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poc: marginal101x
Management of Marginal Lands and Carbon Sequestration estimation through Remote Sensing and GIS

Management of Marginal Lands and Carbon Sequestration estimation through Remote Sensing and GIS
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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.
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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 and 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 MaiL geoportal 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 a exam at the end of each unit.

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

To pass the course you will have an exam at the end of each unit which will count for 20% of the final grade. In total there are 5 exams, one for each unit. The course will be considered passed if the final grade is equal to or higher than 50% of the maximum possible grade. The practices and the questions placed after each video serve to reinforce what you have learned, but they do not count for the final grade. You have 1 attempt to answer each multiple-choice question.

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.

Luis A. Ruiz

Luis A. Ruiz is a professor of remote sensing and digital image processing at the Department of Cartographic Engineering, Geodesy and Photogrammetry of the Polytechnic University of Valencia (Spain), co-director of the GeoEnvironmental Cartography and Remote Sensing group, and Editor-in-Chief of the Spanish Journal of Remote Sensing. His research interests are focused on forest structure and fuel characterization using LiDAR, multispectral satellite and aerial imagery; developing object-based image analysis methods for LULC change detection and geodatabase updating.

Lampros Papalampros

Forest Engineer graduated in 2006 from the Aristotle University of Thessaloniki, Department of Forestry and Natural Environment, Greece. In 2013 he acquired an M.Sc. degree in “Photogrammetric production and remote sensing management in GIS environment” from the School of Rural and Surveying Engineering (Aristotle University of Thessaloniki, Greece). Since 2013, he is studying towards a Ph.D. in the area of Geodesy and Surveying at the same University, and the thesis is about the field of 3D capturing and monitoring of natural environments through novel technologies. He works in HOMEOTECH since 2007 and he has substantial experience in scientific areas such as forest management, cadastre, research programs, LIFE programs, environmental subjects, etc., as he is professionally active since 2004.

Michał Krupiński

Krupiński Michal is a geomatics specialist at Centrum Badań Kosmicznych PAN (Space Research Centre of the Polish Academy of Sciences) since 2010. Involved in number of scientific and R&D projects focused on geospatial data analysis. His research interest includes novel methods for satellite imagery classification, imaging spectroscopy, multifractals, marginal lands and ecosystem services.

Maria Tassopoulou

Maria Tassopoulou is PhD, Surveying Engineer (Aristotle University of Thessaloniki, Department of Rural and Surveying Engineering, Greece, MSc in Restoration of Cultural Monuments (School of Architecture, Aristotle University School of Engineering, Greece). Her areas of expertise include Photogrammetry, GIS, Remote Sensing, Cartography, and 3D modelling using photogrammetric techniques and laser scanners. From 2014, she is member – senior researcher of the Laboratory of Photogrammetry and Remote Sensing, Dept. of Rural and Surveyor Engineering, AUTH.

Natalia Verde

Surveying Engineer with a MSc in Geoinformatics (Aristotle University of Thessaloniki, Greece). Since 2018 she is a PhD candidate at the Aristotle University of Thessaloniki (AUTH) in the Laboratory of Photogrammetry and Remote Sensing. Her PhD is on mapping Sustainable Development Goals indicators at a national scale, using Remote Sensing and cloud computing technologies. She has a strong background in coding and her research interests also include GIS and Photogrammetry.

Eleftherios Mystakidis

Eleftherios Mystakidis is a Forest Engineer who graduated in 2013 from the Aristotle University of Thessaloniki, Department of Forestry and Natural Environment, Greece. He has been working at HOMEOTECH since 2013, providing technical support on projects involving GIS applications, databases and field surveying, participating in the redaction of forest and environmental studies (management, fire protection, road construction and maintenance, technical works, management of protected areas, etc) and field works for surveying and data collection.

Bettina Felten

Bettina Felten is a GIS and remote sensing specialist at IABG Geodata Factory in Dresden, Germany, since September 2018. Her main focus is automated image analysis in the context of remote projects. Bettina is graduated from Wageningen University and Research with a M.Sc. Geo-Information Science and Remote Sensing.

Samuel Nyarko

Samuel Nyarko is a master’s student of Big Data and Artificial Intelligence at SRH Berlin University of Applied Science with seven years of working experience in geospatial technology application on large-scale multinational projects. Samuel is a co-founder and former CEO of Integrated Geospatial Intelligence Application Centre in Ghana. His research interest lies in the combined computer vision and remote sensing applications on mineral resource exploration in particular and also for any object identification, classification, and localization endeavors.

Fernando Bezares

Fernando Bezares is an environmental engineer specialised in geographic information systems applied to the forestry sector working in Cesefor’s IT department. He has worked at the United Nations FAO defining remote sensing based methodologies for the indicators associated with the UN Sustainability Goals. Fernando is a graduate of the first promotion of the Data Forest Master (2017-2019, University of Valladolid). His Final Master's Work (Automatic forest mass segmentation tool from remote sensors: LiDAR and Sentinel-2) was awarded at the SECF 2019 awards.

Jesús Torralba Pérez

Jesús Torralba Pérez is a Forestry and Natural Environmental Engineer from the University of Castilla-La Mancha (Spain). He studied a master’s degree in Remote Sensing at the Universidad Mayor (Chile). Since the end of 2017, he is a PhD student in Geomatics Engineering at the GeoEnvironmental Cartography and Remote Sensing Group (CGAT) at the Polytechnic University of Valencia. His PhD is focused on the characterization of forest structure and forest fuel by integrating analysis of methods based on Terrestrial Laser Scanning (TLS), airborne LiDAR and imaging. His interests and experiences are focused on the analysis and management the forest resources and natural environments with remote sensing and GIS in the framework of climate change.

Juan Pedro Carbonell-Rivera

Juan Pedro Carbonell Rivera is an engineer in Geomatics and Topography by the Universitat Politècnica de València (UPV) with a M.Sc. in Geomatic Engineering and Geoinformation by the UPV and a M.Sc. of the International Master in Geomatics of the Hochschule Karlsruhe – Technik und Wirtschaft (HsKA). Since September 2018 he is PhD student in Geomatics Engineering UPV. His research is focused on characterisation of forest structure by integrated analysis of methods based on Unmanned Aerial Vehicle (UAV) derived imagery.

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MOOC es el acrónimo en inglés de Massive Online Open Course (que puede traducirse como Curso masivo abierto online).

Las siglas MOOC se utilizan para referirse a una iniciativa que comenzó en 2011 con un curso de Introducción a la Inteligencia artificial de la Universidad de Stanford en el que se inscribieron 160.000 estudiantes de 190 países, y que se ha convertido ya en un movimiento global al que se han incorporado las mejores universidades del mundo con la creación de diversas plataformas MOOC y miles de cursos que comparten un objetivo: proporcionar acceso gratuito a una formación superior de calidad a cualquiera que tenga una conexión a internet.

Los MOOCs están basados en la integración de tecnologías que existen desde hace tiempo en Internet: el vídeo en red, la evaluación automática a distancia y los foros de preguntas y respuestas en la web. Pero es ahora cuando los usuarios se han acostumbrado a usar el vídeo en red y las redes sociales en su día a día en Internet, lo que facilita que se creen comunidades de alumnos situados en cualquier parte del mundo que comparten conocimientos y experiencias y se ayudan en el proceso de aprendizaje, dándole a la formación online un nuevo enfoque colaborativo. Esto, unido a la disponibilidad de acceso a los cursos MOOC en cualquier momento y desde cualquier lugar en donde se tenga acceso a Internet, ha permitido crear una nueva comunidad global de estudiantes.

Los MOOCs de UPV[X] están diseñados para ayudarte a aprender trabajando a tu ritmo de forma independiente y proporcionarte el apoyo de la comunidad de aprendizaje si te hace falta.

Para ello los MOOCs están estructurados en módulos que suelen corresponder a una semana de trabajo (aunque no siempre es así). Los módulos se estructuran en lecciones con un vídeo, que dura normalmente entre 3 y 10 minutos, y una pregunta de refuerzo al final para ayudarte a fijar su contenido. Las lecciones están orientadas a tratar un concepto (o un número reducido de ellos) de forma que sean lo más atómicas que sea posible.

Los cursos MOOC de UPV[X] pueden tener distintos esquemas de evaluación, pero lo habitual es que al terminar cada módulo haya un examen (normalmente tipo test) que te permitirá comprobar tu evolución y fijar los conocimientos, y que cuenta en cierto porcentaje para la evaluación final. Con ello tendrás una realimentación frecuente de tu progreso que te permitirá ir mejorar tu proceso de aprendizaje.

Al acabar el MOOC habrá a un examen final para evaluar si has alcanzado los objetivos de aprendizaje que contará en un porcentaje mayor para la evaluación final.

En los MOOC también hay un foro de debate moderado por el equipo del curso donde los alumnos pueden consultar sus dudas sobre el contenido del curso y comentar aquellas cosas que les parezcan interesantes. El acceso a una comunidad online de internautas con tus mismos intereses y que están aprendiendo a la vez enriquece la experiencia del curso.

Esperamos sinceramente que disfrutes de nuestros MOOCs y aprendas con nosotros