Google Earth Engine for Water Resources Management

GIS and Remote Sensing plays a critical role in the management of water resources. Many practitioners in this field are constrained by the availability of tools and computing resources to use these techniques effectively. Recent advances in cloud computing technology have given rise to platforms such as Google Earth Engine, which provide free access to a large pool of computational resources and datasets. The course is designed for researchers in the water sector, academicians, water managers, and stakeholders with basic knowledge of Remote Sensing. It will enable them to leverage this platform for water resource management applications.

Objective

The aim of this course is to introduce Google Earth Engine platform to apply remote sensing techniques using openly available Earth Observation datasets. The course will also teach participants how to build water related applications for state and country-wide analysis, mapping and monitoring.

Duration

24 Hours Live Instruction with 8 Hours of Self-study materials (Videos+Assignment)

The course is typically conducted in 6 sessions of 4 hours each.

Prerequisites

  • Familiarity with remote sensing concepts

Learning Outcomes

  • Implement remote sensing workflows in Earth Engine.
  • Scale your analysis to large regions and over long periods of time.
  • Build interactive apps for data exploration.

Course Outline

Course Pre-work (Self-study, 2-hours)

  • Introduction to Remote Sensing (video)
  • Introduction to Earth Engine (video)

Session 1: Surface Water Mapping (Live Session, 4-hours)

  • Introduction to the Code Editor
  • Fundamentals of Javascript programming
  • Working with Image Collections
  • Working with Feature Collections
  • Creating Mosaics and Composites
  • Calculating Indices (NDVI, MNDWI, AWEI)
  • Computation on Images (Simple Thresholding, Raster Algebra)
  • Image Processing (Kernels, Convolutions, and Morphological Operations)
  • Image Masking
  • Calculating Surface Water Area
  • Raster to Vector Conversion
  • Exporting Data

Session 2: Working with Gridded Rainfall Data (Live Session, 4-hours)

  • Introduction to Gridded Precipitation and Climate Datasets
  • Map/Reduce Programming Concepts
  • Calculating Total Rainfall in a Region
  • Creating Time-series Charts
  • Exporting a Time-series of Rainfall in a Region
  • Calculating Long-Term Monthly Average Rainfall

Session 3: Time Series Analysis (Live Session, 4-hours)

  • Creating a Pixel-wise Precipitation Time Series Dataset
  • Calculating Sen’s Slope Statistic
  • Mann-Kendall Trend Test
  • Significance Testing

Assignments (Self Study, 2-hours)

  • Assignment 1: Extracting Waterbodies in a Watershed
  • Assignment 2: Time Series Analysis of Evapotranspiration

Session 4: Land Use Land Cover Mapping (Live Session, 4-hours)

  • Introduction to Machine Learning and Supervised Classification
  • Land cover classification
  • Accuracy Assessment
  • Hyperparameter Tuning

Session 5: Flood Mapping (Live Session, 4-hours)

  • Introduction to Radar Remote Sensing
  • Visualizing SAR Imagery
  • Change Detection Methods for Detecting Floods
  • Rapid Flood Mapping using UN-SPIDER methodology

Session 6: Drought Mapping and Monitoring (Live Session, 4-hours)

  • Introduction to Drought Mapping and Monitoring
  • Multi-temporal analysis of MODIS-based Vegetation Condition Index (VCI) for Drought Monitoring and Early Warning.

Certification

Upon successful completion of all the live online sessions and completing the assignments, participants will be issued an employer-verifiable certificate from Spatial Thoughts. Learn more.


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