Practice Opening and Plotting Landsat Data in Python Using Rasterio
A set of activities for you to practice your skills using Landsat Data in Open Source Python.
last updated: 27 Jan 2022
Remote Sensing - Landsat
Find and Download Landsat 8 Remote Sensing Data From the USGS Earth Explorer Website
Learn how to find and download Landsat 8 remote sensing data from the USGS Earth Explorer website.
last updated: 19 Jan 2022
How to Replace Raster Cell Values with Values from A Different Raster Data Set in Python
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: 27 Jan 2022
Clean Remote Sensing Data in Python - Clouds, Shadows & Cloud Masks
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: 27 Jan 2022
Open and Crop Landsat Remote Sensing Data in Open Source Python
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: 27 Jan 2022
Work with Landsat Remote Sensing Data in Python
Landsat 8 data are downloaded in tif file format. Learn how to open and manipulate Landsat 8 data in Python. Also learn how to create RGB and color infrared Landsat image composites.
last updated: 11 Jun 2021
Calculate and Plot Difference Normalized Burn Ratio (dNBR) using Landsat 8 Remote Sensing Data in Python
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: 14 Oct 2021
Calculate NDVI Using NAIP Remote Sensing Data in the Python Programming Language
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: 28 Jan 2021
Calculate Vegetation Indices in Python
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: 28 Jan 2021
Get NAIP Remote Sensing Data From the Earth Explorer Website
In this lesson you will review how to find and download USDS NAIP imagery from the USGS Earth Explorere website.
last updated: 28 Jan 2021
How multispectral imagery is drawn on computers - Additive Color Models
Learn the basics of how addidative colors models are used to render RGB images in Python.
last updated: 28 Jan 2021
Introduction to Multispectral Remote Sensing Data in Python
Multispectral remote sensing data can be in different resolutions and formats and often has different bands. Learn about the differences between NAIP, Landsat and MODIS remote sensing data as it is used in Python.
last updated: 28 Jan 2021
Calculate and Plot Difference Normalized Burn Ratio (dNBR) from Landsat Remote Sensing Data in R
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
Work with the Difference Normalized Burn Index - Using Spectral Remote Sensing to Understand the Impacts of Fire on the Landscape
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
How to Replace Raster Cell Values with Values from A Different Raster Data Set in R
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
Get Landsat Remote Sensing Data From the Earth Explorer Website
In this lesson you will review how to find and download Landsat imagery from the USGS Earth Explorere website.
last updated: 03 Sep 2019
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: 30 Mar 2020
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: 08 Jan 2020
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: 03 Sep 2019
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: 03 Sep 2019
Introduction to Spatial and Spectral Resolution: Multispectral Imagery
Multispectral imagery can be provided at different resolutions and may contain different bands or types of light. Learn about spectral vs spatial resolution as it relates to spectral data.
last updated: 30 Mar 2020