# Landsat data in R - fire ecology & remote sensing

## Welcome to Week 6!

Welcome to week 6 of Earth Analytics! This week we will learn about understanding fire patterns and behaviour using spectral remote sensing data. We will learn how to work with multi-band rasters in R. We will also learn some important principles of remote sensing data. Finally we will learn how to calculate vegetation indices in R.

TimeTopicSpeaker
3:00 - 3:30Review last week’s assignment / questions
3:30 - 4:15Understanding fire with Remote sensing dataMegan Cattau
4:30 - 5:50Coding Session: Spectral RS data in RLeah

## Homework Submission

### Produce a .pdf report

Create a new R markdown document. Name it: lastName-firstInitial-week6.Rmd Within your .Rmd document, include the plots listed below. When you are done with your report, use knitr to convert it to PDF format. Submit both the .Rmd file and the .pdf file. Be sure to name your files as instructed above!

#### Use knitr code chunk arguments

In your final report, use the following knitr code chunk arguments to hide messages and warnings and code as you see fit.

• message = FALSE, warning = FALSE Hide warnings and messages in a code chunk
• echo = FALSE Hide code and just show code output
• results = 'hide' Hide the verbose output from some functions like readOGR().

1. What is the key difference between active and passive remote sensing system.
2. Describe atleast 3 differences between lidar vs landsat remote sensing data.
3. Explain what a vegetation index is.

#### Include the plots below.

For all plots

1. Be sure to describe what each plot shows in your final report using a figure caption argument in your code chunks: fig.cap=”caption here”.

#### Plot 1

Create a MAP of the difference between NDVI pre vs post fire with Landsat data (Post fire - pre-fire NDVI).

#### Plot 2

Create a MAP of the difference between NBR pre vs post fire with Landsat data (Pre fire - post-fire NBR). Classify that data using the classification thresholds below. Be sure to include a legend on your map that helps someone looking at it understand differences.

SEVERITY LEVELNBR RANGE
Enhanced Regrowth-700 to -100
Unburned-100 to +100
Low Severity+100 to +270
Moderate Severity+270 to +660
High Severity+660 to +1300

Note: if your min and max NBR values are outside of the range above, you can adjust -700 to be your smallest raster value and for high severity you can adjust 1300 to be your largest NBR raster value.

#### Plot 3

Create a classified map of post fire NDVI with Landsat data using classification values that you think make sense based upon exploring the data.

#### Plot 4

Create a map of post fire NBR with Landsat data.

## Homework due: Thursday March 2 2017 @ 5PM.

Submit your report in both .Rmd and .PDF` format to the D2l dropbox.

#### .Pdf Report structure & code: 10%

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
PDF and RMD submitted Only one of the 2 files are submitted No files submitted
Code is written using “clean” code practices following the Hadley Wickham style guideSpaces are placed after all # comment tags, variable names do not use periods, or function names.Clean coding is used in some of the code but spaces or variable names are incorrect 2-4 times Clean coding is not implemented consistently throughout the report.
Code chunk contains code and runsAll code runs in the documentThere are 1-2 errors in the code in the document that make it not run The are more than 3 code errors in the document
All required R packages are listed at the top of the document in a code chunk. Some packages are listed at the top of the document and some are lower down.
Lines of code are broken up at commas to make the code more readable

#### Knitr pdf output: 20%

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
Code chunk arguments are used to hide warnings
Code chunk arguments are used to hide code and just show output
PDf report emphasizes the write up and the code outputs rather than showing each step of the code

#### Report questions: 20%

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
What is the key difference between active and passive remote sensing system is answered correctly.
Describe atleast 3 differences between how lidar vs landsat remote sensing data is answered correctly.
Explain what a vegetation index is answered correctly.

### Plots are worth 50% of the assignment grade

#### Create a MAP of the difference between NDVI pre vs post fire (Post fire - pre-fire NDVI).

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
Pre and post fire NDVI are created
Plot renders on the pdf.
Plot contains a meaningful title.
Plot has a 2-3 sentence figure caption that clearly describes plot contents.

#### Plot 2 Create a MAP of the difference between NBR pre vs post fire (Post fire - pre-fire NBR).

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
Pre and post fire NBR are created
Plot renders on the pdf.
Plot contains a meaningful title.
Plot has a 2-3 sentence figure caption that clearly describes plot contents.
Plot includes a clear legend with each “level” of burn severity labeled clearly.

#### Plots 3 - Create a classified map of post fire NDVI using classification values that you think make sense based upon exploring the data.

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
A classified map has been created and renders in the report.
The values chosen to create the classified plot clearly show differences in NDVI values.
The colors chosen to create the classified plot clearly show differences in NDVI values.
Plot has a clear title that describes the data being shown.
Plot has a clear legend that shows the classes chosen and associated colors rendered on the map.
Plots have a 2-3 sentence caption that clearly describes plot contents.

#### Plots 4 Create a classified map of post fire NBR using the classification thresholds.

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
Plot renders on the pdf.
Plot contains a meaningful title.
Plot has a 2-3 sentence figure caption that clearly describes plot contents.
Plot data are classified using the thresholds specified.
Plot data colors clearly show areas of most intense burn compared to areas of no or less burn severity.
Plot has a clear legend that shows the classes chosen and associated colors rendered on the map.

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