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Remote Sensing for Organic and Conventional Corn Assessment

Balashova, Natalia
http://orcid.org/0000-0002-9159-1779
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1446803968

Abstract Details

2015, Master of Science (MS), Bowling Green State University, Geology.
The aim of this study is to evaluate the effectiveness of remote sensing in distinguishing organic and conventional corn. The hypothesis of the research is that the difference between organic and conventional corn can be detected based on the dissimilarities in their vigor and maturity, which are commonly altered by different agricultural management and nutrient application to soil. Hyperspectral in situ measurements as well as multispectral reflectance along with narrow and wide band vegetation indices were assessed. Two available cloud-free Landsat 7 and Landsat 8 data sets were used in the analysis, one for the mid- growing season and another for the pre-harvest (maturity) season. Overall, the organic corn demonstrates higher values of chlorophyll- and nitrogen- related narrow band indices at the mid-season and at maturity stage, based on the in situ measurements. The results indicate a significant difference between two types of corn, particularly at 410, 545, 710, and 760 nm. A 750/550 ratio and MSR705 index, calculated from reflectances at 445, 705, and 740 nm, are the most effective indices for the corn separation. The wide band indices, calculated from the satellite data in the blue, red, NIR, and MIR regions, are effective in corn type determination. Landsat 8 color composite images with indices 1/4, 6/2, and 5/7 (Coastal Aerosol/red, SWIR1/blue, NIR/SWIR2) as well as 1/4, 6/2, 5/4 (Coastal Aerosol/red, SWIR1/blue, NIR/red) indices are able to separate the organic and conventional parcels in the mid-season and pre-harvest time in the study. Landsat 8 has more potential than Landsat 7 to discriminate corn type (organic vs. conventional) in both the mid-season and pre-harvest time. More research should be conducted in order to understand the factors that cause the differences between the two types of corn. Chlorophyll measurements, soil chemistry data, soil textural analysis, and soil moisture records were not available for this study.
Anita Simic, Ph.D. (Advisor)
Peter Gorsevski, Ph.D. (Committee Member)
Enrique Gomezdelcampo, Ph.D. (Committee Member)
91 p.
Remote Sensing
Remote sensing; corn; organic and conventional farming; Landsat; hyperspectral reflectance; multispectral measurements

Recommended Citations

Citations

  • Balashova, N. (2015). Remote Sensing for Organic and Conventional Corn Assessment [Master's thesis, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1446803968

    APA Style (7th edition)

  • Balashova, Natalia. Remote Sensing for Organic and Conventional Corn Assessment. 2015. Bowling Green State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1446803968.

    MLA Style (8th edition)

  • Balashova, Natalia. "Remote Sensing for Organic and Conventional Corn Assessment." Master's thesis, Bowling Green State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1446803968

    Chicago Manual of Style (17th edition)

bgsu1446803968
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© 2015, some rights reserved.Creative Commons License Remote Sensing for Organic and Conventional Corn Assessment by Natalia Balashova is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Bowling Green State University and OhioLINK.