# Understanding fire using spectral remote sensing data - Earth analytics course module

Welcome to the first lesson in the Understanding fire using spectral remote sensing data module. This teaching module overviews the use of spectral remote sensing data to better understand fire activity. In it we will review spectral remote sensing as a passive type of remote sensing and how to work with space-borne vs airborne remote sensing data in R. We cover raster stacks in R, plotting multi band composite images, calculating vegetation indices and creating functions to make the processing more efficient in R.

# Lesson 1. Introduction to spectral remote sensing data

## Learning Objectives

After completing this tutorial, you will be able to:

• Define spectral and spatial resolution. Explain how the two types of resolution are different.
• Describe atleast 3 differences between NAIP imagery, Landsat 8 and MODIS in terms of how the data are collected, how frequently they are collected and the spatial & spectral resolution.
• Describe the spatial and temporal tradeoffs between data collected from a satellite vs. an airplane.

## What you need

You will need a computer with internet access to complete this lesson and the data for week 6 of the course.

In the previous weeks of this course, we talked about lidar remote sensing. If you recall, a lidar instrument is an active remote sensing instrument. This means that the instrument emits energy actively rather than collecting information about light energy from another source (the sun). This week we will work with spectral remote sensing. Spectral remote sensing is a passive remote sensing type. This means the the sensor is measuring light energy from an existing source - in this case the sun.

## Electromagnetic spectrum

To better understand spectral remote sensing we need to review some basic principles of the electromagnetic spectrum.

The electromagnetic spectrum is composed of a range of different wavelengths or “colors / types” of light energy. A spectral remote sensing instrument collects light energy within specific regions of the electromagnetic spectrum. We call each region in the spectrum a band.

Above: a video overview of spectral remote sensing.

Above: Watch the first 8 minutes for a nice overview of spectral remote sensing.

# Key Attributes of spectral remote sensing data

## Space vs. airborne data

First, it is important to understand how the data are collected. Data can be collected from the ground, the air (using airplanes or helicopters) or from space. You can imagine that data that are collected from space are often of a lower spatial resolution compared to data collected from an airplane. The tradeoff however is that data collected from an satellite often offer better (even global) coverage.

For example the landsat 8 satellite has a 16 day repeat cycle for the entire globe. This means that you can find a new image for an area, every 16 days. It takes a lot of time and financial resources collect airborne data. Thus data are often only available for smaller geographic areas. Also, you may not find the data are available for multiple time periods OR, in the case of NAIP, you may have a new dataset ever 2-4 years.

## Bands and Wavelengths

When talking about spectral data, we need to understand both the electromagnetic spectrum and image bands. Spectral remote sensing data are collected by powerful camera like instruments known as imaging spectrometers. Imaging spectrometers collect reflected light energy in “bands”.

A band represents a segment of the electromagnetic spectrum. You can think of it as a bin of one “type” of light. For example, the wavelength values between 800nm and 850nm might be one band captured by an imaging spectrometer. The imaging spectrometer collects reflected light energy within a pixel area on the ground. Because an imaging spectrometer collects many different types of light - for each pixel, the amount of light energy for each type of light or band, will be recorded. So for example a camera records the amount of red, green and blue light for each pixel.

Often when you work with a multispectral dataset, the band information is reported as the center wavelength value. This value represents the center point value of the wavelengths represented in that band. Thus in a band spanning 800-850 nm, the center would be 825).

## Spectral Resolution

The spectral resolution of a dataset that has more than one band, refers to the spectral width of each band in the dataset. In the image above, a band was defined as spanning 800-810nm. The spectral width or spectral resolution of the band is thus 10 nanometers. To see an example of this, check out the band widths for the Landsat sensors.

While a general spectral resolution of the sensor is often provided, not all sensors collect information within bands of uniform widths.

## Spatial Resolution

The spatial resolution of a raster represents the area on the ground that each pixel covers. If you have smaller pixels in a raster the data will appear more “detailed”. If you have large pixels in a raster, the data will appear more coarse or “fuzzy”.

If high resolution the data show us more about what is happening on the earth’s surface why wouldn’t we always just collect high resolution data (smaller pixels?)

## NAIP, Landsat & MODIS

In this week’s class, we will look at 3 types of spectral remote sensing data.

1. NAIP
2. Landsat
3. MODIS

### NAIP imagery

We will work with NAIP imagery in the next lesson. NAIP imagery typically has red, green and blue bands. However, sometimes, there is a 4th near-infrared band available. NAIP imagery typically is 1m spatial resolution. This means that each pixel represents 1 meter on the earth’s surface. NAIP is often collected using a camera mounted on an airplane.

### Landsat 8 imagery

Compared to NAIP, Landsat data are collected using an instrument mounted on a satellite which orbits the globe, continuously collecting images. The landsat instrument collects data at 30 meter spatial resolution but also has 11 bands distributed across the electromagnetic spectrum compared to the 3 or 4 that NAIP imagery has. Landsat also has one panchromatic band that collects information across the visible portion of the spectrum at 15 m spatial resolution.

Landsat 8 bands 1-9 are listed below:

#### Landsat 8 Bands

BandWavelength range (nanometers)Spatial Resolution (m)Spectral Width (nm)
Band 1 - Coastal aerosol430 - 450302.0
Band 2 - Blue450 - 510306.0
Band 3 - Green530 - 590306.0
Band 4 - Red640 - 670300.03
Band 5 - Near Infrared (NIR)850 - 880303.0
Band 6 - SWIR 11570 - 1650308.0
Band 7 - SWIR 22110 - 22903018
Band 8 - Panchromatic500 - 6801518
Band 9 - Cirrus1360 - 1380302.0

Above: Source - <a href=”http://landsat.usgs.gov

### MODIS imagery

The Moderate Resolution Imaging Spectrometer (MODIS) instrument is another satellite based instrument that continuously collects data over the Earth’s surface. MODIS collects spectral information at several spatial resolutions including 250m, 500m and 1000m. We will be working with the 500 m spatial resolution MODIS data in this class. MODIS has 36 bands to work with however in class we will focus on the first 7 bands.

#### First 7 MODIS Bands

Below, you can see the first 7 bands of the MODIS instrument

BandWavelength range (nm)Spatial Resolution (m)Spectral Width (nm)
Band 1 - red620 - 6702502.0
Band 2 - near infrared841 - 8762506.0
Band 3 - blue/green459 - 4795006.0
Band 4 - green545 - 5655003.0
Band 5 - near infrared1230 – 12505008.0
Band 6 - mid-infrared1628 – 165250018
Band 7 - mid-infrared2105 - 215550018

In the next lesson, we will dive further into multi-band imagery. We will begin to work with NAIP imagery in R.