Suggested Prior Knowledge

Recognizing that users may come from a wide range of backgrounds, provided here are several resources for foundational knowledge either assumed, or not detailed throughout the training. These range from remote sensing and programming basics and guidebooks, to foundations of lidar and remote sensing applications training resources.

Remote Sensing Basics

Intro to Remote Sensing

• NASA ARSET - Fundamental of Remote Sensing
  • A self-paced course to learn the underlying science behind remote sensing and characteristics of satellites and sensors. Outlined are the advantages and disadvantages of remote sensing, and basic no-cost and open source tools.
  • ARSET overall offers online and in-person trainings for beginners and advanced practitioners alike. Trainings cover a range of NASA datasets, web portals, and analysis tools and their applications to health and air quality, agriculture, climate and resilience, disasters, ecological conservation, and water resources management.

• SERVIR Amazonia - Introduction to Remote Sensing
  • Several introductory trainings are included within this resource.
  • Spanish version
  • SERVIR Amazonia was a geospatial technology transfer program based in Colombia as part of a global collaborative network promoting the use of satellite remote sensing in decision-making. The center offered training and geospatial services to Indigenous organizations and facilitated technical collaboration with governments, universities, research institutions, and NGOs in the Amazon and Caribbean regions.

Intro to Geospatial Information Systems (GIS)

• NASA Earth Science GIS Learning Resources
  • Access tutorials, data recipes, and webinars to level up your skills using Geographic Information Systems (GIS) desktop applications and data tools.
  • NASA is a leading organization in the development of Earth science and Earth observing remote sensing commonly based in GIS technologies and skills.

• QGIS Training Manual
  • QGIS is a free and open source downloadable platform for visualizing and analyzing geospatial data.

• SERVIR Amazonia - Introduction to GIS with QGIS

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Drone Basics

• SERVIR Amazonia - Drone Flight and Image Processing

• Unmanned Aircraft Systems Educator Guide
• Drones and You

Mapping Validation with Collect Earth Online (CEO)

• SERVIR Amazonia - Map Validation Using Collect Earth Online
• Spanish version
• CEO is a free and open-source system for interpreting results with high-resolution satellite imagery, enabling users to validate results with a semi-automated system.

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Programming Basics

Python Programming

• Learn Python - Free Interactive Python Tutorial
• Python for Beginners

Google Colab

• Google Colab Guide
• Google Colab FAQ

Note: requires a Google account

Programming in R

• Kaggle R Guide

Note: requires installing RStudio and Rtools

Github

• Github Docs - Getting started with Github
• Introduction to Github

Note: requires a Github account

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Google Earth Engine

• Beginner’s Cookbook
• Getting started with Earth Engine
• Cloud-Based Remote Sensing with Google Earth Engine
• End-to-End Google Earth Engine (Full Course)

Note: requires a Google Earth Engine account. The resources may be free or require payment depending on your affiliation.

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Lidar Basics

Lidar Courses and Trainings

• NOAA Office for Coastal Management - Intro to Lidar
• NOAA Office for Coastal Management Lidar 101
• Introduction to NEON Discrete Lidar Data in Python
• Ecosystem related lidar trainings
  • Getting started with LiDAR processing for forestry and natural resources
  • ARSET - Biodiversity Applications for Airborne Imaging Systems
    • Includes content for NASA’s airborne LVIS laser altimeter.
  • ARSET - Use of Solar Induced Fluorescence and LIDAR to Assess Vegetation Change and Vulnerability
    • Spanish version
    • Includes use of ICESat-2 (space-based lidar)
  • ARSET - Invasive Species Monitoring with Remote Sensing
    • Includes use of GEDI ecosystem lidar.
  • Portuguese forest lidar course: Applications of lidar for forest inventory

History of Spaceborne Lidar Missions

• Evolution of Lidar
• History and Applications of Spaceborne Lidars
• Requirements for a global lidar system: spaceborne lidar with wall-to-wall coverage
• Spaceborne lidar surveying and mapping
• NASA Leaders in Lidar Video Series
• The waveform processing algorithms for GEDI are largely based off LVIS, NASA’s Land, Vegetation and Ice Sensor (Blair et al., 1999)
  • Plays a key role in the calibration and validation of GEDI lidar waveform simulator and data product algorithms.
• ICESat-1 and ICESat-2
  • ICESat-2 Applications
  • Explore ICESat-2 Data
• GEDI: Dubayah, R., et al., (2020). The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography. Science of Remote Sensing. Vol. 1. https://doi.org/10.1016/j.srs.2020.100002

Foundations of Lidar

• Lidar Quality and Formatting Specifications
  • LAS specification guide
  • ASPRS. 2007. “Common Lidar Data Exchange Format – .LAS Industry Initiative.” www.asprs.org/a/society/committees/lidar/lidar_format.html.
• Laser Scanning
  • Pulsed Lasers and Equations
  • Handbook of Optical and Laser Scanning

Other Suggestions

Some knowledge of working with large datasets
Some knowledge of ecology, forestry, land system science

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Glossary of Terms

AGB (Above Ground Biomass): The total mass of living vegetation above the soil, often measured in Mg ha−1 (megagrams per hectare) or tons/ha.

ALS (Airborne Laser Scanning): A remote sensing technique using lasers mounted on aircraft to collect data about the Earth’s surface and vegetation structure.

ATLAS (Advanced Topographic Laser Altimeter System): A satellite LiDAR instrument on board the ICESat-2 mission.
Bias: A measure of the average difference between estimated or predicted values and observed values, indicating systematic over or underestimation.

CHM (Canopy Height Model): A raster surface model representing the height of the top of the vegetation canopy.

DBH (Diameter at Breast Height): A standard measurement of tree trunk diameter, typically taken at 1.37 meters above the ground.

DEM (Digital Elevation Model): A digital representation of terrain elevation.

DSM (Digital Surface Model): A digital representation of the Earth’s surface, including vegetation, buildings, and other features.

DTM (Digital Terrain Model): A digital representation of the bare Earth’s surface, excluding vegetation and artificial features. (Barbosa_2022, Ma_2024)

EVI (Enhanced Vegetation Index): A vegetation index designed to be more sensitive to variations in canopy structure and less affected by atmospheric conditions than NDVI.

FHD (Foliage Height Diversity): A measure of canopy complexity related to biodiversity.

Footprint: The area on the ground illuminated by a laser pulse from a LiDAR system.

Fuel Load: The amount of fuel (combustible material) present in a given area, often categorized by fuel type (e.g., surface fuels, herbaceous fuels).

GEDI (Global Ecosystem Dynamics Investigation): A spaceborne LiDAR instrument on board the International Space Station (ISS) specifically optimized to estimate vegetation structures.

GLAS (Geoscience Laser Altimeter System): The first operational satellite laser altimeter, carried onboard ICESat-1.

GLCM (Grey-scale Co-occurrence Matrix): A method used to analyze texture in images.

GNSS-R (Global Navigation Satellite System Reflectometry): A remote sensing technique that uses reflected GNSS signals to measure Earth surface properties.

HBfuels (Herbaceous Fuels): Fuel components consisting of non-woody grasses, herbs, and forbs.

ICESat (Ice, Cloud, and land Elevation Satellite): A series of NASA satellite missions carrying laser altimeters.

ISS (International Space Station): The orbiting space station where the GEDI instrument is located.

KS Test (Kolmogorov–Smirnov Test): A non-parametric test used to compare a sample with a reference probability distribution or to compare two samples.

LiDAR (Light Detection and Ranging): A remote sensing method that uses pulsed laser light to measure distance and create precise 3D information.

MODIS (Moderate Resolution Imaging Spectroradiometer): A key instrument aboard the Terra and Aqua satellites, used for Earth observation.

NDI (Normalized Difference Index): A spectral index derived from SAR data.

NDMI (Normalized Difference Moisture Index): A vegetation index related to water content in vegetation.

NDVI (Normalized Difference Vegetation Index): A widely used vegetation index calculated from red and near-infrared light reflectance.

PAI (Plant Area Index): A measure of the total one-sided area of photosynthetic tissue per unit ground surface area.

PRF (Pulse Repetition Frequency): The number of laser pulses emitted per second by a LiDAR system.

R2 (Coefficient of Determination): A statistical measure that represents the proportion of the variance for a dependent variable that’s explained by an independent variable or variables in a regression model.

RH Metrics (Relative Height Metrics): Metrics derived from LiDAR waveforms that represent the height above ground as a function of backscattered energy.

RMSE (Root Mean Square Error): A measure of the average magnitude of the errors between predicted and observed values.

SHfuels (Shrubs and Herbaceous Fuels): A fuel component representing the sum of surface fuels and herbaceous fuels.

SRTM (Shuttle Radar Topography Mission): A mission that provided a digital elevation model of the Earth’s surface.

SUfuels (Surface Fuels): Fuel components consisting of duff, litter, and downed woody debris.

UAV (Unmanned Aerial Vehicle): An uncrewed aircraft, often used as a platform for remote sensing sensors like LiDAR.

Waveform (LiDAR): The digitized signal received by a LiDAR sensor, representing the distribution of reflected energy over time.