EURAC Research Institute for Applied Remote Sensing

Bolzano, Italy

EURAC Research Institute for Applied Remote Sensing

Bolzano, Italy

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Vaglio Laurin G.,University of Rome Tor Vergata | Vaglio Laurin G.,Centro Euro Mediterraneo per i Cambiamenti Climatici Euro Mediterranean Center for Climate Change | Del Frate F.,University of Rome Tor Vergata | Pasolli L.,EURAC Research Institute for Applied Remote Sensing | And 4 more authors.
International Journal of Remote Sensing | Year: 2013

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. © 2013 Copyright Taylor and Francis Group, LLC.


Santi E.,CNR Institute of Applied Physics Nello Carrara | Paloscia S.,CNR Institute of Applied Physics Nello Carrara | Pettinato S.,CNR Institute of Applied Physics Nello Carrara | Callegari M.,EURAC Research Institute for Applied Remote Sensing | And 2 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

In this work, the characterization and extraction of snowpack parameters from X-band SAR imagery has been addressed. A preliminary sensitivity analysis was carried out by exploiting datasets of snowpack parameters (depth, density, snow grain radius, temperature and wetness) collected from the available meteorological stations on two test sites in the Italian Alps. This is a crucial step, since it provides indications on the sensitivity of the input features (i.e., backscattering coefficients and ancillary data) to variations in the target snow parameters. X-band data has been found to contribute to retrieval of the snow water equivalent under specific conditions, i.e., that the snow cover is characterized by a snow depth of roughly 60-70 cm (snow water equivalent >100-150mm) and with relatively large crystal dimensions. After this phase, the retrieval process is addressed. The method is based on a Neural Network retrieval algorithm trained by using a DRTM electromagnetic model in order to estimate the snow water equivalent. The proposed approach also makes use of the threshold criterion for detecting the wet snow cover extent on which the retrieval cannot be performed. The method has been developed and calibrated on the Cordevole plateau located in the Dolomites, Eastern Italian Alps, where ground data collected by the Avalanche Center in Arabba and meteorological data measured by a network of automatic stations were available. The method was then validated on a second site located in South Tyrol region (Eastern Italian Alps), where also manual and automatic ground measurements of snow parameters were available. The activity was carried out in the framework of two projects funded by the Italian Space Agency (HYDROCOSMO and SNOX) for the exploitation of X-band satellite SAR data for the analysis and characterization of snow in mountain areas. © 2012 SPIE.


Pasolli L.,EURAC Research Institute for Applied Remote Sensing | Notarnicola C.,EURAC Research Institute for Applied Remote Sensing | Zebisch M.,EURAC Research Institute for Applied Remote Sensing
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

This study presents an analysis on the retrieval of soil moisture content from medium resolution wide swath SAR images for monitoring regional scale spatial and temporal patterns of this variable in the challenging Alpine environment. The possibility to retrieve soil moisture content from satellite high resolution SAR imagery in Alpine areas was successfully investigated in a previous contribution. The rationale behind this work is the fact that multi-scale and multi-sensor products could lead to a more general and comprehensive understanding of the phenomena at the ground, since different perspectives and trade-offs among spatial and temporal resolution can be exploited. In more detail, the analysis proposed here aims at: i) assessing the effectiveness of the proposed retrieval algorithm when applied to medium resolution wide swath SAR imagery; and ii) investigating the feasibility of mapping spatial patterns and temporal dynamics of soil moisture content at a regional scale. ENVISAT ASAR Wide Swath images acquired over the Alto Adige/Süd Tirol Province during the years 2010-2011 are used for the experimental analysis. Achieved results are compared with ground measurements and meteorological data, indicating good agreement in terms of both spatial distribution and temporal dynamics of estimated soil moisture content values. © 2012 SPIE.


Vaglio Laurin G.,University of Rome Tor Vergata | Del Frate F.,University of Rome Tor Vergata | Pasolli L.,EURAC Research Institute for Applied Remote Sensing | Notarnicola C.,EURAC Research Institute for Applied Remote Sensing
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

The potential of SAR data in discriminating vegetation/forest types it is here explored using Neural Networks (NN) in an Alpine environment. Amplitude data from two SAR polarimetric sensors, namely RADARSAT2 Standard Quad Polarization (SQP) and ALOS PALSAR Fine Beam Dual (FBD), were used separately and in conjunction to discriminate four vegetation types: conifer forest, broadleaved forest, riparian vegetation, and dwarf pine and shrubs (mainly composed by Pinus mugo species). Results indicate successful separation of needle-leaved from broadleaved and/or riparian vegetation, but scarce ability to discriminate the other two types. ALOS PALSAR produced better results in separating vegetation types with respect to RADARSAT2 reaching in the best case a K Cohen's coefficient equal to 0.88. Results obtained from combination of the two SAR data were successful, but still in the range of those obtained by single scene usage. © 2012 IEEE.

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