The course will provide the knowledge needed to design and implement a carbon monitoring system targeted to the specific ecosystem and research question/application. It will provide also the knowledge to find existing data and information from existing sources and critically evaluate them.
EXPECTED LEARNING OUTCOMES.
1) Knowledge and understanding: at the end of the course the student will have the necessary tools to define the best strategy to monitor the ecosystem carbon cycle, the different options available and the overall knowledge to monitor the terrestrial ecosystems carbon and other greenhouse gases (GHG) exchange with the atmosphere in context of climate change.
2) Applied knowledge and understanding: the course will provide the necessary cognitive tools to allow the choice of the most suitable techniques for the study of the ecosystem carbon and other GHGs balances and the options to correctly collect, organize, store and analyse the measurements.
3) Making judgments: once the training course is over, the student will have the tools for a strong autonomy of judgement on issues related to the interactions between climate, atmosphere and ecosystems in the context of the carbon exchange and sequestration and on the options available for the quantification and monitoring of the GHGs exchange in natural ecosystems..
4) Communication skills: at the end of the training course, the student must demonstrate that he or she is able to communicate and discuss in a concise but effective way the issues dealt with during the course, demonstrating the ability to integrate the knowledge acquired.
5) Learning skills: at the end of the course the student must have learned the concepts and techniques addressed and know how to define limits and fundamentals.

The course is designed to give an introduction on how to generate information from remote sensing data and how to analyse these data in a geographic information system, in order to map forest resources and monitor relevant changes in forest canopy cover.
The course examines the basics of theoretical issues and image classification to help students understand and choose remote sensing solutions for forest classification and forest monitoring problems. The main topics are covered with many practical exercises of forest classification and forest change detection.

Expected Learning outcomes:
1) Knowledge and understanding: comprehensive knowledge of the basics of theoretical issues behind optical remote sensing and image classification
2) Applied knowledge and understanding: ability to select, conceptualize, and implement image classification techniques of multispectral RS images in QGIS with respect to a given practical application in forest cover mapping and change detection
3) Making judgments: critical analysis and evaluation of the potentials and limitations of different image classification methods
4) Communication skills: Refined presentation skills of an own image classification project for forest applications
5) Learning skills: an own mental model for addressing simple tasks exercises of forest classification and forest change detection (competent practitioner of RS)