Using drones to map habitats: data fusion and classification
Locations and Dates:
Online (self-paced learning in your own time, on our learning platform UKCEH LearnWorlds).
To give you a feel for what this online learning is like, we put together this video about our online learning platform, UKCEH LEarnWorlds. (external link to YouTube)
We also offer bespoke training courses at a time of your choice. From 6-20 learners. Please contact Dr. Ingo Schüder on 01491 69 2225 or 07395 845941or UKCEHtraining@ceh.ac.uk to explore further.
Students: £349 (Use Coupon Code STUDENT)
Group ticket (3 professionals – 10% discount) £1077
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For group and other discounted tickets go here (external link to our Mitingu sign-up and payments platform)
Short Course Description
This online course will provide participants with the basic skills needed to take images collected from a UAV platform such as a quadcopter, process the images into a scene suitable for image processing, and generate classified images. The course will take you through the steps necessary to produce high quality and accurate maps suitable for the mapping of vegetation and landforms. You will do exercises to practice and consolidate your learning.
It will cover the following topics:
- UK Civil Aviation Authority guidelines and the law
- Drone technology
- Aerial photography
- Image stitching software
- Image classification
The self-paced on-demand training offers:
- 8 Sessions
- 12 Exercises
- 16 videos (approx. 4h 20 min playing time)
- Total learning time approx.15 hours
- Gain skills to create high quality and spatially accurate habitat classification maps from data you have captured using your UAV platform.
- Learn how to create digital elevation and surface models.
- Learn how to process these data to map the heights and locations of trees and vegetation or other features
- Learn how to operate your UAV platform within the law
- Gain insight into some of the problems you may encounter when flying your aircraft
- Learn how to bring together the data collected by your aircraft into a spatially accurate and informative map suitable for mapping habitats and change.
Hardware and Software requirements for this course
We recommend you to use a laptop or desktop computer with at least 8 MB RAM if possible.
The course uses a number of different software for the different stages of processing the images. Key data management principles will apply to whatever software you are using.
- R Studio Desktop 1.4 (free open source software)
- R programming language (we will most likely use version 4.0.5) for the classification of data (free open source software)
- spatial analysis software QGIS 3.18 (free open source)
- PhotoScan (aka Metashape) for a demonstration of the photogrammetric processing of digital images. (We will provide a licence key for a 30-day free trial)
Some prior knowledge of these will be an advantage, especially R Studio. The course materials include instructions on where to locate this software and how to install it.
Intermediate - Course participants should be familiar with the manipulation and fusion of spatial data, GIS and/or image processing and UAV platforms for data capture. Knowledge of the programming language R is recommended.
Paul is researching the use of Unmanned Aerial Vehicles (UAVs) for fine-scale mapping and habitat classification of remote environments. He is a qualified RPA-S pilot, and proficient in the use of UAV derived imagery to generate high-resolution habitat classification maps.
Charles George, Earth Observation Scientist, UK Centre for Ecology & Hydrology
Charles is very experienced in using ArcGIS, Photoscan and R.
Previous Course participants on the full interactive course said:
"I have learnt a lot so this has been a very useful course & would recommend."
"I have gained a thorough overall understanding of the process and potential challenges to create habitat maps from drones. This understanding will be very helpful when I use drone data products. Thanks you!"
"The course has a nice blend of exercises and lecture - I liked being able to practice a skill just after learning about it."
"Thanks for a very interesting course - lots of good materials and scripts to refer back to as well."