The interactions between geo-environmental and anthropic processes are increasing due to the ever-growing population and its related side effects (e.g., urban sprawl, land degradation, natural resource and energy consumption, etc.). Natural hazards, land degradation and environmental pollution are three of the possible “interactions” between geosphere and anthroposphere. In this context, spatial and spatiotemporal data are of crucial importance for the identification, analysis and modelling of the processes of interest in Earth and Soil Sciences. The information content of such geo-environmental data requires advanced mathematical, statistical and geomorphometric methodologies in order to be fully exploited.

The session aims to explore the challenges and potentialities of quantitative spatial data analysis and modelling in the context of Earth and Soil Sciences, with a special focus on geo-environmental challenges. Studies implementing intuitive and applied mathematical/numerical approaches and highlighting their key potentialities and limitations are particularly sought after. A special attention is paid to spatial uncertainty evaluation and its possible reduction, and to alternative techniques of representation of spatial data (e.g., visualization, sonification, haptic devices, etc.).

In the session, two main topics will be covered (although the session is not limited to them!):
1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty: geostatistics, machine learning, statistical learning, etc.
2) Analysis and representation of exhaustive spatial data at different scales and resolutions: geomorphometry, image analysis, machine learning, pattern recognition, etc.

Co-organized as GM2.11/SSS12.7
Convener: Jean Golay | Co-conveners: Marco Cavalli, Mohamed Laib, Sebastiano Trevisani
| Wed, 10 Apr, 16:15–18:00
Room 0.96
| Attendance Wed, 10 Apr, 14:00–15:45
Hall X1

Attendance time: Wednesday, 10 April 2019, 14:00–15:45 | Hall X1

Chairperson: Sebastiano Trevisani; Jean Golay
X1.11 |
Roberto Salzano, Rosamaria Salvatori, Mauro Mazzola, and Christina A. Pedersen
X1.12 |
Estimation of wood volume through image interpretation and photogrammetry.
Francesco Pirotti and Erico Kutchartt
X1.13 |
Ioakeim Konstantinidis, Efstratios Karantanellis, Vassileios Marinos, and Ioannis Farmakis
X1.14 |
Shallow water bathymetry using remote sensing: satellite imagery and close-range imagery
Filippo Tonion, Giancarlo Faina, Diego Paltrinieri, and francesco pirotti
X1.16 |
Impact of crop residue burning on spatial and seasonal features of PM2.5, PM10, CO, SO2, O3, and NO2 in China
Wenting Zhang and Sanwei He
X1.17 |
Dimitrios D. Alexakis, Evdokia Tapoglou, Anthi-Eirini K. Vozinaki, and Ioannis K. Tsanis
X1.18 |
A stochastic model of the morphological pattern of thermokarst plains with fluvial erosion
Alexey Victorov and Timofey Orlov
X1.19 |
Dina Al-Sammrraie, Stefan Kreiter, Max Kluger, and Tobias Mörz
X1.20 |
Raphaël Bubloz
X1.21 |
Matteo Albéri, Marica Baldoncini, Stefano Bisogno, Carlo Bottardi, Ivan Callegari, Enrico Chiarelli, Giovanni Fiorentini, Andrea Motti, Norman Natali, Marco Ogna, Kassandra Giulia Cristina Raptis, Andrea Serafini, Gianluigi Simone, Virginia Strati, and Fabio Mantovani
X1.22 |
Mohamed Laib, Luciano Telesca, and Mikhail Kanevski
X1.24 |
Jean Golay, Mohamed Laib, and Mikhail Kanevski