Remote Sensing

An Introduction to Remote Sensing Data

Remote Sensing is studying things without touching them. To study the Earth and the landscape around us, you can use cameras and other sensors such as lidar to capture images and data about the Earth as it 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 smart phone or ipad is an example of a passive remote sensing sensor. To capture a picture, this camera records sunlight, reflected off objects. A passive 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 objects, like trees, on the earth’s surface.

active remote sensing passive remote sensing
LEFT: Remote sensing systems that measure energy that is naturally available are called passive sensors. RIGHT: Active sensors emit their own energy from a source on the instrument itself. Source: Natural Resources Canada.

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

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: 08 Dec 2017

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: 08 Dec 2017

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 Dec 2017

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: 08 Dec 2017

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: 08 Dec 2017

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: 08 Dec 2017

Extract Raster Values Using Vector Boundaries in R

This lesson reviews how to extract pixels from a raster dataset using a vector boundary. We can use the extracted pixels to calculate mean and max tree height for a study area (in this case a field site where we measured tree heights on the ground. Finally we will compare tree heights derived from lidar data compared to tree height measured by humans on the ground.

last updated: 08 Dec 2017

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: 08 Dec 2017

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: 08 Dec 2017

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: 08 Dec 2017

Plot histograms of raster values in R

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. We cover the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

last updated: 08 Dec 2017

Introduction to Lidar Raster Data Products

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. We cover the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

last updated: 08 Dec 2017

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: 08 Dec 2017