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Soil Science Society of America Journal Abstract - Soil & Water Management & Conservation

Broad-Scale Monitoring of Tillage Practices Using Sequential Landsat Imagery


This article in SSSAJ

  1. Vol. 77 No. 5, p. 1755-1764
    Received: Mar 16, 2013
    Published: September 20, 2013

    * Corresponding author(s): baojuan5@vt.edu
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  1. Baojuan Zheng *a,
  2. James B. Campbella,
  3. Yang Shaoa and
  4. Randolph H. Wynneb
  1. a Dep. of Geography Virginia Tech 115 Major Williams Hall Blacksburg, VA 24060
    b Forest Resources and Environmental Conservation Virginia Tech Cheatham Hall Blacksburg, VA 24061


Crop residue management is an important conservation practice that can preserve soil and water resources. Monitoring how farmers manage their land in terms of tillage practices can provide insights on the effectiveness of the conservation programs so that we can improve the implementation of conservation tillage practices to optimize crop yields and environmental conservation. Remote sensing is a cost-effective and time-efficient tool for tillage monitoring. Previous studies have established the minimum Normalized Difference Tillage Index (minNDTI) technique at a pixel level for tillage mapping by extracting the minimum values from time-series Landsat NDTI spectral profiles. The objective of this study was to evaluate the minNDTI technique and to devise strategies for mapping tillage practices at the field level. We incorporated a multiscale image segmentation approach to fill Landsat 7 scan line corrector (SLC)-off data gaps to facilitate site-specific and broad-scale mapping of tillage practices. The gap-filled Landsat 7 images were combined with Landsat 5 imagery to develop a multitemporal image series for tillage mapping. We then applied object-based approaches and utilized the USDA cropland data layer to produce field-level tillage maps, which were evaluated using field observations and Conservation Technology Information Center (CTIC) county-level tillage data. Our results show that the gap-filling procedure is effective for the purpose of tillage mapping. Overall classification accuracies of tillage maps range from 69 to 79%. Comparison between remotely sensed and CTIC tillage data suggests that we can monitor tillage practices systematically using remote sensing imagery.

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