Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy)

Abstract

On the 29th of October 2018, a storm named “Vaia” hit North-Eastern Italy, causing the loss of 8 million m3 of standing trees and creating serious damage to the forested areas, with many economic and ecological implications. This event brought up the necessity of a standard procedure for windthrow detection and monitoring based on satellite data as an alternative to foresters’ fieldwork. The proposed methodology was applied in Carnic Alps (Friuli Venezia Giulia, NE Italy) in natural stands dominated by Picea abies and Abies alba. We used images from the Sentinel-2 mission: 1) to test vegetation indices performance in monitoring the vegetation dynamics in the short period after the storm, and 2) to create a windthrow map for the whole Friuli Venezia Giulia region. Results showed that windthrows in forests have a significant influence on visible and short-wave infrared (SWIR) spectral bands of Sentinel-2, both in the short and the long-term timeframes. NDWI8A and NDWI were the best indices for windthrow detection (R2 = 0.80 and 0.77, respectively) and NDVI, PSRI, SAVI and GNDVI had an overall good performance in spotting wind-damaged areas (R2 = 0.60–0.76). Moreover, these indices allowed to monitor post-Vaia forest die-off and showed a dynamic recovery process in cleaned sites. The NDWI8A index, employed in the vegetation index differencing (VID) change detection technique, delimited damaged areas comparable to the estimations provided by Regional Forest System (2545 ha and 3183 ha, respectively). Damaged forests detected by NDWI8A VID ranged from 500 m to 1500 m a.s.l., mainly covering steep slopes in the south and east aspects (42% and 25%, respectively). Our results suggested that the NDWI8A VID method may be a cost-effective and accurate way to produce windthrow maps, which could limit the risks associated with fieldwork and may provide a valuable tool to plan tree removal interventions in a more efficient way.

Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

“…In fact, Vaglio Laurin et al (2020) [18] with a bi-temporal change detection approch obtained an overall accuracy of 86% for S2 using an image acquired after 7 months from the storm and 68% for S1 using data acquired after 15-20 days after the storm. Also, Olmo et al (2021) [22] using a bi-temporal approach between vegetation indices (vegetation index differences) calculated on S2 bands produced accurate results in detecting VAIA forest windthrow in Friuli Venezia Gilia. Olmo et al [22] showed with a multitemporal analysis based on vegetation indices that, starting from April 2019, it was possible to observe a deviation of reflectance values for damaged and undamaged areas comparing to the years before the storm.…”

“…Also, Olmo et al (2021) [22] using a bi-temporal approach between vegetation indices (vegetation index differences) calculated on S2 bands produced accurate results in detecting VAIA forest windthrow in Friuli Venezia Gilia. Olmo et al [22] showed with a multitemporal analysis based on vegetation indices that, starting from April 2019, it was possible to observe a deviation of reflectance values for damaged and undamaged areas comparing to the years before the storm. Piragnolo et al [23] showed that the multitemporal analysis of vegetation indices calculated on the S2 imagery are useful to predict severity classes of damaged areas using aggregational statistics of VIs as input to random forest machine learning algorithm.…”

“…Many of these studies use dense Landsat or MODIS TS as input data [40,46], while, the use of S2 and CCDC algorithm to map forest areas damaged by windstorms is limited [37]. Many recent studies on VAIA windstorm underlined that S2 is adequate to detect windstorm since S2 program offers innovative features for forest remote sensing by combining high spatial resolution (i.e., 13 bands, from 0.443 to 2.190 µm with the visible (i.e., R, G, B,) and the near infrared bands at 10-m spatial resolution and four red-edge bands at 20-m spatial resolution), wide coverage and a quick revisit time (i.e., every 5 days after the launch of Sentinel-2B satellite in 2017) [15,18,22,23]. However, they considered only a small portion of the area hit by VAIA windstorm and no-ones provided an estimation of damaged area.…”

Estimated Biomass Loss Caused by the Vaia Windthrow in Northern Italy: Evaluation of Active and Passive Remote Sensing Options

“…The Vaia storm, which occurred in Northeastern Italy at the end of October 2018, felled large volumes of timber causing serious ecological and financial losses, with unexpected challenges for forest managers. The wind gusts exceeded 200 km h −1 ; over 490 Municipalities in six administrative Regions registered forest damages over an area of 67,000 km 2 [1][2][3].…”

“…The delineation of impacted areas is frequently carried out with optical data [1][2][3]13], even if cloud cover can delay the availability of images for months as previously evidenced also in the Vaia case [14]. Synthetic Aperture Radar (SAR) data may also be employed, even if it requires expert knowledge.…”