Crop classification and LAI estimation using original and resolution-reduced images from two consumer-grade cameras

Remote Sensing

Volumn 9, Issue 10, 2017, Pages

  Zhang, Jian ^a,b   Yang, Chenghai ^b   Zhao, Biquan ^a   Song, Huaibo ^b,c   Hoffmann, Wesley Clint ^b   Shi, Yeyin ^d   Zhang, Dongyan ^e   Zhang, Guozhong ^a  

^a HUAZHONG AGRICULTURAL UNIVERSITY   (China)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c NORTHWEST A F UNIVERSITY   (China)

^d UNIVERSITY OF NEBRASKA LINCOLN   (United States)

^e ANHUI UNIVERSITY   (China)

Author keywords

Consumer grade camera; Crop identification; Image classification; Leaf area index; Modified NIR camera; Vegetation index

Indexed keywords

CAMERAS; CROPS; IMAGE CLASSIFICATION; IMAGE RESOLUTION; INFRARED DEVICES; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; REGRESSION ANALYSIS; REMOTE SENSING; VEGETATION;

CLASSIFICATION RESULTS; CROP CLASSIFICATION; CROP IDENTIFICATION; LEAF AREA INDEX; NIR CAMERA; REMOTE SENSING APPLICATIONS; SUPERVISED CLASSIFIERS; VEGETATION INDEX;

IMAGE PROCESSING;

EID: 85032878309     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9101054     Document Type: Article

References (56)

1. Schimmelpfennig, D.; Ebel, R. Sequential adoption and cost savings from precision agriculture. J. Agric. Resour. Econ. 2016, 41, 97-115.
2. Nijland,W.; de Jong, R.; de Jong, S.M.;Wulder, M.A.; Bater, C.W.; Coops, N.C. Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agric. For. Meteorol. 2014, 184, 98-106.
3. Light, D.L. Film cameras or digital sensors? The challenge ahead for aerial imaging. Photogramm. Eng. Remote Sens. 1996, 62, 285-291.
4. Quilter, M.C.; Anderson, V.J. Low altitude/large scale aerial photographs: A tool for range and resource managers. Rangel. Arch. 2000, 22, 13-17.
5. Yuan, H.; Yang, G.; Li, C.;Wang, Y.; Liu, J.; Yu, H.; Feng, H.; Xu, B.; Zhao, X.; Yang, X. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of Rf, ANN, and SVM regression models. Remote Sens. 2017, 9, 309.
6. Yang, C.; Odvody, G.N.; Thomasson, J.A.; Isakeit, T.; Nichols, R.L. Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery. Comput. Electron. Agric. 2016, 123, 154-162.
7. 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.
8. Hu, Z.; He, F.; Yin, J.; Lu, X.; Tang, S.; Wang, L.; Li, X. Estimation of fractional vegetation cover based on digital camera survey data and a remote sensing model. J. China Univ. Min. Echnol. 2007, 17, 116-120.
9. Zarco-Tejada, P.J.; Diaz-Varela, R.; Angileri, V.; Loudjani, P. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (uav) and automatic 3d photo-reconstruction methods. Eur. J. Agron. 2014, 55, 89-99.
10. Prosdocimi, M.; Calligaro, S.; Sofia, G.; Dalla Fontana, G.; Tarolli, P. Bank erosion in agricultural drainage networks: New challenges from structure-from-motion photogrammetry for post-event analysis. Earth Surf. Process. Landf. 2015, 40, 1891-1906.
11. Prosdocimi, M.; Burguet, M.; Di Prima, S.; Sofia, G.; Terol, E.; Rodrigo Comino, J.; Cerdà, A.; Tarolli, P. Rainfall simulation and structure-from-motion photogrammetry for the analysis of soil water erosion in mediterranean vineyards. Sci. Total Environ. 2017, 574, 204-215.
12. Shi, Y.; Thomasson, J.A.; Murray, S.C.; Pugh, N.A.; Rooney, W.L.; Shafian, S.; Rajan, N.; Rouze, G.; Morgan, C.L.S.; Neely, H.L.; et al. Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS ONE 2016, 11, e0159781.
13. Yang, C.; Hoffmann,W.C. Low-cost single-camera imaging system for aerial applicators. J. Appl. Remote Sens. 2015, 9, 096064.
14. Wellens, J.; Midekor, A.; Traore, F.; Tychon, B. An easy and low-cost method for preprocessing and matching small-scale amateur aerial photography for assessing agricultural land use in burkina faso. Int. J. Appl. Earth Obs. Geoinf. 2013, 23, 273-278.
15. Diaz-Varela, R.; Zarco-Tejada, P.J.; Angileri, V.; Loudjani, P. Automatic identification of agricultural terraces through object-oriented analysis of very high resolution dsms and multispectral imagery obtained from an unmanned aerial vehicle. J. Environ. Manag. 2014, 134, 117-126.
16. Jensen, T.; Apan, A.; Young, F.; Zeller, L. Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform. Comput. Electron. Agric. 2007, 59, 66-77.
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. Bayer, B.E. Color Imaging Array. US Patent, 3,971,065, 20 July 1976.
19. Lelong, C.C.D. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 2008, 8, 3557-3585.
20. Miller, C.D.; Fox-Rabinovitz, J.R.; Allen, N.F.; Carr, J.L.; Kratochvil, R.J.; Forrestal, P.J.; Daughtry, C.S.T.; McCarty, G.W.; Hively, W.D.; Hunt, E.R. Nir-green-blue high-resolution digital images for assessment of winter cover crop biomass. GISci. Remote Sens. 2011, 48, 86-98.
21. Akkaynak, D.; Treibitz, T.; Xiao, B.; Gürkan, U.A.; Allen, J.J.; Demirci, U.; Hanlon, R.T. Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration. JOSA A 2014, 31, 312-321.
22. 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.
23. 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.
24. Zhang, J.; Yang, C.; Song, H.; Hoffmann, W.; 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.
25. Lebourgeois, V.; Bégué, A.; Labbé, S.; Houlès, M.; Martiné, J.F. A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring. Precis. Agric. 2012, 13, 525-541.
26. Widlowski, J.-L.; Lavergne, T.; Pinty, B.; Gobron, N.; Verstraete, M.M. Towards a high spatial resolution limit for pixel-based interpretations of optical remote sensing data. Adv. Space Res. 2008, 41, 1724-1732.
27. Kobayashi, H.; Suzuki, R.; Nagai, S.; Nakai, T.; Kim, Y. Spatial scale and landscape heterogeneity effects on fapar in an open-canopy black spruce forest in interior alaska. IEEE Geosci. Remote Sens. Lett. 2014, 11, 564-568.
28. Pix4D. Getting GCPs in the Field or through Other Sources. Available online: https://support.pix4d.com/hc/en-us/articles/202557489-Step-1-Before-Starting-a-Project-4-Getting-GCPs-on-the-field-or-throughother-sources-optional-but-recommended-#gsc.tab=0 (acessed on 22 August 2017).
29. Tan, B.; Woodcock, C.; Hu, J.; Zhang, P.; Ozdogan, M.; Huang, D.; Yang, W.; Knyazikhin, Y.; Myneni, R. The impact of gridding artifacts on the local spatial properties of modis data: Implications for validation, compositing, and band-to-band registration across resolutions. Remote Sens. Environ. 2006, 105, 98-114.
30. Schowengerdt, R.A. Remote sensing: Models and Methods for Image Processing; Academic Press: San Diego, CA, USA, 2006; pp. 67-83.
31. Richards, J.A.; Richards, J. Remote Sensing Digital Image Analysis; Springer: Berlin, Germany, 2013.
32. Davis, S.M.; Landgrebe, D.A.; Phillips, T.L.; Swain, P.H.; Hoffer, R.M.; Lindenlaub, J.C.; Silva, L.F. Remote sensing: The Quantitative Approach;McGraw-Hill International Book Co.: New York, NY, USA, 1978; p. 405.
33. Asmala, A. Analysis of maximum likelihood classification on multispectral data. Appl. Math. Sci. 2012, 6, 6425-6436.
34. Kruse, F.; Lefkoff, A.; Boardman, J.; Heidebrecht, K.; Shapiro, A.; Barloon, P.; Goetz, A. The spectral image processing system (sips)-Interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993, 44, 145-163.
35. Hsu, C.; Chang, C.; Lin, C. A Practical Guide to Support Vector Classification. Available online: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (acessed on 22 August 2017).
36. Story, M.; Congalton, R.G. Accuracy assessment: A user's perspective. Photogramm. Eng. Remote Sens. 1986, 52, 397-399.
37. Stehman, S.V.; Czaplewski, R.L. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sen. Environ. 1998, 64, 331-344.
38. Rosenfield, G.H.; Fitzpatrick-Lins, K. A coefficient of agreement as a measure of thematic classification accuracy. Photogramm. Eng. Remote Sens. 1986, 52, 223-227.
39. Wehrhan, M.; Rauneker, P.; Sommer, M. Uav-based estimation of carbon exports from heterogeneous soil landscapes-A case study from the carbozalf experimental area. Sensors 2016, 16, 255.
40. Kelcey, J.; Lucieer, A. Sensor correction and radiometric calibration of a 6-band multispectral imaging sensor for UAV remote sensing. ISPRS Int. Arch. Photogramm. Remote Sens. Space Inf. Sci. 2012, 1, 393-398.
41. Li, H.; Liu, W.; Dong, B.; Kaluzny, J.V.; Fawzi, A.A.; Zhang, H.F. Snapshot hyperspectral retinal imaging using compact spectral resolving detector array. J. Biophotonics 2017, 10, 830-839.
42. Rouse, J.W., Jr.; Haas, R.; Schell, J.; Deering, D. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ. 1974, 351, 309-317.
43. Jordan, C.F. Derivation of leaf-area index from quality of light on the forest floor. Ecology 1969, 663-666.
44. Richardson, A.J.; Everitt, J.H. Using spectral vegetation indices to estimate rangeland productivity. Geocarto Int. 1992, 7, 63-69.
45. Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from eos-modis. Remote Sens. Environ. 1996, 58, 289-298.
46. Roujean, J.-L.; Breon, F.-M. Estimating par absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 1995, 51, 375-384.
47. Woebbecke, D.; Meyer, G.; Von Bargen, K.; Mortensen, D. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 1995, 38, 259-269.
48. Meyer, G.E.; Hindman, T.W.; Laksmi, K. Machine vision detection parameters for plant species identification. Proc. SPIE 1999, 3543.
49. Camargo Neto, J. A Combined Statistical-Soft Computing Approach for Classification and MappingWeed Species in Minimum-Tillage Systems. Ph.D. Thesis, University of Nebraska, Lincoln, NE, USA, 2004.
50. Kataoka, T.; Kaneko, T.; Okamoto, H. Crop growth estimation system using machine vision. In Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Kobe, Japan, 20-24 July 2003; pp. 1079-1083.
51. Woebbecke, D.M.; Meyer, G.E.; Von Bargen, K.; Mortensen, D.A. Plant species identification, size, and enumeration using machine vision techniques on near-binary images. Proc. SPIE 1993, 1836, 208-219.
52. Petropoulos, G.P.; Vadrevu, K.P.; Xanthopoulos, G.; Karantounias, G.; Scholze, M. A comparison of spectral angle mapper and artificial neural network classifiers combined with landsat tm imagery analysis for obtaining burnt area mapping. Sensors 2010, 10, 1967-1985.
53. Yang, C.; Everitt, J.H.; Murden, D. Evaluating high resolution spot 5 satellite imagery for crop identification. Comput. Electron. Agric. 2011, 75, 347-354.
54. Rasmussen, J.; Ntakos, G.; Nielsen, J.; Svensgaard, J.; Poulsen, R.N.; Christensen, S. Are vegetation indices derived from consumer-grade cameras mounted on uavs sufficiently reliable for assessing experimental plots? Eur. J. Agron. 2016, 74, 75-92.
55. Haghighattalab, A.; González Pérez, L.;Mondal, S.; Singh, D.; Schinstock, D.; Rutkoski, J.; Ortiz-Monasterio, I.; Singh, R.P.; Goodin, D.; Poland, J. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Method. 2016, 12, 35.
56. Heesup, Y.; Hak-Jin, K.; Kido, P.; Kyungdo, L.; Sukyoung, H. Use of an uav for biomass monitoring of hairy vetch. In Proceedings of the ASABE Annual InternationalMeeting, New Orleans, IN, USA, 26-29 July 2015; p. 1.