Specialised Training Module Forests
This action is part of a series of actions in the continuity of existing or programmed work. The rationale consists of creating a whole series of consistent training modules on remote sensing, with the objective of producing (mainly) open education resources on remote sensing and thematic applications. The series aims at promoting and disseminating research results of the Joint Research Units (like TETIS, Montpellier, France) and more broadly of the THEIA thematic cluster, which focuses on continental surfaces.
This particular action aims at creating a specialized module for the use of Sentinel images and other data in a specific domain related to forests for educational and vocational training. The module addresses three types of forests: temperate, Mediterranean and tropical.
Methodologies used include the use of Sentinel data (Optical and RADAR) and LIDAR technologies (using essentially aerial borne data) and stereophotogrammetry.
The module aims at giving an overview of existing methods for the evaluation of various indexes or indicators related to forests and at comparing them in terms of scales and practical use. LIDAR technologies have proved their usefulness for the estimation of biomass and other variables. Fusion of data from different sensors – including Sentinel 1and 2 – and joint analysis are on the research front and the perspectives provided by these techniques are also presented and discussed.
A 2-day module will be created for e-learning and it will be useable for hybrid completion. Remote sensing specialists will be implicated to produce the course material.
Outputs and Results
- Online courses will be open and accessible
- Face-to-face courses and practical work will be included in master programs or proposed in short vocational training sessions
- The result consists of a module, comprising an online course, and a set of data aimed at being used in tutored sessions. The online course gives the theoretical aspects, which are destined to be put in practice during the sessions
- Two training sessions are planned and should each be attended by at least 20 students; the material will be used for further sessions, depending on demand