Satellite times-series analysis and assessment of the BFAST algorithm to detect possible abrupt changes in forest seasonality utilising Sentinel-1 and Sentinel-2 data. Case study: Paphos forest, Cyprus

Abstract

Climate change can be described as the dominant factor all these decades concerning changes in forest phenology while, at the same time, temperature affects the development time (Barrett & Brown, 2021; X.Zhou et al., 2020; Suepa et al., 2016). Satellite image-time series data have proven their value regarding forest health and forest phenology observation. Monitoring continuous plant phenology is critical for the ecosystem at a regional and global level since the high sensitivity of vegetation life cycle to climate change; the so-called phenophases are essential biological indicators to comprehend how climate change has impacted these ecosystems and how this will change the ensuing years. (Buitenwerf, Rose, and Higgins 2015; Johansson et al. 2015).  This study conducts a time-series analysis using the breaks for additive season and trend (BFAST) time-series decomposition algorithm, to detect possible abrupt changes in forest seasonality and the impacts of extreme climatic events on forest health, examining Sentinel-1 and Sentinel-2 data for the period 2017-2021. The backscatter coefficient from Sentinel-1, Normalised Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and Green Chlorophyll Index (GCI) were created by Sentinel-2 and assessed to find possible correlations between them. All the satellite time-series data derived through the Google Earth Engine platform.The study area is the Paphos Forest, managed by the Department of Forest which could be described as a representative Mediterranean forest; thus, it is vital to monitor it because Mediterranean forests are expected to experience the first climate change in Europe. More specifically, the study focus on the Nortwest, West and Southwest areas of the Paphos Forest since the SAR images are from Ascending orbit. Moreover, Paphos forest has unspoiled vegetation, and a highly reduced number of forest wildfires have occurred in recent years, favouring the reliability of the research's results.   AcknowledgementsThe authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.