Forest Mapping and Monitoring with SAR Data [Advanced]

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 
Measurements of forest cover and change are vital to understanding the global carbon cycle and the contribution of forests to carbon sequestration. Many nations are engaged in international agreements, such as the Reducing Emissions from Deforestation and Degradation (REDD+) initiative, which includes tracking annual deforestation rates and developing early warning systems of forest loss. Remote sensing data are integral to data collection for these metrics, however, the use of optical remote sensing for monitoring forest health can be challenging in tropical, cloud-prone regions.
Radar remote sensing overcomes these challenges because of its ability to “see” the surface through clouds or regardless of day or night conditions. In addition, the radar signal can penetrate through the vegetation canopy and provide information relevant to structure and density. Although the capabilities and benefits of SAR data for forest mapping and monitoring are known, it is underutilized operationally due to data complexities and limited user-friendly tutorials.

This advanced webinar series will introduce participants to 1.) SAR time series analysis of forest change using Google Earth Engine (GEE), 2.) land cover classification with radar and optical data with GEE, 3.) mapping mangroves with SAR, and 4.) forest stand height estimation with SAR. Each training will include a theoretical portion describing the use of SAR for landcover mapping as related to the focus of the session followed by a demonstration that will show participants how to access, download, and analyze SAR data for forest mapping and monitoring. These demonstrations will use freely-available, open-source data, and software.

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

  • Interpret radar data for forest mapping
  • Understand how radar data can be applied to land cover mapping
  • Become familiar with open source tools used to analyze radar data
  • Conduct a land cover classification with radar and optical data
  • Map mangrove forests with radar data
  • Understand how forest stand height can be mapped using radar data
  • Apply SAR time-series analysis to map forest change
  • Learn about upcoming radar missions at NASA

Course Format: 

  • Four parts with sessions offered in English and Spanish
  • Four exercises
  • One Google Form homework

prerequisites: Attendees who have not completed the following may not be prepared for the pace of this training:

Part 1: Time Series Analysis of Forest Change

  • Introduction to analysis and interpretation of SAR data for forest mapping
  • Exercise: Time Series of Forest Change using GEE
  • Q&A

Part 2: Land Cover Classification with Radar and Optical Data

  • Review of the unique attributes of radar and optical data as related to forest mapping and how they can be complementary
  • Classification algorithms and improvements with optical imagery
  • Exercise: Land Cover Classification with Radar and Optical using GEE
  • Q&A

Part 3: Mangrove Mapping

  • Introduction to analysis and interpretation of SAR data for mangrove mapping
  • Exercise: Mapping Mangroves with the Sentinel Toolbox
  • Q&A

Part 4: Forest Stand Height (with Guest Speaker Paul Siqueria)

  • Introduction to the use of SAR data for mapping forest stand height
  • Applications and looking forward to NISAR 2022
  • Demo: Estimating Forest Stand Height
  • Q&A

​Each part of 4 includes links to the recordings, presentation slides, exercises, and Question & Answer Transcripts.

Authoring Person(s) Name: 
Erika Podes
Amber McCullum
Juan Luis Torres Perez
Sean McCartney
Authoring Organization(s) Name: 
NASA Applied Remote Sensing Training Program (ARSET)
License - link to legal statement specifying the copyright status of the learning resource: 
Creative Commons Attribution 2.0 Generic - CC BY 2.0
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
Also available in - other languages in which the learning resource has been translated or made available other than the primary: 
More info about
Keywords - short phrases describing what the learning resource is about: 
Agriculture data
Data analysis
Environmental change records
Environmental management
Forest mapping applications
Land management
Landcover applications
Open access
Remote sensing
Satellite imagery
Software management
Sustainable Development Goals (SDGs)
Synthetic aperture radar data (SAR)
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Education: Science and Mathematics Education
Physical Sciences and Mathematics: Earth Sciences
Physical Sciences and Mathematics: Environmental Sciences
Published / Broadcast: 
Tuesday, May 12, 2020
Publisher - organization credited with publishing or broadcasting the learning resource: 
NASA Applied Remote Sensing Training Program (ARSET)
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.
Contributor Name: 
Paul Siqueria
Contact Person(s): 
Brock Blevins
Contact Organization(s): 
NASA Applied Remote Sensing Training Program (ARSET)
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.
Target Audience - intended audience for which the learning resource was created: 
Citizen scientist
Data manager
Data policymaker
Early-career research scientist
Mid-career research scientist
Research scientist
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)