The 2013 Colorado Front Range Flood

Why was the Flooding so Destructive?

A major disturbance event like this flood causes significant changes in a landscape. The St. Vrain River in the image above completely shifted its course of flow in less than 5 days! This brings major changes for aquatic organisms, like crayfish, that lived along the old stream bed that is now bare and dry, or for, terrestrial organisms, like a field vole, that used to have a burrow under what is now the St. Vrain River. Likewise, the people living in the house that is now on the west side of the river instead of the eastern bank have a completely different yard and driveway!

1. Why might this storm have caused so much flooding?
2. What other weather patterns could have contributed to pronounced flooding?

Introduction to Disturbance Events

Within ecology, disturbance events are those events which cause dramatic change in an ecosystem through a temporary, often rapid, change in environmental conditions. Although the disturbance events themselves can be of short duration, the ecological effects last decades, if not longer.

Common examples of natural ecological disturbances include hurricanes, fires, floods, earthquakes and windstorms.

Disturbance events can also be human caused: clear cuts when logging, fires to clear forests for cattle grazing or the building of new housing developments are all common disturbances.

Ecological communities are often more resilient to some types of disturbance than others. Some communities are even dependent on cyclical disturbance events. Lodgepole pine (Pinus contorta) forests in the Western US are dependent on frequent stand-replacing fires to release seeds and spur the growth of young trees.

However, in discussions of ecological disturbance events we think about events that disrupt the status of the ecosystem and change the structure of the landscape.

In this lesson we will dig into the causes and disturbances caused during a storm in September 2013 along the Colorado Front Range.

Driver: Climatic & Atmospheric Patterns

Drought

How do we measure drought?

Bonus: There are several other commonly used drought indices. The National Drought Mitigation Center provides a comparison of the different indices.

This interactive plot shows the Palmer Drought Severity Index from 1991 through 2015 for Colorado.

Palmer Drought Severity Index for Colorado 1991-2015. Source: National Ecological Observatory Network (NEON) based on data from National Climatic Data Center–NOAA.

Questions

Use the figure above to answer these questions:

1. In this dataset, what years are near normal, extreme drought, and extreme wet on the Palmer Drought Severity Index?
2. What are the patterns of drought within Colorado that you observe using this Palmer Drought Severity Index?
3. What were the drought conditions immediately before the September 2013 floods?

Over this decade and a half, there have been several cycles of dry and wet periods. The 2013 flooding occurred right at the end of a severe drought.

There is a connection between the dry soils during a drought and the potential for flooding. In a severe drought the top layers of the soil dry out. Organic matter, spongy and absorbent in moist topsoil, may desiccate and be transported by the winds to other areas. Some soil types, like clay, can dry to a near-impermeable brick causing water to flow across the top instead of sinking into the soils.

Atmospheric Conditions

In early September 2013, a slow moving cold front moved through Colorado intersecting with a warm, humid front. The clash between the cold and warm fronts yielded heavy rain and devastating flooding across the Front Range in Colorado.

The storm that caused the 2013 Colorado flooding was kept in a confined area over the Eastern Range of the Rocky Mountains in Colorado by these water vapor systems.

Driver: Precipitation

How do we measure precipitation?

Precipitation can be measured by different types of gauges; some must be manually read and emptied, others automatically record the amount of precipitation. If the precipitation is in a frozen form (snow, hail, freezing rain) the contents of the gauge must be melted to get the water equivalency for measurement. Rainfall is generally reported as the total amount of rain (millimeters, centimeters, or inches) over a given per period of time.

Boulder, Colorado lays on the eastern edge of the Rocky Mountains where they meet the high plains. The average annual precipitation is near 20". However, the precipitation comes in many forms -- winter snow, intense summer thunderstorms, and intermittent storms throughout the year.

The figure below show the total precipitation each month from 1948 to 2013 for the National Weather Service's COOP site Boulder 2 (Station ID:050843) that is centrally located in Boulder, CO.

Notice the general pattern of rainfall across the 65 years.

1. How much rain generally falls within one month?
2. Is there a strong annual or seasonal pattern? (Remember, with interactive Plotly plots you can zoom in on the data)
3. Do any other events over the last 65 years equal the September 2013 event?

Now that we've looked at 65 years of data from Boulder, CO. Let's focus more specifically on the September 2013 event. The plot below shows daily precipitation between August 15 - October 15, 2013.

Explore the data and answer the following questions:

1. What dates were the highest precipitation values observed?
2. What was the total precipitation on these days?
3. In what units is this value?

Optional Data Activity: Visualize Precipitation Data in R to Better Understand the 2013 Colorado Floods.

Driver: Stream Discharge

The USGS has a distributed network of aquatic sensors located in streams across the United States. This network monitors a suit of variables that are important to stream morphology and health. One of the metrics that this sensor network monitors is stream discharge, a metric which quantifies the volume of water moving down a stream. Discharge is an ideal metric to quantify flow, which increases significantly during a flood event.

How is stream discharge measured?

Most USGS streamgages operate by measuring the elevation of the water in the river or stream and then converting the water elevation (called 'stage') to a streamflow ('discharge') by using a curve that relates the elevation to a set of actual discharge measurements. This is done because currently the technology is not available to measure the flow of the water accurately enough directly. From the USGS National Streamflow Information Program

What was the stream discharge prior to and during the flood events?

The data for the stream gauge along Boulder Creek 5 miles downstream of downtown Boulder is reported in daily averages. Take a look at the interactive plot below to see how patterns of discharge seen in these data?

Questions:

Optional Data Activity: Visualize Stream Discharge Data in R to Better Understand the 2013 Colorado Floods.

Impact: Flood

Return Interval

A 1000 year Flood!!! Understanding Return Periods

When talking about major disturbance events we often hear "It was a 1000-year flood" or "That was a 100-year storm". What does this really mean?

Check out this video explanation from The Weather Channel on how return intervals are calculated and what they mean to us.

And it isn't just floods, major hurricanes are forecast to strike New Orleans, Louisiana once every 20 years. Yet in 2005 New Orleans was pummeled by 4 hurricanes and 1 tropical storm. Hurricane Cindy in July 2013 caused the worst black out in New Orleans for 40 years. Eight weeks later Hurricane Katrina came ashore over New Orleans, changed the landscape of the city and became the costliest natural disaster to date in the United States. It was frequently called a 100-year storm.

If we say the return period is 20 years then how did 4 hurricanes strike New Orleans in 1 year?

The return period of extreme events is also referred to as recurrence interval. It is an estimate of the likelihood of an extreme event based on the statistical analysis of data (including flood records, fire frequency, historical climatic records) that an event of a given magnitude will occur in a given year. The probability can be used to assess the risk of these events for human populations but can also be used by biologists when creating habitat management plans or conservation plans for endangered species. The concept is based on the magnitude-frequency principle, where large magnitude events (such as major hurricanes) are comparatively less frequent than smaller magnitude incidents (such as rain showers). (For more information visit
Climatica's Return Periods of Extreme Events.)

Question

Your friend is thinking about buying a house near Boulder Creek. The house is above the level of seasonal high water but was flooded in the 2013 flood. He realizes how expensive flood insurance is and says, "Why do I have to buy this insurance, a flood like that won't happen for another 100 years? I won't live here any more." How would you explain to him that even though the flood was a 100-year flood he should still buy the flood insurance?

Flood Plains

Impact: Erosion & Sedimentation

How can we evaluate the impact of a flooding event?

1. Economic Impacts

We could look at economic damages to homes, businesses, and other infrastructure. Click here to view the City of Boulder's maps for flood damages.

2. Before & After Photos

We could view photos from before and after the disturbance event to see where erosion or sedimentation has occurred.

Images are great for an overall impression of what happened, where soil has eroded, and where soil or rocks have been deposited. But it is hard to get measurements of change from these 2D images. There are several ways that we can measure the apparent erosion and soil deposition.

3. Field Surveys

Standard surveys can be done to map the three-dimensional position of points allowing for measurement of distance between points and elevation. However, this requires extensive effort by land surveyors to visit each location and collect a large number of points to precisely map the region. This method can be very time intensive.

This method is challenging to use over a large spatial scale.

4. Stereoscopic Images

We could view stereoscopic images, two photos taken from slightly different perspectives to give the illusion of 3D, one can view, and even measure, elevation changes from 2D pictures.

However, this relies on specialized equipment and is challenging to automate.

5. LiDAR

A newer technology is Light Detection and Ranging (LiDAR or lidar).

Watch this video to see how LiDAR works.

Using LiDAR to Measure Change

LiDAR data allows us to create models of the earth's surface. The general term for a model of the elevation of an area is a Digital Elevation Model (DEM). DEMs come in two common types:

• Digital Terrain Models (DTM): The elevation of the ground (terrain).
• Digital Surface Models (DSM): The elevation of everything on the surface of the earth, including trees, buildings, or other structures. Note: some DSMs have been post-processed to remove buildings and other human-made objects.

Digital Terrain Models (DTMs)

Here we have Digital Terrain Model of lower Four-Mile Canyon Creek from before the 2013 flood event (left) and from after the 2013 flood event (right). We can see some subtle differences in the elevation, especially along the stream bed, however, even on this map it is still challenging to see.

Digital Elevation Model of Difference (DoD)

If we have a DEM from before and after an event, we can can create a model that shows the change that occurred during the event. This new model is called a Digital Elevation Model of Difference (DoD).

Questions

Here we are using DTMs to create our Digital Elevation Model of Difference (DoD) to look at the changes after a flooding event. What types of disturbance events or what research question might one want to look at DoDs from Digital Surface Models?

Four Mile Canyon Creek DoD

Optional Data Activity: Visualize Topography Change using LiDAR-derived Data in R to Better Understand the 2013 Colorado Floods.

Using Data to Understand Disturbance Events

We've used the data from drought, atmospheric conditions, precipitation, stream flow, and the digital elevation models to help us understand what caused the 2013 Boulder County, Colorado flooding event and where there was change in the stream bed around Four Mile Canyon Creek at the outskirts of Boulder, Colorado.

Quantifying the change that we can see from images of the flooding streams or post-flood changes to low lying areas allows scientists, city planners, and homeowners to make choices about future land use plans.

Follow-up Questions

1. What other types of questions could this or similar data be used to answer?
2. What types of disturbance events in your local area could you use data to quantify the causes and impact of?

Several factors contributed to extreme flooding that occurred in Boulder, Colorado in 2013. In this data activity, we explore and visualize the data for precipitation (rainfall) data collected by the National Weather Service's Cooperative Observer Program. The tutorial is part of the Data Activities that can be used with the Quantifying The Drivers and Impacts of Natural Disturbance Events Teaching Module.

Research Question

What were the patterns of precipitation leading up to the 2013 flooding events in Colorado?

Precipitation Data

The heavy precipitation (rain) that occurred in September 2013 caused much damage during the 2013 flood by, among other things, increasing stream discharge (flow). In this lesson we will download, explore, and visualize the precipitation data collected during this time to better understand this important flood driver.

Where can we get precipitation data?

The precipitation data are obtained through NOAA's NECI database. There are numerous datasets that can be found and downloaded via the CDO Search portal.

The precipitation data that we will use is from the Cooperative Observer Network (COOP).

"Through the National Weather Service (NWS) Cooperative Observer Program (COOP), more than 10,000 volunteers take daily weather observations at National Parks, seashores, mountaintops, and farms as well as in urban and suburban areas. COOP data usually consist of daily maximum and minimum temperatures, snowfall, and 24-hour precipitation totals." Quoted from NOAA's National Centers for Environmental Information

Data are collected at different stations, often on paper data sheets like the one below, and then entered into a central database where we can access that data and download it in the .csv (Comma Separated Values) format.

Obtain the Data

If you have not already opened the CDO Search portal, do so now.

Note: If you are using the pre-compiled data subset that can be downloaded as a compressed file above, you should still read through this section to know where the data comes from before proceeding with the lesson.

Step 1: Search for the data

To obtain data we must first choose a location of interest. The COOP site Boulder 2 (Station ID:050843) is centrally located in Boulder, CO.

Then we must decide what type of data we want to download for that station. As shown in the image below, we selected:

• the desired date range (1 January 2003 to 31 December 2013),
• the type of dataset ("Precipitation Hourly"),
• the search type ("Stations") and
• the search term (e.g. the # for the station located in central Boulder, CO: 050843).

Once the data are entered and you select Search, you will be directed to a new page with a map. You can find out more information about the data by selecting View Full Details. Notice that this dataset goes all the way back to 1 August 1948! However, we've selected only a portion of this time frame.

Step 2: Request the data

Once you are sure this is the data that you want, you need to request it by selecting Add to Cart. The data can then be downloaded as a .csv file which we will use to conduct our analyses. Be sure to double check the date range before downloading.

On the options page, we want to make sure we select:

• Station Name
• Geographic Location (this gives us longitude & latitude; optional)
• Include Data Flags (this gives us information if the data are problematic)
• Units (Standard)
• Precipitation (w/ HPCP automatically checked)

On the next page you must enter an email address for the dataset to be sent to.

Step 3: Get the data

As this is a small dataset, it won't take long for you to get an email telling you the dataset is ready. Follow the link in the email to download your dataset. You can also view documentation (metadata) for the data.
Each data subset is downloaded with a unique order number. The order number in our example dataset is 805325. If you are using a dataset you've downloaded yourself, make sure to substitute in your own order number in the code below.

To ensure that we remember what our data file is, we've added a descriptor to the order number: 805325-precip_daily_2003-2013. You may wish to do the same.

Work with Precipitation Data

R Libraries

We will use ggplot2 to efficiently plot our data and plotly to create i nteractive plots.

# load packages
library(ggplot2) # create efficient, professional plots
library(plotly) # create cool interactive plots

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggmap':
## 
##     wind

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

# set your working directory to ensure R can find the file we wish to import 
# and where we want to save our files. Be sure to move the download into 
# your working direectory!
wd <- "~/Git/data/" # This will depend on your local environment
setwd(wd)

Import Precipitation Data

We will use the 805325-Preciptation_daily_2003-2013.csv file in this analysis. This dataset is the daily precipitation date from the COOP station 050843 in Boulder, CO for 1 January 2003 through 31 December 2013.

Since the data format is a .csv, we can use read.csv to import the data. After we import the data, we can take a look at the first few lines using head(), which defaults to the first 6 rows, of the data.frame. Finally, we can explore the R object structure.

# import precip data into R data.frame by 
# defining the file name to be opened

precip.boulder <- read.csv(paste0(wd,"disturb-events-co13/precip/805325-precip_daily_2003-2013.csv"), stringsAsFactors = FALSE, header = TRUE )

# view first 6 lines of the data
head(precip.boulder)

##       STATION    STATION_NAME ELEVATION LATITUDE LONGITUDE
## 1 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811
## 2 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811
## 3 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811
## 4 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811
## 5 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811
## 6 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811
##             DATE HPCP Measurement.Flag Quality.Flag
## 1 20030101 01:00  0.0                g             
## 2 20030201 01:00  0.0                g             
## 3 20030202 19:00  0.2                              
## 4 20030202 22:00  0.1                              
## 5 20030203 02:00  0.1                              
## 6 20030205 02:00  0.1

# view structure of data
str(precip.boulder)

## 'data.frame':	1840 obs. of  9 variables:
##  $ STATION         : chr  "COOP:050843" "COOP:050843" "COOP:050843" "COOP:050843" ...
##  $ STATION_NAME    : chr  "BOULDER 2 CO US" "BOULDER 2 CO US" "BOULDER 2 CO US" "BOULDER 2 CO US" ...
##  $ ELEVATION       : num  1650 1650 1650 1650 1650 ...
##  $ LATITUDE        : num  40 40 40 40 40 ...
##  $ LONGITUDE       : num  -105 -105 -105 -105 -105 ...
##  $ DATE            : chr  "20030101 01:00" "20030201 01:00" "20030202 19:00" "20030202 22:00" ...
##  $ HPCP            : num  0 0 0.2 0.1 0.1 ...
##  $ Measurement.Flag: chr  "g" "g" " " " " ...
##  $ Quality.Flag    : chr  " " " " " " " " ...

About the Data

Viewing the structure of these data, we can see that different types of data are included in this file.

• STATION and STATION_NAME: Identification of the COOP station.
• ELEVATION, LATITUDE and LONGITUDE: The spatial location of the station.
• DATE: Gives the date in the format: YYYYMMDD HH:MM. Notice that DATE is currently class chr, meaning the data are interpreted as a character class and not as a date.
• HPCP: The total precipitation given in inches (since we selected Standard for the units), recorded for the hour ending at the time specified by DATE. Importantly, the metadata (see below) notes that the value 999.99 indicates missing data. Also important, hours with no precipitation are not recorded.
• Measurement Flag: Indicates if there are any abnormalities with the measurement of the data. Definitions of each flag can be found in Table 2 of the documentation.
• Quality Flag: Indicates if there are any potential quality problems with the data. Definitions of each flag can be found in Table 3 of the documentation.

Additional information about the data, known as metadata, is available in the PRECIP_HLY_documentation.pdf file that can be downloaded along with the data. (Note, as of Sept. 2016, there is a mismatch in the data downloaded and the documentation. The differences are in the units and missing data value: inches/999.99 (standard) or millimeters/25399.75 (metric)).

Clean the Data

Before we can start plotting and working with the data we always need to check several important factors:

• data class: is R interpreting the data the way we expect it. The function str() is an important tools for this.
• NoData Values: We need to know if our data contains a specific value that means "data are missing" and if this value has been assigned to NA in R.

Convert Date-Time

As we've noted, the date field is in a character class. We can convert it to a date/time class that will allow R to correctly interpret the data and allow us to easily plot the data. We can convert it to a date/time class using as.POSIXct().

# convert to date/time and retain as a new field
precip.boulder$DateTime <- as.POSIXct(precip.boulder$DATE, 
                                  format="%Y%m%d %H:%M") 
                                  # date in the format: YearMonthDay Hour:Minute 

# double check structure
str(precip.boulder$DateTime)

##  POSIXct[1:1840], format: "2003-01-01 01:00:00" "2003-02-01 01:00:00" ...

• For more information on date/time classes, see the NEON tutorial Dealing With Dates & Times in R - as.Date, POSIXct, POSIXlt.

NoData Values

We've also learned that missing values, also known as NoData values, are labelled with the placeholder 999.99. Do we have any NoData values in our data?

# histogram - would allow us to see 999.99 NA values 
# or other "weird" values that might be NA if we didn't know the NA value
hist(precip.boulder$HPCP)

Looking at the histogram, it looks like we have mostly low values (which makes sense) but a few values up near 1000 -- likely 999.99. We can assign these entries to be NA, the value that R interprets as no data.

# assing NoData values to NA
precip.boulder$HPCP[precip.boulder$HPCP==999.99] <- NA 

# check that NA values were added; 
# we can do this by finding the sum of how many NA values there are
sum(is.na(precip.boulder))

## [1] 94

There are 94 NA values in our dataset. This is missing data.

Questions:

1. Do we need to worry about the missing data?
2. Could they affect our analyses?

This depends on what questions we are asking. Here we are looking at general patterns in the data across 10 years. Since we have just over 3650 days in our entire dataset, missing 94 probably won't affect the general trends we are looking at.

Can you think of a research question where we would need to be concerned about the missing data?

Plot Precipitation Data

Now that we've cleaned up the data, we can view it. To do this we will plot using ggplot() from the ggplot2 package.

#plot the data
precPlot_hourly <- ggplot(data=precip.boulder,  # the data frame
      aes(DateTime, HPCP)) +   # the variables of interest
      geom_bar(stat="identity") +   # create a bar graph
      xlab("Date") + ylab("Precipitation (Inches)") +  # label the x & y axes
      ggtitle("Hourly Precipitation - Boulder Station\n 2003-2013")  # add a title

precPlot_hourly

## Warning: Removed 94 rows containing missing values (position_stack).

As we can see, plots of hourly date lead to very small numbers and is difficult to represent all information on a figure. Hint: If you can't see any bars on your plot you might need to zoom in on it.

Plots and comparison of daily precipitation would be easier to view.

Plot Daily Precipitation

There are several ways to aggregate the data.

Daily Plots

If you only want to view the data plotted by date you need to create a column with only dates (no time) and then re-plot.

# convert DATE to a Date class 
# (this will strip the time, but that is saved in DateTime)
precip.boulder$DATE <- as.Date(precip.boulder$DateTime, # convert to Date class
                                  format="%Y%m%d %H:%M") 
                                  #DATE in the format: YearMonthDay Hour:Minute 

# double check conversion
str(precip.boulder$DATE)

##  Date[1:1840], format: "2003-01-01" "2003-02-01" "2003-02-03" "2003-02-03" ...

precPlot_daily1 <- ggplot(data=precip.boulder,  # the data frame
      aes(DATE, HPCP)) +   # the variables of interest
      geom_bar(stat="identity") +   # create a bar graph
      xlab("Date") + ylab("Precipitation (Inches)") +  # label the x & y axes
      ggtitle("Daily Precipitation - Boulder Station\n 2003-2013")  # add a title

precPlot_daily1

## Warning: Removed 94 rows containing missing values (position_stack).

R will automatically combine all data from the same day and plot it as one entry.

Daily Plots & Data

If you want to record the combined hourly data for each day, you need to create a new data frame to store the daily data. We can use the aggregate() function to combine all the hourly data into daily data. We will use the date class DATE field we created in the previous code for this.

# aggregate the Precipitation (PRECIP) data by DATE
precip.boulder_daily <-aggregate(precip.boulder$HPCP,   # data to aggregate
	by=list(precip.boulder$DATE),  # variable to aggregate by
	FUN=sum,   # take the sum (total) of the precip
	na.rm=TRUE)  # if the are NA values ignore them
	# if this is FALSE any NA value will prevent a value be totalled

# view the results
head(precip.boulder_daily)

##      Group.1   x
## 1 2003-01-01 0.0
## 2 2003-02-01 0.0
## 3 2003-02-03 0.4
## 4 2003-02-05 0.2
## 5 2003-02-06 0.1
## 6 2003-02-07 0.1

So we now have daily data but the column names don't mean anything. We can give them meaningful names by using the names() function. Instead of naming the column of precipitation values with the original HPCP, let's call it PRECIP.

# rename the columns
names(precip.boulder_daily)[names(precip.boulder_daily)=="Group.1"] <- "DATE"
names(precip.boulder_daily)[names(precip.boulder_daily)=="x"] <- "PRECIP"

# double check rename
head(precip.boulder_daily)

##         DATE PRECIP
## 1 2003-01-01    0.0
## 2 2003-02-01    0.0
## 3 2003-02-03    0.4
## 4 2003-02-05    0.2
## 5 2003-02-06    0.1
## 6 2003-02-07    0.1

Now we can plot the daily data.

# plot daily data
precPlot_daily <- ggplot(data=precip.boulder_daily,  # the data frame
      aes(DATE, PRECIP)) +   # the variables of interest
      geom_bar(stat="identity") +   # create a bar graph
      xlab("Date") + ylab("Precipitation (Inches)") +  # label the x & y axes
      ggtitle("Daily Precipitation - Boulder Station\n 2003-2013")  # add a title

precPlot_daily

Compare this plot to the plot we created using the first method. Are they the same?

Subset the Data

Instead of looking at the data for the full decade, let's now focus on just the 2 months surrounding the flood on 11-15 September. We'll focus on the window from 15 August to 15 October.

Just like aggregating, we can accomplish this by interacting with the larger plot through the graphical interface or by creating a subset of the data and protting it separately.

Subset Within Plot

To see only a subset of the larger plot, we can simply set limits for the scale on the x-axis with scale_x_date().

# First, define the limits -- 2 months around the floods
limits <- as.Date(c("2013-08-15", "2013-10-15"))

# Second, plot the data - Flood Time Period
precPlot_flood <- ggplot(data=precip.boulder_daily,
      aes(DATE, PRECIP)) +
      geom_bar(stat="identity") +
      scale_x_date(limits=limits) +
      xlab("Date") + ylab("Precipitation (Inches)") +
      ggtitle("Precipitation - Boulder Station\n August 15 - October 15, 2013")

precPlot_flood

## Warning: Removed 770 rows containing missing values (position_stack).

Now we can easily see the dramatic rainfall event in mid-September!

Subset The Data

Now let's create a subset of the data and plot it.

# subset 2 months around flood
precip.boulder_AugOct <- subset(precip.boulder_daily, 
                        DATE >= as.Date('2013-08-15') & 
												DATE <= as.Date('2013-10-15'))

# check the first & last dates
min(precip.boulder_AugOct$DATE)

## [1] "2013-08-21"

max(precip.boulder_AugOct$DATE)

## [1] "2013-10-11"

# create new plot
precPlot_flood2 <- ggplot(data=precip.boulder_AugOct, aes(DATE,PRECIP)) +
  geom_bar(stat="identity") +
  xlab("Time") + ylab("Precipitation (inches)") +
  ggtitle("Daily Total Precipitation \n Boulder Creek 2013") 

precPlot_flood2 

Interactive Plots - Plotly

Let's turn our plot into an interactive Plotly plot.

# setup your plot.ly credentials; if not already set up
#Sys.setenv("plotly_username"="your.user.name.here")
#Sys.setenv("plotly_api_key"="your.key.here")


# view plotly plot in R
ggplotly(precPlot_flood2)


# publish plotly plot to your plot.ly online account when you are happy with it
api_create(precPlot_flood2)

Additional Resources

Units & Scale

If you are using a dataset downloaded before 2016, the units were in hundredths of an inch, this isn't the most useful measure. You might want to create a new column PRECIP that contains the data from HPCP converted to inches.

# convert from 100th inch by dividing by 100
precip.boulder$PRECIP<-precip.boulder$HPCP/100

# view & check to make sure conversion occurred
head(precip.boulder)

##       STATION    STATION_NAME ELEVATION LATITUDE LONGITUDE       DATE
## 1 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811 2003-01-01
## 2 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811 2003-02-01
## 3 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811 2003-02-03
## 4 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811 2003-02-03
## 5 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811 2003-02-03
## 6 COOP:050843 BOULDER 2 CO US    1650.5 40.03389 -105.2811 2003-02-05
##   HPCP Measurement.Flag Quality.Flag            DateTime PRECIP
## 1  0.0                g              2003-01-01 01:00:00  0.000
## 2  0.0                g              2003-02-01 01:00:00  0.000
## 3  0.2                               2003-02-02 19:00:00  0.002
## 4  0.1                               2003-02-02 22:00:00  0.001
## 5  0.1                               2003-02-03 02:00:00  0.001
## 6  0.1                               2003-02-05 02:00:00  0.001

Question

Compare HPCP and PRECIP. Did we do the conversion correctly?

This tutorial will walk you through the fundamental principles of working with image raster data in R.

Raster Data

Raster or "gridded" data are data that are saved in pixels. In the spatial world, each pixel represents an area on the Earth's surface. An color image raster is a bit different from other rasters in that it has multiple bands. Each band represents reflectance values for a particular color or spectra of light. If the image is RGB, then the bands are in the red, green and blue portions of the electromagnetic spectrum. These colors together create what we know as a full color image.

Work with Multiple Rasters

In a previous tutorial, we loaded a single raster into R. We made sure we knew the CRS (coordinate reference system) and extent of the dataset among other key metadata attributes. This raster was a Digital Elevation Model so there was only a single raster that represented the ground elevation in each pixel. When we work with color images, there are multiple rasters to represent each band. Here we'll learn to work with multiple rasters together.

Raster Stacks

A raster stack is a collection of raster layers. Each raster layer in the raster stack needs to have the same

• projection (CRS),
• spatial extent and
• resolution.

You might use raster stacks for different reasons. For instance, you might want to group a time series of rasters representing precipitation or temperature into one R object. Or, you might want to create a color images from red, green and blue band derived rasters.

In this tutorial, we will stack three bands from a multi-band image together to create a composite RGB image.

First let's load the R packages that we need: sp and raster.

# load the raster, sp, and rgdal packages
library(raster)
library(sp)
library(rgdal)

# set the working directory to the data
#setwd("pathToDirHere")
wd <- ("~/Git/data/")
setwd(wd)

Next, let's create a raster stack with bands representing

• blue: band 19, 473.8nm
• green: band 34, 548.9nm
• red; band 58, 669.1nm

This can be done by individually assigning each file path as an object.

# import tiffs
band19 <- paste0(wd, "NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band19.tif")
band34 <- paste0(wd, "NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band34.tif")
band58 <- paste0(wd, "NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band58.tif")

# View their attributes to check that they loaded correctly:
band19

## [1] "~/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band19.tif"

band34

## [1] "~/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band34.tif"

band58

## [1] "~/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band58.tif"

Note that if we wanted to create a stack from all the files in a directory (folder) you can easily do this with the list.files() function. We would use full.names=TRUE to ensure that R will store the directory path in our list of rasters.

# create list of files to make raster stack
rasterlist1 <- list.files(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB", full.names=TRUE))

rasterlist1

## character(0)

Or, if your directory consists of some .tif files and other file types you don't want in your stack, you can ask R to only list those files with a .tif extension.

rasterlist2 <-  list.files(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB", full.names=TRUE, pattern="tif"))

rasterlist2

## character(0)

Back to creating our raster stack with three bands. We only want three of the bands in the RGB directory and not the fourth band90, so will create the stack from the bands we loaded individually. We do this with the stack() function.

# create raster stack
rgbRaster <- stack(band19,band34,band58)

# example syntax for stack from a list
#rstack1 <- stack(rasterlist1)

This has now created a stack that is three rasters thick. Let's view them.

# check attributes
rgbRaster

## class      : RasterStack 
## dimensions : 502, 477, 239454, 3  (nrow, ncol, ncell, nlayers)
## resolution : 1, 1  (x, y)
## extent     : 256521, 256998, 4112069, 4112571  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## names      : band19, band34, band58 
## min values :     84,    116,    123 
## max values :  13805,  15677,  14343

# plot stack
plot(rgbRaster)

From the attributes we see the CRS, resolution, and extent of all three rasters. The we can see each raster plotted. Notice the different shading between the different bands. This is because the landscape relects in the red, green, and blue spectra differently.

Check out the scale bars. What do they represent?

This reflectance data are radiances corrected for atmospheric effects. The data are typically unitless and ranges from 0-1. NEON Airborne Observation Platform data, where these rasters come from, has a scale factor of 10,000.

Plot an RGB Image

You can plot a composite RGB image from a raster stack. You need to specify the order of the bands when you do this. In our raster stack, band 19, which is the blue band, is first in the stack, whereas band 58, which is the red band, is last. Thus the order for a RGB image is 3,2,1 to ensure the red band is rendered first as red.

Thinking ahead to next time: If you know you want to create composite RGB images, think about the order of your rasters when you make the stack so the RGB=1,2,3.

We will plot the raster with the rgbRaster() function and the need these following arguments:

• R object to plot
• which layer of the stack is which color
• stretch: allows for increased contrast. Options are "lin" & "hist".

Let's try it.

# plot an RGB version of the stack
plotRGB(rgbRaster,r=3,g=2,b=1, stretch = "lin")

Note: read the raster package documentation for other arguments that can be added (like scale) to improve or modify the image.

Explore Raster Values - Histograms

You can also explore the data. Histograms allow us to view the distrubiton of values in the pixels.

# view histogram of reflectance values for all rasters
hist(rgbRaster)

## Warning in .hist1(raster(x, y[i]), maxpixels = maxpixels, main =
## main[y[i]], : 42% of the raster cells were used. 100000 values used.

## Warning in .hist1(raster(x, y[i]), maxpixels = maxpixels, main =
## main[y[i]], : 42% of the raster cells were used. 100000 values used.

## Warning in .hist1(raster(x, y[i]), maxpixels = maxpixels, main =
## main[y[i]], : 42% of the raster cells were used. 100000 values used.

Note about the warning messages: R defaults to only showing the first 100,000 values in the histogram so if you have a large raster you may not be seeing all the values. This saves your from long waits, or crashing R, if you have large datasets.

Crop Rasters

You can crop all rasters within a raster stack the same way you'd do it with a single raster.

# determine the desired extent
rgbCrop <- c(256770.7,256959,4112140,4112284)

# crop to desired extent
rgbRaster_crop <- crop(rgbRaster, rgbCrop)

# view cropped stack
plot(rgbRaster_crop)

Raster Bricks in R

In our rgbRaster object we have a list of rasters in a stack. These rasters are all the same extent, CRS and resolution. By creating a raster brick we will create one raster object that contains all of the rasters so that we can use this object to quickly create RGB images. Raster bricks are more efficient objects to use when processing larger datasets. This is because the computer doesn't have to spend energy finding the data - it is contained within the object.

# create raster brick
rgbBrick <- brick(rgbRaster)

# check attributes
rgbBrick

## class      : RasterBrick 
## dimensions : 502, 477, 239454, 3  (nrow, ncol, ncell, nlayers)
## resolution : 1, 1  (x, y)
## extent     : 256521, 256998, 4112069, 4112571  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : memory
## names      : band19, band34, band58 
## min values :     84,    116,    123 
## max values :  13805,  15677,  14343

While the brick might seem similar to the stack (see attributes above), we can see that it's very different when we look at the size of the object.

• the brick contains all of the data stored in one object
• the stack contains links or references to the files stored on your computer

Use object.size() to see the size of an R object.

# view object size
object.size(rgbBrick)

## 5762000 bytes

object.size(rgbRaster)

## 49984 bytes

# view raster brick
plotRGB(rgbBrick,r=3,g=2,b=1, stretch = "Lin")

Notice the faster plotting? For a smaller raster like this the difference is slight, but for larger raster it can be considerable.

Write to GeoTIFF

We can write out the raster in GeoTIFF format as well. When we do this it will copy the CRS, extent and resolution information so the data will read properly into a GIS program as well. Note that this writes the raster in the order they are in. In our case, the blue (band 19) is first but most programs expect the red band first (RGB).

One way around this is to generate a new raster stack with the rasters in the proper order - red, green and blue format. Or, just always create your stacks R->G->B to start!!!

# Make a new stack in the order we want the data in 
orderRGBstack <- stack(rgbRaster$band58,rgbRaster$band34,rgbRaster$band19)

# write the geotiff
# change overwrite=TRUE to FALSE if you want to make sure you don't overwrite your files!
writeRaster(orderRGBstack,paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB/rgbRaster.tif"),"GTiff", overwrite=TRUE)

Import A Multi-Band Image into R

You can import a multi-band image into R too. To do this, you import the file as a stack rather than a raster (which brings in just one band). Let's import the raster than we just created above.

# import multi-band raster as stack
multiRasterS <- stack(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB/rgbRaster.tif")) 

# import multi-band raster direct to brick
multiRasterB <- brick(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB/rgbRaster.tif")) 

# view raster
plot(multiRasterB)

plotRGB(multiRasterB,r=1,g=2,b=3, stretch="lin")

Plotly - Interactive (and Online) Plots

Plotly bills itself as "a collaborative platform for modern data science". You can use it to build interactive plots that can easily be shared with others (like the Quantifying The Drivers and Impacts of Natural Disturbance Events – The 2013 Colorado Floods lessons).

You will need an free online Plotly account to post & share you plots online. But you can create the plots and use them on your local computer without an account. If you do not wish to share plots online you can skip to Step 3: Create Plotly plot.

Additional information on the plotly R package can be found on the Plotly R Getting Started page.

Note: Plotly doesn't just work with R -- other programs include Python, MATLAB, Excel, and JavaScript.

Step 1: Create account

If you do not already have an account, you need to set up an account by visiting the Plotly website and following the directions there.

Step 2: Connect account to R

To share plots from R (or RStudio) to Plotly, you have to connect to your account. This is done through an API (Application Program Interface). You can find your username & API key in your profile settings on the Plotly website under the "API key" menu option.

To link your account to your R, use the following commands, substituting in your own username & key as appropriate.

# set plotly user name
Sys.setenv("plotly_username"="YOUR_USERNAME")
# set plotly API key
Sys.setenv("plotly_api_key"="YOUR_KEY")

Step 3: Create Plotly plot

There are lots of ways to plot with the plotly package. We briefly describe two basic functions plotly() and ggplotly(). For more information on plotting in R with Plotly, check out the Plotly R library page.

Here we use the example dataframe economics that comes with the package.

# load packages
library(ggplot2) # to create plots and feed to ggplotly()
library(plotly)  # to create interactive plots

# view str of example dataset
str(economics)

## tibble [574 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ date    : Date[1:574], format: "1967-07-01" "1967-08-01" ...
##  $ pce     : num [1:574] 507 510 516 512 517 ...
##  $ pop     : num [1:574] 198712 198911 199113 199311 199498 ...
##  $ psavert : num [1:574] 12.6 12.6 11.9 12.9 12.8 11.8 11.7 12.3 11.7 12.3 ...
##  $ uempmed : num [1:574] 4.5 4.7 4.6 4.9 4.7 4.8 5.1 4.5 4.1 4.6 ...
##  $ unemploy: num [1:574] 2944 2945 2958 3143 3066 ...

# plot with the plot_ly function
unempPerCapita <- plot_ly(x =economics$date, y = economics$unemploy/economics$pop)

To make your plotly plot in R, run the following line:

unempPerCapita 

Note: This plot is interactive within the R environment but is not as posted on this website.

If you already use ggplot to create your plots, you can directly turn your ggplot objects into interactive plots with ggplotly().

## plot with ggplot, then ggplotly

unemployment <- ggplot(economics, aes(date,unemploy)) + geom_line()
unemployment

To make your plotly plot in R, run the following line:

ggplotly(unemployment)

Note: This plot is interactive within the R environment but is not as posted on this website.

Step 4: Publish to Plotly

The function plotly_POST() allows you to post any plotly plot to your account.

# publish plotly plot to your plotly online account
api_create(unemployment)

Examples

The plots below were generated using R code that harnesses the power of the ggplot2 and the plotly packages. The plotly code utilizes the RopenSci plotly packages - check them out!

About Hierarchical Data Formats - HDF5

The Hierarchical Data Format version 5 (HDF5), is an open source file format that supports large, complex, heterogeneous data. HDF5 uses a "file directory" like structure that allows you to organize data within the file in many different structured ways, as you might do with files on your computer. The HDF5 format also allows for embedding of metadata making it self-describing.

Hierarchical Structure - A file directory within a file

The HDF5 format can be thought of as a file system contained and described within one single file. Think about the files and folders stored on your computer. You might have a data directory with some temperature data for multiple field sites. These temperature data are collected every minute and summarized on an hourly, daily and weekly basis. Within one HDF5 file, you can store a similar set of data organized in the same way that you might organize files and folders on your computer. However in a HDF5 file, what we call "directories" or "folders" on our computers, are called groups and what we call files on our computer are called datasets.

2 Important HDF5 Terms

• Group: A folder like element within an HDF5 file that might contain other groups OR datasets within it.
• Dataset: The actual data contained within the HDF5 file. Datasets are often (but don't have to be) stored within groups in the file.

An HDF5 file containing datasets, might be structured like this:

HDF5 is a Self Describing Format

HDF5 format is self describing. This means that each file, group and dataset can have associated metadata that describes exactly what the data are. Following the example above, we can embed information about each site to the file, such as:

• The full name and X,Y location of the site
• Description of the site.
• Any documentation of interest.

Similarly, we might add information about how the data in the dataset were collected, such as descriptions of the sensor used to collect the temperature data. We can also attach information, to each dataset within the site group, about how the averaging was performed and over what time period data are available.

One key benefit of having metadata that are attached to each file, group and dataset, is that this facilitates automation without the need for a separate (and additional) metadata document. Using a programming language, like R or Python, we can grab information from the metadata that are already associated with the dataset, and which we might need to process the dataset.

Compressed & Efficient subsetting

The HDF5 format is a compressed format. The size of all data contained within HDF5 is optimized which makes the overall file size smaller. Even when compressed, however, HDF5 files often contain big data and can thus still be quite large. A powerful attribute of HDF5 is data slicing, by which a particular subsets of a dataset can be extracted for processing. This means that the entire dataset doesn't have to be read into memory (RAM); very helpful in allowing us to more efficiently work with very large (gigabytes or more) datasets!

Heterogeneous Data Storage

HDF5 files can store many different types of data within in the same file. For example, one group may contain a set of datasets to contain integer (numeric) and text (string) data. Or, one dataset can contain heterogeneous data types (e.g., both text and numeric data in one dataset). This means that HDF5 can store any of the following (and more) in one file:

• Temperature, precipitation and PAR (photosynthetic active radiation) data for a site or for many sites
• A set of images that cover one or more areas (each image can have specific spatial information associated with it - all in the same file)
• A multi or hyperspectral spatial dataset that contains hundreds of bands.
• Field data for several sites characterizing insects, mammals, vegetation and meteorology.
• A set of images that cover one or more areas (each image can have unique spatial information associated with it)
• And much more!

Open Format

The HDF5 format is open and free to use. The supporting libraries (and a free viewer), can be downloaded from the HDF Group website. As such, HDF5 is widely supported in a host of programs, including open source programming languages like R and Python, and commercial programming tools like Matlab and IDL. Spatial data that are stored in HDF5 format can be used in GIS and imaging programs including QGIS, ArcGIS, and ENVI.

Summary Points - Benefits of HDF5

• Self-Describing The datasets with an HDF5 file are self describing. This allows us to efficiently extract metadata without needing an additional metadata document.
• Supporta Heterogeneous Data: Different types of datasets can be contained within one HDF5 file.
• Supports Large, Complex Data: HDF5 is a compressed format that is designed to support large, heterogeneous, and complex datasets.
• Supports Data Slicing: "Data slicing", or extracting portions of the dataset as needed for analysis, means large files don't need to be completely read into the computers memory or RAM.
• Open Format - wide support in the many tools: Because the HDF5 format is open, it is supported by a host of programming languages and tools, including open source languages like R and Python and open GIS tools like QGIS.

In this tutorial, we will show how to read and extract NEON reflectance data stored within an HDF5 file using R.

About Hyperspectral Remote Sensing Data

The electromagnetic spectrum is composed of thousands of bands representing different types of light energy. Imaging spectrometers (instruments that collect hyperspectral data) break the electromagnetic spectrum into groups of bands that support classification of objects by their spectral properties on the Earth's surface. Hyperspectral data consists of many bands - up to hundreds of bands - that span a portion of the electromagnetic spectrum, from the visible to the Short Wave Infrared (SWIR) regions.

The NEON imaging spectrometer (NIS) collects data within the 380 nm to 2510 nm portions of the electromagnetic spectrum within bands that are approximately 5 nm in width. This results in a hyperspectral data cube that contains approximately 426 bands - which means BIG DATA.

The HDF5 data model natively compresses data stored within it (makes it smaller) and supports data slicing (extracting only the portions of the data that you need to work with rather than reading the entire dataset into memory). These features make it ideal for working with large data cubes such as those generated by imaging spectrometers, in addition to supporting spatial data and associated metadata.

In this tutorial we will demonstrate how to read and extract spatial raster data stored within an HDF5 file using R.

Read HDF5 data into R

We will use the terra and rhdf5 packages to read in the HDF5 file that contains hyperspectral data for the NEON San Joaquin (SJER) field site. Let's start by calling the needed packages and reading in our NEON HDF5 file.

Please be sure that you have at least version 2.10 of rhdf5 installed. Use: packageVersion("rhdf5") to check the package version.

# Load `terra` and `rhdf5` packages to read NIS data into R

library(terra)

library(rhdf5)

library(neonUtilities)

Set the working directory to ensure R can find the file we are importing, and we know where the file is being saved. You can move the file that is downloaded afterward, but be sure to re-set the path to the file.

wd <- "~/data/" #This will depend on your local environment

setwd(wd)

We can use the neonUtilities function byTileAOP to download a single reflectance tile. You can run help(byTileAOP) to see more details on what the various inputs are. For this exercise, we'll specify the UTM Easting and Northing to be (257500, 4112500), which will download the tile with the lower left corner (257000,4112000). By default, the function will check the size total size of the download and ask you whether you wish to proceed (y/n). This file is ~672.7 MB, so make sure you have enough space on your local drive. You can set check.size=FALSE if you want to download without a prompt.

byTileAOP(dpID='DP3.30006.001',

          site='SJER',

          year='2021',

          easting=257500,

          northing=4112500,

          check.size=TRUE, # set to FALSE if you don't want to enter y/n

          savepath = wd)

This file will be downloaded into a nested subdirectory under the ~/data folder, inside a folder named DP3.30006.001 (the Data Product ID). The file should show up in this location: ~/data/DP3.30006.001/neon-aop-products/2021/FullSite/D17/2021_SJER_5/L3/Spectrometer/Reflectance/NEON_D17_SJER_DP3_257000_4112000_reflectance.h5.

# Define the h5 file name to be opened

h5_file <- paste0(wd,"DP3.30006.001/neon-aop-products/2021/FullSite/D17/2021_SJER_5/L3/Spectrometer/Reflectance/NEON_D17_SJER_DP3_257000_4112000_reflectance.h5")

You can use h5ls and/or View(h5ls(...)) to look at the contents of the hdf5 file, as follows:

# look at the HDF5 file structure 

View(h5ls(h5_file,all=T))

When you look at the structure of the data, take note of the "map info" dataset, the Coordinate_System group, and the wavelength and Reflectance datasets. The Coordinate_System folder contains the spatial attributes of the data including its EPSG Code, which is easily converted to a Coordinate Reference System (CRS). The CRS documents how the data are physically located on the Earth. The wavelength dataset contains the wavelength values for each band in the data. The Reflectance dataset contains the image data that we will use for both data processing and visualization.

More Information on raster metadata:

• Raster Data in R - The Basics - this tutorial explains more about how rasters work in R and their associated metadata.

• About Hyperspectral Remote Sensing Data -this tutorial explains more about metadata and important concepts associated with multi-band (multi and hyperspectral) rasters.

We can use the h5readAttributes() function to read and extract metadata from the HDF5 file. Let's start by learning about the wavelengths described within this file.

# get information about the wavelengths of this dataset

wavelengthInfo <- h5readAttributes(h5_file,"/SJER/Reflectance/Metadata/Spectral_Data/Wavelength")

wavelengthInfo

## $Description
## [1] "Central wavelength of the reflectance bands."
## 
## $Units
## [1] "nanometers"

Next, we can use the h5read function to read the data contained within the HDF5 file. Let's read in the wavelengths of the band centers:

# read in the wavelength information from the HDF5 file

wavelengths <- h5read(h5_file,"/SJER/Reflectance/Metadata/Spectral_Data/Wavelength")

head(wavelengths)

## [1] 381.6035 386.6132 391.6229 396.6327 401.6424 406.6522

tail(wavelengths)

## [1] 2485.693 2490.703 2495.713 2500.722 2505.732 2510.742

Which wavelength is band 21 associated with?

(Hint: look at the wavelengths vector that we just imported and check out the data located at index 21 - wavelengths[21]).

Band 21 has a associated wavelength center of 481.7982 nanometers (nm) which is in the blue portion (~380-500 nm) of the visible electromagnetic spectrum (~380-700 nm).

Bands and Wavelengths

A band represents a group of wavelengths. For example, the wavelength values between 695 nm and 700 nm might be one band captured by an imaging spectrometer. The imaging spectrometer collects reflected light energy in a pixel for light in that band. Often when you work with a multi- or hyperspectral dataset, the band information is reported as the center wavelength value. This value represents the mean value of the wavelengths represented in that band. Thus in a band spanning 695-700 nm, the center would be 697.5 nm). The full width half max (FWHM) will also be reported. This value can be thought of as the spread of the band around that center point. So, a band that covers 800-805 nm might have a FWHM of 5 nm and a wavelength value of 802.5 nm.

The HDF5 dataset that we are working with in this activity may contain more information than we need to work with. For example, we don't necessarily need to process all 426 bands available in a full NEON hyperspectral reflectance file - if we are interested in creating a product like NDVI which only uses bands in the Near InfraRed (NIR) and Red portions of the spectrum. Or we might only be interested in a spatial subset of the data - perhaps an area where we have collected corresponding ground data in the field.

The HDF5 format allows us to slice (or subset) the data - quickly extracting the subset that we need to process. Let's extract one of the green bands - band 34.

By the way - what is the center wavelength value associated with band 34?

Hint: wavelengths[34].

How do we know this band is a green band in the visible portion of the spectrum?

In order to effectively subset our data, let's first read the reflectance metadata stored as attributes in the "Reflectance_Data" dataset.

# First, we need to extract the reflectance metadata:

reflInfo <- h5readAttributes(h5_file, "/SJER/Reflectance/Reflectance_Data")

reflInfo

## $Cloud_conditions
## [1] "For cloud conditions information see Weather Quality Index dataset."
## 
## $Cloud_type
## [1] "Cloud type may have been selected from multiple flight trajectories."
## 
## $Data_Ignore_Value
## [1] -9999
## 
## $Description
## [1] "Atmospherically corrected reflectance."
## 
## $Dimension_Labels
## [1] "Line, Sample, Wavelength"
## 
## $Dimensions
## [1] 1000 1000  426
## 
## $Interleave
## [1] "BSQ"
## 
## $Scale_Factor
## [1] 10000
## 
## $Spatial_Extent_meters
## [1]  257000  258000 4112000 4113000
## 
## $Spatial_Resolution_X_Y
## [1] 1 1
## 
## $Units
## [1] "Unitless."
## 
## $Units_Valid_range
## [1]     0 10000

# Next, we read the different dimensions



nRows <- reflInfo$Dimensions[1]

nCols <- reflInfo$Dimensions[2]

nBands <- reflInfo$Dimensions[3]



nRows

## [1] 1000

nCols

## [1] 1000

nBands

## [1] 426

The HDF5 read function reads data in the order: Bands, Cols, Rows. This is different from how R reads data. We'll adjust for this later.

# Extract or "slice" data for band 34 from the HDF5 file

b34 <- h5read(h5_file,"/SJER/Reflectance/Reflectance_Data",index=list(34,1:nCols,1:nRows)) 



# what type of object is b34?

class(b34)

## [1] "array"

A Note About Data Slicing in HDF5

Data slicing allows us to extract and work with subsets of the data rather than reading in the entire dataset into memory. In this example, we will extract and plot the green band without reading in all 426 bands. The ability to slice large datasets makes HDF5 ideal for working with big data.

Next, let's convert our data from an array (more than 2 dimensions) to a matrix (just 2 dimensions). We need to have our data in a matrix format to plot it.

# convert from array to matrix by selecting only the first band

b34 <- b34[1,,]



# display the class of this re-defined variable

class(b34)

## [1] "matrix" "array"

Arrays vs. Matrices

Arrays are matrices with more than 2 dimensions. When we say dimension, we are talking about the "z" associated with the data (imagine a series of tabs in a spreadsheet). Put the other way: matrices are arrays with only 2 dimensions. Arrays can have any number of dimensions one, two, ten or more.

Here is a matrix that is 4 x 3 in size (4 rows and 3 columns):

Dimensions in Arrays

An array contains 1 or more dimensions in the "z" direction. For example, let's say that we collected the same set of species data for every day in a 30 day month. We might then have a matrix like the one above for each day for a total of 30 days making a 4 x 3 x 30 array (this dataset has more than 2 dimensions). More on R object types here (links to external site, DataCamp).

Next, let's look at the metadata for the reflectance data. When we do this, take note of 1) the scale factor and 2) the data ignore value. Then we can plot the band 34 data. Plotting spatial data as a visual "data check" is a good idea to make sure processing is being performed correctly and all is well with the image.

# look at the metadata for the reflectance dataset

h5readAttributes(h5_file,"/SJER/Reflectance/Reflectance_Data")

## $Cloud_conditions
## [1] "For cloud conditions information see Weather Quality Index dataset."
## 
## $Cloud_type
## [1] "Cloud type may have been selected from multiple flight trajectories."
## 
## $Data_Ignore_Value
## [1] -9999
## 
## $Description
## [1] "Atmospherically corrected reflectance."
## 
## $Dimension_Labels
## [1] "Line, Sample, Wavelength"
## 
## $Dimensions
## [1] 1000 1000  426
## 
## $Interleave
## [1] "BSQ"
## 
## $Scale_Factor
## [1] 10000
## 
## $Spatial_Extent_meters
## [1]  257000  258000 4112000 4113000
## 
## $Spatial_Resolution_X_Y
## [1] 1 1
## 
## $Units
## [1] "Unitless."
## 
## $Units_Valid_range
## [1]     0 10000

# plot the image

image(b34)

What do you notice about the first image? It's washed out and lacking any detail. What could be causing this? It got better when plotting the log of the values, but still not great.

# this is a little hard to visually interpret - what happens if we plot a log of the data?

image(log(b34))

Let's look at the distribution of reflectance values in our data to figure out what is going on.

# Plot range of reflectance values as a histogram to view range

# and distribution of values.

hist(b34,breaks=50,col="darkmagenta")

# View values between 0 and 5000

hist(b34,breaks=100,col="darkmagenta",xlim = c(0, 5000))

# View higher values

hist(b34, breaks=100,col="darkmagenta",xlim = c(5000, 15000),ylim = c(0, 750))

As you're examining the histograms above, keep in mind that reflectance values range between 0-1. The data scale factor in the metadata tells us to divide all reflectance values by 10,000. Thus, a value of 5,000 equates to a reflectance value of 0.50. Storing data as integers (without decimal places) compared to floating points (with decimal places) creates a smaller file. This type of scaling is commin in remote sensing datasets.

Notice in the data that there are some larger reflectance values (>5,000) that represent a smaller number of pixels. These pixels are skewing how the image renders.

Data Ignore Value

Image data in raster format will often contain a data ignore value and a scale factor. The data ignore value represents pixels where there are no data. Among other causes, no data values may be attributed to the sensor not collecting data in that area of the image or to processing results which yield null values.

Remember that the metadata for the Reflectance dataset designated -9999 as data ignore value. Thus, let's set all pixels with a value == -9999 to NA (no value). If we do this, R won't render these pixels.

# there is a no data value in our raster - let's define it

noDataValue <- as.numeric(reflInfo$Data_Ignore_Value)

noDataValue

## [1] -9999

# set all values equal to the no data value (-9999) to NA

b34[b34 == noDataValue] <- NA



# plot the image now

image(b34)

Reflectance Values and Image Stretch

Our image still looks dark because R is trying to render all reflectance values between 0 and 14999 as if they were distributed equally in the histogram. However we know they are not distributed equally. There are many more values between 0-5000 than there are values >5000.

Images contain a distribution of reflectance values. A typical image viewing program will render the values by distributing the entire range of reflectance values across a range of "shades" that the monitor can render - between 0 and 255. However, often the distribution of reflectance values is not linear. For example, in the case of our data, most of the reflectance values fall between 0 and 0.5. Yet there are a few values >0.8 that are heavily impacting the way the image is drawn on our monitor. Imaging processing programs like ENVI, QGIS and ArcGIS (and even Adobe Photoshop) allow you to adjust the stretch of the image. This is similar to adjusting the contrast and brightness in Photoshop.

The proper way to adjust our data would be to apply what's called an image stretch. We will learn how to stretch our image data later. For now, let's plot the values as the log function on the pixel reflectance values to factor out those larger values.

image(log(b34))

The log applied to our image increases the contrast making it look more like an image. However, look at the images below. The top one is an RGB image as the image should look. The bottom one is our log-adjusted image. Notice a difference?

Transpose Image

Notice that there are three data dimensions for this file: Bands x Rows x Columns. However, when R reads in the dataset, it reads them as: Columns x Bands x Rows. The data are flipped. We can quickly transpose the data to correct for this using the t or transpose command in R.

The orientation is rotated in our log adjusted image. This is because R reads in matrices starting from the upper left hand corner. While most rasters read pixels starting from the lower left hand corner. In the next section, we will deal with this issue by creating a proper georeferenced (spatially located) raster in R. The raster format will read in pixels following the same methods as other GIS and imaging processing software like QGIS and ENVI do.

# We need to transpose x and y values in order for our 

# final image to plot properly

b34 <- t(b34)

image(log(b34), main="Transposed Image")

Create a Georeferenced Raster

Next, we will create a proper raster using the b34 matrix. The raster format will allow us to define and manage:

• Image stretch
• Coordinate reference system & spatial reference
• Resolution
• and other raster attributes...

It will also account for the orientation issue discussed above.

To create a raster in R, we need a few pieces of information, including:

• The coordinate reference system (CRS)
• The spatial extent of the image

Define Raster CRS

First, we need to define the Coordinate reference system (CRS) of the raster. To do that, we can first grab the EPSG code from the HDF5 attributes, and covert the EPSG to a CRS string. Then we can assign that CRS to the raster object.

# Extract the EPSG from the h5 dataset

h5EPSG <- h5read(h5_file, "/SJER/Reflectance/Metadata/Coordinate_System/EPSG Code")



# convert the EPSG code to a CRS string

h5CRS <- crs(paste0("+init=epsg:",h5EPSG))



# define final raster with projection info 

# note that capitalization will throw errors on a MAC.

# if UTM is all caps it might cause an error!

b34r <- rast(b34, 
        crs=h5CRS)



# view the raster attributes

b34r

## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 0, 1000, 0, 1000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :    32 
## max value   : 13129

# let's have a look at our properly oriented raster. Take note of the 

# coordinates on the x and y axis.



image(log(b34r), 
      xlab = "UTM Easting", 
      ylab = "UTM Northing",
      main = "Properly Oriented Raster")

Next we define the extents of our raster. The extents will be used to calculate the raster's resolution. Fortunately, the spatial extent is provided in the HDF5 file "Reflectance_Data" group attributes that we saved before as reflInfo.

# Grab the UTM coordinates of the spatial extent

xMin <- reflInfo$Spatial_Extent_meters[1]

xMax <- reflInfo$Spatial_Extent_meters[2]

yMin <- reflInfo$Spatial_Extent_meters[3]

yMax <- reflInfo$Spatial_Extent_meters[4]



# define the extent (left, right, top, bottom)

rasExt <- ext(xMin,xMax,yMin,yMax)

rasExt

## SpatExtent : 257000, 258000, 4112000, 4113000 (xmin, xmax, ymin, ymax)

# assign the spatial extent to the raster

ext(b34r) <- rasExt



# look at raster attributes

b34r

## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :    32 
## max value   : 13129

Learn more about working with Raster data in R in the Data Carpentry workshop: Introduction to Geospatial Raster and Vector Data with R.

We can adjust the colors of our raster as well, if desired.

# let's change the colors of our raster and adjust the zlim 

col <- terrain.colors(25)



image(b34r,  
      xlab = "UTM Easting", 
      ylab = "UTM Northing",
      main= "Spatially Referenced Raster",
      col=col, 
      zlim=c(0,3000))

We've now created a raster from band 34 reflectance data. We can export the data as a raster, using the writeRaster command. Note that it's good practice to close the H5 connection before moving on!

# write out the raster as a geotiff

writeRaster(b34r,

            file=paste0(wd,"band34.tif"),

            overwrite=TRUE)



# close the H5 file

H5close()

In this tutorial, we will learn how to create multi (3) band images from hyperspectral data. We will also learn how to perform some basic raster calculations (known as raster math in the GIS world).

About Hyperspectral Data

We often want to generate a 3 band image from multi or hyperspectral data. The most commonly recognized band combination is RGB which stands for Red, Green and Blue. RGB images are just like an image that your camera takes. But other band combinations can be useful too. For example, near infrared images highlight healthy vegetation, which makes it easy to classify or identify where vegetation is located on the ground.

Create a Raster Stack in R

In the previous lesson, we exported a single band of the NEON Reflectance data from a HDF5 file. In this activity, we will create a full color image using 3 (red, green and blue - RGB) bands. We will follow many of the steps we followed in the Intro to Working with Hyperspectral Remote Sensing Data in HDF5 Format in R tutorial. These steps included loading required packages, downloading the data (optionally, you don't need to do this if you downloaded the data from the previous lesson), and reading in our file and viewing the hdf5 file structure.

First, let's load the required R packages, terra and rhdf5.

library(terra)

library(rhdf5)

library(neonUtilities)

Next set the working directory to ensure R can find the file we wish to import. Be sure to move the download into your working directory!

# set working directory (this will depend on your local environment)

wd <- "~/data/"

setwd(wd)

We can use the neonUtilities function byTileAOP to download a single reflectance tile. You can run help(byTileAOP) to see more details on what the various inputs are. For this exercise, we'll specify the UTM Easting and Northing to be (257500, 4112500), which will download the tile with the lower left corner (257000, 4112000).

byTileAOP(dpID = 'DP3.30006.001',

          site = 'SJER',

          year = '2021',

          easting = 257500,

          northing = 4112500,

          savepath = wd)

This file will be downloaded into a nested subdirectory under the ~/data folder, inside a folder named DP3.30006.001 (the Data Product ID). The file should show up in this location: ~/data/DP3.30006.001/neon-aop-products/2021/FullSite/D17/2021_SJER_5/L3/Spectrometer/Reflectance/NEON_D17_SJER_DP3_257000_4112000_reflectance.h5.

Now we can read in the file. You can move this file to a different location, but make sure to change the path accordingly.

# Define the h5 file name to be opened

h5_file <- paste0(wd,"DP3.30006.001/neon-aop-products/2021/FullSite/D17/2021_SJER_5/L3/Spectrometer/Reflectance/NEON_D17_SJER_DP3_257000_4112000_reflectance.h5")

As in the last lesson, let's use View(h5ls) to take a look inside this hdf5 dataset:

View(h5ls(h5_file,all=T))

To spatially locate our raster data, we need a few key attributes:

• The coordinate reference system
• The spatial extent of the raster

We'll begin by grabbing these key attributes from the H5 file.

# define coordinate reference system from the EPSG code provided in the HDF5 file

h5EPSG <- h5read(h5_file,"/SJER/Reflectance/Metadata/Coordinate_System/EPSG Code" )

h5CRS <- crs(paste0("+init=epsg:",h5EPSG))



# get the Reflectance_Data attributes

reflInfo <- h5readAttributes(h5_file,"/SJER/Reflectance/Reflectance_Data" )



# Grab the UTM coordinates of the spatial extent

xMin <- reflInfo$Spatial_Extent_meters[1]

xMax <- reflInfo$Spatial_Extent_meters[2]

yMin <- reflInfo$Spatial_Extent_meters[3]

yMax <- reflInfo$Spatial_Extent_meters[4]



# define the extent (left, right, top, bottom)

rastExt <- ext(xMin,xMax,yMin,yMax)



# view the extent to make sure that it looks right

rastExt

## SpatExtent : 257000, 258000, 4112000, 4113000 (xmin, xmax, ymin, ymax)

# Finally, define the no data value for later

h5NoDataValue <- as.integer(reflInfo$Data_Ignore_Value)

cat('No Data Value:',h5NoDataValue)

## No Data Value: -9999

Next, we'll write a function that will perform the processing that we did step by step in the Intro to Working with Hyperspectral Remote Sensing Data in HDF5 Format in R. This will allow us to process multiple bands in bulk.

The function band2Rast slices a band of data from the HDF5 file, and extracts the reflectance array for that band. It then converts the data into a matrix, converts it to a raster, and finally returns a spatially corrected raster for the specified band.

The function requires the following variables:

• file: the hdf5 reflectance file
• band: the band number we wish to extract
• noDataValue: the noDataValue for the raster
• extent: a terra spatial extent (SpatExtent) object .
• crs: the Coordinate Reference System for the raster

The function output is a spatially referenced, R terra object.

# file: the hdf5 file

# band: the band you want to process

# returns: a matrix containing the reflectance data for the specific band



band2Raster <- function(file, band, noDataValue, extent, CRS){
    # first, read in the raster
    out <- h5read(file,"/SJER/Reflectance/Reflectance_Data",index=list(band,NULL,NULL))
	  # Convert from array to matrix
	  out <- (out[1,,])
	  # transpose data to fix flipped row and column order 
    # depending upon how your data are formatted you might not have to perform this
    # step.
	  out <- t(out)
    # assign data ignore values to NA
    # note, you might chose to assign values of 15000 to NA
    out[out == noDataValue] <- NA
	  
    # turn the out object into a raster
    outr <- rast(out,crs=CRS)
   
    # assign the extents to the raster
    ext(outr) <- extent
   
    # return the terra raster object
    return(outr)
}

Now that the function is created, we can create our list of rasters. The list specifies which bands (or dimensions in our hyperspectral dataset) we want to include in our raster stack. Let's start with a typical RGB (red, green, blue) combination. We will use bands 14, 9, and 4 (bands 58, 34, and 19 in a full NEON hyperspectral dataset).

# create a list of the bands (R,G,B) we want to include in our stack

rgb <- list(58,34,19)



# lapply tells R to apply the function to each element in the list

rgb_rast <- lapply(rgb,FUN=band2Raster, file = h5_file,
                   noDataValue=h5NoDataValue, 
                   ext=rastExt,
                   CRS=h5CRS)

Check out the properties or rgb_rast:

rgb_rast

## [[1]]
## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :     0 
## max value   : 14950 
## 
## [[2]]
## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :    32 
## max value   : 13129 
## 
## [[3]]
## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :     9 
## max value   : 11802

Note that it displays properties of 3 rasters. Finally, we can create a raster stack from our list of rasters as follows:

rgbStack <- rast(rgb_rast)

In the code chunk above, we used the lapply() function, which is a powerful, flexible way to apply a function (in this case, our band2Raster() function) multiple times. You can learn more about lapply() here.

NOTE: We are using the raster stack object in R to store several rasters that are of the same CRS and extent. This is a popular and convenient way to organize co-incident rasters.

Next, add the names of the bands to the raster so we can easily keep track of the bands in the list.

# Create a list of band names

bandNames <- paste("Band_",unlist(rgb),sep="")



# set the rasterStack's names equal to the list of bandNames created above

names(rgbStack) <- bandNames



# check properties of the raster list - note the band names

rgbStack

## class       : SpatRaster 
## dimensions  : 1000, 1000, 3  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## names       : Band_58, Band_34, Band_19 
## min values  :       0,      32,       9 
## max values  :   14950,   13129,   11802


# scale the data as specified in the reflInfo$Scale Factor

rgbStack <- rgbStack/as.integer(reflInfo$Scale_Factor)



# plot one raster in the stack to make sure things look OK.

plot(rgbStack$Band_58, main="Band 58")

We can play with the color ramps too if we want:

# change the colors of our raster 

colors1 <- terrain.colors(25)

image(rgbStack$Band_58, main="Band 58", col=colors1)

# adjust the zlims or the stretch of the image

image(rgbStack$Band_58, main="Band 58", col=colors1, zlim = c(0,.5))

# try a different color palette

colors2 <- topo.colors(15, alpha = 1)

image(rgbStack$Band_58, main="Band 58", col=colors2, zlim=c(0,.5))

The plotRGB function allows you to combine three bands to create an true-color image.

# create a 3 band RGB image

plotRGB(rgbStack,
        r=1,g=2,b=3,
        stretch = "lin")

A note about image stretching: Notice that we use the argument stretch="lin" in this plotting function, which automatically stretches the brightness values for us to produce a natural-looking image.

Once you've created your raster, you can export it as a GeoTIFF using writeRaster. You can bring this GeoTIFF into any GIS software, such as QGIS or ArcGIS.

# Write out final raster	

# Note: if you set overwrite to TRUE, then you will overwrite (and lose) any older version of the tif file! 

writeRaster(rgbStack, file=paste0(wd,"NEON_hyperspectral_tutorial_example_RGB_image.tif"), overwrite=TRUE)

Raster Math - Creating NDVI and other Vegetation Indices in R

In this last part, we will calculate some vegetation indices using raster math in R! We will start by creating NDVI or Normalized Difference Vegetation Index.

About NDVI

NDVI is a ratio between the near infrared (NIR) portion of the electromagnetic spectrum and the red portion of the spectrum.

$$ NDVI = \frac{NIR-RED}{NIR+RED} $$

Please keep in mind that there are different ways to aggregate bands when using hyperspectral data. This example is using individual bands to perform the NDVI calculation. Using individual bands is not necessarily the best way to calculate NDVI from hyperspectral data.

# Calculate NDVI

# select bands to use in calculation (red, NIR)

ndviBands <- c(58,90)



# create raster list and then a stack using those two bands

ndviRast <- lapply(ndviBands,FUN=band2Raster, file = h5_file,
                   noDataValue=h5NoDataValue, 
                   ext=rastExt, CRS=h5CRS)

ndviStack <- rast(ndviRast)



# make the names pretty

bandNDVINames <- paste("Band_",unlist(ndviBands),sep="")

names(ndviStack) <- bandNDVINames



# view the properties of the new raster stack

ndviStack

## class       : SpatRaster 
## dimensions  : 1000, 1000, 2  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## names       : Band_58, Band_90 
## min values  :       0,      11 
## max values  :   14950,   14887

#calculate NDVI

NDVI <- function(x) {
	  (x[,2]-x[,1])/(x[,2]+x[,1])
}

ndviCalc <- app(ndviStack,NDVI)

plot(ndviCalc, main="NDVI for the NEON SJER Field Site")

# Now, play with breaks and colors to create a meaningful map

# add a color map with 4 colors

myCol <- rev(terrain.colors(4)) # use the 'rev()' function to put green as the highest NDVI value

# add breaks to the colormap, including lowest and highest values (4 breaks = 3 segments)

brk <- c(0, .25, .5, .75, 1)



# plot the image using breaks

plot(ndviCalc, main="NDVI for the NEON SJER Field Site", col=myCol, breaks=brk)

This activity will walk you through the fundamental principles of working with raster data in R.

What is Raster Data?

Raster or "gridded" data are data that are saved in pixels. In the spatial world, each pixel represents an area on the Earth's surface. For example in the raster below, each pixel represents a particular land cover class that would be found in that location in the real world.

More on rasters in the The Relationship Between Raster Resolution, Spatial extent & Number of Pixels tutorial.

To work with rasters in R, we need two key packages, sp and raster. To install the raster package you can use install.packages('raster'). When you install the raster package, sp should also install. Also install the rgdal package install.packages('rgdal'). Among other things, rgdal will allow us to export rasters to GeoTIFF format.

Once installed we can load the packages and start working with raster data.

# load the raster, sp, and rgdal packages
library(raster)
library(sp)
library(rgdal)

# set working directory to data folder
#setwd("pathToDirHere")
wd <- ("~/Git/data/")
setwd(wd)

Next, let's load a raster containing elevation data into our environment. And look at the attributes.

# load raster in an R object called 'DEM'
DEM <- raster(paste0(wd, "NEON-DS-Field-Site-Spatial-Data/SJER/DigitalTerrainModel/SJER2013_DTM.tif"))

# look at the raster attributes. 
DEM

## class      : RasterLayer 
## dimensions : 5060, 4299, 21752940  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 254570, 258869, 4107302, 4112362  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : /Users/olearyd/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/DigitalTerrainModel/SJER2013_DTM.tif 
## names      : SJER2013_DTM

Notice a few things about this raster.

• dimensions: the "size" of the file in pixels

• nrow, ncol: the number of rows and columns in the data (imagine a spreadsheet or a matrix).
• ncells: the total number of pixels or cells that make up the raster.

• resolution: the size of each pixel (in meters in this case). 1 meter pixels means that each pixel represents a 1m x 1m area on the earth's surface.
• extent: the spatial extent of the raster. This value will be in the same coordinate units as the coordinate reference system of the raster.
• coord ref: the coordinate reference system string for the raster. This raster is in UTM (Universal Trans Mercator) zone 11 with a datum of WGS 84. More on UTM here.

Work with Rasters in R

Now that we have the raster loaded into R, let's grab some key raster attributes.

Define Min/Max Values

By default this raster doesn't have the min or max values associated with it's attributes Let's change that by using the setMinMax() function.

# calculate and save the min and max values of the raster to the raster object
DEM <- setMinMax(DEM)

# view raster attributes
DEM

## class      : RasterLayer 
## dimensions : 5060, 4299, 21752940  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 254570, 258869, 4107302, 4112362  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : /Users/olearyd/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/DigitalTerrainModel/SJER2013_DTM.tif 
## names      : SJER2013_DTM 
## values     : 228.1, 518.66  (min, max)

Notice the values is now part of the attributes and shows the min and max values for the pixels in the raster. What these min and max values represent depends on what is represented by each pixel in the raster.

You can also view the rasters min and max values and the range of values contained within the pixels.

#Get min and max cell values from raster
#NOTE: this code may fail if the raster is too large
cellStats(DEM, min)

## [1] 228.1

cellStats(DEM, max)

## [1] 518.66

cellStats(DEM, range)

## [1] 228.10 518.66

View CRS

First, let's consider the Coordinate Reference System (CRS).

#view coordinate reference system
DEM@crs

## CRS arguments:
##  +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs

This raster is located in UTM Zone 11.

View Extent

If you want to know the exact boundaries of your raster that is in the extent slot.

# view raster extent
DEM@extent

## class      : Extent 
## xmin       : 254570 
## xmax       : 258869 
## ymin       : 4107302 
## ymax       : 4112362

Raster Pixel Values

We can also create a histogram to view the distribution of values in our raster. Note that the max number of pixels that R will plot by default is 100,000. We can tell it to plot more using the maxpixels attribute. Be careful with this, if your raster is large this can take a long time or crash your program.

Since our raster is a digital elevation model, we know that each pixel contains elevation data about our area of interest. In this case the units are meters.

This is an easy and quick data checking tool. Are there any totally weird values?

# the distribution of values in the raster
hist(DEM, main="Distribution of elevation values", 
     col= "purple", 
     maxpixels=22000000)

It looks like we have a lot of land around 325m and 425m.

Plot Raster Data

Let's take a look at our raster now that we know a bit more about it. We can do a simple plot with the plot() function.

# plot the raster
# note that this raster represents a small region of the NEON SJER field site
plot(DEM, 
		 main="Digital Elevation Model, SJER") # add title with main

R has an image() function that allows you to control the way a raster is rendered on the screen. The plot() function in R has a base setting for the number of pixels that it will plot (100,000 pixels). The image command thus might be better for rendering larger rasters.

# create a plot of our raster
image(DEM)

# specify the range of values that you want to plot in the DEM
# just plot pixels between 250 and 300 m in elevation
image(DEM, zlim=c(250,300))

# we can specify the colors too
col <- terrain.colors(5)
image(DEM, zlim=c(250,375), main="Digital Elevation Model (DEM)", col=col)

Plotting with Colors

In the above example. terrain.colors() tells R to create a palette of colors within the terrain.colors color ramp. There are other palettes that you can use as well include rainbow and heat.colors.

• More on color palettes in R here.
• Another good post on colors.
• RColorBrewer is another powerful tool to create sets of colors.

Breaks and Colorbars in R

A digital elevation model (DEM) is an example of a continuous raster. It contains elevation values for a range. For example, elevations values in a DEM might include any set of values between 200 m and 500 m. Given this range, you can plot DEM pixels using a gradient of colors.

By default, R will assign the gradient of colors uniformly across the full range of values in the data. In our case, our DEM has values between 250 and 500. However, we can adjust the "breaks" which represent the numeric locations where the colors change if we want too.

# add a color map with 5 colors
col=terrain.colors(5)

# add breaks to the colormap (6 breaks = 5 segments)
brk <- c(250, 300, 350, 400, 450, 500)

plot(DEM, col=col, breaks=brk, main="DEM with more breaks")

We can also customize the legend appearance.

# First, expand right side of clipping rectangle to make room for the legend
# turn xpd off
par(xpd = FALSE, mar=c(5.1, 4.1, 4.1, 4.5))

# Second, plot w/ no legend
plot(DEM, col=col, breaks=brk, main="DEM with a Custom (but flipped) Legend", legend = FALSE)

# Third, turn xpd back on to force the legend to fit next to the plot.
par(xpd = TRUE)

# Fourth, add a legend - & make it appear outside of the plot
legend(par()$usr[2], 4110600,
        legend = c("lowest", "a bit higher", "middle ground", "higher yet", "highest"), 
        fill = col)

Notice that the legend is in reverse order in the previous plot. Let’s fix that. We need to both reverse the order we have the legend laid out and reverse the the color fill with the rev() colors.

# Expand right side of clipping rect to make room for the legend
par(xpd = FALSE,mar=c(5.1, 4.1, 4.1, 4.5))
#DEM with a custom legend
plot(DEM, col=col, breaks=brk, main="DEM with a Custom Legend",legend = FALSE)
#turn xpd back on to force the legend to fit next to the plot.
par(xpd = TRUE)

#add a legend - but make it appear outside of the plot
legend( par()$usr[2], 4110600,
        legend = c("Highest", "Higher yet", "Middle","A bit higher", "Lowest"), 
        fill = rev(col))

Try the code again but only make one of the changes -- reverse order or reverse colors-- what happens?

The raster plot now inverts the elevations! This is a good reason to understand your data so that a simple visualization error doesn't have you reversing the slope or some other error.

We can add a custom color map with fewer breaks as well.

#add a color map with 4 colors
col=terrain.colors(4)
#add breaks to the colormap (6 breaks = 5 segments)
brk <- c(200, 300, 350, 400,500)
plot(DEM, col=col, breaks=brk, main="DEM with fewer breaks")

A discrete dataset has a set of unique categories or classes. One example could be land use classes. The example below shows elevation zones generated using the same DEM.

Basic Raster Math

We can also perform calculations on our raster. For instance, we could multiply all values within the raster by 2.

#multiple each pixel in the raster by 2
DEM2 <- DEM * 2

DEM2

## class      : RasterLayer 
## dimensions : 5060, 4299, 21752940  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 254570, 258869, 4107302, 4112362  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : memory
## names      : SJER2013_DTM 
## values     : 456.2, 1037.32  (min, max)

#plot the new DEM
plot(DEM2, main="DEM with all values doubled")

Cropping Rasters in R

You can crop rasters in R using different methods. You can crop the raster directly drawing a box in the plot area. To do this, first plot the raster. Then define the crop extent by clicking twice:

1. Click in the UPPER LEFT hand corner where you want the crop box to begin.
2. Click again in the LOWER RIGHT hand corner to define where the box ends.

You'll see a red box on the plot. NOTE that this is a manual process that can be used to quickly define a crop extent.

#plot the DEM
plot(DEM)

#Define the extent of the crop by clicking on the plot
cropbox1 <- drawExtent()

#crop the raster, then plot the new cropped raster
DEMcrop1 <- crop(DEM, cropbox1)

#plot the cropped extent
plot(DEMcrop1)

You can also manually assign the extent coordinates to be used to crop a raster. We'll need the extent defined as (xmin, xmax, ymin , ymax) to do this. This is how we'd crop using a GIS shapefile (with a rectangular shape)

#define the crop extent
cropbox2 <-c(255077.3,257158.6,4109614,4110934)
#crop the raster
DEMcrop2 <- crop(DEM, cropbox2)
#plot cropped DEM
plot(DEMcrop2)

Image (RGB) Data in R

Go to our tutorial Image Raster Data in R - An Intro to learn more about working with image formatted rasters in R.