Discrimination of species composition types of a grazed pasture landscape using Sentinel-1 and Sentinel-2 data

Abstract

Species composition is one of the important measurable indices of alpha diversity and hence aligns with the measurable Essential Biodiversity Variables meant to fulfil the Aichi Biodiversity Targets by 2020. Graziers also seek for pasture fields with varied species composition for their livestock, but visual determination of the species composition is not practicable for graziers with large fields. Consequently, this study demonstrated the capability of Sentinel-1 Synthetic Aperture Radar (S1) and Sentinel-2 Multispectral Instrument (S2) to discriminate pasture fields with single-species composition, two-species composition and multi-species composition for a pastoral landscape in Australia. The study used K-Nearest Neighbours (KNN), Random Forest (RF) and Support Vector Machine (SVM) classifiers to evaluate the strengths of S1-alone and S2-alone features and the combination of these S1 and S2 features to discriminate the composition types. For the S1 experiment, KNN which was the reference classifier achieved an overall accuracy of 0.85 while RF and SVM produced 0.74 and 0.89, respectively. The S2 experiment produced accuracies higher than the S1 in that the overall performance of the KNN classifier was 0.87 while RF and SVM were 0.93 and 0.89, respectively. The combination of the S1 and S2 features elicited the highest accuracy estimates of the classifiers in that the KNN classifier recorded 0.89 while RF and SVM produced 0.96 and 0.93, respectively. In conclusion, the inclusion of S1 features improve the classifiers created with S2 features only.

Keywords

• Support vector machine
• Random forest
• Botanical composition
• Pasture species classification
• Synthetic aperture radar
• Satellite remote sensing