Erts.” NASA Special Publication 351: 309.
   Google Scholar
• Saari, H., I. Pellikka, L. Pesonen, S. Tuominen, J. Heikkilä, C. Holmlund, … T. Antila. 2011. “Unmanned Aerial Vehicle (UAV) Operated Spectral Camera System for Forest and Agriculture Applications.” In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII 8174: 81740H–81740H–15. doi:10.1117/12.897585.
   Google Scholar
• Salamí, E., C. Barrado, and E. Pastor. 2014. “UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas.” Remote Sensing 6 (11): 11051–11081. doi:10.3390/rs61111051.
   Web of Science ®Google Scholar
• Siebert, S., and J. Teizer. 2014. “Mobile 3D Mapping for Surveying Earthwork Projects Using an Unmanned Aerial Vehicle (UAV) System.” Automation in Construction 41: 1–14. doi:10.1016/j.autcon.2014.01.004.
   Web of Science ®Google Scholar
• Simelli, I., and A. Tsagaris (2015). The Use of Unmanned Aerial Systems (UAS) in Agriculture. Presented at the 7th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2015), Kavala, Greece.
   Google Scholar
• Sullivan, J. M. 2006. “Evolution or Revolution? The Rise of UAVs.” IEEE Technology and Society Magazine 25 (3): 43–49. doi:10.1109/MTAS.2006.1700021.
   Web of Science ®Google Scholar
• Suomalainen, J., N. Anders, S. Iqbal, G. Roerink, J. Franke, P. Wenting, … L. Kooistra. 2014. “A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles.” Remote Sensing 6 (11): 11013–11030. doi:10.3390/rs61111013.
   Web of Science ®Google Scholar
• Thenkabail, P. S., R. B. Smith, and E. De Pauw. 2000. “Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics.” Remote Sensing of Environment 71 (2): 158–182. doi:10.1016/S0034-4257(99)00067-X.
   Web of Science ®Google Scholar
• Thiel, C., and C. Schmullius. 2016. “Comparison of UAV Photograph-Based and Airborne LiDAR-Based Point Clouds Over Forest from a Forestry Application Perspective.” International Journal of Remote Sensing 0 (0): 1–16. doi:10.1080/01431161.2016.1225181.
   Web of Science ®Google Scholar
• Torres-Sánchez, J., F. López-Granados, and J. M. Peña. 2015. “An Automatic Object-Based Method for Optimal Thresholding in UAV Images: Application for Vegetation Detection in Herbaceous Crops.” Computers and Electronics in Agriculture 114: 43–52. doi:10.1016/j.compag.2015.03.019.
   Web of Science ®Google Scholar
• Torres-Sánchez, J., J. M. Peña, A. I. De Castro, and F. López-Granados. 2014. “Multi-Temporal Mapping of the Vegetation Fraction in Early-Season Wheat Fields Using Images from UAV.” Computers and Electronics in Agriculture 103: 104–113. doi:10.1016/j.compag.2014.02.009.
   Web of Science ®Google Scholar
• Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. doi:10.1016/0034-4257(79)90013-0.
   Web of Science ®Google Scholar
• Turner, D., A. Lucieer, and C. Watson (2011). Development of an Unmanned Aerial Vehicle (UAV) for Hyper Resolution Vineyard Mapping Based on Visible, Multispectral, and Thermal Imagery, In Proceedings of 34th International symposium on remote sensing of environment (p. 4).
   Google Scholar
• Turner, D., A. Lucieer, and C. Watson. 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 Sensing 4 (12): 1392–1410. doi:10.3390/rs4051392.
   Web of Science ®Google Scholar
• Turner, W., S. Spector, N. Gardiner, M. Fladeland, E. Sterling, and M. Steininger. 2003. “Remote Sensing for Biodiversity Science and Conservation.” Trends in Ecology & Evolution 18 (6): 306–314. doi:10.1016/S0169-5347(03)00070-3.
   Web of Science ®Google Scholar
• Uto, K., H. Seki, G. Saito, and Y. Kosugi. 2013. “Characterization of Rice Paddies by a UAV-Mounted Miniature Hyperspectral Sensor System.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (2): 851–860. doi:10.1109/JSTARS.2013.2250921.
   Web of Science ®Google Scholar
• Wagner, M. 2015. Unmanned Aerial Vehicles (SSRNScholarly Paper No. ID 2584652). Rochester, NY: Social Science Research Network. Retrieved from. http://papers.ssrn.com/abstract=2584652.
   Google Scholar
• Wallace, L. (2013). Assessing the Stability of Canopy Maps Produced from UAV-LiDAR Data. In 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS (pp. 3879–3882). 10.1109/IGARSS.2013.6723679
   Google Scholar
• Wallace, L., A. Lucieer, Z. Malenovský, D. Turner, and P. Vopěnka. 2016. “Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds.” Forests 7 (3): 62. doi:10.3390/f7030062.
   Web of Science ®Google Scholar
• Wallace, L., A. Lucieer, C. Watson, and D. Turner. 2012. “Development of a UAV-LiDAR System with Application to Forest Inventory.” Remote Sensing 4 (6): 1519–1543. doi:10.3390/rs4061519.
   Web of Science ®Google Scholar
• Wallace, L., A. Lucieer, and C. S. Watson. 2014. “Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data.” IEEE Transactions on Geoscience and Remote Sensing 52 (12): 7619–7628. doi:10.1109/TGRS.2014.2315649.
   Web of Science ®Google Scholar
• Wallace, L. O., A. Lucieer, D. Turner, and C. S. Watson (2011). Error Assessment and Mitigation for Hyper-Temporal UAV-Borne LiDAR Surveys of Forest Inventory. Presented at the SilviLaser 2011, Hobart, Tasmania. Retrieved from http://events.cdesign.com.au/ei/viewpdf.esp?id=297&file=P:/Eventwin/docs/pdf/silvi2011Abstract00033.pdf
   Google Scholar
• Ward, S., J. Hensler, B. Alsalam, and L. F. Gonzalez (2016). Autonomous UAVs Wildlife Detection Using Thermal Imaging, Predictive Navigation and Computer Vision. In Australian Research Centre for Aerospace Automation; School of Electrical Engineering & Computer Science; Institute for Future Environments; Science & Engineering Faculty. Yellowstone Conference Center, Big Sky, Montana. Retrieved from http://eprints.qut.edu.au/92309/
   Google Scholar
• Watts, A. C., V. G. Ambrosia, and E. A. Hinkley. 2012. “Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use.” Remote Sensing 4 (6): 1671–1692. doi:10.3390/rs4061671.
   Web of Science ®Google Scholar
• Wehrhan, M., P. Rauneker, and M. Sommer. 2016. “UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes—A Case Study from the CarboZALF Experimental Area.” Sensors 16 (2): 255. doi:10.3390/s16020255.
   PubMed Web of Science ®Google Scholar
• Woebbecke, D. M., G. E. Meyer, K. Von Bargen, and D. A. Mortensen (1995). Color Indices for Weed Identification under Various Soil, Residue, and Lighting Conditions. Transactions of the ASAE (USA). Retrieved from http://agris.fao.org/agris-search/search.do?recordID=US9561468
   Google Scholar
• Xu, H. 2006. “Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery.” International Journal of Remote Sensing 27 (14): 3025–3033. doi:10.1080/01431160600589179.
   Web of Science ®Google Scholar
• Zarco-Tejada, P. J., A. Berjón, R. López-Lozano, J. R. Miller, P. Martín, V. Cachorro, … A. De Frutos. 2005. “Assessing Vineyard Condition with Hyperspectral Indices: Leaf and Canopy Reflectance Simulation in a Row-Structured Discontinuous Canopy.” Remote Sensing of Environment 99 (3): 271–287. doi:10.1016/j.rse.2005.09.002.
   Web of Science ®Google Scholar
• Zarco-Tejada, P. J., V. González-Dugo, and J. A. Berni. 2012. “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 Sensing of Environment 117: 322–337. doi:10.1016/j.rse.2011.10.007.
   Web of Science ®Google Scholar
• Zhang, C., and J. M. Kovacs. 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.
   Web of Science ®Google Scholar