# Lidar Data in R - Remote Sensing Uncertainty

## Welcome to Week 5!

Welcome to week 5 of Earth Analytics! This week, you will explore the concept of uncertainty surrounding lidar raster data (and remote sensing data in general). You will use the same data that you downloaded last week for class. You will also use pipes again this week to work with tabular data.

TimeTopicSpeaker
9:30 - 9:40Review
9:40 - 10:30Guest speaker - Chris Crosby, UNAVCO / Open Topography
10:30 - 12:20Coding: Use lidar to characterize vegetation / uncertainty

## Homework Submission

### Produce a Report

Create a new R markdown document. Name it: lastName-firstInitial-week5.Rmd Within your .Rmd document, include the plots listed below. When you are done with your report, use knitr to convert it to html format. Submit both the .Rmd file and the .html 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
• fig.cap = "caption here" Add a caption to a figure. When you do this, each figure needs to be in it’s own code chunk!

1. Write at least 2 paragraphs: In this class you learned the relationship between lidar derived canopy height models and measured tree height. Use that plots that you create below, the readings and the course lessons to answer the following questions
• Which lidar tree height metric, (max vs. mean height) more closely relates to human measured tree height?
• What sources of uncertainty (as discussed in class and the readings) may impact relationship between lidar vs human measured tree height?
• Do you notice any differences in the relationship between the lidar vs human measured tree height between SJER vs SOAP field sites? Explain.
2. Write at least 1 paragraph: List a minimum of 3 sources of uncertainty associated with the lidar derived tree heights and 3 sources of uncertainty associated with in situ measurements of tree height. For each source of uncertainty, specify whether it is a random or systematic error. Be sure to reference the plots and readings as necessary.

#### Include the Plots Below

Be sure to describe what each plot shows in your final report. Your plots do not need to be in the order below. I just listed them this way to make it easier to keep track of and grade!

#### Plots 1 - 2

Overlay the field site point locations on top of the canopy height model for both the SJER and the SOAP field sites.

#### Plots 3 - 6: Scatterplots

For both the SJER and SOAP field sites, create scatter plots that compare:

• MAXIMUM canopy height model height in meters, extracted within a 20 meter radius, compared to MAXIMUM tree height derived from the in situ field site data.
• AVERAGE canopy height model height in meters, extracted within a 20 meter radius, compared to AVERAGE tree height derived from the in situ field site data.

#### Plot 7 - 10 Difference Bar Plots

For both the SJER and SOAP field sites, create barplots that show the DIFFERENCE between:

• Extracted lidar max canopy height model height compared to measured max height per plot.
• Extracted lidar average canopy height model compared to measured average height per plot.

Add a regression line to each scatterplot. For both plots write a thoughtful paragraph describing what the regression relationship tells you about the relationship between lidar and measured vegetation height. Does the comparison between lidar and measured average height look stronger? Or Maximum height? Why might one be “better” or a strong relationship than the other.

### IMPORTANT: For All Plots

• Label x and y axes appropriately - include units
• Add a title to your plot that describes what the plot shows
• Add a brief, 1-3 sentence caption below each plot that describes what it shows HINT: you can use the knitr argument fig.cap = "Caption here" if you are knitting to pdf to automatically add captions.

## Homework Due: Monday October 9th 2017 @ 8am.

Submit your report in both .Rmd and .html format to the D2l dropbox. Once again you are welcome to submit a .pdf instead of .html if you wish!

## Report Structure, Code Syntax & Knitr Output: 10%

Full CreditNo Credit
.html or .pdf and .Rmd files submitted
Code is written using “clean” code practices following the Hadley Wickham style guide
Code chunks contain code and code runs
All required R packages are listed at the top of the document in a code chunk.
Lines of code are broken up at commas to reduce the line width and make the code more readable
Code chunk arguments are used to hide warnings and code and just show output
.html / .pdf report emphasizes the write up and the code outputs rather than showing each step of the code (note we will still look at and grade your code but it should not appear in your report)

## Report Questions: 40%

Full CreditNo Credit
Student compared the scatter plots of average and max height and determined which relationship is “better” (more comparable 1:1 ) for both field sites
Student discusses 2-3 potential sources of uncertainty that may have impacted these relationships
Student discusses differences in the relationships observed between the two field sites (SJER vs SOAP)
1-2 readings from the homework are referenced in the report. (You can chose whether you’d like to use bookdown or create manual references)
3 sources of uncertainty associated with 1) the lidar derived tree heights and 2) insitu tree height measurements are correctly identified as discussed in class and the readings
Student identifies uncertainty sources listed above as systematic vs random

## Plots are Worth 50% of the Assignment Grade

### Plots 1 - 2 - Basemap - plot locations overlayed on top of the CHM for each field site.

Full CreditNo Credit
Plots have a title that describes plot contents.
Plots have a 2-3 sentence caption that clearly describes plot contents.

### Plots 3 - 6 - Scatterplots Insitu vs Lidar for San Joachin (SJER) & Soaproot (SOAP) Saddle sites

Full CreditNo Credit
Scatter plot of maximum measured vs lidar tree height is included
Scatter plot of average measured vs lidar tree height is included
Plots have a title that describes plot contents.
X & Y axes are labeled appropriately.
Plots have a 2-3 sentence caption that clearly describes plot contents.

### Plots 7 - 10 - Difference Bar Plot: Insitu vs Lidar

Full CreditNo Credit
Bar plot of maximum measured minus lidar tree height is included
Bar plot of average measured minus lidar tree height is included
Plots have a title that clearly describes plot contents
X & Y axes are labeled appropriately
Plots have a 2-3 sentence caption that clearly describes plot contents

### Graduate Regression Scatter Plot 1

10% of the regression plot grade

Full CreditNo Credit
Bar plot of maximum measured minus lidar tree height is included
Bar plot of average measured minus lidar tree height is included
Plots have a title that clearly describes plot contents
X & Y axes are labeled appropriately
Plots have a 2-3 sentence caption that clearly describes plot contents

90% of the regression plot grade

Full CreditNo Credit
1-2 Paragraphs are included that describe what these plots show in terms of the relationship between lidar and measured tree height and which metrics may or may not be better (average vs maximum height)

## Example Homework Plots

The plots below are examples of what your plot could look like. Feel free to customize or modify plot settings as you see fit! Note that I did not number the plots this week to allow you to place plots where you’d like in your report.

Updated: