Module 4 Overview


Source: Example figure from this section. Comparing non-spaceborne lidar samples over two Amazonian forest sites with red locations for forest field inventory sites. Izaya Numata

Localized Forest Biomass Estimation

Module 4 will have advanced applications focused on GEDI derived products for forest disturbance and forest carbon dynamics monitoring. Presented here are methods, techniques, and modeling for forest biomass estimation using field, airborne and satellite lidar (GEDI) data developed in Acre, in the southwestern Amazon of Brazil. Participants will understand the advantages, limitations, and considerations for applying such methodologies on their own.

The Target Audience

Technical teams from government/non-government institutions, scientists, researchers, and university students (including undergraduates and graduates) working on ecosystem and carbon management issues, vegetation evaluation and monitoring, conservation and restoration planning.

Learning Objectives

  1. Learn how to develop local forest biomass models using field and lidar data and apply it to forest biomass mapping.
  2. Empower users to develop GEDI-based AGBD data more suitable for particular areas of interest by local AGB model.

Partners, Contributors, and Acknowledgements

Izaya Numata, South Dakota State University
Nadeem Fareed, University of Florida
Marcus d’Oliveira, Embrapa-Acre
Willian Antonio Melo, Universidade Federal do Acre
Sonaira Souza Silva, Universidade Federal do Acre
Sidney Novoa, Conservacion Amazonica
Karis Tenneson, Spatial Informatics Group
Andrea Nicolau, Spatial Informatics Group
Mark Cochrane, University of Maryland, Center for Environmental Science

Module Topics Overview

  • Background on forest biomass estimation using forest inventory and UAV lidar data
  • Exercise 1
    • GEDI waveform simulation with airborne lidar using rGEDI in R
  • Exercise 2
    • Development of local AGB model with field AGB and simulated ALS Relative Height metrics in R
  • Exercise 3
    • Above ground biomass mapping in Google Earth Engine
    • Biomass change detection fusing lidar with the CCDC algorithm.

Pre-Requisites

  • RStudio platform and Rtools, rGEDI, rGEDIsimulator, and rGESII packages.
  • Google Drive account
  • Google Earth Engine account

Table of contents

Curtosy of EarthRISE at NASA Marshall Space Flight Center and the Lab for Applied Scienecs at the University of Alabama in Huntsville