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Short lectures and presentations



Introduction to the training

Introducing the tutors, objectives, topics and schedule of the training. Getting to know each other.

  • download or open the presentation from your local VM @: ~/ost4sem/lecture/introduction.pdf


Introduction to Linux and Open Source tools

Linux environment, why and what to use; Why to use Unix/Linux and command line for solving complex research questions.

  • download or open the presentation from your local VM @: ~/ost4sem/lecture/handsonLinux_OS.pdf


Perform GIS and Remote Sensing analysis under Linux OS

  • download .**
    Giuseppe Amatulli, Ph.D.; Program in Spatial Biodiveristy Science and Conservation (SBSC) - Yale University
    RSUG meeting - Monday 4 November from 5 to 6pm in ESC 110. Yale University

    In the last decades there has been an explosion of available data for environmental spatio-temporal research. This “big data” allows us to address a number of old and new important research questions with unprecedented rigor and generality. Beside this, reproducible research requires code that is easily published without license constraints and complex work-flows that are able to integrate different data analysis methods. Open-source software under Linux OS provides a valid and powerful alternative which can be used in desktop PCs, laptops and also in remote servers, such as the one at Yale HPC.

During the seminar I will show how to install a Linux-like Virtual Machine (LVM) in your lap-top (so bring your lap-top) and how to use the http://www.spatial-ecology.net platform to become a self-taught programmer. We will work with the most powerful GIS and RS libraries such as GDAL/OGR and their related applications.

The LVM is an ad-hoc customization of the Ubuntu distribution with Remote Sensing, GIS and Statistics open source software; with sample geo-data, scripts, and example exercises directly linked with the material stored at http://www.spatial-ecology.net. All the materials, data and software are under the Common Public License agreement so feel free to redistribute or install in other PCs.

In order to speed up the LVM installation process please follow the “Access spatial-ecology Ubuntu from a Virtual Machine “ procedure.

This procedure does not install a Linux beside Windows or MacOS, but inside Windows or MacOS. In other words your main OS will be remain Windows or MacOS, and you will boot the PC as before. There is not any risk for your data or for your main OS.

How to take part in this training

General presentation of the training working environment: Virtual machine, Ubuntu OS and the training wiki infrastructure.

  • download or open the presentation from your local VM @: ~/ost4sem/lecture/handsonost4sem.pdf


Introduction to spatial ecological modeling

Defining and discussing ecological models and spatial ecology. Presentation of various applications, and examples of models.

  • download or open the presentation from your local VM @: ~/ost4sem/lecture/specomodel_intro.pdf

Case study: Forest Suitability and Climate Change

In this case study we process data for Forest Habitat Suitability Modeling and Climate Change projections.
Presenting the theory and basic assumptions in niche modeling and potential habitat suitability mapping. The Random Forest machine learning ensemble model and the technical procedure for plotting current and future habitat distribution maps.

  • download or open the presentation from your local VM @: ~/ost4sem/lecture/jrc24feb_2010.pdf

Case study: Forest fire and climate change

In this case study we process data for projecting future burnt area in the EU-Mediterranean countries under IPCC SRES A2/B2 climate change scenarios.
Presenting the statistical modeling of historical (1985-2004) monthly burnt areas in European Mediterranean countries, as a function of monthly weather data and derived fire danger indexes, and how to analyse potential trends under present and future climate conditions.

  • download or open the presentation from your local VM @: ~/ost4sem/lecture/fireclimatechange.pdf

Case study: Natural vegetation and climate change

In this case study we modelling and map the Natural Forest Distribution in Italy and predict habitat suitability changes for the year 2080 under IPCC SRES A2a scenario. We use the randomforest machine learning algorithm in R and prepare data in bash and GRASS.

Introduction to UNIX LINUX

General presentation of Unix Linux and the Bash shell programming environment.

  • download or open the presentation from your local VM @: ~/ost4sem/lecture/unixlinux.pdf


Introduction to AWK

General presentation of AWK programming language for processing text-based data. NOT AVAILABLE * download or open the presentation from your local VM @: ~/ost4sem/lecture/awk.pdf

Introduction to GDAL OGR

General presentation of GDAL and OGR: Geospatial Data Abstraction Libraries. NOT AVAILABLE * download or open the presentation from your local VM @: ~/ost4sem/lecture/gdal.pdf

Introduction to R

General presentation of the R language and environment for statistical computing and graphics.

  • Download or open the presentation from your local VM @: ~/ost4sem/lecture/rintro.pdf

    ===== Introduction to GRASS ===== General presentation of GRASS: Geographical Resources Analysis Support System. * Download or open the presentation from your local VM @: ~/ost4sem/lecture/grass
    intro.pdf


Introduction to Quantum Gis

General presentation of Qgis: a user friendly Open Source Geographic Information System licensed under the GNU General Public License.

  • Download or open the presentation from your local VM @: ~/ost4sem/lecture/qgis_intro.pdf


Introduction to Gnuplot

General presentation of Gnuplot: a portable command-line driven graphing utility, for visualising mathematical functions and data interactively. NOT AVAILABLE * Download or open the presentation from your local VM @: ~/ost4sem/lecture/gnuplot.pdf

More Image processing

A presentation and hands on guide to:

  • geo data processing using QGIS gui
  • introduction to spatialite
  • Ogr simple features library
  • Satellite Image classification
  • Advanced geoprocessing
wiki/lectures.txt · Last modified: 2015/06/27 06:08 (external edit)