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.
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
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: 10 Jan 2018
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: 10 Jan 2018
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: 10 Jan 2018
In this lesson you will review how to find and download Landsat imagery from the USGS Earth Explorere website.
last updated: 10 Jan 2018
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
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: 10 Jan 2018
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: 08 Dec 2017
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
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: 10 Jan 2018
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: 10 Jan 2018
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: 10 Jan 2018
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
This lesson presents how to classify a raster dataset and export it as a new raster in R.
last updated: 10 Jan 2018
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
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
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
This lesson reviews how a lidar data point cloud is converted to a raster format such as a geotiff.
last updated: 10 Jan 2018
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: 10 Jan 2018
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: 10 Jan 2018
Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.
last updated: 10 Jan 2018
Learn why documentation is important when analyzing data by evaluating someone elses report on the Colorado floods.
last updated: 10 Jan 2018
Learn how to use the time series feature in Google Earth to view before / after images of a location.
last updated: 10 Jan 2018