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Article

Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.)

1
NASA DEVELOP National Program, NASA Langley Research Center MS 307, Hampton, VA 23681, USA
2
Natural Resource Ecology Laboratory, Colorado State University, 1499 Campus Delivery, Fort Collins, CO 80523, USA
3
Earth System Science Center, The University of Alabama in Huntsville, 320 Sparkman Drive, Huntsville, AL 35805, USA
4
SERVIR Science Coordination Office, NASA Marshall Space Flight Center, 320 Sparkman Drive, Huntsville, AL 35805, USA
5
United States Department of Agriculture, Agricultural Research Service, National Laboratory for Genetic Resources Preservation, 1111 South Mason Street, Fort Collins, CO 80521, USA
6
Minnesota Department of Natural Resources, 1601 Minnesota Drive, Brainerd, MN 56401, USA
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Department of Math, Science and Technology, University of Minnesota, Crookston, 2900 University Ave., Crookston, MN 56716, USA
8
International Center for Tropical Agriculture (CIAT), Km 17, Recta Cali-Palmira, Apartado Aéreo 6713, Cali 763537, Colombia
9
Department of Biology, Saint Louis University, 1 N. Grand Blvd., St. Louis, MO 63103, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(18), 3023; https://doi.org/10.3390/rs12183023
Received: 30 June 2020 / Revised: 18 August 2020 / Accepted: 9 September 2020 / Published: 16 September 2020
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. View Full-Text
Keywords: wildrice; crop wild relative; emergent aquatic vegetation; Landsat 8 OLI; random forest; Sentinel-1 C-band SAR; Google Earth Engine wildrice; crop wild relative; emergent aquatic vegetation; Landsat 8 OLI; random forest; Sentinel-1 C-band SAR; Google Earth Engine
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MDPI and ACS Style

O’Shea, K.; LaRoe, J.; Vorster, A.; Young, N.; Evangelista, P.; Mayer, T.; Carver, D.; Simonson, E.; Martin, V.; Radomski, P.; Knopik, J.; Kern, A.; Khoury, C.K. Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.). Remote Sens. 2020, 12, 3023. https://doi.org/10.3390/rs12183023

AMA Style

O’Shea K, LaRoe J, Vorster A, Young N, Evangelista P, Mayer T, Carver D, Simonson E, Martin V, Radomski P, Knopik J, Kern A, Khoury CK. Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.). Remote Sensing. 2020; 12(18):3023. https://doi.org/10.3390/rs12183023

Chicago/Turabian Style

O’Shea, Kristen, Jillian LaRoe, Anthony Vorster, Nicholas Young, Paul Evangelista, Timothy Mayer, Daniel Carver, Eli Simonson, Vanesa Martin, Paul Radomski, Joshua Knopik, Anthony Kern, and Colin K. Khoury 2020. "Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.)" Remote Sensing 12, no. 18: 3023. https://doi.org/10.3390/rs12183023

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