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.
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: 11 Sep 2020
A set of activities for you to practice your skills using Landsat Data in Open Source Python.
last updated: 11 Sep 2020
Learn how to find and download Landsat 8 remote sensing data from the USGS Earth Explorer website.
last updated: 11 Sep 2020
Most remote sensing data sets contain no data values that represent pixels that contain invalid data. Learn how to handle no data values in Python for better raster processing.
last updated: 11 Sep 2020
Landsat remote sensing data often has pixels that are covered by clouds and cloud shadows. Learn how to remove cloud covered landsat pixels using open source Python.
last updated: 11 Sep 2020
Learn how to open up and create a stack of Landsat images and crop them to a certain extent using open source Python.
last updated: 11 Sep 2020
The Normalized Burn Index is used to quantify the amount of area that was impacted by a fire. Learn how to calculate the normalized burn index and classify your data using Landsat 8 data in Python.
last updated: 11 Sep 2020
A vegetation index is a single value that quantifies vegetation health or structure. Learn how to calculate the NDVI vegetation index using NAIP data in Python.
last updated: 11 Sep 2020
A vegetation index is a value that quantifies vegetation health or structure. Learn how to calculate the NDVI and NBR vegetation indices to study vegetation health and wildfire impacts in Python.
last updated: 11 Sep 2020
Scientists often use remote sensing methods to study the impacts of wildfire through calculations of vegetation indices before and after wildfire. Learn more about how remote sensing can be used to study wildfire impacts.
last updated: 11 Sep 2020
Scientists often use field survey methods to study the impacts of wildfire through measurements of biomass and soil. Learn more about how survey methods can be used to study wildfire impacts.
last updated: 11 Sep 2020
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: 11 Sep 2020
In this lesson you will review how to find and download USDS NAIP imagery from the USGS Earth Explorere website.
last updated: 11 Sep 2020
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: 11 Sep 2020
Learn the basics of how addidative colors models are used to render RGB images in Python.
last updated: 11 Sep 2020
This lesson defines 3 lidar data products: the digital elevation model (DEM), the digital surface model (DSM) and the canopy height model (CHM).
last updated: 11 Sep 2020
Rasters are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how a lidar data point cloud is converted to a raster format such as a GeoTIFF.
last updated: 11 Sep 2020
This lesson covers what a lidar point cloud is. You will use the free plas.io point cloud viewer to explore a point cloud.
last updated: 11 Sep 2020
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: 11 Sep 2020
A flood event often changes the terrain as water moves sediment and debris across the landscape. Learn how terrain changes are measured using lidar remote sensing data.
last updated: 11 Sep 2020
The amount and/or duration of rainfall can impact how severe a flood is. Learn how rainfall is measured and used to understand flood impacts.
last updated: 11 Sep 2020
Learn more about the stream discharge data that is used in this data story.
last updated: 11 Sep 2020
Changes in the atmosphere, including how quickly a storm moves can impact the severity of a flood. Learn more about how atmospheric conditions impact flood events.
last updated: 11 Sep 2020
The 2013 flood event caused significant damage throughout the state of Colorado, USA. Learn about what caused the 2013 floods in Colorado and also some of the impacts.
last updated: 11 Sep 2020
Reclassifying raster data allows you to use a set of defined values to organize pixel values into new bins or categories. Learn how to classify a raster dataset and export it as a new raster in Python.
last updated: 11 Sep 2020
Often you need to process two raster datasets together to create a new raster output and then save that output as a new file. Learn how to subtract rasters and create a new GeoTIFF file using open source Python.
last updated: 11 Sep 2020
You can perform the same raster processing steps in Python that you would in a tool like ArcGIS. Learn how to process spatial raster data using Open Source Python.
last updated: 11 Sep 2020
Challenge your skills. Practice opening, cleaning and plotting raster data in Python
last updated: 11 Sep 2020
Raster metadata includes the coordinate reference system (CRS), resolution, and spatial extent. Learn about these metadata and how to access them in Python
last updated: 11 Sep 2020
Raster data are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how to open, plot, and explore raster files in Python.
last updated: 11 Sep 2020
Rasters are gridded data composed of pixels that store values. Learn more about the structure of raster data and how to use them to store data, such as imagery or elevation values.
last updated: 11 Sep 2020
Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.
last updated: 06 Mar 2020
Learn how to use the time series feature in Google Earth to view before and after images of a location.
last updated: 03 Apr 2020
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: 03 Sep 2019
In this lesson you review how to calculate difference normalized burn ratio using pre and post fire NBR rasters in R. You finally will classify the dNBR raster.
last updated: 13 Mar 2020
In this lesson you review the normalized burn ratio (NBR) index which can be used to identify the area and severity of a fire. Specifically you will calculate NBR using Landsat 8 spectral remote sensing data in raster, .tif format.
last updated: 13 Mar 2020
Often data have missing or bad data values that you need to replace. Learn how to replace missing or bad data values in a raster, with values from another raster in the same pixel location using the cover function in R.
last updated: 03 Sep 2019
In this lesson you will review how to find and download Landsat imagery from the USGS Earth Explorere website.
last updated: 03 Sep 2019
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: 30 Mar 2020
Learn how to find and download Landsat 8 remote sensing data from the USGS Earth Explorer website.
last updated: 04 Mar 2020
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: 13 Mar 2020
The Normalized Burn Index is used to quantify the amount of area that was impacted by a fire. Learn how to calculate the normalized burn index and classify your data using Landsat 8 data in open source Python.
last updated: 04 Mar 2020
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: 08 Jan 2020
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: 03 Sep 2019
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: 03 Sep 2019
It is important to compare differences between tree height measurements made by humans on the ground to those estimated using lidar remote sensing data. Learn how to perform this analysis and calculate error or uncertainty in R.
last updated: 03 Sep 2019
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: 03 Sep 2019
There are difference sources of error when you measure tree height using Lidar. Learn about accuracy, precision and the sources of error associated with lidar remote sensing data.
last updated: 03 Sep 2019
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: 13 Mar 2020
This lesson presents how to classify a raster dataset and export it as a new raster in R.
last updated: 13 Mar 2020
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: 03 Sep 2019
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: 03 Sep 2019
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: 13 Mar 2020
This lesson reviews how a lidar data point cloud is converted to a raster format such as a geotiff.
last updated: 03 Sep 2019
This lesson covers what a lidar point cloud is. We will use the free plas.io point cloud viewer to explore a point cloud.
last updated: 08 Jan 2020
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 Mar 2020
Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.
last updated: 03 Sep 2019
Learn why documentation is important when analyzing data by evaluating someone elses report on the Colorado floods.
last updated: 03 Sep 2019
Learn how to use the time series feature in Google Earth to view before / after images of a location.
last updated: 30 Mar 2020