Friday Forum: Remote Sensing of the Venice Lagoon and its Watershed

February 17, 2012

LSRC D106

Sonia Silvestri

The Venice lagoon (Italy) is one of the largest lagoons in Europe, with a high environmental and socio-economic significance, also linked to the historical and cultural value of the city of Venice. In this presentation, remote sensing is used to look at the spatial and temporal variability of some environmental processes and to provide a visualization of such variability. Remote sensing facilitates the acquisition of spatial data and the interpretation of its space-time properties, providing access to the geometric patterns that can not be obtained using point measurements, particularly in a dynamic environment such as a coastal lagoon. Applications on the Venice lagoon watershed, where aquifer pollution may have fundamental impacts on the water quality of the lagoon, will also be presented.

Friday Forum: From Perspective Representation to (Digital) Reality

February 10, 2012

LSRC D106

12:00-1:00pm

Andrea Giordano

Historical paintings and engravings are an important documentary source for studying the history of urban transformations in architecture and city design. By applying perspective transformations and architectural engineering rules, we can digitally reproduce 3D models of the buildings and spaces portrayed in the pictures and thereby find out valuable details related to the processes of transformation and change in the city.This talk will present our work applying these techniques to several historical paintings.

Intertidal Bio-Geomorphic Patterns and the History of the Lagoon of Venice

February 3, 2012

LSRC D106

12:00-1:00pm

Marco Marani

The Venice lagoon is a natural lab for which long-term observations exist, documenting changes in geomorphological and ecological structures in response to climatic and anthropogenic forcings. I will briefly review the environmental changes documented over several centuries in the Venice lagoon through maps and documents, discussing the controlling processes and showing how remote sensing and mathematical modelling can help us extract process characteristics from patterns.