Serpentinites Outcrops Detection by Using Using Satellite (S2, WV3, Hyperion) Imagery and Airborne Hyperspectral Thermal Data: the San Severino Area and the Taro River Valley (Italy) Case Studies

In Italy, serpentinites and ultramafic rocks are common along the Alps and Apennines Mountains.

The mapping of the serpentinites outcrops is of interest to the public Authorities because these units could potentially contain asbestos mineral (i.e. Naturally Occurring Asbestos - NOA) such as chrysotile, tremolite and actinolite, which show a high potential hazard to human health. NOA when in fibers could be inhaled and lead to lung cancer.

In the framework of the BRIC 2016-2018 research activities supported by the Italian National Institute for Insurance against Accidents at Work with project 57/2016 has the objective to review the different sensing technologies from proximal to remote (from airborne to satellite) sensing to detect the NOA and the man made materials containing asbestos.

Asbestos minerals spectral features are at 1.385 , 2.323 and 9.6 μm. SWIR spectral regions have been exploited by both airborne and spaceborne imagery, while the LWIR spectral region has been less investigated. In particular, LWIR spectral range shows a clear absorption peak not overlapping to the other minerals normally associated with asbestos (e.g. limestone) including the manmade asbestos cement roof shields.

To better allows the exploitation of the LWIR spectral range potential for the detection and identification of the asbestos cement material and NOA, an airborne survey has been carried out with the airborne hyperspectral TASI-600 sensor (32 spectral bands in the LWIR range).

Two areas have been analysed: the San Severino, in the Southern Apennines, and specific sectors along Taro River in the Norther Apennines. The San Severino site has been investigated by WorldView-3, Sentinel-2 and Hyperion imagery, while a sector of the Taro river valley has been analysed by S2 images and an ad hoc TASI airborne survey. The following algorithms commonly have been applied to both datasets: the Spectral Angle Mapper (SAM), the Adaptive Coherence Estimator (ACE) and the Support Vector Machine (SVM).

T his communication will present the NOA detection results on the two test areas by using the different classification procedures to the available remote data set. In the study areas, we found a general a good agreement (k > 0.8) between the remotely detected NOA outcrops and the more recent geologic map.