GIS and Remote Sensing analyses with R (GARM01)

oliver-hookerOliver Hooker
  • 10 Jan

GIS and Remote Sensing analyses with R (GARM01) 

https://www.prstatistics.com/course/gis-and-remote-sensing-analyses-with-r-garm01/ 

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14th - 18th February 2022 

Course Overview: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 5-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. email oliverhooker@prstatistics.com with any questions 

Course program 

Monday 14th – Classes from 09:00 to 17:00
Theory – Introduction to GIS.
Practical – Introduction to GIS with R: Import and plot data.
Theory – Coordinate systems.
Practical – Projecting vectorial & raster files.

Tuesday 15th – Classes from 09:00 to 17:00
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.

Wednesday 16th – Classes from 09:00 to 17:00
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.
RS software.
Practical – Getting and plotting RS data. Downloading, reading, and plotting RS data in R.
Manipulating satellite data.

Thursday 217th – Classes from 09:00 to 17:00
Theory – Working with RS variables. Image classification, Vegetation indexes, data fusion.
Practical – Calculating RS variables with RStoolbox: Vegetation indexes and classification
methods.
Theory: Remote Sensing applications to biology
Practical: Statistical analyses with RS data

Friday 18th – Classes from 09:00 to 17:00
Final practical