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Open AccessArticle

Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data

1
Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
2
Food Agility Cooperative Research Centre, University of New England, Armidale, NSW 2351, Australia
3
Central Tablelands Local Land Services, Mudgee, NSW 2850, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 253; https://doi.org/10.3390/rs11030253
Received: 10 December 2018 / Revised: 17 January 2019 / Accepted: 23 January 2019 / Published: 27 January 2019
In livestock grazing environments, the knowledge of C3/C4 species composition of a pasture field is invaluable, since such information assists graziers in making decisions around fertilizer application and stocking rates. The general aim of this research was to explore the potential of multi-temporal Sentinel-1 (S1) Synthetic Aperture Radar (SAR) to discriminate between C3, C4, and mixed-C3/C4 compositions. In this study, three Random Forest (RF) classification models were created using features derived from polarimetric SAR (polSAR) and grey-level co-occurrence textural metrics (glcmTEX). The first RF model involved only polSAR features and produced a prediction accuracy of 68% with a Kappa coefficient of 0.49. The second RF model used glcmTEX features and produced prediction accuracies of 76%, 62%, and 75% for C3, C4, and mixed C3/C4 grasses, respectively. The glcmTEX model achieved an overall prediction accuracy of 73% with a Kappa coefficient of 0.57. The polSAR and glcmTEX features were then combined (COMB model) to improve upon their individual classification performances. The COMB model produced prediction accuracies of 89%, 81%, and 84% for C3, C4, and mixed C3/C4 pasture grasses, and an overall prediction accuracy of 86% with a Kappa coefficient of 0.77. The contribution of the various model features could be attributed to the changes in dominant species between sampling sites through time, not only because of climatic variability but also because of preferential grazing. View Full-Text
Keywords: satellite remote sensing; pasture grass classification; C-band synthetic aperture radar; grey-level co-occurrence matrix satellite remote sensing; pasture grass classification; C-band synthetic aperture radar; grey-level co-occurrence matrix
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MDPI and ACS Style

Crabbe, R.A.; Lamb, D.W.; Edwards, C. Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data. Remote Sens. 2019, 11, 253. https://doi.org/10.3390/rs11030253

AMA Style

Crabbe RA, Lamb DW, Edwards C. Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data. Remote Sensing. 2019; 11(3):253. https://doi.org/10.3390/rs11030253

Chicago/Turabian Style

Crabbe, Richard A.; Lamb, David W.; Edwards, Clare. 2019. "Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data" Remote Sens. 11, no. 3: 253. https://doi.org/10.3390/rs11030253

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