Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification

TL;DR

This study compares Sentinel-2 satellite imagery with aerial imagery for classifying invasive serrated tussock grass, finding Sentinel-2 offers a cost-effective, scalable alternative with comparable accuracy by leveraging spectral and seasonal data.

Contribution

It demonstrates that multi-temporal Sentinel-2 imagery, combined with spectral and seasonal features, can effectively classify invasive species at landscape scale, rivaling aerial imagery performance.

Findings

Sentinel-2 achieved 68% accuracy, slightly higher than aerial imagery.

Multi-seasonal features improve classification performance.

Satellite imagery offers a scalable, cost-effective solution for invasive species monitoring.

Abstract

Invasive species pose major global threats to ecosystems and agriculture. Serrated tussock (\textit{Nassella trichotoma}) is a highly competitive invasive grass species that disrupts native grasslands, reduces pasture productivity, and increases land management costs. In Victoria, Australia, it presents a major challenge due to its aggressive spread and ecological impact. While current ground surveys and subsequent management practices are effective at small scales, they are not feasible for landscape-scale monitoring. Although aerial imagery offers high spatial resolution suitable for detailed classification, its high cost limits scalability. Satellite-based remote sensing provides a more cost-effective and scalable alternative, though often with lower spatial resolution. This study evaluates whether multi-temporal Sentinel-2 imagery, despite its lower spatial resolution, can provide a comparable and cost-effective alternative for landscape-scale monitoring of serrated tussock by leveraging its higher spectral resolution and seasonal phenological information. A total of eleven models have been developed using various combinations of spectral bands, texture features, vegetation indices, and seasonal data. Using a random forest classifier, the best-performing Sentinel-2 model (M76*) has achieved an Overall Accuracy (OA) of 68\% and an Overall Kappa (OK) of 0.55, slightly outperforming the best-performing aerial imaging model's OA of 67\% and OK of 0.52 on the same dataset. These findings highlight the potential of multi-seasonal feature-enhanced satellite-based models for scalable invasive species classification.