Earth Science and Data Science Lessons

Use Scientific Programming in R and Python for Earth Science

Earth Science is the study of the Earth’s processes and systems. Earth systems include both the environment and human impacts on and interactions with the environment. Often the data required to study Earth Systems are large and complex. In the lessons below, which cover R and Python, you’ll discover how to collect, process and analyze Earth science data to better understand our planet.

Remote Sensing to Study Wildfire

Learn about how scientists use remote sensing methods to study the impacts of wildfire through calculations of vegetation indices before and after wildfire.

last updated: 02 Jan 2019

Field Methods to Study Wildfire

Learn about how scientists use field survey methods to study the impacts of wildfire through measurements of biomass and soil.

last updated: 02 Jan 2019

An Overview of the Cold Springs Wildfire

The Cold Springs wildfire burned a total of 528 acres of land between July 9, 2016 and July 14, 2016. Learn more about this wildfire and how scientists study wildfire using both field and remote sensing methods.

last updated: 02 Jan 2019

Learn to Use NAIP Multiband Remote Sensing Images in Python

Learn how to open up a multi-band raster layer or image stored in .tiff format in Python using Rasterio. Learn how to plot histograms of raster values and how to plot 3 band RGB and color infrared or false color images.

last updated: 30 Oct 2018

Open, Plot and Explore Lidar Data in Raster Format with Python

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You will learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

last updated: 30 Oct 2018

Work with MODIS Remote Sensing Data in R.

In this lesson you will explore how to import and work with MODIS remote sensing data in raster geotiff format in R. You will cover importing many files using regular expressions and cleaning raster stack layer names for nice plotting.

last updated: 10 Jan 2018

Clean Remote Sensing Data in R - Clouds, Shadows & Cloud Masks

In this lesson, you will learn how to deal with clouds when working with spectral remote sensing data. You will learn how to mask clouds from landsat and MODIS remote sensing data in R using the mask() function. You will also discuss issues associated with cloud cover - particular as they relate to a research topic.

last updated: 10 Jan 2018

Work with MODIS Remote Sensing Data in Python

MODIS is a satellite remote sensing instrument that collects data daily across the globe at 250-500 m resolution. Learn how to import, clean up and plot MODIS data in Python.

last updated: 23 Oct 2018

Clean Remote Sensing Data in Python - Clouds, Shadows & Cloud Masks

In this lesson, you will learn how to deal with clouds when working with spectral remote sensing data. You will learn how to mask clouds from landsat and MODIS remote sensing data in R using the mask() function. You will also discuss issues associated with cloud cover - particular as they relate to a research topic.

last updated: 06 Nov 2018

Landsat Remote Sensing tif Files in R

In this lesson you will cover the basics of using Landsat 7 and 8 in R. You will learn how to import Landsat data stored in .tif format - where each .tif file represents a single band rather than a stack of bands. Finally you will plot the data using various 3 band combinations including RGB and color-infrared.

last updated: 30 Jul 2018

Calculate NDVI in R: Remote Sensing Vegetation Index

NDVI is calculated using near infrared and red wavelengths or types of light and is used to measure vegetation greenness or health. Learn how to calculate remote sensing NDVI using multispectral imagery in R.

last updated: 30 Jul 2018

How Multispectral Imagery is Drawn on Computers - Additive Color Models

In this lesson you will learn the basics of using Landsat 7 and 8 in R. You will learn how to import Landsat data stored in .tif format - where each .tif file represents a single band rather than a stack of bands. Finally you will plot the data using various 3 band combinations including RGB and color-infrared.

last updated: 08 Dec 2017

Extract Raster Values Using Vector Boundaries in R

This lesson reviews how to extract pixels from a raster dataset using a vector boundary. You can use the extracted pixels to calculate mean and max tree height for a study area (in this case a field site where tree heights were measured on the ground. Finally you will compare tree heights derived from lidar data compared to tree height measured by humans on the ground.

last updated: 30 Jul 2018

Clip Raster in R

You can clip a raster to a polygon extent to save processing time and make image sizes smaller. Learn how to crop a raster dataset in R.

last updated: 10 Jan 2018

Classify a Raster in R.

This lesson presents how to classify a raster dataset and export it as a new raster in R.

last updated: 10 Jan 2018

Create a Canopy Height Model With Lidar Data

A canopy height model contains height values trees and can be used to understand landscape change over time. Learn how to use LIDAR elevation data to calculate canopy height and change in terrain over time.

last updated: 10 Jan 2018

Plot Histograms of Raster Values in R

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

last updated: 10 Jan 2018

Introduction to Lidar Raster Data Products

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

last updated: 30 Jul 2018

What is Lidar Data

This lesson reviews what lidar remote sensing is, what the lidar instrument measures and discusses the core components of a lidar remote sensing system.

last updated: 30 Jul 2018