Megan CattauMegan Cattau has contributed to the materials listed below.
Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Megan Cattau has contributed to the following lessons:
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.
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.
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.
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.
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.