Temperature variation influences the length of the growing season in Alpine areas, causing shift in phenological phases of vegetation, with a reduction of ecosystems resilience. Notably, changes of the start and end of the growing season might have a significant impact on fragile mountain ecosystems. Optical remote sensing, through spectral vegetation indices, has been extensively used for monitoring vegetation dynamics. However, there are challenges of processing optical data, namely clouds and their shadows, which interferes with remote sensing studies. As opposed to optical images, Synthetic Aperture Radar (SAR) can provide a systematic data source of land surface and land cover changes that are insensitive to cloud contamination. Moreover, the free data policy adopted for the Copernicus programme allows us to use time-series from Sentinel-1 and Sentinel-2 with 20 and 10 m of spatial resolution, respectively. In this study we analysed the vegetation phenology in the alpine areas of South-Tyrol (Italy) by constructing time-series from both SAR and optical sensors, validating subsequently our results with a network of ground stations at different altitude (i.e. phenocams, NDVI sensors and temperature - soil moisture data loggers). After a noise removal using different techniques, several filters were applied to SAR and optical time-series. From the modelled values we extracted the Start of Season (SOS), Maximum and End of Season (EOS), and we validated our results using information from NDVI sensors and phenocams. From our study we can assume that multitemporal SAR signal and specifically the VH polarization can be used to detect phenological dynamics in grassland and multitemporal SAR signal is well correlated to the NDVI from Sentinel 2 and ground observations of vegetation indices.

SAR and Optical satellite sensors to detect phenology in Alpine areas

Renato Gerdol;
2018

Abstract

Temperature variation influences the length of the growing season in Alpine areas, causing shift in phenological phases of vegetation, with a reduction of ecosystems resilience. Notably, changes of the start and end of the growing season might have a significant impact on fragile mountain ecosystems. Optical remote sensing, through spectral vegetation indices, has been extensively used for monitoring vegetation dynamics. However, there are challenges of processing optical data, namely clouds and their shadows, which interferes with remote sensing studies. As opposed to optical images, Synthetic Aperture Radar (SAR) can provide a systematic data source of land surface and land cover changes that are insensitive to cloud contamination. Moreover, the free data policy adopted for the Copernicus programme allows us to use time-series from Sentinel-1 and Sentinel-2 with 20 and 10 m of spatial resolution, respectively. In this study we analysed the vegetation phenology in the alpine areas of South-Tyrol (Italy) by constructing time-series from both SAR and optical sensors, validating subsequently our results with a network of ground stations at different altitude (i.e. phenocams, NDVI sensors and temperature - soil moisture data loggers). After a noise removal using different techniques, several filters were applied to SAR and optical time-series. From the modelled values we extracted the Start of Season (SOS), Maximum and End of Season (EOS), and we validated our results using information from NDVI sensors and phenocams. From our study we can assume that multitemporal SAR signal and specifically the VH polarization can be used to detect phenological dynamics in grassland and multitemporal SAR signal is well correlated to the NDVI from Sentinel 2 and ground observations of vegetation indices.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2399230
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