ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live
30 September 2024 - 4 October 2024
£490.00
Event Date
Monday, September 30th, 2024
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course Details
Course overview:
Ecological remote sensing is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time.
This course mainly focuses on the application of free and open source algorithms – which ensure high reproducibility and robustness of ecological analysis – to study ecological change in space and time by remotely sensed imagery. Particular emphasis will be given to: 1) remote sensing principles, 2) remotely sensed data gathering and analysis, 3) monitoring ecosystem change in space and time by remote sensing data.
The course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling.
By the end of the course, participants will:
• be able to create their own projects on monitoring of spatial and temporal changes of ecosystems with remote sensing data
• be able to report in LaTeX and R Markdown the achieved results
Intended Audiences
Intended Audience
• Practitioners, students, academics
• People new to R
Venue
Delivered remotely
Course Information
Time zone – Central European Time
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
Theoretical presentations will introduce coding sessions. The whole course is intended to be practical.
Assumed quantitative knowledge
No previous knowledge of R is needed.
Assumed computer background
A basic computer background is needed.
Equipment and software requirements
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.
Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).
A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.
Package needed for the course:
– imageRy
– overlap
– spatstat
– terra
– vegan
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
COURSE PROGRAMME
Monday 30th – Classes from 09:30 to 17:30
– R (intro)
[Introduction to the R Software and the Free and Open Source philosophy: how to deal with R making your first code!]
[Spatial R]
[Reference systems: introduction to the main coordinate systems]
– Visualizing data
[Visualizing multi- e hyper-spectral data]
Tuesday 1st – Classes from 09:30 to 17:30
– Spectral indices extracted from satellite imagery
[Main spectral indices extracted from remote sensing data]
– Remote sensing data classification
[Generating land cover maps from remotely sensed data]
Wednesday 2nd – Classes from 09:30 to 17:30
– Land use change in space and time
[Analysis ecosystem change in space and time: the case of Mato Grosso]
[Time series: ice melt in Greenland]
Thursday 3rd – Classes from 09:30 to 17:30
– External remote sensing data
[Download and use remote sensing data from internet sources]
[Downloading and visualising Copernicus data]
– Image data processing
[Ecosystem variability]
[Multivariate analysis on remotely sensed data]
Friday 4th – Classes from 09:30 to 17:30
– Reporting
[LaTeX for scientific reporting via articles]
[LaTeX/Beamer for scientific reporting via presentations]
[R Markdown for scientific reporting via internet pages]
Course Instructor
Dr. Duccio Rocchini
Coming soon…