Plant trait time series from Sentinel-2 to detect drought stress in a mid-latitude forest ecosystem

Forest ecosystem conservation is of crucial importance for preserving biodiversity, regulating climate patterns, and providing ecosystem services essential for human well-being. The Ticino Park temperate mixed forest represents the last remaining natural ecosystem of the Po Valley region. Recognized as a UNESCO-MAB Biosphere Reserve, this precious ecosystem has been increasingly affected by natural and human-induced disturbances, including severe drought, exacerbated by climate change.

Remote sensing has proven to be a cost-effective tool for the indirect estimation and mapping of forest characteristics and conditions at different spatial and temporal scales. This study aims to develop a better understanding of the relationship between drought-induced forest stress, spectral changes observed from Sentinel-2 satellite data, and how these relate to functional traits and species composition. We believe this will help to identify spectral indicators and metrics for the early detection of drought-induced forest mortality.

In summer 2022, an intensive field campaign was carried out in the Ticino Park Forest. First, in June, data on functional traits, specifically Leaf Area Index (LAI), Leaf Chlorophyll Content (LCC) and Leaf water content (LWC), were collected within 31 homogeneous 30x30 mforest stands. Secondly, in September, a subset of 19 of the 31 stands initially sampled were revisited (for a total of 52 sampling stations). In addition, vascular plant species composition was analysed in 64 selected stands to define the different vegetation associations and calculate the corresponding Ellenberg indexes in order to ecologically characterise the sites. Meanwhile, the standardized precipitation-evapotranspiration index (SPEI) from 2017 to 2023 was calculated to assess the severity and duration of drought events in the Ticino Park area.

Concerning the remote sensing analysis, the time series of cloud-free Sentinel-2 images collected over the Ticino Park from 2017 to 2023 were processed to compute LAI, Canopy Chlorophyll content (CCC = LCC x LAI) and Canopy water content (CWC = LWC x LAI) maps of each image through the Sentinel Application Platform (SNAP) biophysical processor tool. LAI, CCC and CWC maps were validated using LAI, CCC and CWC field measurements. These plant functional trait time series were used to quantify the deviation of LAI, CCC and CWC at a precise location and time from the 2017-2023 multi-year daily averages, thus obtaining the standard anomalies. Generalized additive models (GAMs) were then applied to examine the correlation between functional trait anomalies and a series of factors expected to influence the response of plant traits to water stress, such as SPEI value, vegetation association, and other environmental characteristics.

This study is a first attempt to analyse by remote sensing-based approaches the vegetation response to extreme weather conditions, accounting for differences in local climatic conditions, species ecology, and environmental variables.