Marginal Lands and Carbon Sequestration Analysis
Learn to analyze marginal lands and estimate carbon sequestration using remote sensing and GIS techniques in this comprehensive course.
Learn to analyze marginal lands and estimate carbon sequestration using remote sensing and GIS techniques in this comprehensive course.
This course teaches students to understand and analyze Marginal Lands (MLs) using advanced remote sensing and GIS techniques. Students will learn methodologies for ML identification, classification, and carbon sequestration estimation. The course covers biomass estimation using optical sensors, RADAR, and LiDAR, along with practical applications in the Google Earth Engine platform. Based on the European MAIL project, participants will gain hands-on experience with large dataset manipulation and the MaiL Geoportal. The course includes self-evaluation questions, weekly practice exercises, and unit exams to ensure comprehensive learning.
Instructors:
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What you'll learn
Understand the concept and definition of Marginal Lands
Master methodological approaches for identifying and classifying Marginal Lands
Learn forest biomass estimation techniques using optical sensors, RADAR, and LiDAR
Develop skills in carbon storage calculation and accounting
Gain practical experience with Google Earth Engine for Marginal Lands management
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This course focuses on the analysis and management of Marginal Lands (MLs) using remote sensing and GIS technologies. It introduces students to ML identification methodologies, biomass estimation techniques using various sensors, and carbon sequestration calculation methods. The curriculum is based on the European MAIL project and includes practical applications using Google Earth Engine. Students learn to handle large datasets and use the MaiL Geoportal for efficient analysis. The course combines theoretical knowledge with practical exercises and includes regular assessments to ensure learning effectiveness.
Definition & descriptions of MLs
Module 1
Identification & classification of MLs
Module 2
Biomass estimation from remote sensing data
Module 3
Carbon storage and accounting
Module 4
GEE based platform for MLs management
Module 5
Fee Structure
Instructors
1 Course
Distinguished Geospatial Technology Expert and Remote Sensing Scholar
Charalampos Georgiadis serves as Associate Professor at the School of Rural and Surveying Engineering at Aristotle University of Thessaloniki. His academic journey includes a Diploma in Rural and Surveying Engineering from AUTH, an M.Sc. in Protection, Preservation, and Restoration of Cultural Monuments, and a PhD from the Department of Spatial Information Science and Engineering at the University of Maine, USA
1 Course
Distinguished Remote Sensing Expert and Environmental Monitoring Pioneer
Luis A. Ruiz serves as Professor of Remote Sensing and Digital Image Processing at Universitat Politècnica de València, where he specializes in forest structure analysis and land use monitoring. His research focuses on developing innovative methods for forest fuel characterization using LiDAR technology and multispectral imagery. His expertise spans object-based image analysis for land use and land cover change detection, with particular emphasis on geodatabase updating and environmental monitoring. His work has significantly advanced the field of remote sensing applications in forestry and environmental science, combining traditional remote sensing techniques with cutting-edge object-oriented analysis methods. Through his research and teaching, he continues to pioneer new approaches in environmental monitoring and forest structure assessment, contributing to both theoretical advancement and practical applications in the field.
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