The course will cover the basics to perform spatial analyses using R as a Geographical Information System (GIS) platform and Remote Sensing as main data source. The course will provide a brief theoretical background of GIS tools and Remote Sensing data and techniques. By the end of this 4-day practical course, attendees will have the capacity to search satellite imagery, to manipulate Remote Sensing data, to create new variables, as well as to choose the best spatial tools and techniques to perform spatial analyses and interpret their results.
The course will be mainly practical, with some theoretical lectures. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Attendees will learn to use the Rpackage RSToolbox for Remote Sensing image processing and analysis such as calculating spectral indices, principal component transformation, or unsupervised and supervised classification.
This course is orientated to PhD and MSc students, as well as other students and researchers working on biogeography, spatial ecology, or related disciplines.
Last Up-Dated – 17:02:2022
Duration – Approx. 32 hours
ECT’s – Equal to 2 ECT’s
Language – English
Introductory lectures on the concepts and applications of GIS and Remote Sensing.
Practical lectures on most used spatial tools. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practical modules will be provided by the instructor, but participants are welcome to bring their own data.
Basic knowledge in Geographical Information Systems, Remote Sensing, and spatial analyses.
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact firstname.lastname@example.org.
If you are unsure about course suitability, please get in touch by email to find out more email@example.com
Theory – Introduction to GIS.
Practical – Introduction to GIS with R: Import and plot data.
Theory – Coordinate systems.
Practical – Projecting vectorial & raster files.
Theory – Vector database operations.
Practical – Attribute and spatial queries: join/merge, filter/subset, select by attribute, select by
location, summarize, add/calculate new attributes (columns), plot attributes.
Theory – Vector analyses.
P: Vector analyses – buffer, merge, dissolve, intersect, union, select, calculate areas.
Theory – Raster GIS.
Practical – Raster analyses: rasterize, crop, mask, merge, distance surface, zonal statistics.
Theory – Introduction to Remote Sensing. RS as main data source: RS sensors & variables.
Practical – Getting and plotting RS data. Downloading, reading, and plotting RS data in R.
Manipulating satellite data.
Theory – Working with RS variables. Image classification, Vegetation indexes, data fusion.
Practical – Calculating RS variables with RStoolbox: Vegetation indexes and classification
Theory: Remote Sensing applications to biology
Practical: Statistical analyses with RS data.
Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe