Objective:

The main objective of the Unit “Land, Vegetation and Snow” is the retrieval of biophysical parameters from remotely sensed images. In this context the activities are mainly focused on two aspects: 

1. Research and development of advanced methods in the direction of improving accuracy and reliability of the parameter estimation process from both optical and radar images
2. Methods implementation for creating user-oriented applications

Another relevant aspect of the group work is related to the development and adaptation of the estimation techniques in order to take into account the peculiarity of the complex Alpine and mountainous environment and landscape.

Expertise & Capacity:

The expertise of the “Land, Vegetation and Snow” Unit is mainly:

• Standard and advanced retrieval methodologies for the estimation of soil, vegetation and snow biophysical parameters, such as moisture content, Leaf Area Index and Snow Water Equivalent, from remotely sensed images
• Snow cover algorithms with medium and high geometrical resolution satellite images for monitoring the changes of the snow cover on the Alpine regions
• Semi-automatic classification procedures for determining the land-use/land-cover from high and very high geometrical resolution images
• Eco-hydrological modeling

Projects:

• ClimAlpTour - Climate Change and its Impact on Tourism in the Alpine Space (Interreg IV B - Alpine Space)
• GMES-Sentinel1 - Soil Moisture Algorithm Development (funded by the ESA - European Space Agency)  
• HABIT-CHANGE - Adaptive Management of Climate-induced Changes of Habitat Diversity in Protected Areas (INTERREG IV B CENTRAL EUROPE programme)
• IRKIS - Soil moisture monitoring with satellite sata (Interreg IV A Italy - Switzerland)
• KLIMAST - Climate change in South Tyrol - how sensitive are we? (financed by the Autonomous Province of Bolzano)
• MS.MONINA - Multi-scale service for monitoring NATURA 2000 habitats of European community interest (Seventh Framework Programme - FP7/2007-2013) 
• SNOX - SNOw cover and glacier monitoring in alpine areas by COSMO-SkyMed X-Band data (financed by the Italian Space Agency)
• SnowEurope - Daily Snow Cover prototype from Europe (funded by JRC - Joint Research Center)

Publications:

1. L. Pasolli, C. Notarnicola, L. Bruzzone, G. Bertoldi, S. Della Chiesa, V. Hell, G. Niedrist,  U. Tappeiner, M. Zebisch, F. Del Frate, and G. Vaglio Laurin, 2011, Estimation of soil moisture in an alpine catchment with RADARSAT2 images, Applied and Environmental Soil Science, Volume 2011, Article ID 175473, 12 pages, doi:10.1155/2011/175473.
2. S. Kass, C. Notarnicola, M. Zebisch, 2011, Identification of orchards and vineyards with different texture-based measurements by using an object-oriented classification approach, IJGIS, International Journal of Geographical Information Science, in press.
3. L. Pasolli, C. Notarnicola, L. Bruzzone, 2011, Estimating Soil Moisture from Microwave Remotely Sensed Data with the ε-insensitive Support Vector Regression, accepted for IEEE Transactions on Geoscience and Remote Sensing Letters, in press.
4. G. Bertoldi, C. Notarnicola, G. Leitinger, S. Endrizzi, M. Zebisch, S. Della Chiesa, U. Tappeiner, 2010, Topographical and ecohydrological controls on land surface temperature in an alpine catchment, Ecohydrology, Volume 3, Issue 2, pages 189-204, June 2010, DOI: 10.1002/eco.129.
5. C. Notarnicola, 2009, Group Inversion Approach” for detection of soil moisture temporal-invariant locations, Remote Sen., 2009, 1, pp. 1338-1352, doi:10.3390.
6. C. Notarnicola, A. Mondini, 2009, Use of historical orthophotos and digital elevation model to link watershed land use changes and storm flow response in a karst environment”, J. Appl. Remote Sens. Vol. 3, 033574 (Dec. 18, 2009).
7. C. Notarnicola, 2009, Ensemble of retrieval algorithms and electromagnetic models for soil and vegetation water content estimation from SAR images, Geoscience and Remote Sensing, ISBN 978-953-307-003-2, In-Tech, September 2009.
8. C. Notarnicola, M. Angiulli, F. Posa, 2008, Soil Moisture Retrieval from Remotely Sensed Data: Neural Network Approach versus Bayesian Method, IEEE Transactions on Geoscience and Remote Sensing, vol.46, no.2, February 2008, p.547-557.