Document Type

Article

Publication Date

6-17-2024

Department

Department of Civil, Environmental, and Geospatial Engineering

Abstract

It is important to detect wintertime green vegetated cover (WGVC), since it includes cover crop information, which is one of the most important agricultural best management practices used today. Related to this, cover crop area, which is part of WGVC, has been estimated using survey methods traditionally, but remote sensing can be used as a more time- and cost-effective assessment. Previously developed pixel-based methods to assess cover crops using remote sensing can have a salt-and-pepper effect, which lowers classification accuracy. Therefore, object-based classification was applied to estimate the spatial distribution of WGVC across the entire state of Delaware. To reduce the financial burdens of fee-based software products, the workflow was formalized only with open-source remote sensing software and publicly available imagery. WGVC in this study was defined as any vegetation planted or surviving during winter on field crop areas. Obviously, the WGVC area estimated in this study was far more extensive than the surveyed area of conventionally defined cover crops, which had a narrower definition. Applying this methodology across Delaware, the total WGVC was estimated to be 137,297 ha between 12/26/2021 and 04/30/2022. The classification accuracy of each date was evaluated using samples collected from pan-sharpened Landsat 8 and 9 images, and the accuracies were higher than 85%. Kappa statistics were above 74% in all cases. The workflow in this study may improve time, labor, and cost efficiency in other areas.

Publisher's Statement

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. Publisher’s version of record: https://doi.org/10.1117/1.JRS.18.024515

Publication Title

Journal of Applied Remote Sensing

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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