Exploring Sentinel-1 Radar Polarisation and Landsat Series Data to Detect Forest Disturbance from Dust Events: A Case Study of the Paphos Forest in Cyprus - Theocharidis, Christos; Eliades, Marinos; Kolokoussis, Polychronis; Miltiadou, Milto; Danezis, Chris; Gitas, Ioannis; Kontoes, Charalampos; Hadjimitsis, Diofantos G. (My University, 2025)
Monitoring forest health has become essential due to increasing pressures caused by climate change and dust events, particularly in semi-arid regions. This study investigates the impact of dust events on forest vegetation in Paphos forest in Cyprus, which is a semi-arid area prone to frequent dust storms. Using multispectral and radar satellite data from Sentinel-1 and Landsat series, vegetation responses to eight documented dust events between 2015 and 2019 were analysed, employing BFAST (Breaks For Additive Season and Trend) algorithms to detect abrupt changes in vegetation indices and radar backscatter. The outcomes showed that radar data were particularly effective in identifying only the most significant dust events (PM10 > 100 μg/m3, PM2.5 > 30 μg/m3), indicating that SAR (Synthetic Aperture Radar) is more responsive to pronounced dust deposition, where backscatter changes reflect more substantial vegetation stress. Conversely, optical data were sensitive to a wider range of events, capturing responses even at lower dust concentrations (PM10 > 50 μg/m3, PM2.5 > 20 μg/m3) and detecting minor vegetation stress through indices like SAVI, EVI, and AVI. The analysis highlighted that successful detection relies on multiple factors beyond sensor type, such as rainfall timing and imagery availability close to the dust events. This study highlights the importance of an integrated remote sensing approach for effective forest health monitoring in regions prone to dust events.
Keywords: time-series analysis; SAR; BFAST; dust storm; forest degradation; forest phenology; decomposition; climate change