Lessons: Use Remote sensing data in R or Python

About Remote Sensing Data

Remote sensing is the science of studying things without touching them. You can use remote sensing systems, to study how Earth systems change over time. For example, scientists use, high powered cameras, not unlike the camera in your smartphone, mounted on airplanes and satellites to capture images of the earth as it changes over time. Other sensors such as lidar (light detection and ranging) are used to collect height data which can be used to measure how trees and forests and even development changes over time.

Active vs Passive Remote Sensing

There are two types of remote sensing sensors: active and passive sensors. Passive sensors measure existing energy, often from the sun. The camera in your smartphone or iPad is an example of a passive remote sensing sensor. To capture a picture, this camera records sunlight, reflected off objects. In contract, an active remote sensing sensor creates its own energy source. Lidar (also sometimes referred to as active laser scanning) is an example of an active remote sensing sensor. Lidar systems have a laser on board that emits light that then reflects off of objects, like trees, on the Earth’s surface.

Learn why Earth Data Science skills are important for finding your next job.

Below you will find lessons that cover how to find, download, work with, visualize and analyze remote sensing data including Landsat, MODIS, NAIP and LiDAR in R or Python.

How to Download MACA2 Climate Data Using Python

MACA V2 climate data provides but historica and future predictions of climate variables using different models. Learn how to download netcdf 4 format programatically using open source Python and open the data with xarray.

last updated: 19 Nov 2021

Introduction to the CMIP and MACA v2 Climate Data

In this lesson you will learn the basics of what CMIP5 and MACA v 2 data are and how global climate data are downscaled to higher resolutions to support regional analysis.

last updated: 19 Nov 2021

Introduction to the NetCDF4 Hierarchical Data Format

In this lesson you will learn about that netcdf 4 data format which is a format, commonly used to store climate data. In later lessons you will learn how to open climate data using open source Python tools.

last updated: 12 Nov 2020

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 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

Remote Sensing to Study Wildfire

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

Learn to Use NAIP Multiband Remote Sensing Images in Python

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

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

An Overview of the 2013 Colorado Floods

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

Crop Spatial Raster Data With a Shapefile in Python

Sometimes a raster dataset covers a larger spatial extent than is needed for a particular purpose. In these cases, you can crop a raster file to a smaller extent. Learn how to crop raster data using a shapefile and export it as a new raster in open source Python

last updated: 09 Nov 2020

Classify and Plot Raster Data in Python

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: 09 Nov 2020

Open, Plot and Explore Raster Data with Python

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 using Rasterio.

last updated: 05 Nov 2020

What is Raster Data

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: 05 Nov 2020

Work with MODIS Remote Sensing Data in R.

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

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

How to Open and Work with NAIP Multispectral Imagery in R

In this lesson you learn how to open up a multi-band raster layer or image stored in .tiff format in R. You are introduced to the stack() function in R which can be used to import more than one band into a stack object in R. You also review using plotRGB to plot a multi-band image using RGB, color-infrared to other band combinations.

last updated: 03 Sep 2019

Extract Raster Values Using Vector Boundaries in R

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

Clip Raster in R

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

Classify a Raster in R.

This lesson presents how to classify a raster dataset and export it as a new raster in R.

last updated: 13 Mar 2020

Create a Canopy Height Model With Lidar Data

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

Plot Histograms of Raster Values in R

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

Introduction to Lidar Raster Data Products

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

What is Lidar Data

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

Compare Lidar to Measured Tree Height

To explore uncertainty in remote sensing data, it is helpful to compare ground-based measurements and data that are collected via airborne instruments or satellites. Learn how to create scatter plots that compare values across two datasets.

last updated: 12 Feb 2021

Extract Raster Values at Point Locations in Python

For many scientific analyses, it is helpful to be able to select raster pixels based on their relationship to a vector dataset (e.g. locations, boundaries). Learn how to extract data from a raster dataset using a vector dataset.

last updated: 02 Feb 2021

Compare Lidar With Human Measured Tree Heights - Remote Sensing Uncertainty

Uncertainty quantifies a range of values within which a measurement value could be within, considering a specified level of confidence. Learn about the types of uncertainty that you can expect when working with tree height data both derived from lidar remote sensing and human measurements and learn about sources of error including systematic vs. random error.

last updated: 02 Feb 2021