Improved remote sensing methods to detect northern wild rice (Zizania palustris l.)

Kristen O'Shea, Jillian LaRoe, Anthony Vorster, Nicholas Young, Paul Evangelista, Timothy Mayer, Daniel Carver, Eli Simonson, Vanesa Martin, Paul Radomski, Joshua Knopik, Anthony Kern, Colin K. Khoury

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Declining populations of Zizania palustris L. (northern wildrice, or wildrice) during the last century drives the demand for new and innovative techniques to support monitoring of this culturally and ecologically significant crop wild relative. We trained three wildrice detection models in R and Google Earth Engine using data from annual aquatic vegetation surveys in northern Minnesota. Three different training datasets, varying in the definition of wildrice presence, were combined with Landsat 8 Operational Land Imager (OLI) and Sentinel-1 C-band synthetic aperture radar (SAR) imagery to map wildrice in 2015 using random forests. Spectral predictors were derived from phenologically important time periods of emergence (June-July) and peak harvest (August-September). The range of the Vertical Vertical (VV) polarization between the two time periods was consistently the top predictor. Model outputs were evaluated using both point and area-based validation (polygon). While all models performed well in the point validation with percent correctly classified ranging from 83.8% to 91.1%, we found polygon validation necessary to comprehensively assess wildrice detection accuracy. Our practical approach highlights a variety of applications that can be applied to guide field excursions and estimate the extent of occurrence at landscape scales. Further testing and validation of the methods we present may support multiyear monitoring which is foundational for the preservation of wildrice for future generations.

    Original languageEnglish (US)
    Article number3023
    JournalRemote Sensing
    Volume12
    Issue number18
    DOIs
    StatePublished - Sep 2 2020

    Bibliographical note

    Funding Information:
    Funding: This research was funded by USDA Agricultural Research Service (award #007271-00002). The USDA is an equal opportunity employer and provider. Support was also provided by the National Aeronautics and Space Administration through contract NNL16AA05C and cooperative agreement NNX14AB60A. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Aeronautics and Space Administration.

    Funding Information:
    This research was funded by USDA Agricultural Research Service (award #007271-00002). The USDA is an equal opportunity employer and provider. Support was also provided by the National Aeronautics and Space Administration through contract NNL16AA05C and cooperative agreement NNX14AB60A. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Aeronautics and Space Administration. We would like to thank KatieWalker, Charles Whittemore, and Leorah McGinnis from the NASA DEVELOP program for their support of preliminary method development. We also thank Ann Geisman, Stephanie Simon, Donna Perleberg, and Calub Shavlik from the Minnesota Department of Natural Resources for providing data, local insight, and expert knowledge of the area

    Publisher Copyright:
    © 2020 by the authors.

    Keywords

    • Crop wild relative
    • Emergent aquatic vegetation
    • Google earth engine
    • Landsat 8 oli
    • Random forest
    • Sentinel-1 c-band SAR
    • Wildrice

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