Course Overview
This 5-day training course offered at IRES is an intensive and hands-on experience with Google Earth Engine, a powerful cloud-based platform for planetary-scale geospatial analysis. Participants will learn to harness the full potential of Google Earth Engine to analyze vast amounts of satellite imagery and geospatial data. The course covers essential topics such as data acquisition, processing, visualization, and analysis. It also includes advanced scripting techniques using JavaScript, enabling participants to automate and optimize geospatial workflows. Real-world projects and case studies are integrated to enhance practical understanding and application of the tools and concepts learned.
Course Duration
5 days
Target Audience
- GIS professionals and geospatial analysts
- Environmental scientists and ecologists
- Remote sensing specialists
- Researchers and academics in the field of geography and environmental science
- Urban planners and land use managers
- Climate change analysts and policy makers
Personal Impact
- Develop proficiency in using Google Earth Engine for geospatial analysis and visualization.
- Learn to process and analyze large-scale satellite imagery efficiently.
- Gain skills in writing scripts for automating geospatial tasks.
- Enhance your ability to conduct advanced spatial and temporal analysis.
- Stay at the forefront of geospatial technology and remote sensing capabilities.
Organizational Impact
- Empower your organization to perform high-resolution geospatial analysis with minimal infrastructure costs.
- Improve decision-making with access to up-to-date and comprehensive geospatial data.
- Enhance capabilities in monitoring environmental changes and assessing impacts.
- Optimize workflows and increase efficiency in handling large geospatial datasets.
- Support sustainability and conservation efforts with accurate and timely geospatial insights.
Course Outline
Course Objectives
- To provide a solid understanding of Google Earth Engine and its capabilities.
- To teach participants how to access and manipulate satellite imagery and geospatial data.
- To introduce scripting in Google Earth Engine for advanced geospatial analysis.
- To demonstrate practical applications of Google Earth Engine in various fields.
- To equip participants with skills to automate geospatial workflows using JavaScript.
Course Modules
Course Outline
Module 1: Introduction to Google Earth Engine
- Overview of Google Earth Engine platform and its applications
- Understanding Earth Engine datasets and data catalog
- Setting up and navigating the Earth Engine Code Editor
- Basic JavaScript programming concepts for Earth Engine
- Working with raster and vector data in Google Earth Engine
- Case Study: Exploring and visualizing global land cover data using Google Earth Engine
Module 2: Image Processing and Analysis in Google Earth Engine
- Introduction to satellite imagery and remote sensing principles
- Importing and visualizing satellite data (Landsat, Sentinel, MODIS)
- Preprocessing imagery: cloud masking, atmospheric correction, and image mosaicking
- Performing band calculations and spectral indices (NDVI, EVI, etc.)
- Temporal analysis and time-series visualization
- Real-Life Project: Analyzing deforestation trends using time-series data from Landsat
Module 3: Advanced Scripting Techniques and Data Visualization
- Writing custom functions and mapping over image collections
- Using reducers for zonal statistics and image reduction
- Creating interactive visualizations and charts
- Exporting analysis results to Google Drive or other formats
- Integrating Google Earth Engine with other GIS tools (QGIS, ArcGIS)
- Case Study: Developing a script to monitor urban expansion using Sentinel-2 data
Module 4: Change Detection and Machine Learning Applications
- Techniques for change detection and land cover classification
- Introduction to machine learning concepts in Earth Engine
- Supervised and unsupervised classification methods
- Training and applying classifiers (Random Forest, CART)
- Accuracy assessment and validation of classification results
- Real-Life Project: Implementing a land cover classification workflow using machine learning
Module 5: Applications and Case Studies in Google Earth Engine
- Application of Google Earth Engine in climate change analysis
- Monitoring water resources and drought assessment
- Urban heat island analysis and mitigation strategies
- Applications in agriculture: crop monitoring and yield estimation
- Building custom applications with Earth Engine Apps
- Case Study: Developing a comprehensive environmental monitoring application using Google Earth Engine
Related Courses
Course Administration Details
Methodology
These instructor-led training sessions are delivered using a blended learning approach and include presentations, guided practical exercises, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience as professionals and trainers in these fields. All facilitation and course materials are offered in English. Participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
Training Venue
The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation and Airport Transfer
Accommodation and Airport Transfer are arranged upon request. For reservations contact the Training Officer.
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on:
Payment
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to [email protected]
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