Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition

Abstract

Assessing the condition of ecosystems is imperative for understanding their degree of degradation and managing their conservation. The increasing availability of remote sensing products offers unprecedented opportunities for mapping vegetation with high detail and accuracy. However, mapping complex ecosystems, like grasslands, remains challenging due to their heterogeneity in vegetation composition and structure. Furthermore, degraded ecosystems affected by invasive vegetation present different condition levels within vegetation classes, limiting the accuracy of classifications and condition assessment. Here, we evaluated the capacity of Sentinel-2 multispectral time series imagery as an input for classifying different levels of cover within a vegetation class to detect the subtle differences needed to assess the condition of degraded ecosystems. Our study was conducted in the iron-grasslands of South Australia, a perennial tussock grassland dominated by iron-grasses (Lomandra spp.) and severely affected by invasive annual grasses. We developed random forest models to discriminate classes defined by the cover of iron-grasses, wild oats (Avena barbata), and woodland (training points = 250). We tested the importance of data seasonality, spatial resolution, spectral bands, and vegetation indices. The combination of spatial, temporal, and spectral detail produced the best classification results. Random forest classifications performed best at 10 m resolution, suggesting that detailed resolution outweighs spectral detail for discriminating vegetation patterns in systems with high spatial heterogeneity. The model at 10 m resolution combining all periods and all variables (spectral bands and vegetation indices) produced a mean kappa coefficient of 56% and a mean overall accuracy of 67%. The dry season imagery and vegetation indices emerged as the most informative, suggesting that vegetation classes presented different phenological properties critical for their discrimination. Our study contributes to mapping complex ecosystems, facilitating the discrimination of different levels of condition in grasslands degraded by invasive species, and thus benefits the conservation of native grasslands and other ecosystems.