Remote sensing data analysis and coding in R for ecology
Learn remote sensing data analysis in R. Gain hands-on skills in coding, spatial data, ecosystem monitoring, and ecological change detection.
Duration: 40 hours
Format: Recorded ‘on-demand’ Format
£450Registration Fee
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- Overview
- Instructors
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Course Description
Ecological remote sensing is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time. This 40 hour 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.
What You’ll Learn
During the course we will cover the following:
- Navigate the R environment, write basic code, and understand the principles of free and open-source software.
- Apply spatial analysis techniques in R, including working with reference and coordinate systems.
- Visualise and interpret multi- and hyperspectral remote sensing data, and extract key spectral indices.
- Perform classification of remote sensing data and generate accurate land cover maps.
- Analyse ecosystem change across space and time using real-world datasets, such as deforestation in Mato Grosso or ice melt in Greenland.
- Access, download, and process remote sensing data from online sources, including Copernicus products.
- Apply multivariate statistical methods to remotely sensed data to explore ecosystem variability and environmental patterns.
- Produce professional scientific reports and outputs using LaTeX, Beamer, and R Markdown for articles, presentations, and web-based communication.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is intended for practitioners, students, and academics who are new to R and would like to build a solid foundation in the language. No prior knowledge of R is required, though a basic computer background is expected. The course will be especially valuable for those working on data-driven projects in research, industry, or applied fields who wish to develop skills in data manipulation, analysis, and reproducible workflows using R.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Prof. Duccio Rocchini
Duccio is a spatial ecologist and remote sensing specialist whose work focuses on the integration of computational methods and open-source tools to measure and monitor biodiversity. His research explores the theoretical and applied dimensions of spatial ecology, with a strong emphasis on using satellite data, spectral diversity metrics, and landscape models to understand biodiversity patterns across scales.
Duccio’s career spans international collaborations with institutions such as the U.S. Geological Survey, the University of Nottingham (UK), the Ashoka Trust for Research in Ecology and the Environment (India), and UCLA. His early work in remote sensing and biodiversity laid the foundation for his continued focus on reproducible, open-source approaches in ecology.
Since 2019, Duccio has been Full Professor at the Alma Mater Studiorum – University of Bologna. Prior to this, he served as Associate Professor at the University of Trento and as a researcher at Fondazione Edmund Mach, where he was part of the GRASS GIS and Remote Sensing Group. He is a prominent advocate for open algorithms in spatial ecology and actively contributes to methodological advances in ecological modelling.
Education & Career
• PhD in Ecology with a focus on remote sensing and biodiversity (2005)
• Full Professor, University of Bologna (since 2019)
• Associate Professor, University of Trento (2017–2019)
• Researcher at Fondazione Edmund Mach, Trento (2009–2017)
• Collaborator with international institutions including USGS, UCLA, and ATREE (India)
Research Focus
Duccio’s research investigates how remotely sensed data and spatial models can quantify biodiversity and ecological change. He specialises in spectral diversity, spatial scaling, landscape metrics, and the development of open-source tools to support ecological theory and conservation applications.
Current Projects
• Development of spectral diversity indices for biodiversity monitoring
• Open-source algorithm design for spatial and theoretical ecology
• Cross-institutional research on scaling laws and landscape complexity in biodiversity science
Professional Consultancy
Duccio advises research groups and institutions on remote sensing applications, biodiversity modelling, and open-source GIS implementation. He is also active in promoting open science and reproducible research within the ecological community.
Teaching & Skills
• Expert in remote sensing, GIS, spatial modelling, and spectral diversity
• Experienced educator in ecology, spatial analysis, and biodiversity assessment
• Advocate for open-source tools and reproducible computational ecology
Links
• ResearchGate
Session 1 – 02:00:00 – Introduction to the R Software and the Free and Open Source philosophy: how to deal with R, making your first code!
Session 2 – 02:00:00 – Spatial R
Session 3 – 02:00:00 – Reference systems: introduction to the main coordinate systems
Session 4 – 02:00:00 – Visualizing multi- e hyper-spectral data
Session 5 – 02:40:00 – Main spectral indices extracted from remote sensing data
Session 6 – 02:40:00 – Remote sensing data classification
Session 7 – 02:40:00 –Generating land cover maps from remotely sensed data
Session 8 – 04:00:00 –Analysis ecosystem change in space and time: the case of Mato Grosso
Session 9 – 04:00:00 –Time series: ice melt in Greenland
Session 10 – 02:00:00 – Download and use remote sensing data from internet sources
Session 11 – 02:00:00 – Downloading and visualising Copernicus data
Session 12 – 02:00:00 – Ecosystem variability
Session 13 – 02:00:00 – Multivariate analysis on remotely sensed data
Session 14 – 02:40:00 – LaTeX for scientific reporting via articles
Session 15 – 02:40:00 – LaTeX/Beamer for scientific reporting via presentations
Session 16 – 02:40:00 – R Markdown for scientific reporting via internet pages
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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