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A typical course schedule.
Data used to learn.
Tools we teach to use.


During the intensive practical training we will demonstrate different free and open sources tools to carry out spatial ecological modelling and spatial analysis in ecology. The course comprises a set of short lectures that present modelling concepts and procedures, as well as practical sessions for students where students can use the software and scripting routines to achieve large scale data analysis and standalone implementation processes. The first part of the training will be dedicated largely to lectures and exercises of diverse open source software. During the hands-on software practical sessions, participants will actively interact with different tools and learn how to carry out spatial data analysis by manipulating data from different case studies. Various exercises will be proposed and students will have the opportunity to improve their computational skills in both testing ecological theory and solving practical questions, while producing maps and summary statistics. Particular emphasis will be placed on forest ecosystems applications. During the second part of the training course students will practice their acquired knowledge in a short personal project. An open discussion will be carried out on how to approach each student's project with the tools presented during the training. The students will be supervised in their work and will have the opportunity to analyse their own dataset and solve their own project. This short project will allow them to briefly go through the principal phases of spatial ecological applications: conceptualization, data preparation, model fitting, model evaluation, spatial prediction, assessment of model applicability, application of the model results and summary statistics.


At the end of the course participants should acquire a basic knowledge of a selection of open source tools that are available in the context of spatial ecological modelling and GIS/spatial analysis. Following the course, students should be able to progress independently and learn how to process data using open source software. The course gives a useful background for data analysis in the context of forest, biodiversity, conservation, landscape planning and other related topics such as precision farming, geostatistics, socioeconomic issues, remote sensing and non-spatial modelling.


Linux shell scripting, GRASS and Qgis geographic information systems, R language and environment for statistical computing and graphics, AWK programming language for processing textbased data, gnuplot program for two and three-dimensional plots of functions and data, geotools library Gdal/ogr, PK-tools, OFGT for the manipulation of geospatial data. Target students - 15 to 20 students maximum.
This training is addressed to a diverse population of students at masters or doctoral level, researchers and professionals with a common interest in spatial data analysis and ecological modelling. Students should have a basic knowledge of GIS to be able to follow the course and improve their computational skills. Students are expected to provide an idea for a personal project as well as the necessary data to be processed in order to fulfill the project task. If needed a project proposal with its related data can be provided. Appropriate exercises will be provided allowing students to practice the use of tools and methods in spatial data handling. The exercises and examples are applied within a large variety of topics: forestry, landscape planning, predictive modelling, mapping, nature conservation and spatially related fields of study. In agreement we can adjust key examples and focus exercises on other special needs.

PC requirements

The course is carried out in common pc (desk-top or lap-top) running any kind of Operation System. A linux-like virtual machine will be used as core were teaching the proposed tools. The GIS Virtual Machine is installed in the student's pc without requiring a particular power, even a pc of 2-3 years can be used. The GIS Virtual Machine includes the software, study material, data, scripts and exercises that the students needs. After the course the students can take all virtual machine folders and install them on his personal pc, having in this way the full working environment of the course, and thereby making the continuation of self-learning easier.

wiki/introduction.txt · Last modified: 2021/01/20 20:36 (external edit)