The High Arctic and alpine areas are going through a rapid climate change. Temperature variation influences the length of the growing season, 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 and arctic ecosystems. Optical remote sensing, through spectral vegetation indices, has been extensively used for monitoring vegetation dynamics. Moreover, with a 10 m spatial resolution of Sentinel-2A and 2B it is now possible to study phenology at a level of vegetation community. However, there are challenges of processing optical data, namely clouds and their shadows, which interferes with remote sensing studies. Cloud detection in the Arctic and alpine areas is especially time consuming since cloud-contaminated conditions are frequent. Hence, optical data require corrections according to different environmental conditions in order to be enabled in vegetation studies. To assess the accuracy of satellite processed products, a field validation is necessary. Nevertheless, due to remote location, extreme climatic conditions, short growing season and high cost of sampling, field-based manual phenological observations in mountain regions are often problematic. To overcome traditional approaches, proximal sensors were proven to be a key method to validate phenological phases. Indices derived from digital cameras and NDVI sensors offer the opportunity of recording data with high spatial and temporal resolution in remote areas, that can consequently be used in optical satellite validation. In mountain regions, where phenological phases are strictly dependent on altitude and exposition, a network of proximal sensors needs to cover a wide spatial and vertical gradient. In this study, we present a network of phenocams and NDVI sensors in Adventdalen (Svalbard archipelago) and alpine areas of Dovrefjell National park (Norway) and South-Tyrol (Italy). The aim of the project is to validate phenological phases derived from Sentinel-2A and 2B. After a pre-processing phase, seasonal parameters will be mapped, as the onset and end of the growing season. In particular, the aim of the project is: ∙ to validate a phenolgy map of a clear sky NDVI time-series; ∙ to analyse proximal sensor products of phenology on different vegetation communities, altitude and exposition

Proximal sensing phenology data to validate Sentinel-2 in cold regions

Renato Gerdol;
2018

Abstract

The High Arctic and alpine areas are going through a rapid climate change. Temperature variation influences the length of the growing season, 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 and arctic ecosystems. Optical remote sensing, through spectral vegetation indices, has been extensively used for monitoring vegetation dynamics. Moreover, with a 10 m spatial resolution of Sentinel-2A and 2B it is now possible to study phenology at a level of vegetation community. However, there are challenges of processing optical data, namely clouds and their shadows, which interferes with remote sensing studies. Cloud detection in the Arctic and alpine areas is especially time consuming since cloud-contaminated conditions are frequent. Hence, optical data require corrections according to different environmental conditions in order to be enabled in vegetation studies. To assess the accuracy of satellite processed products, a field validation is necessary. Nevertheless, due to remote location, extreme climatic conditions, short growing season and high cost of sampling, field-based manual phenological observations in mountain regions are often problematic. To overcome traditional approaches, proximal sensors were proven to be a key method to validate phenological phases. Indices derived from digital cameras and NDVI sensors offer the opportunity of recording data with high spatial and temporal resolution in remote areas, that can consequently be used in optical satellite validation. In mountain regions, where phenological phases are strictly dependent on altitude and exposition, a network of proximal sensors needs to cover a wide spatial and vertical gradient. In this study, we present a network of phenocams and NDVI sensors in Adventdalen (Svalbard archipelago) and alpine areas of Dovrefjell National park (Norway) and South-Tyrol (Italy). The aim of the project is to validate phenological phases derived from Sentinel-2A and 2B. After a pre-processing phase, seasonal parameters will be mapped, as the onset and end of the growing season. In particular, the aim of the project is: ∙ to validate a phenolgy map of a clear sky NDVI time-series; ∙ to analyse proximal sensor products of phenology on different vegetation communities, altitude and exposition
2018
Phenology; Sentinel-2; Proximal sensing; High Arctic; Mountain regions
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2399226
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