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Management of Marginal Lands and Carbon Sequestration estimation through Remote Sensing and GIS

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Duration

5 weeks

About this course

The course is targeted to students, researchers, or stakeholders who are interested in the analysis and management of Marginal Lands (MLs). This course is designed in 5 units in which it is expected to transfer the results of the European project “Identifying Marginal Lands in Europe and strengthening their contribution potentialities in a CO2 sequestration strategy (MAIL)”. Project under the European Union's Horizon 2020 Marie Skłodowska-Curie research and innovation program (Grant Agreement No. 823805). All videos are based on the procedures followed in the MAIL project for Europe, which means that some of the procedures described may not be exactly transferable to other parts of the world. The mechanics of the course are simple: follow the learning sequences proposed by the platform by watching the videos and asking the questions and activities that are proposed. In some cases, you will find optional readings that you can read to expand on the subject.

What will I learn

In this MOOC you will learn: (1) the definition of MLs; (2) a methodological proposal for the identification and classification of MLs; (3) a short review of forest biomass estimation with optical sensors, RADAR, LiDAR; (4) a proposal of carbon calculation; and (5) an introduction to the MLs management application created in GEE in the framework of the project. During the course, we will give you some recommendations for the manipulation of large datasets and we will teach you how to work with the GEE application as a user. All this with the aim that you will be able to better understand the concept of MLs and will be able to analyze large MLs extensions in a fast way. Within the course, you will have questions for self-evaluation after each video, weekly practice, and at the end of the course an exam where you will be asked about the content of the whole course.

Prerequisites

No previous knowledge is required to enroll the course.

Units

  1. Week 1: Definition & descriptions of MLs.
  2. Week 2: Identification & classification of MLs.
  3. Week 3: Biomass estimation from remote sensing data.
  4. Week 4: Carbon storage and accounting.
  5. Week 5: GEE based platform for MLs management.
  6. Final exam

Evaluation

Only the final exam counts for passing the course, the practice and the questions placed after each video serve to reinforce what you have learned but do not count towards your final grade. You have 1 attempt to answer each multiple-choice question. The course will be considered passed if the final score is equal to or higher than 50% of the maximum possible grade. The practices are self-evaluated, although if you think we can give you some comments or if you have many doubts, you can send an email to the corresponding teacher in each section.

Teachers of the course

Charalampos Georgiadis

Charalampos Georgiadis is an Associate Professor at the School of Civil Engineering in the Aristotle University of Thessaloniki. 1997, Diploma Rural and Surveying Engineering (AUTH), 2000, M.sc. Protection, Preservation, and Restoration of Cultural Monuments (AUTH), 2005 PhD Department of Spatial Information Science and Engineering (University of Maine, USA).

His research interests include among others Photogrammetry, Remote Sensing, Cartography, GIS, UAV, Mobile Mapping Systems, 3D modelling, laser scanners, Image processing and analysis, Spatial data analysis. Scientific Responsible or member of research groups in 47 Research projects Funded by National, European or USA Organizations. He has 59 publications in journals and scientific conferences and he is scientific reviewer in 12 Journals.

AUTH

Charalampos Georgiadis
Natalia Verde
Maria Tassopoulou

HOMEOTECH

Lampros Papalampros

IABG

Bettina Felten
Samuel Nyarko

CBK PAN

Michał Krupiński

Cesefor

Fernando Bezares

UPV

Jesús Torralba Pérez
Juan Pedro Carbonell Rivera

  1. Código del curso

    MAIL301

  2. Inicio de las clases

  3. Fin de las clases

  4. Esfuerzo estimado

    05:00
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