Investigating the potential of Sentinel-1 to detect varying spatial heterogeneity in pasture cover in grasslands

Richard Crabbe, David W. Lamb, Clare Edwards

Research output: Contribution to journalArticlepeer-review

Abstract

Selective grazing by livestock may be indicative of a site’s grass species diversity and depending on the grazing intensity; this may or may not promote further diversity. However, the detection of sites with spatial heterogeneity in pasture cover as a manifestation of selective grazing has not yet been investigated using satellite remote sensing. Thus, this study was conducted to address the question; can Sentinel-1 detect spatial heterogeneity induced by livestock grazing in grassy fields? Since Synthetic Aperture Radar (SAR) imaging is noted to be sensitive to vegetation architectural arrangement, this study used Sentinel-1 C-band SAR to detect spatial heterogeneity created by selective livestock grazing. The study examined a range of semivariogram, grey-level co-occurrence matrix (GLCM), and eigenvector-eigenvalue polarimetric decomposition features. The coefficient of variation estimates of the GLCM contrast feature consistently produced the strongest correlation (R 2 = 0.71) with Lloyd’s Patchiness Index and semivariogram sill while the polarimetric scattering entropy (range estimates) produced a significant linear correlation with semivariogram sill (R 2 = 0.55, p < 0.05). Inferably, the GLCM contrast and polarimetric scattering entropy can predict spatial heterogeneity in a grazing environment. This is the first time polarimetric scattering entropy estimated from Sentinel-1 has been used for the detection of spatial heterogeneity in a grazing landscape, which makes this study different from past similar studies. Nonetheless, we recommend the testing of this parameter (polarimetric scattering entropy) with a multitemporal SAR data and encourage future studies to investigate the potential of Sentinel-1 for the detection of spatial distances between grass clumps.

Original languageEnglish
Pages (from-to)254-265
Number of pages12
JournalInternational Journal of Remote Sensing
Volume42
Issue number1
Early online date10 Nov 2020
DOIs
Publication statusPublished - Jan 2021

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