Skara
The combination of time series of satellite imagery and high-performance cloud-computing capabilities to handle ‘big data’ provides an asset for timely detection of forest damages that can be complementary to traditional methods and help overcoming some of the limitations of national inventories.
The aim of the course is provide the students with theoretical and practical knowledge of working with data from remote sensing and field inventory and how to combine these in order to address issues connected to change and damage detection in forest ecosystems. The course will introduce the students to the techniques and platforms used in forest remote sensing, including imaging and laser scanning techniques and platforms like satellites, planes, drones and mobile devices. The students will learn different techniques for extracting forest information with a focus on change detection from these different data sources. Theory in basic understanding of how remotely sensed data will be transformed into forest information using statistical modelling will be given. Practical skills will be taught through computer exercises as well as hands-on experience of data collection in the field by using drones. Data processing steps like: spectral calibration; feature extraction; histogram matching, change detection and time series analysis will be explained and practiced.
Register for the reserve list of the course: The course is full. From today (2024-03-27) when you register to the course you are put in a reserve list.
Pre-workshop self-study, including reading course textbook, Remote sensing and image interpretation (Lillesand, kiefer & Chipman), scientific articles as well as view recorded video lectures to get a basic understanding of the topic. One week (40h).
Workshop at Remingstorp supertest site (26th of May to 2nd of June). Including lectures/seminars; computer exercises, data acquisition using drones; in-situ calibration and fieldtrip.
Preliminary schedule:
26/5
Morning - afternoon: Travel to Remingstorp/Skara
Evening: Get-together party
27/5
Morning - afternoon: Introduction exercises in remote sensing (Satellite images, Lidar, Photogrammetry)
Evening: Drone demo
28/5
Morning: Field visit: Spectral data acquisition
Afternoon: Lab: Spectral calibration
Evening: Reflection and discussion
29/5
Morning: Field visit: 3D data acquisition
Afternoon: Lab: 3D remote sensing
Evening: Reflection and discussion
30/5
Morning: Field visit: Multiple data set acquisition
Afternoon: Lab: Mixing different sensor data/Change detection
Evening: Reflection and discussion
31/5
Morning: Introduction High performance computing (HPC)
Afternoon: Lab: Introduction High performance computing (HPC)
Evening: Reflection and discussion
1/6
Morning: High performance computing (HPC)
Afternoon: Lab: HPC
Evening: Introduction to home assignment
2/6
Pack up and travel home
Post-Workshop self-study. Teacher-guided HPC exercises, where the student will conduct a change detection analysis as an individual project. Two weeks (80h)