Mountain areas are vulnerable, heterogeneous, and dynamic regions continuously changed by human land use, hazard phenomena, and increased socio-economic competition. Endemic mountain plant species are threatened by the upwards migration of more competitive sub-alpine shrubs and tree species, leading to considerable loss of endemic species in mountain regions and severe loss in the economic value of EU forest land. 
Proper forest management relies on natural vegetation monitoring and should at least be sufficiently detailed to discover and identify changes in the extent of main vegetation types.

A new study recently published on the International Journal of Remote Sensing by a team of scientists (among them the CMCC researchers Gaia Vaglio Laurin and Riccardo Valentini from IAFENT Division) analyze some potential monitoring tools based on advanced remote sensing sensors.
Advanced remote sensing sensors, such as those exploiting very high-resolution synthetic aperture radar (SAR), hyperspectral, or light detection and ranging (lidar) data, are in fact not fully integrated in operational activities and their testing is important for possible incorporation in routine monitoring of forested areas and to increase the quantity and quality of environmental information.
The study in particular explore for the first time the potential of ALOS PALSAR and RADARSAT-2 SAR scenes’ synergic use for discrimination of different vegetation types – with a focus on woody vegetation – in an untested alpine heterogeneous and fragmented landscape, thus assessing the effectiveness of advanced SAR systems to provide detailed mapping in natural environments. A second innovative feature is the integration of a lidar-based CHM with SAR data to evaluate the advantages offered by the addition of this frequently available data type.

The abstract of the paper:
Natural vegetation monitoring in the alpine mountain range is a priority in the European Union in view of climate change effects. Many potential monitoring tools, based on advanced remote sensing sensors, are still not fully integrated in operational activities, such as those exploiting very high-resolution synthetic aperture radar (SAR) or light detection and ranging (lidar) data. Their testing is important for possible incorporation in routine monitoring and to increase the quantity and quality of environmental information. In this study the potential of ALOS PALSAR and RADARSAT-2 SAR scenes’ synergic use for discrimination of different vegetation types was tested in an alpine heterogeneous and fragmented landscape. The integration of a lidar-based canopy height model (CHM) with SAR data was also tested. A SPOT image was used as a benchmark to evaluate the results obtained with different input data. Discrimination of vegetation types was performed with maximum likelihood classification and neural networks. Six tested data combinations obtained more than 85% overall accuracy, and the most complex input which integrates the two SARs with lidar CHM outperformed the result based on SPOT. Neural network algorithms provided the best results. This study highlights the advantages of integrating SAR sensors with lidar CHM for vegetation monitoring in a changing environment.

Read the integral version of the paper:
Vaglio Laurin, G., Del Frate, F., Pasolli, L., Notarnicola, C., Guerriero, L., Valentini, R.:
Discrimination of vegetation types in alpine sites with ALOS PALSAR-, RADARSAT-2-, and lidar-derived information,
International Journal of Remote Sensing, Vol. 34, Iss. 19, 2013
doi: 10.1080/01431161.2013.810823