Asner, G.P.; Mascaro, J.; Muller-Landau, H.C.; Vieilledent, G.; Vaudry, R.; Rasamoelina, M.; Hall, J.S.; VanBreugel, M., 2012: A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia, 168, pp.1147–1160.
Bendig, J., Bolten, A., & Bareth, G., 2013a: UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability. Photogrammetrie - Fernerkundung - Geoinformation, 6,551–562.
Bendig, J., Willkomm, M., Tilly, N., Gnyp, M. L., Bennertz, S., Qiang, C., ...& Bareth, G., 2013b: Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring in Northeast China. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 40, pp. 45-50.
Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G., 2014: Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6(11), 10395–10412.
Bendig, J., Willkomm, M., Tilly, N., Gnyp, M. L., Bennertz, S., Lenz-Wiedemann, V. I. S., … Cao, Q., 2015a: Very high resolution Crop Surface Models (CSM) from UAV-based stereo images for rice growth monitoring in Northeast China. Gis.Science - Die Zeitschrift Fur Geoinformatik, XL(1), pp. 1–9.
Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., … Bareth, G., 2015b: 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.
Besl, P. J. and McKay, H. D., 1992: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239 -256. Campbell, G.S.; Norman, J.M., 2012: An introduction to environmental biophysics. Springer Science & Business Media; ISBN 1461216265.
Chang, A., Jung, J., Maeda, M. M., & Landivar, J., 2017: Crop height monitoring with digital imagery from Unmanned Aerial System (UAS). Computers and Electronics in Agriculture, 141, pp. 232–237. https://doi.org/10.1016/j.compag.2017.07.008
Dandois, J. P. and Ellis, E. C., 2013: High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sensing of Environment, 136, pp. 259-276.
Dandois, J., Olano, M., & Ellis, E., 2015: Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sensing, 7(10), pp. 13895-13920.
Diaz-Varela, R. A., Zarco-Tejada, P. J., Angileri, V., & Loudjani, P., 2014: Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. Journal of Environmental Management, 134, pp.117–126. https://doi.org/10.1016/j.jenvman.2014.01.006
Díaz-Varela, R. A., Rosa, R. D., León, L. and Zarco-Tejada, P. J., 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.
Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E., 2013: Topographic structure from motion: A new development in photogrammetric measurement. Earth Surface Processes and Landforms, 38(4), pp. 421–430. https://doi.org/10.1002/esp.3366
Furukawa, Y.; Curless, B.; Seitz, S.M.; Szeliski, R., 2010: Towards internet-scale multi-view stereo. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 1434–1441.
Furukawa, Y.; Ponce, J., 2007: Accurate, Dense, and Robust Multi-View Stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1, pp. 1–14.
Grenzdörffer, G. J., 2014: Crop height determination with UAS point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(1), pp. 135–140. https://doi.org/10.5194/isprsarchives-XL-1-135-2014
Guerra-Hernández, J., Cosenza, D. N., Rodriguez, L. C. E., Silva, M., Tomé, M., Díaz-Varela, R. A., & González-Ferreiro, E., 2018: Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations. International Journal of Remote Sensing, 39(15–16), pp. 5211–5235. https://doi.org/10.1080/01431161.2018.1486519
Gómez-Candón, D., De Castro, A. I., & López-Granados, F., 2014: Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), pp. 44–56. https://doi.org/10.1007/s11119-013-9335-4
Hassan, M. A., Yang, M., Rasheed, A., Yang, G., Reynolds, M., Xia, X., … He, Z., 2019: A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 282(October 2018), pp. 95–103. https://doi.org/10.1016/j.plantsci.2018.10.022
Hobbs, R.J.; Mooney, H.A., 2012: Remote sensing of biosphere functioning; Springer Science & Business Media; Vol. 79; ISBN 146123302X.
Holman, F.H.; Riche, A.B.; Michalski, A.; Castle, M.; Wooster, M.J.; Hawkesford, M.J., 2016: High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens. 2016, 8, pp. 1031.
Hoshikawa, K., 1994: Encyclopedia Nipponica. Tokyo, Syogakukan Publ, 12, pp.943.
Hosoi, F. and Omasa, K., 2006: Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning lidar.
IEEE Transactions of Geoscience and Remote Sensing, 44(12), pp. 3610-3618. Hosoi, F., Nakabayashi, K., & Omasa, K., 2011: 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors, 11(2), pp. 2166-2174.
Jay, S., Rabatel, G., Hadoux, X., Moura, D., & Gorretta, N., 2015: In-field crop row phenotyping from 3D modeling performed using Structure from Motion. Computers and Electronics in Agriculture, 110, pp. 70–77. https://doi.org/10.1016/j.compag.2014.09.021
Jensen, J. L. R. and Mathews, A. J., 2016: Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy height in a woodland ecosystem. At http://www.mdpi.com/2072-4292/8/1/50. Remote Sensing, Basel, 8(1), 50, pp. 1-13. Accessed 30 May 2016.
Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A., 2017: Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, pp. 105–114. https://doi.org/10.1016/j.rse.2017.06.007
Jones, H. G. and Vaughan R. A., 2010: Remote sensing of vegetation –principles, techniques, and applications-. Oxford University Press Inc., New York, 353 pp.
Jones, H.G., 2013: Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology; 3rd ed.; Cambridge University Press: Cambridge; ISBN 9780521279598.
Kim, D.; Yun, H.S.; Jeong, S.; Kwon, Y.; Kim, S.; Suk, W.; Id, L.; Kim, H., 2018: Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery. Remote Sens., 10, pp. 563.
Lati, R.N., Filin, S., Eizenberg, H., 2013: Estimating plant growth parameters using an energy minimization-based stereovision model. Comput. Electron. Agric. 98, pp. 260–271.
Lin, Y.; Hyyppä, J.; Jaakkola, A., 2011: Mini-UAV-borne LIDAR for fine-scale mapping. IEEE Geosci. Remote Sens. Lett., 8, pp. 426–430.
Lowe, D.G., 1999: Object recognition from local scale-invariant features. In Proceedings of the iccv, 99, pp. 1150–1157.
Lowe, D.G., 2004: Distinctive Image Features from. Int. J. Comput. Vis., 60,pp. 91–110.
Malambo, L., Popescu, S. C., Murray, S. C., Putman, E., Pugh, N. A., Horne, D. W., … Bishop, M., 2018: Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. International Journal of Applied Earth Observation and Geoinformation, 64(June 2017), pp. 31–42. https://doi.org/10.1016/j.jag.2017.08.014
Mathews, A.J.; Jensen, J.L.R., 2013: Visualizing and quantifying vineyard canopy LAI using an unmanned aerial vehicle (UAV) collected high density structure from motion point cloud. Remote Sens., 5, pp. 2164–2183.
Matsuda, M., Hosaka, Y. and Omasa, K., 2010: Quality Assessment of Grains Using Functional Remote Sensing. Iden, 64(2), 81-86()
Means, J. E., Acker, S. A., Fitt, B. J., Renslow, M., Emerson, L. and Hendrix, C. J., 2000: Predicting forest stand characteristics with air borne scanning
lidar. Photogrammetric Engineering and Remote Sensing, 66(11), pp. 1367-1372.
Meng, Z., Whitmore, N. D., Valasek, P. A., Shen, Y., Wyatt, K. D., & Liu, W., 2000: 3-D Hale-McClellan prestack depth migration with enhanced extrapolation operators. SEG Technical Program Expanded Abstracts, 19(1), pp. 485–488. https://doi.org/10.1190/1.1816102
Morsdorf, F.; Kötz, B.; Meier, E.; Itten, K.I.; Allgöwer, B., 2006: Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sens. Environ., 104, pp. 50–61.
Müller-Linow, M., Pinto-Espinosa, F., Scharr, H., & Rascher, U., 2015: The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool. Plant methods, 11(1), pp. 11.
Nilsson, M., 1996: Estimation of tree heights and stand volume using an airborne lidar system. Remote Sensing of Environment, 56(1), pp. 1-7.
Omasa, K.; Aiga, I., 1987: Environmental measurement: Image instrumentation for evaluating pollution effects on plants. In Systems & Control Encyclopedia; M.G., Ed.; Pergamon Press: Oxford; pp. 1516–1522.
Omasa, K., 1990: Image instrumentation methods of plant analysis. In Modern Methods of Plant Analysis. Physical Methods in Plant Sciences; Linskens, H.F., Jackson, J.F., Eds.; Springer-Verlag: Berlin; pp. 203–243.
Omasa, K., Akiyama, Y., Ishigami, Y. and Yoshimi, K., 2000: 3-D remote sensing of woody canopy heights using a scanning helicopter-borne lidar system with high spatial resolution.Journal of Remote Sensing Society of Japan, 20(4),pp. 394-406.
Omasa, K., Qiu, G. Y., Watanuki, K., Yoshimi, K. and Akiyama, Y., 2003: Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees. Environmental Science and Technology, 37, pp. 1198-1201.
Omasa, K., 2006: Image Sensing and Phytobiological Information. In CIGR Handbook of Agricultural Engineering Information Technology; Munack, A., Ed.; American Society of Agricultural and Biological Engineers: St. Joseph; pp.217–230.
Omasa, K., Hosoi F., and Konishi A., 2007: 3D lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany, 58, pp. 881-898. Sankey, T., Donager, J., McVay, J., & Sankey, J. B., 2017: UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sensing of Environment, 195, pp. 30-43.
Sellers, P.J.; Rasool, S.I.; Bolle, H.-J., 2002: A Review of Satellite Data Algorithms for Studies of the Land Surface. Bull. Am. Meteorol. Soc., 71, pp. 1429–1447.
Shewchuk, J. R., 1997: Delaunay refinement mesh generation. Carnegie-Mellon Univ Pittsburgh Pa School of Computer Science, Ph.D. Thesis.
Shewchuk, J. R., 2002: Delaunay refinement algorithms for triangular mesh generation. Computational geometry, 22(1-3), pp. 21-74.
Teng, P., Zhang, Y., Shimizu, Y., Hosoi, F. and Omasa, K., 2016: Accuracy Assessment in 3D Remote Sensing of Rice Plants in Paddy Field Using a Small UAV. Eco-Engineering, 28(4), pp. 107-112.
Teng, P.; Fukumaru, Y.; Zhang, Y.; Aono, M.; Shimizu, Y.; Hosoi, F.; Omasa, K., 2018: Accuracy Assessment in 3D Remote Sensing of Japanese Larch Trees using a Small UAV. Eco-Engineering, 30, pp. 1–6.
Tomasi, C.; Kanade, T., 1993: Shape and motion from image streams: a factorization method. Proc. Natl. Acad. Sci. U. S. A., 90, pp. 9795–9802.
Triggs, B.; McLauchlan, P.F.; Hartley, R.I.; Fitzgibbon, A.W., 2000: Bundle Adjustment — A Modern Synthesis. Vis. Algorithms Theory Pract., 1883, pp. 298–372.
Wallace, L. O., Lucieer, A., & Watson, C. S., 2012a: Assessing the Feasibility of Uav-Based Lidar for High Resolution Forest Change Detection. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7(September), pp. 499–504. https://doi.org/10.5194/isprsarchives-xxxix-b7-499-2012
Wallace, L.; Lucieer, A.; Watson, C.; Turner, D., 2012b: Development of a UAV-LiDAR system with application to forest inventory. Remote Sens., 4, pp.1519–1543.
Wallace, L., Lucieer, A., & Watson, C. S., 2014: Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR ata. IEEE Transactions on Geoscience and Remote Sensing, 52(12), pp. 7619–7628. https://doi.org/10.1109/TGRS.2014.2315649
Wallace, L., Lucieer, A., Malenovský, Z., Turner, D. and Vopěnka, P., 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), pp. 1–16.
White, J. C., Stepper, C., Tompalski, P., Coops, N. C. and Wulder, M. A., 2015: Comparing ALS and image-based point cloud metrics and modelled forest inventory attributes in a complex coastal forest environment. Forests, 6(10), pp. 3704-3732.
Whitlock, C.H.; Charlock, T.P.; Staylor, W.F.; Pinker, R.T.; Laszlo, I.; Ohmura, A.; Gilgen, H.; Konzelman, T.; DiPasquale, R.C.; Moats, C.D., 1995: First global WCRP shortwave surface radiation budget dataset. Bull. Am. Meteorol. Soc., 76, pp. 905–922.
Yu, N., Li, L., Schmitz, N., Tian, L. F., Greenberg, J. A., & Diers, B. W., 2016: Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sensing of Environment, 187, pp. 91–101. https://doi.org/10.1016/j.rse.2016.10.005
Zahawi, R. A., Dandois, J. P., Holl, K. D., Nadwodny, D., Reid, J. L., & Ellis, E. C., 2015: Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation, 186, pp. 287–295. https://doi.org/10.1016/j.biocon.2015.03.031
Zarco-Tejada, P. J., González-Dugo, V., & Berni, J. A. J., 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, pp. 322–337. https://doi.org/10.1016/j.rse.2011.10.007
Zarco-Tejada, P. J., Guillén-Climent, M. L., Hernández-Clemente, R., Catalina, A., González, M. R., & Martín, P., 2013: Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and Forest Meteorology, 171–172, pp. 281–294. https://doi.org/10.1016/j.agrformet.2012.12.013
Zarco-Tejada, P. J., Diaz-Varela, R., Angileri, V. and Loudjani, P., 2014: Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 55, pp. 89-99.
Zhang, H., Sun, Y., Chang, L., Qin, Y., Chen, J., Qin, Y., … Wang, Y., 2018a: Estimation of grassland canopy height and aboveground biomass at the quadrat scale using unmanned aerial vehicle. Remote Sensing, 10(6). https://doi.org/10.3390/rs10060851
Zhang, Y., Teng, P., Shimizu, Y., Hosoi, F. and Omasa, K., 2016: Estimating 3D leaf and stem shape of nursery paprika plants by a novel multi-camera photography system. Sensors, 16(874), pp. 1-18.
Zhang, Y.; Teng, P.; Aono, M.; Shimizu, Y.; Hosoi, F.; Omasa, K., 2018b: 3D monitoring for plant growth parameters in field with a single camera by multi-view approach. J. Agric. Meteorol., 74, pp. 129–139.
秋山侃・冨久尾歩・平野聡・石塚直樹・小川茂男・岡本勝男・ 齋藤元也・内田諭・山本由紀代・吉迫宏・瑞慶村知佳, 2014:農業リモートセンシング・ハンドブック, システム農学会, 東京, 512 pp.
松田真典・保坂幸男・大政謙次, 2010: 機能リモートセンシングによる穀類の品質評価, 遺伝, 64(2), 81-86