Evaluation of orthomosics and digital surface models derived from aerial imagery for crop type mapping

Remote Sensing

Volumn 9, Issue 3, 2017, Pages

  Wu, Mingquan ^a,b   Yang, Chenghai ^b   Song, Xiaoyu ^b,c   Hoffmann, Wesley Clint ^b   Huang, Wenjiang ^a   Niu, Zheng ^a   Wang, Changyao ^a   Li, Wang ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c INSTITUTE OF PLANT AND ENVIRONMENT PROTECTION   (China)

Author keywords

Aerial imagery; Consumer grade cameras; Crop height; Crop mapping; Object based classification

Indexed keywords

AERIAL PHOTOGRAPHY; CAMERAS; CULTIVATION; INFRARED DEVICES; MAPPING; MAXIMUM LIKELIHOOD;

AERIAL IMAGERY; CROP HEIGHT; CROP MAPPING; CROP TYPE MAPPINGS; DIGITAL SURFACE MODELS; MAXIMUM LIKELIHOOD METHODS; OBJECT-BASED CLASSIFICATIONS; OBJECT-BASED METHODS;

CROPS;

EID: 85019083166     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9030239     Document Type: Article

References (35)

1. Yang, C.; Westbrook, J.K.; Suh, C.P.-C.; Martin, D.E.; Hoffmann, W.C.; Lan, Y.; Fritz, B.K.; Goolsby, J.A. An airborne multispectral imaging system based on two consumer-grade cameras for agricultural remote sensing. Remote Sens. 2014, 6, 5257-5278.
2. Grenzdörffer, G.; Engel, A.; Teichert, B. The photogrammetric potential of low-cost UAVs in forestry and agriculture. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, 31, 1207-1214.
3. Honkavaara, E.; Saari, H.; Kaivosoja, J.; Pölönen, I.; Hakala, T.; Litkey, P.; Mäkynen, J.; Pesonen, L. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sens. 2013, 5, 5006-5039.
4. George, E.A.; Tiwari, G.; Yadav, R.; Peters, E.; Sadana, S. UAV systems for parameter identification in agriculture. In Proceedings of the IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS), Trivandrum, India, 23-24 August 2013; pp. 270-273.
5. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712.
6. Gómez-Candón, D.; De Castro, A.; 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.
7. Torres-Sánchez, J.; Peña, J.; De Castro, A.; López-Granados, F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric. 2014, 103, 104-113.
8. Hardin, P.; Jackson, M.; Anderson, V.; Johnson, R. Detecting squarrose knapweed (Centaurea virgata Lam. Ssp. Squarrosa Gugl.) using a remotely piloted vehicle: A Utah case study. GISci. Remote Sens. 2007, 44, 203-219.
9. Berni, J.A.; Zarco-Tejada, P.J.; Suárez, L.; Fereres, E. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722-738.
10. Swain, K.C.; Thomson, S.J.; Jayasuriya, H.P. Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. Trans. ASABE 2010, 53, 21-27.
11. Hunt, E.R.; Hively, W.D.; Fujikawa, S.J.; Linden, D.S.; Daughtry, C.S.; McCarty, G.W. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens. 2010, 2, 290-305.
12. Yang, C. A high-resolution airborne four-camera imaging system for agricultural remote sensing. Comput. Electron. Agric. 2012, 88, 13-24.
13. Yang, C.; Everitt, J.H.; Davis, M.R.; Mao, C. A CCD camera-based hyperspectral imaging system for stationary and airborne applications. Geocarto Int. 2003, 18, 71-80.
14. Zhang, J.; Yang, C.; Song, H.; Hoffmann, W.C.; Zhang, D.; Zhang, G. Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sens. 2016, 8, 257.
15. Nguy-Robertson, A.L.; Brinley Buckley, E.M.; Suyker, A.S.; Awada, T.N. Determining factors that impact the calibration of consumer-grade digital cameras used for vegetation analysis. Int. J. Remote Sens. 2016, 37, 3365-3383.
16. Song, H.; Yang, C.; Zhang, J.; Hoffmann,W.C.; He, D.; Thomasson, J.A. Comparison of mosaicking techniques for airborne images from consumer-grade cameras. J. Appl. Remote Sens. 2016, 10, 016030.
17. Westbrook, J.K.; Eyster, R.S.; Yang, C.; Suh, C.P.-C. Airborne multispectral identification of individual cotton plants using consumer-grade cameras. Remote Sens. Appl. Soc. Environ. 2016, 4, 37-43.
18. Goel, P.; Prasher, S.; Landry, J.A.; Patel, R.; Viau, A.; Miller, J. Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing. Trans. ASAE 2003, 46, 1235-1464.
19. Calderón, R.; Navas-Cortés, J.A.; Lucena, C.; Zarco-Tejada, P.J. 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. 2013, 139, 231-245.
20. Song, H.; Yang, C.; Zhang, J.; He, D.; Thomasson, J.A. Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot. J. Appl. Remote Sens. 2015, 9, 096013.
21. Bendig, J.; Bolten, A.; Bennertz, S.; Broscheit, J.; Eichfuss, S.; Bareth, G. Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB imaging. Remote Sens. 2014, 6, 10395-10412.
22. Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 2015, 39, 79-87.
23. Grenzdörffer, G.J. Crop height determination with UAS point clouds. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2014, 40, 135.
24. Li,W.; Niu, Z.; Chen, H.; Li, D.;Wu, M.; Zhao,W. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol. Indic. 2016, 67, 637-648.
25. Papadavid, G.; Hadjimitsis, D.; Toulios, L.; Michaelides, S. Mapping potato crop height and leaf area index through vegetation indices using remote sensing in Cyprus. J. Appl. Remote Sens. 2011, 5, 053526.
26. Verhoeven, G.; Vermeulen, F. Engaging with the canopy-Multi-dimensional vegetation mark visualisation using archived aerial images. Remote Sens. 2016, 8, 752.
27. Sharma, L.K.; Bu, H.; Franzen, D.W.; Denton, A. Use of corn height measured with an acoustic sensor improves yield estimation with ground based active optical sensors. Comput. Electron. Agric. 2016, 124, 254-262.
28. Shuai, G.; Zheng, N.; Gang, S.; Dan, Z.; Kun, J.; Yuchu, Q. Height Extraction of Maize Using Airborne Full-waveform LIDAR Data and a Deconvolution Algorithm. IEEE Geosci. Remote Sens. Lett. 2015, 12, 1978-1982.
29. Tilly, N.; Hoffmeister, D.; Cao, Q.; Huang, S.; Lenz-Wiedemann, V.; Miao, Y.; Bareth, G. Multitemporal crop surface models: Accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J. Appl. Remote Sens. 2014, 8, 083671.
30. Tilly, N.; Hoffmeister, D.; Cao, Q.; Lenz-Wiedemann, V.; Miao, Y.; Bareth, G. Precise plant height monitoring and biomass estimation with Terrestrial Laser Scanning in paddy rice. In Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Conference, Antalya, Turkey, 11-13 November 2013; Volume 1113.
31. Zhang, L.; Grift, T.E. A LIDAR-based crop height measurement system for Miscanthus giganteus. Comput. Electron. Agric. 2012, 85, 70-76.
32. Yang, C.; Odvody, G.N.; Fernandez, C.J.; Landivar, J.A.; Minzenmayer, R.R.; Nichols, R.L. Evaluating unsupervised and supervised image classification methods for mapping cotton root rot. Precis. Agric. 2015, 16, 201-215.
33. Wu, M.; Zhang, X.; Huang, W.; Niu, Z.; Wang, C.; Li, W.; Hao, P. Reconstruction of daily 30 m data from HJ CCD, GF-1 WFV, Landsat, and MODIS data for crop monitoring. Remote Sens. 2015, 7, 16293-16314.
34. Bareth, G.; Bendig, J.; Tilly, N.; Hoffmeister, D.; Aasen, H.; Bolten, A. A comparison of UVA-and TLS-derived plant height for crop monitoring: Using polygon grids for the analysis of crop surface models (CSMs). Photogramm. Fernerkund. Geoinf. 2016, 2016, 85-94.
35. Li, W.; Niu, Z.; Chen, H.; Li, D. Characterizing canopy structural complexity for the estimation of maize LAI based on ALS data and UAV stereo images. Int. J. Remote Sens. 2016.