Monday

Tuesday

Wednesday

Thursday

Friday

9-10:30

Introduction to the school (school directors). Student self-presentation.

Statistical models and sampling design. Power analysis (Acutis,  Perego).

Conducting a meta-analysis in agro-environmental science.

Literature search, data collection, data extraction and database creation. Combining effect sizes and weighting (Valkama)

Topography for agro-environmental modelling. Fundamentals of Terrain analysis (TA).

From soil catena to 3D landscape

(Märker)

Land Cover and Soil monitoring in the EU An overview of environmental datasets for land cover and digital soil mapping. (Schillaci, Saia, Ceccherini).

Machine learning for environmental modelling.  Machine learning (random forest, neural network) as a tool for agro- environmental modelling, land cover mapping, vegetation indices.

(Ceriani and Schillaci)

10.30-11:00

Coffee  break

Coffee e break

Coffee  break

Coffee  break

Coffee  break

11:00-12:30

Introduction to mixed model (Perego). Practical activity on sample size determination (Acutis, Perego)

Heterogeneity and weighting. Publication bias. Format of report and review of case study. Criticism of meta-analysis

 (Valkama)

Environmental process modelling with emphasis on soil erosion and storm flow (Märker)

Remote sensing with Google Earth Engine (Ceccherini).

Deep learning classification of satellite images using, examples and applications using Pyton, R and GIS. (Ceriani)

12:30-14:00

Lunch

Lunch

Lunch

Lunch

Lunch

14:00 – 15:30

Spatial data analysis: geostatistics and mixed effect models accounting for spatial autocorrelation (Stellacci, Barca)

Testing the students.

Practical: Software for meta-analysis. Exercises (Valkama)

Practical: examples and applications of TA. GIS-based assessment models for soil erosion (Märker)

Examples and applications Remote sensing with Google Earth Engine (Ceccherini).

Practical: Examples and applications of machine and deep learning (Ceriani)

15:30 – 15.40

Break

Break

Break

Break

Break

15:40-17:10

Practical applications of spatial data analysis in R environment . (Stellacci, Barca)

Practical: Database creation and running a meta-analysis. (Valkama)

Practical: examples and applications of TA. GIS-based assessment models for soil erosion (Märker)

Examples and applications of Soil Organic Carbon modelling using R and GIS. (Schillaci, Saia, Ceccherini).

Practical: Examples and applications of machine and deep learning (Schillaci and Ceriani)

Social dinner
(at 19:30)

Certificate ceremony (after the end of the lecture)