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: 19 Nov 2021
A set of activities for you to practice your skills using Landsat Data in Open Source Python.
last updated: 27 Jan 2022
Learn how to find and download Landsat 8 remote sensing data from the USGS Earth Explorer website.
last updated: 19 Jan 2022
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
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
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
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
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
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
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: 28 Jan 2021
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: 28 Jan 2021
Learn the basics of how addidative colors models are used to render RGB images in Python.
last updated: 28 Jan 2021
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
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