Exploring short-term climate change effects on rangelands and broad-leaved forests by free satellite data in Aosta Valley (Northwest Italy)

Satellite remote sensing is a power tool for the long-term monitoring of vegetation. This work, with reference to a regional case study, investigates remote sensing potentialities for describing the annual phenology of rangelands and broad-leaved forests at the landscape level with the aim of detecting eventual effects of climate change in the Alpine region of the Aosta Valley (Northwest (NW) Italy). A first analysis was aimed at estimating phenological metrics (PMs) from satellite images time series and testing the presence of trends along time. A further investigation concerned evapotranspiration from vegetation (ET) and its variation along the years. Additionally, in both the cases the following meteorological patterns were considered: air temperature anomalies, precipitation trends and the timing of yearly seasonal snow melt. The analysis was based on the time series (TS) of different MODIS collections datasets together with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) collection obtained through Google Earth Engine. Ground weather stations data from the Centro Funzionale VdA ranging from 2000 to 2019 were used. In particular, the MOD13Q1 v.6, MOD16A2 and MOD10A1 v.6 collections were used to derive PMs, ET and snow cover maps. The SRTM (shuttle radar topography mission) DTM (digital terrain model) was also used to describe local topography while the Coordination of Information on the Environment (CORINE) land cover map was adopted to investigate land use classes. Averagely in the area, rangelands and broad-leaved forests showed that the length of season is getting longer, with a general advance of the SOS (start of the season) and a delay in the EOS (end of the season). With reference to ET, significant increasing trends were generally observed. The water requirement from vegetation appeared to have averagely risen about 0.05 Kg·m−2 (about 0.5%) per year in the period 2000–2019, for a total increase of about 1 Kg·m−2 in 20 years (corresponding to a percentage difference in water requirement from vegetation of about 8%). This aspect can be particularly relevant in the bottom of the central valley, where the precipitations have shown a statistically significant decreasing trend in the period 2000–2019 (conversely, no significant variation was found in the whole territory). Additionally, the snowpack timing persistence showed a general reduction trend. PMs and ET and air temperature anomalies, as well as snow cover melting, proved to have significantly changed their values in the last 20 years, with a continuous progressive trend. The results encourage the adoption of remote sensing to monitor climate change effects on alpine vegetation, with particular focus on the relationship between phenology and other abiotic factors permitting an effective technological transfer

Reading Greenness in Urban Areas: Possible Roles of Phenological Metrics from the Copernicus HR-VPP Dataset

Vegetation phenology is that branch of science that describes periodic plant life cycle events across the growing seasons. Remote sensing typically monitors these significant events by means of time series of vegetation indices, permitting to characterize vegetation dynamics. It is well known that vegetation in urban areas, i.e., green spaces in general, may benefit human health mainly by mitigating noise and air pollution, promoting physical or social activities, and improving mental health. Based on the influence that green space exposure seems to exert on Public Health and using a multidisciplinary approach, we mapped phenological behavior of urban green areas to explore yearly persistence of their potential favorable effect, such as heat reduction, air purification, noise mitigation, and promotion of physical/social activities and improvement of mental health. The study area corresponds to the municipality of Torino (about 800,000 inhabitants, NW, Italy). Renouncing to a rigorous at-species level phenological description, this work investigated macro-phenology of vegetated areas for the 2018, 2019 and 2020 years with reference to the new free and open Copernicus HR-VPP dataset. Vegetation type, deduced with reference to the 2019 BDTRE official technical map of the Piemonte Region, was considered and related to the correspondent macro-phenology using a limited number of metrics from the HR-VPP dataset. Investigation was aimed at exploring their capability of providing synthetic and easy-to-use information for urban planners. No validation was achieved about phenological metrics values (assuming their accuracy correspondent to the nominal one reported in the associated manuals). Nevertheless, a spatial validation was operated to investigate the capability of the dataset to properly recognize vegetated areas, thus providing correspondent metrics. Preliminary results showed a spatial inconsistency related to the HR-VPP dataset, that greatly overestimates (about 50%) vegetated areas in the city, assigning metric values to pixels that, if compared with technical maps, do not fall within vegetated areas. The work found out that, among HR-VPP metrics, LOS (Length Of Season) and SPROD (Seasonal Productivity) well characterized vegetation patches, making it possible to clearly read vegetation behavior, which can be effectively exploited to zone the city and make management of green areas and real estate considerations more effective