UAV multispectral survey to map soil and crop for precision farming applications

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 2016-January, Issue , 2016, Pages 1023-1029

  Sona, Giovanna ^a   Passoni, Daniele ^a   Pinto, Livio ^a   Pagliari, Diana ^a   Masseroni, Daniele ^b   Ortuani, Bianca ^b   Facchi, Arianna ^b  

^a POLITECNICO DI MILANO   (Italy)

^b UNIVERSITY OF MILAN   (Italy)

Author keywords

DEM; Multispectral images; NDVI; Orthophoto; Precision farming; Soil and crop characterization; UAV

Indexed keywords

ANTENNAS; CROPS; DATA ACQUISITION; INFORMATION MANAGEMENT; REMOTE SENSING; SOILS; SURVEYS; UNMANNED AERIAL VEHICLES (UAV);

DIGITAL SURFACE MODEL (DSM); GROUND RESOLUTION; MULTISPECTRAL IMAGES; NDVI; ORTHO PHOTOS; PRECISION FARMING; RECENT RESEARCHES; SCIENTIFIC SOFTWARES;

IMAGE ENHANCEMENT;

EID: 84987898729     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5194/isprsarchives-XLI-B1-1023-2016     Document Type: Conference Paper

References (38)

1. Agisoft, 2006. Agisoft software from http://www.agisoft.com
2. Agüera, F., Carvajal, F., Pérez, M., 2011. Measuring sunflower nitrogen status from an unmanned aerial vehicle-based system and an on the ground device. ISPRS Archives, UAV-g 2011, 14-16 September, Zurich, Switzerland, Vol. 38(1)/C22, pp. 33-37.
3. Antonucci, F., Pallottino, F., Costa, C., Rimatori, V., Giorgi, S., Papetti, P., Menesatti, P., 2011. Development of a Rapid Soil Water Content Detection Technique Using Active Infrared Thermal Methods for In-Field Applications. Sensors, 11, pp. 10114-10128.
4. ArcGIS, 2016. ArcGIS software from http//:www.arcgis.com
5. Berni, J.A.J., Zarco-Tejada, P.J., Suárez, L., Fereres, E., 2009. Thermal and narrowband multispectral Remote Sensing for vegetation monitoring from an Unmanned Aerial Vehicle. Geoscience and Remote Sensing, Vol. 47(3), pp. 722-738.
6. Chang, C.W., Laird, D., Mausbach, M.J., Hurburgh, C.R. Jr., 2001. Near-Infrared Reflectance Spectroscopy-Principal Components Regression Analyses of Soil Properties. Soil Sci. Soc. Am. J., 65, 480-490.
7. Colomina, I., Molina, P., 2014. Unmanned Aerial Systems for Photogrammetry and Remote Sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 92, pp. 79-97.
8. Corwin, D.L., Lesch, S.M., Shouse, P.J., Soppe, R., Ayars, J.E., 2003. Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity. Agron. Journal, 95, pp. 352-364
9. Corwin, D.L., 2008. Past, Present, and Future Trends of Soil Electrical Conductivity Measurements Using Geophysical Methods. In: Allred, B.J., Daniels, J.J., Reza Eshani, M., editors. Handbook of Agricultural Geophysics. CRC Press, Taylor & Francis Group; New York, NY, USA., pp. 17-44
10. Dandois J.P., Olano M., Ellis E.C., 2015. Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure, Remote Sensing 7, 13895-13920.
11. DeBell L., Anderson K., Brazier R.E., King N., Jones L., 2015. Water resource management at catchment scales using lightweight UAVs: current capabilities and future perspectives. Journal of Unmanned Vehicle Systems 3.1 pp 1-24.
12. Dunford, R., Michel, K., Gagnage, M., Piegay, H., Trémelo, M.L., 2009. Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest. International Journal of Remote Sensing, Vol. 30(19), pp. 4915-4935
13. ENVI, 2013. ENVI software from http://www.exelisvis.com/ENVIProducts
14. Facchi et al. 2015. Report of the Project "STeP - Sensing Technologies for Precision Agriculture", funded by the Department of Agricultural and Environmental Sciences of the University of Milan (UNIMI) through the Development Plan UNIMI 2014.
15. Forlani, G., 1990. Compensazione congiunta di osservazioni geodetiche e fotogrammetriche: il programma calge e la simulazione di un esempio per cartografia a grande scala. Ricerche di Geodesia e Fotogrammetria. Vol. 8. Casa Editrice Clup. Milano.
16. Gholizadeh A., Boruvka L., Saberioon M., Vasat R., 2013. Visible, near-infrared and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: state of the art and key issues. Applied spectroscopy 67(12), pp 1349-1362.
17. Fridgen J.L., Kitchen N.R., Sudduth K.A., Drummond S.T., Wiebold W.J., Fraisse C.W. 2004. Management zone analyst (MZA): Software for sub-field management zone delineation. Agron. J., 96, pp. 100-108
18. Gini, R., Passoni, D., Pinto, L., Sona, G., 2014. Use of Unmanned Aerial Systems for multispectral survey and tree classification: a test in a park area of northern Italy. European Journal of Remote Sensing, Vol. 47, pp. 251-269.
19. Gomez, C, Viscarra Rossel, R.A., McBratney, A.B., 2008. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma, 146, pp. 403-411
20. Hunt, E.R.J., Cavigelli, M., Daugherty, C.S.T., McMurtrey, J.E., Walthall, C.L., 2005. Evaluation of digital photography from model aircraft for Remote Sensing of crop biomass and nitrogen status. Precision Agriculture, Vol. 6, pp. 359-378.
21. Laliberte, A. S., Goforth, M.A., Steele, C.M., Rango, A., 2011. Multispectral Remote Sensing from Unmanned Aircraft: image processing workflows and applications for rangeland environment. Remote Sensing, Vol. 3(11), pp. 2529-2551.
22. López-Lozano, R., Casterad, M.A., Herrero, J., 2010. Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping. Computers and Electronics in Agriculture, 73, pp. 219-229
23. Lu, S., Ju, Z., Ren, T., Horton, R., 2009. A general approach to estimate soil water content from thermal inertia. Agricultural and. Forest Meteorology., 149, pp. 1693-1698
24. Maltese, A., Cammalleri, C, Ciraolo, G., D'Asaro, F., 2010. A thermal inertia model for soil water content retrieval using thermal and multispectral images. Proc. SPIE 7824, "Remote Sensing for Agriculture, Ecosystems and Hydrology XII", 78241G.
25. Minacapilli, M., Cammalleri, C, Ciraolo, G., D'Asaro, F., Iovino, M., Maltese, A., 2012. Thermal Inertia Modeling for Soil Surface Water Content Estimation: A Laboratory Experiment. Soil Science Society of Amrica Journal, 76, pp. 92-100.
26. Minacapilli, M., Iovino, M., Blanda, F., 2009. High resolution remote estimation of soil surface water content by a thermal inertia approach. Journal of. Hydroogy, 379, pp. 229-238
27. Moral, F.J., Terron, J.M., Marques da Silva, J.R., 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil and Tillage Research, 106, pp. 335-343
28. Morari, F., Castrignanò, A., Pagliarin, C, 2009. Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors. Computers and Electronics in Agriculture, 68, pp. 97-107.
29. Nebiker, S., Annen, A., Scherrer, M., Oesch, D., 2008. A lightweight multispectral sensor for micro UAV - Opportunities for very high resolution airborne Remote Sensing. ISPRS Archives, ISPRS Congress, 3-11 July, Beijing, China, Vol. 37/B1. pp. 1193-1199.
30. Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J., 1992. Soil pattern recognition with fuzzy c-means: Application to classification and soil-landform interrelationships. Soil Science Society of America Journal, 56, pp. 506-516
31. Ortuani B., Chiaradia E.A., Priori S., L'Abate G., Canone D., Comunian A., Giudici M., Mele M., Facchi A. 2016. Mapping soil water capacity through EMI survey to delineate site specific management units within an irrigated field. Soil Science (in press).
32. Photomodeler, 2012. Photomodeler scanner software from http://www.photomodeler.com
33. Pix4D, 2015. Pix4D software from http//:www.pix4d.com
34. Scudiero, E., Teatini, P., Corwin, D.L., Deiana, R., Berti, A., Morari, F., 2013. Delineation of site-specific management units in a saline region at the Venice Lagoon margin, Italy, using soil reflectance and apparent electrical conductivity. Computers and Electronics in Agriculture, 99, pp. 54-64
35. Tetracam, 2012. Tetracam ADCLite http://www.tetracam.com
36. Van Meirvenne, M., Islam, M.M., De Smedt, P., Meerschman, E., Van De Vijver, E., Saey, T., 2013. Key variables for the identification of soil management classes in the aeolian landscapes of north-west Europe. Geoderma, 199, pp. 99-105.
37. Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., Skjemstad, J.O., 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, pp. 59-75
38. Zhang, W., Wang, K., Chen, H., He, X., Zhang, J., 2012. Ancillary information improves kriging on soil organic carbon data for a typical karst peak cluster depression landscape. Journal of the Science of Food and Agricolture, 92, pp. 1094-1102