Cultural skills (knowledge of): spatial attributes of ecological processes - spatial ecology: concepts and applications - GIS: functionality, data model and types of software
Methodological skills (knowing how to perform): practical use of GIS software - retrieval, analysis and interpretation of spatial data - identification and evaluation of ecological spatial patterns
Methodological skills (knowing how to perform): practical use of GIS software - retrieval, analysis and interpretation of spatial data - identification and evaluation of ecological spatial patterns
teacher profile teaching materials
- Intro to spatial ecology and cartography
- GIS: functions, geographical approach and modeling of reality
- Types of GIS software: open source and proprietary software
- Data models: vectorial (points, lines and polygons) and raster (pixel)
- Principles and methods in remote sensing: electromagnetic reflectance, remote sensed image resolution, active and passive sensors, remote sensing platforms
- Species distributions and biodiversity mapping
Practicals (software: QGIS and R)
- Visualization of geographical objects(features) on a map
- Preparation of plant and animal maps
- Preparation and analysis of environmental maps (land use/land cover, habitat maps, photosynthetic activity, etc.) in time and in space
- Principles and methods of cartographic extraction of bio-environmental features
- Introduction to Species Distribution Modeling (SDM)
Software:
QGIS.org, 2022. QGIS Geographic Information System. QGIS Association. http://www.qgis.org
R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Office hours by appointment via email: marta.carboni@uniroma3.it
Programme
Theory- Intro to spatial ecology and cartography
- GIS: functions, geographical approach and modeling of reality
- Types of GIS software: open source and proprietary software
- Data models: vectorial (points, lines and polygons) and raster (pixel)
- Principles and methods in remote sensing: electromagnetic reflectance, remote sensed image resolution, active and passive sensors, remote sensing platforms
- Species distributions and biodiversity mapping
Practicals (software: QGIS and R)
- Visualization of geographical objects(features) on a map
- Preparation of plant and animal maps
- Preparation and analysis of environmental maps (land use/land cover, habitat maps, photosynthetic activity, etc.) in time and in space
- Principles and methods of cartographic extraction of bio-environmental features
- Introduction to Species Distribution Modeling (SDM)
Prerequisiti
There are no mandatory prerequisites. However, it is useful to have already taken the course in “Statistical analysis of ecological data”Core Documentation
Materials, PDFs of lecture slides and scripts are made available during the courseSoftware:
QGIS.org, 2022. QGIS Geographic Information System. QGIS Association. http://www.qgis.org
R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Office hours by appointment via email: marta.carboni@uniroma3.it
Type of evaluation
Evaluation will consist in a final exam with an oral discussion and a practical exam and in a written report based on a GIS project.