Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/29625
Title: Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data
Contributor(s): Crabbe, Richard Azu (author); Lamb, David William  (author); Edwards, Clare  (author)
Publication Date: 2019-01-27
Open Access: Yes
DOI: 10.3390/rs11030253
Handle Link: https://hdl.handle.net/1959.11/29625
Abstract: 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.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 11(3), p. 1-20
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2072-4292
Fields of Research (FoR) 2008: 070104 Agricultural Spatial Analysis and Modelling
090905 Photogrammetry and Remote Sensing
Fields of Research (FoR) 2020: 300206 Agricultural spatial analysis and modelling
401304 Photogrammetry and remote sensing
Socio-Economic Objective (SEO) 2008: 830403 Native and Residual Pastures
Socio-Economic Objective (SEO) 2020: 100503 Native and residual pastures
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article

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