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

Authors :
Kristen O'Shea
Vanesa Martín
Joshua Knopik
Eli Simonson
Paul Radomski
Nicholas E. Young
Daniel Carver
Paul H. Evangelista
Anthony G. Vorster
Jillian LaRoe
Colin K. Khoury
T. Mayer
Anthony Kern
Source :
Remote Sensing; Volume 12; Issue 18; Pages: 3023, Remote Sensing, Vol 12, Iss 3023, p 3023 (2020)
Publication Year :
2020
Publisher :
Multidisciplinary Digital Publishing Institute, 2020.

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.

Details

Language :
English
ISSN :
20724292
Database :
OpenAIRE
Journal :
Remote Sensing; Volume 12; Issue 18; Pages: 3023
Accession number :
edsair.doi.dedup.....494deed6f6784fb055a7ba7e2d9f830b
Full Text :
https://doi.org/10.3390/rs12183023