Description

Google Earth Engine (GEE) for remote sensing applications is quickly becoming one of the most utilized tools in the scientific and decision-making community. GEE provides unparalleled access to large-scale data analysis through cloud computing technology. The online interface allows users to access and analyze stores of NASA Earth data without the need for any locally stored data or software. This training, led by NASA's Applied Remote Sensing Training (ARSET) program, covers the GEE Code Editor, hands-on exercises on change detection, time series analysis, land cover classification, and accuracy assessment of optical imagery. These processes are an integral part of optical imagery analysis for many applications, including monitoring forest disturbance, wildfire mapping, identifying land cover degradation, mapping ecosystem connectivity, and identification of land surface changes due to urban growth.

Prerequisites

• Fundamentals of Remote Sensing
• Participants should create a GEE account

Objectives

By the end of this training, attendees will be able to:

• Navigate the GEE interface to explore remote sensing datasets relevant to land monitoring
• Execute JavaScript commands to retrieve satellite data and process imagery for analysis
• Complete a supervised land classification along with an accuracy assessment in GEE
• Apply algorithms to derive a time series of environmental parameters and calculate differences between years to detect landscape changes
• Illustrate the capabilities of cloud-based raster computing for land management applications

Target Audience

This training is designed for local, regional, state, federal, and non-governmental organizations involved in land management, conservation, biodiversity, and forest monitoring.

Course Format

• Three 2-hour parts

Sessions

Part 1: Google Earth Engine Basics and General Applications

Part 1 components:

• Introduction to Google Earth Engine Functionalities and Available Data Types
• Basics Functions in GEE JavaScript API Activity
• Python API Demonstration
• Q&A

Materials

• Part 1 Presentation Slides (PDF, 3.5 MB)
• Part 1 Q&A Transcript (PDF, 0.1 MB)

Part 2: Land Cover Classification and Accuracy Assessment in Google Earth Engine

Part 2 components:

• Land Cover Classification and Accuracy Assessment Overview
• Landsat Data Retrieval and Imagery Preparation
• Supervised Land Cover Classification
• Accuracy Assessment of Land Classifications
• Q&A

Materials

• Part 2 Presentation Slides (PDF, 2.9 MB)
• Part 2 Q&A Transcript (PDF, 0.2 MB)

Part 3: Time Series Analysis and Change Detection in Google Earth Engine

Part 3 components:

• Overview of Time Series and Change Detection
• Environmental Parameter Calculations
• Time Series Display of Environmental Data
• Change Detection Calculation and Mapping
• Q&A

Materials

• Part 3 Presentation Slides (PDF, 8.0 MB)
• Part 3 Q&A Transcript (PDF, 0.1 MB)