Speaker
Description
Detection of land surface phenology at the landscape scale enables to investigate the spatio-temporal patterns of plant phenology and their relationship with environmental variability and climatic drivers. In fact, vegetation phenology is highly sensitive to climate conditions and is a climate change fingerprint.
Along with the availability of high resolution satellite time series and proven methodologies to extract temporal information from satellite acquisitions, comes the need for procedures to generate high resolution Earth observation derived phenological metrics that could serve a wide range of applications, like monitor ecological status, environmental conditions, climate change impacts on ecosystems, cropland practices, and ecosystem disturbances. Forest disturbances, like logging and wildfires, determine a loss in terms of woody biomass and modify Earth carbon balance. Earth observation capacity to monitor forest disturbance and regeneration plays a fundamental role in supporting sustainable ecosystem management and surveillance. Procedures for monitoring and classifying forest disturbances using satellite Earth observation data have improved over the past years, with the development of many algorithms that exploit dense time series at high spatial resolution. Characterization of forest disturbance, like identification of event occurrence dates, represents public authorities and practitioners information needs related to altered ecosystems.
Results from the applications of Sentinel-1 satellite derived high spatial resolution phenological metrics in the field of forest disturbance are here presented. The proposed procedure estimates phenological metrics using local curve fitting and local derivatives to identify phenophases, operating without thresholds or a priori information. A set of different Radar Vegetation Indices (RVI), estimated from Sentinel-1 satellites observations, has been tested to derive phenological metrics using a local smoothing procedure. Proper radiometric radiometric terrain correction allowed the synergic use of multiple relative orbit acquisitions, and the use of weighted smoothing procedure enhance vegetation indices time series integrity. A comparison with Leaf Area Index (LAI) derived phenological metrics, estimated from high spatial resolution optical satellite time series, shows strengths and weaknesses for the two source datasets.
High spatial resolution smoothed time series and phenological metrics open up to the provision of novel temporal information about forest phenology anomalies, and useful monitoring system to scrutinize spatio-temporal patterns of forest disturbances, as demonstrated from a showcase of forest logging in central Italy. This study highlights the importance of integrated data and methologies to support the processes of vegetation recognition and monitoring activities. Earth observation SAR time series represent a promising tool for a wide range of applications, as monitor terrestrial ecosystems, environmental conditions, and cropland practices.
