Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy)

Tommaso Orusa,Enrico Borgogno Mondino

doi:10.3390/cli9030047

Abstract

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.

Highlights

Satellite-based remote sensing has been proved to effectively support vegetation monitoring [1]
An ecological and evolutionary dilemma is posed to a variety of organisms, in particular vegetation, nowadays because of the environmental modifications related to climate change: can they respond properly to these ongoing changes? Does their phenology remain synchronous with other mechanisms or species that they interact with? Can they adapt their responses consistently with regard to the timing of reproduction? These questions are difficult to be answered without long-term observations and experiments
Exploring short-term climate change effects on vegetation through pheno-meteorological modeling and relative trends such as ET and phenological metrics (PMs), snowpack time persistence to the ground derived from satellites EO data with different time scales provide useful information about biosphere–atmosphere exchanges of carbon, energy, and water at both regional and global scale

Summary

Introduction

Satellite-based remote sensing has been proved to effectively support vegetation monitoring [1]. The long-term monitoring of rangelands behavior could drive one to better understand the dynamics of reaction/adaptation (resilience) of vegetation to climate change. In this context, remotely sensed data from EO (earth observation) missions operating over a longtime range could proficiently support this analysis. Frost damage might increase if the date of the last spring frost posticipates and if the rate of change in frost dates is slower than the one of snowmelt dates Under these conditions, the knowledge of phenological trends at a wide scale within a certain area is crucial to adapt and plan pasture management under climate change. Observed trends of leaf senescence delay by remote sensing appearing less pronounced and being spatially heterogeneous are

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