# Lidar data in R - remote sensing uncertainty

## Welcome to Week 5!

Welcome to week 5 of Earth Analytics! This week, we will dive deeper into working with spatial data in R. We will learn how to handle data in different coordinate reference systems, how to create custom maps and legends and how to extract data from a raster file. We are on our way towards integrating many different types of data into our analysis which involves knowing how to deal with things like coordinate reference systems and varying data structures.

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 .pdf 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

1. Write atleast 2 paragraphs: In this class we learned about lidar and canopy height models. We then compared height values extracted from a canopy height model compared to height values measured by humans at each study site. Compare the results of the scatter plots below (plots 3 and 4). Which lidar estimate (max vs average) does a better job of comparing measured average or max tree height ? Any ideas why one is better than the other? Discuss this referencing what you see in the plots and the readings assigned for homework.
2. Write atleast 1 paragraph: List atleast 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 source. Be sure to reference the plots in your report when discussing this. Note: the assigned readings will help you write this paragraph.

#### Include the plots below.

Be sure to describe what each plot shows in your final report.

#### Plot 3 & 4 scatterplots

Create two 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 5 & 6 difference bar plots

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 2-3 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: Friday Feb 24 2017 @ noon.

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: 10%

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: 40%

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
Student clearly defines Coordinate Reference System (CRS) (1 paragraph is well written and correctly describes what a CRS is.)
Describe what you need to do when you want to plot 2 spatial datasets in 2 different Coordinate Reference System (CRS) (paragraph is well written and correctly describes the key step.)
Student compared the scatter plots of average and max height and determined which relationship is “better” (more comparable)
Student references what they see in the scatter plots and the difference bar plots to make their argument for which relationships (average height vs max height) is better. The argument is based upon data results and what they learned in the readings / class.
1-2 readings from the homework are referenced in this paragraph.
3 sources of uncertainty associated with the lidar derived tree heights and the in situ measurements of tree height are discussed in the homework.
The sources of uncertainty either reference the readings or are other sources discussed in class or observed by the student.

## Plots are worth 40% of the assignment grade

### Plot 1 ggmap() or maps basemap plot 1

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
Code chunk arguments are used to hide warnings
Plot renders on the pdf.
Study area location is correct.
Plots have a 2-3 sentence caption that clearly describes plot contents.

### Plot 2 Field site detail map

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
Roads and plots are included on the map
AOI boundary is included on the map.
Road lines are symbolized by type.
Plot location points are symbolized by type.
Plots has a title that clearly defines plot contents.
Plots have a 2-3 sentence caption that clearly describes plot contents.

## Plots 3 & 4 - scatterplots insitu vs lidar

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo 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 5 & 6 - difference bar plot: insitu vs lidar

Full CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo 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 CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo Credit
Bar plot of maximum measured minus lidar tree height is included.   Plot is not included
Bar plot of average measured minus lidar tree height is included.   Plot is not 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 CreditPartial Credit ~BPartial Credit ~CPartial Credit ~DNo 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).

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