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Original Articles

Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry

ORCID Icon, ORCID Icon, ORCID Icon, , ORCID Icon &
Pages 687-699 | Received 12 May 2018, Accepted 09 Nov 2018, Published online: 07 Feb 2019

References

  • Becker-Reshef I, Vermote E, Lindeman M, Justice C. 2010. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens Environ. 114(6):1312–1323.
  • 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 Sens. 6(11):10395–10412.
  • Bortolot ZJ, Wynne RH. 2005. Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data. ISPRS J Photogramm Remote Sens. 59(6):342–360.
  • Cooper S, Roy D, Schaaf C, Paynter I. 2017. Examination of the potential of terrestrial laser scanning and structure-from-motion photogrammetry for rapid nondestructive field measurement of grass biomass. Remote Sens. 9(6):531.
  • Cunliffe AM, Brazier RE, Anderson K. 2016. Remote sensing of environment ultra- fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens Environ [Internet]. 183:129–143.
  • Eitel JUH, Magney TS, Vierling LA, Greaves HE, Zheng G. 2016. Remote sensing of environment an automated method to quantify crop height and calibrate satellite-derived biomass using hypertemporal lidar. Remote Sens Environ. 187:414–422.
  • Feng Z, Chen Y, Hakala T, Zhou H, Wang Y, Karjalainen M. 2018. Estimating ground level and canopy top elevation with airborne microwave profiling radar. IEEE Trans Geosci Remote Sens. 56(4):2283–2294.
  • Fern R, Foxley E, Bruno A, Morrison M. 2018. Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecol Indic. 94:16–21.
  • Geipel J, Link J, Claupein W. 2014. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system. Remote Sens. 6(11):10335–10355.
  • Goldstein E, Oliver A, DeVries E, Moore L, Jass T. 2015. Ground control point requirements for structure-from-motion derived topography in low- slope coastal environments. PeerJ Prepr. 3:e1444v1:1–9.
  • Goswami S, Gamon JA, Vargas S, Craig T. 2015. Relationships of NDVI, Biomass and Leaf Area Index (LAI) for six key plant species in Barrow. Alaska. PeerJ Prepr. 230313:1–17.
  • Harwin S, Lucieer A. 2012. Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from Unmanned Aerial Vehicle (UAV) imagery. Remote Sens. 4(6):1573–1599.
  • Iqbal F, Lucieer A, Barry K, Wells R. 2017. Poppy crop height and capsule volume estimation from a single UAS flight. Remote Sens. 9(7):647.
  • Jimenez-Berni J, Deery D, Rozas-Larrondo P, Condon A, Rebetzke G, James R, Bovill W, Furbank R, Sirault X. 2018. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front Plant Sci. 9:1–18.
  • Kachamba DJ, Ørka HO, Gobakken T, Eid T, Mwase W. 2016. Biomass estimation using 3D data from unmanned aerial vehicle imagery in a tropical Woodland. Remote Sens. 8:1–18.
  • Koch B. 2010. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm Remote Sens. 65(6):581–590.
  • Koutsoudis A, Vidmar B, Ioannakis G, Arnaoutoglou F, Pavlidis G, Chamzas C. 2014. Multi-image 3D reconstruction data evaluation. J Cult Herit. 15(1):73–79.
  • Liu H, Zhu H, Wang P. 2017. Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data. Int J Remote Sens. 38(8-10):2117–2134.
  • Lu B, He Y. 2017. Species classification using Unmanned Aerial Vehicle (UAV) -acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS J Photogramm Remote Sens. 128:73–85.
  • Luo S, Chen J, Wang C, Xi X, Zeng H, Peng D, Li D. 2016. Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters. Opt Express. 24(11):11578–11593.
  • Mondino EB, Gajetti M. 2017. Preliminary considerations about costs and potential market of remote sensing from UAV in the Italian viticulture context. Eur J Remote Sens. 50(1):310–319.
  • Nex F, Remondino F. 2014. UAV for 3D mapping applications: A review. Appl Geomat. 6(1):1–15.
  • Qazi W, Baig S, Gilani H, Waqar M, Dhakal A, Ammar A. 2017. Comparison of forest aboveground biomass estimates from passive and active remote sensing sensors over Kayar Khola. J Appl Remote Sens. 11(2):026038 1-16.
  • Rosnell T, Honkavaara E. 2012. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors. 12(1):453–480.
  • Solberg S, Brunner A, Hanssen KH, Lange H, Naesset E, Rautiainen M, Stenberg P. 2009. Mapping LAI in a Norway spruce forest using airborne laser scanning. Remote Sens Environ. 113(11):2317–2327. Internet].
  • Tonkin T, Midgley N. 2016. Ground-Control Networks for Image Based Surface Reconstruction : An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry. Remote Sens. 8:16–19.
  • Vazirabad Y, Karslioglu M. 2011. Lidar for Biomass Estimation. Biomass, Darko Matovic, IntechOpen, DOI: 10.5772/16919. Available from: https://www.intechopen.com/books/biomass-detection-production-and-usage/lidar-for-biomass-estimation/; p. 3–26 (accessed on 5/5/2018).
  • Walter J, Edwards J, McDonald G, Kuchel H. 2018. Photogrammetry for the estimation of wheat biomass and harvest index. F Crop Res. 216:165–174.
  • Wong WVC, Tsuyuki S, Phua M, Ioki K, Takao G. 2015. Forest biophysical characteristics estimation using digital aerial photogrammetry and airborne laser scanning for tropical Montane Forest. Proceedings of ACRS 2015: the 36th Asian conference on remote sensing: fostering resilient growth in Asia, 19–23 October 2015, Quezon City, Philippines. Asian Association on Remote Sensing (AARS), 2015.
  • Wu C, Shen H, Shen A, Dend J, Gan M, Zhu J, Xu H, Wang K. 2016. Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery. J Appl Remote Sens. 10(3): 035010 1–17.
  • Yang S, Feng Q, Liang T, Liu B, Zhang W, Xie H. 2018. Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region. Remote Sens Environ [. 204:448–455.
  • Zhu X, Liu D. 2015. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS J Photogramm Remote Sens. 102:222–231.

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