Natural Resources, School of


Date of this Version

Spring 4-18-2013


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Natural Resource Sciences, Under the Supervision of Professor Ayse Kilic. Lincoln, Nebraska: May, 2013

Copyright (c) 2013 Sonisa Sharma


Tillage management practices are an important component to crop production and to federal and state conservation efforts and crop subsidy programs. Crop residue created by conservation tillage reduces soil erosion and reduce evaporation from exposed soil. Agro-hydrological models require information on tillage practices to estimate their impacts on soil-water-holding capacity, total evapotranspiration, carbon sequestration, water runoff and water and wind erosion for agricultural lands. Classification of tillage practices using remote sensing offers promise for the rapid collection of tillage information on individual fields over large areas. Using satellite imagery proves to be challenging due to the similarity in spectral signatures for soils and crop residues and the typically broad spectral bands used by moderate resolution satellites needed to cover large areas and with frequent revisit time. In this study, Landsat 5 images from Path 29, Row 32 in years 2008 and 2009 acquired over southeastern Nebraska (NE) were used to discriminate tillage practices using a Quadratic Discriminant Analysis (QDA). Ground truth data regarding the presence or absence of no-till practices were collected by the US Department of Agriculture–Natural Resources and Conservation Service (USDA–NRCS) at 31 locations in Adams and Fillmore Counties. Results indicated that Landsat‑TM bands 1, 3, 4, 5 and 7 classified 75-91% correctly for no-till and 20-55% for till in March and May of 2008 and 2009 respectively. Similarly, the Landsat based tillage indices such as simple tillage index, and the normalized difference tillage index and Normalized difference of Bands 1 and 5 discriminated tillage practices in March and May of 2008 and 2009 images with 81-91% for no-till and 60%, 12-26% for till respectively. When prediction was performed using training model May 2009, there was 81% classification accuracy under no-till and 24 % under till for May 2008 image. The QDA approach with Landsat 5 data appears to be efficient and effective in classifying tillage practices over large areas.

Adviser: Ayse Kilic