Intro to Raster Data

Last updated on 2024-10-15 | Edit this page

Overview

Questions

  • What is a raster dataset?
  • How do I work with and plot raster data in R?
  • How can I handle missing or bad data values for a raster?

Objectives

  • Describe the fundamental attributes of a raster dataset.
  • Explore raster attributes and metadata using R.
  • Import rasters into R using the terra package.
  • Plot a raster file in R using the ggplot2 package.
  • Describe the difference between single- and multi-band rasters.

Things You’ll Need To Complete This Episode

See the lesson homepage for detailed information about the software, data, and other prerequisites you will need to work through the examples in this episode.

In this episode, we will introduce the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R. We will discuss some of the core metadata elements that we need to understand to work with rasters in R, including CRS and resolution. We will also explore missing and bad data values as stored in a raster and how R handles these elements.

We will continue to work with the dplyr and ggplot2 packages that were introduced in the Introduction to R for Geospatial Data lesson. We will use two additional packages in this episode to work with raster data - the terra and sf packages. Make sure that you have these packages loaded.

R

library(terra)
library(ggplot2)
library(dplyr)

Introduce the Data

If not already discussed, introduce the datasets that will be used in this lesson. A brief introduction to the datasets can be found on the Geospatial workshop homepage.

For more detailed information about the datasets, check out the Geospatial workshop data page.

View Raster File Attributes


We will be working with a series of GeoTIFF files in this lesson. The GeoTIFF format contains a set of embedded tags with metadata about the raster data. We can use the function describe() to get information about our raster data before we read that data into R. It is ideal to do this before importing your data.

R

describe("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")

OUTPUT

 [1] "Driver: GTiff/GeoTIFF"
 [2] "Files: data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif"
 [3] "Size is 1697, 1367"
 [4] "Coordinate System is:"
 [5] "PROJCRS[\"WGS 84 / UTM zone 18N\","
 [6] "    BASEGEOGCRS[\"WGS 84\","
 [7] "        DATUM[\"World Geodetic System 1984\","
 [8] "            ELLIPSOID[\"WGS 84\",6378137,298.257223563,"
 [9] "                LENGTHUNIT[\"metre\",1]]],"
[10] "        PRIMEM[\"Greenwich\",0,"
[11] "            ANGLEUNIT[\"degree\",0.0174532925199433]],"
[12] "        ID[\"EPSG\",4326]],"
[13] "    CONVERSION[\"UTM zone 18N\","
[14] "        METHOD[\"Transverse Mercator\","
[15] "            ID[\"EPSG\",9807]],"
[16] "        PARAMETER[\"Latitude of natural origin\",0,"
[17] "            ANGLEUNIT[\"degree\",0.0174532925199433],"
[18] "            ID[\"EPSG\",8801]],"
[19] "        PARAMETER[\"Longitude of natural origin\",-75,"
[20] "            ANGLEUNIT[\"degree\",0.0174532925199433],"
[21] "            ID[\"EPSG\",8802]],"
[22] "        PARAMETER[\"Scale factor at natural origin\",0.9996,"
[23] "            SCALEUNIT[\"unity\",1],"
[24] "            ID[\"EPSG\",8805]],"
[25] "        PARAMETER[\"False easting\",500000,"
[26] "            LENGTHUNIT[\"metre\",1],"
[27] "            ID[\"EPSG\",8806]],"
[28] "        PARAMETER[\"False northing\",0,"
[29] "            LENGTHUNIT[\"metre\",1],"
[30] "            ID[\"EPSG\",8807]]],"
[31] "    CS[Cartesian,2],"
[32] "        AXIS[\"(E)\",east,"
[33] "            ORDER[1],"
[34] "            LENGTHUNIT[\"metre\",1]],"
[35] "        AXIS[\"(N)\",north,"
[36] "            ORDER[2],"
[37] "            LENGTHUNIT[\"metre\",1]],"
[38] "    USAGE["
[39] "        SCOPE[\"Engineering survey, topographic mapping.\"],"
[40] "        AREA[\"Between 78°W and 72°W, northern hemisphere between equator and 84°N, onshore and offshore. Bahamas. Canada - Nunavut; Ontario; Quebec. Colombia. Cuba. Ecuador. Greenland. Haiti. Jamica. Panama. Turks and Caicos Islands. United States (USA). Venezuela.\"],"
[41] "        BBOX[0,-78,84,-72]],"
[42] "    ID[\"EPSG\",32618]]"
[43] "Data axis to CRS axis mapping: 1,2"
[44] "Origin = (731453.000000000000000,4713838.000000000000000)"
[45] "Pixel Size = (1.000000000000000,-1.000000000000000)"
[46] "Metadata:"
[47] "  AREA_OR_POINT=Area"
[48] "Image Structure Metadata:"
[49] "  COMPRESSION=LZW"
[50] "  INTERLEAVE=BAND"
[51] "Corner Coordinates:"
[52] "Upper Left  (  731453.000, 4713838.000) ( 72d10'52.71\"W, 42d32'32.18\"N)"
[53] "Lower Left  (  731453.000, 4712471.000) ( 72d10'54.71\"W, 42d31'47.92\"N)"
[54] "Upper Right (  733150.000, 4713838.000) ( 72d 9'38.40\"W, 42d32'30.35\"N)"
[55] "Lower Right (  733150.000, 4712471.000) ( 72d 9'40.41\"W, 42d31'46.08\"N)"
[56] "Center      (  732301.500, 4713154.500) ( 72d10'16.56\"W, 42d32' 9.13\"N)"
[57] "Band 1 Block=1697x1 Type=Float64, ColorInterp=Gray"
[58] "  Min=305.070 Max=416.070 "
[59] "  Minimum=305.070, Maximum=416.070, Mean=359.853, StdDev=17.832"
[60] "  NoData Value=-9999"
[61] "  Metadata:"
[62] "    STATISTICS_MAXIMUM=416.06997680664"
[63] "    STATISTICS_MEAN=359.85311802914"
[64] "    STATISTICS_MINIMUM=305.07000732422"
[65] "    STATISTICS_STDDEV=17.83169335933"                                                                                                                                                                                                                                          

If you wish to store this information in R, you can do the following:

R

HARV_dsmCrop_info <- capture.output(
  describe("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")
)

Each line of text that was printed to the console is now stored as an element of the character vector HARV_dsmCrop_info. We will be exploring this data throughout this episode. By the end of this episode, you will be able to explain and understand the output above.

Open a Raster in R


Now that we’ve previewed the metadata for our GeoTIFF, let’s import this raster dataset into R and explore its metadata more closely. We can use the rast() function to open a raster in R.

Data Tip - Object names

To improve code readability, file and object names should be used that make it clear what is in the file. The data for this episode were collected from Harvard Forest so we’ll use a naming convention of datatype_HARV.

First we will load our raster file into R and view the data structure.

R

DSM_HARV <-
  rast("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")

DSM_HARV

OUTPUT

class       : SpatRaster
dimensions  : 1367, 1697, 1  (nrow, ncol, nlyr)
resolution  : 1, 1  (x, y)
extent      : 731453, 733150, 4712471, 4713838  (xmin, xmax, ymin, ymax)
coord. ref. : WGS 84 / UTM zone 18N (EPSG:32618)
source      : HARV_dsmCrop.tif
name        : HARV_dsmCrop
min value   :       305.07
max value   :       416.07 

The information above includes a report of min and max values, but no other data range statistics. Similar to other R data structures like vectors and data frame columns, descriptive statistics for raster data can be retrieved like

R

summary(DSM_HARV)

WARNING

Warning: [summary] used a sample

OUTPUT

  HARV_dsmCrop
 Min.   :305.6
 1st Qu.:345.6
 Median :359.6
 Mean   :359.8
 3rd Qu.:374.3
 Max.   :414.7  

but note the warning - unless you force R to calculate these statistics using every cell in the raster, it will take a random sample of 100,000 cells and calculate from that instead. To force calculation all the values, you can use the function values:

R

summary(values(DSM_HARV))

OUTPUT

  HARV_dsmCrop
 Min.   :305.1
 1st Qu.:345.6
 Median :359.7
 Mean   :359.9
 3rd Qu.:374.3
 Max.   :416.1  

To visualise this data in R using ggplot2, we need to convert it to a dataframe. We learned about dataframes in an earlier lesson. The terra package has an built-in function for conversion to a plotable dataframe.

R

DSM_HARV_df <- as.data.frame(DSM_HARV, xy = TRUE)

Now when we view the structure of our data, we will see a standard dataframe format.

R

str(DSM_HARV_df)

OUTPUT

'data.frame':	2319799 obs. of  3 variables:
 $ x           : num  731454 731454 731456 731456 731458 ...
 $ y           : num  4713838 4713838 4713838 4713838 4713838 ...
 $ HARV_dsmCrop: num  409 408 407 407 409 ...

We can use ggplot() to plot this data. We will set the color scale to scale_fill_viridis_c which is a color-blindness friendly color scale. We will also use the coord_quickmap() function to use an approximate Mercator projection for our plots. This approximation is suitable for small areas that are not too close to the poles. Other coordinate systems are available in ggplot2 if needed, you can learn about them at their help page ?coord_map.

R

ggplot() +
    geom_raster(data = DSM_HARV_df , aes(x = x, y = y, fill = HARV_dsmCrop)) +
    scale_fill_viridis_c() +
    coord_quickmap()
Raster plot with ggplot2 using the viridis color scale
Raster plot with ggplot2 using the viridis color scale

Plotting Tip

More information about the Viridis palette used above at R Viridis package documentation.

Plotting Tip

For faster, simpler plots, you can use the plot function from the terra package.

See ?plot for more arguments to customize the plot

R

plot(DSM_HARV)

This map shows the elevation of our study site in Harvard Forest. From the legend, we can see that the maximum elevation is ~400, but we can’t tell whether this is 400 feet or 400 meters because the legend doesn’t show us the units. We can look at the metadata of our object to see what the units are. Much of the metadata that we’re interested in is part of the CRS. We introduced the concept of a CRS in an earlier lesson.

Now we will see how features of the CRS appear in our data file and what meanings they have.

View Raster Coordinate Reference System (CRS) in R

We can view the CRS string associated with our R object using thecrs() function.

R

crs(DSM_HARV, proj = TRUE)

OUTPUT

[1] "+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs"

Challenge

What units are our data in?

+units=m tells us that our data is in meters.

Understanding CRS in Proj4 Format


The CRS for our data is given to us by R in proj4 format. Let’s break down the pieces of proj4 string. The string contains all of the individual CRS elements that R or another GIS might need. Each element is specified with a + sign, similar to how a .csv file is delimited or broken up by a ,. After each + we see the CRS element being defined. For example projection (proj=) and datum (datum=).

UTM Proj4 String

A projection string (like the one of DSM_HARV) specifies the UTM projection as follows:

+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0

  • proj=utm: the projection is UTM, UTM has several zones.
  • zone=18: the zone is 18
  • datum=WGS84: the datum is WGS84 (the datum refers to the 0,0 reference for the coordinate system used in the projection)
  • units=m: the units for the coordinates are in meters
  • ellps=WGS84: the ellipsoid (how the earth’s roundness is calculated) for the data is WGS84

Note that the zone is unique to the UTM projection. Not all CRSs will have a zone. Image source: Chrismurf at English Wikipedia, via Wikimedia Commons (CC-BY).

UTM zones in the USA.
The UTM zones across the continental United States. From: https://upload.wikimedia.org/wikipedia/commons/8/8d/Utm-zones-USA.svg

Calculate Raster Min and Max Values


It is useful to know the minimum or maximum values of a raster dataset. In this case, given we are working with elevation data, these values represent the min/max elevation range at our site.

Raster statistics are often calculated and embedded in a GeoTIFF for us. We can view these values:

R

minmax(DSM_HARV)

OUTPUT

    HARV_dsmCrop
min       305.07
max       416.07

R

min(values(DSM_HARV))

OUTPUT

[1] 305.07

R

max(values(DSM_HARV))

OUTPUT

[1] 416.07

Data Tip - Set min and max values

If the minimum and maximum values haven’t already been calculated, we can calculate them using the setMinMax() function.

R

DSM_HARV <- setMinMax(DSM_HARV)

We can see that the elevation at our site ranges from 305.0700073m to 416.0699768m.

Raster Bands


The Digital Surface Model object (DSM_HARV) that we’ve been working with is a single band raster. This means that there is only one dataset stored in the raster: surface elevation in meters for one time period.

Multi-band raster image

A raster dataset can contain one or more bands. We can use the rast() function to import one single band from a single or multi-band raster. We can view the number of bands in a raster using the nlyr() function.

R

nlyr(DSM_HARV)

OUTPUT

[1] 1

However, raster data can also be multi-band, meaning that one raster file contains data for more than one variable or time period for each cell. Jump to a later episode in this series for information on working with multi-band rasters: Work with Multi-band Rasters in R.

Dealing with Missing Data


Raster data often has a NoDataValue associated with it. This is a value assigned to pixels where data is missing or no data were collected.

By default the shape of a raster is always rectangular. So if we have a dataset that has a shape that isn’t rectangular, some pixels at the edge of the raster will have NoDataValues. This often happens when the data were collected by an airplane which only flew over some part of a defined region.

In the image below, the pixels that are black have NoDataValues. The camera did not collect data in these areas.

In the next image, the black edges have been assigned NoDataValue. R doesn’t render pixels that contain a specified NoDataValue. R assigns missing data with the NoDataValue as NA.

The difference here shows up as ragged edges on the plot, rather than black spaces where there is no data.

If your raster already has NA values set correctly but you aren’t sure where they are, you can deliberately plot them in a particular colour. This can be useful when checking a dataset’s coverage. For instance, sometimes data can be missing where a sensor could not ‘see’ its target data, and you may wish to locate that missing data and fill it in.

To highlight NA values in ggplot, alter the scale_fill_*() layer to contain a colour instruction for NA values, like scale_fill_viridis_c(na.value = 'deeppink')

The value that is conventionally used to take note of missing data (the NoDataValue value) varies by the raster data type. For floating-point rasters, the figure -3.4e+38 is a common default, and for integers, -9999 is common. Some disciplines have specific conventions that vary from these common values.

In some cases, other NA values may be more appropriate. An NA value should be a) outside the range of valid values, and b) a value that fits the data type in use. For instance, if your data ranges continuously from -20 to 100, 0 is not an acceptable NA value! Or, for categories that number 1-15, 0 might be fine for NA, but using -.000003 will force you to save the GeoTIFF on disk as a floating point raster, resulting in a bigger file.

If we are lucky, our GeoTIFF file has a tag that tells us what is the NoDataValue. If we are less lucky, we can find that information in the raster’s metadata. If a NoDataValue was stored in the GeoTIFF tag, when R opens up the raster, it will assign each instance of the value to NA. Values of NA will be ignored by R as demonstrated above.

Challenge

Use the output from the describe() and sources() functions to find out what NoDataValue is used for our DSM_HARV dataset.

R

describe(sources(DSM_HARV))

OUTPUT

 [1] "Driver: GTiff/GeoTIFF"
 [2] "Files: /home/runner/work/r-raster-vector-geospatial/r-raster-vector-geospatial/site/built/data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif"
 [3] "Size is 1697, 1367"
 [4] "Coordinate System is:"
 [5] "PROJCRS[\"WGS 84 / UTM zone 18N\","
 [6] "    BASEGEOGCRS[\"WGS 84\","
 [7] "        DATUM[\"World Geodetic System 1984\","
 [8] "            ELLIPSOID[\"WGS 84\",6378137,298.257223563,"
 [9] "                LENGTHUNIT[\"metre\",1]]],"
[10] "        PRIMEM[\"Greenwich\",0,"
[11] "            ANGLEUNIT[\"degree\",0.0174532925199433]],"
[12] "        ID[\"EPSG\",4326]],"
[13] "    CONVERSION[\"UTM zone 18N\","
[14] "        METHOD[\"Transverse Mercator\","
[15] "            ID[\"EPSG\",9807]],"
[16] "        PARAMETER[\"Latitude of natural origin\",0,"
[17] "            ANGLEUNIT[\"degree\",0.0174532925199433],"
[18] "            ID[\"EPSG\",8801]],"
[19] "        PARAMETER[\"Longitude of natural origin\",-75,"
[20] "            ANGLEUNIT[\"degree\",0.0174532925199433],"
[21] "            ID[\"EPSG\",8802]],"
[22] "        PARAMETER[\"Scale factor at natural origin\",0.9996,"
[23] "            SCALEUNIT[\"unity\",1],"
[24] "            ID[\"EPSG\",8805]],"
[25] "        PARAMETER[\"False easting\",500000,"
[26] "            LENGTHUNIT[\"metre\",1],"
[27] "            ID[\"EPSG\",8806]],"
[28] "        PARAMETER[\"False northing\",0,"
[29] "            LENGTHUNIT[\"metre\",1],"
[30] "            ID[\"EPSG\",8807]]],"
[31] "    CS[Cartesian,2],"
[32] "        AXIS[\"(E)\",east,"
[33] "            ORDER[1],"
[34] "            LENGTHUNIT[\"metre\",1]],"
[35] "        AXIS[\"(N)\",north,"
[36] "            ORDER[2],"
[37] "            LENGTHUNIT[\"metre\",1]],"
[38] "    USAGE["
[39] "        SCOPE[\"Engineering survey, topographic mapping.\"],"
[40] "        AREA[\"Between 78°W and 72°W, northern hemisphere between equator and 84°N, onshore and offshore. Bahamas. Canada - Nunavut; Ontario; Quebec. Colombia. Cuba. Ecuador. Greenland. Haiti. Jamica. Panama. Turks and Caicos Islands. United States (USA). Venezuela.\"],"
[41] "        BBOX[0,-78,84,-72]],"
[42] "    ID[\"EPSG\",32618]]"
[43] "Data axis to CRS axis mapping: 1,2"
[44] "Origin = (731453.000000000000000,4713838.000000000000000)"
[45] "Pixel Size = (1.000000000000000,-1.000000000000000)"
[46] "Metadata:"
[47] "  AREA_OR_POINT=Area"
[48] "Image Structure Metadata:"
[49] "  COMPRESSION=LZW"
[50] "  INTERLEAVE=BAND"
[51] "Corner Coordinates:"
[52] "Upper Left  (  731453.000, 4713838.000) ( 72d10'52.71\"W, 42d32'32.18\"N)"
[53] "Lower Left  (  731453.000, 4712471.000) ( 72d10'54.71\"W, 42d31'47.92\"N)"
[54] "Upper Right (  733150.000, 4713838.000) ( 72d 9'38.40\"W, 42d32'30.35\"N)"
[55] "Lower Right (  733150.000, 4712471.000) ( 72d 9'40.41\"W, 42d31'46.08\"N)"
[56] "Center      (  732301.500, 4713154.500) ( 72d10'16.56\"W, 42d32' 9.13\"N)"
[57] "Band 1 Block=1697x1 Type=Float64, ColorInterp=Gray"
[58] "  Min=305.070 Max=416.070 "
[59] "  Minimum=305.070, Maximum=416.070, Mean=359.853, StdDev=17.832"
[60] "  NoData Value=-9999"
[61] "  Metadata:"
[62] "    STATISTICS_MAXIMUM=416.06997680664"
[63] "    STATISTICS_MEAN=359.85311802914"
[64] "    STATISTICS_MINIMUM=305.07000732422"
[65] "    STATISTICS_STDDEV=17.83169335933"                                                                                                                                                                                                                                          

NoDataValue are encoded as -9999.

Bad Data Values in Rasters


Bad data values are different from NoDataValues. Bad data values are values that fall outside of the applicable range of a dataset.

Examples of Bad Data Values:

  • The normalized difference vegetation index (NDVI), which is a measure of greenness, has a valid range of -1 to 1. Any value outside of that range would be considered a “bad” or miscalculated value.
  • Reflectance data in an image will often range from 0-1 or 0-10,000 depending upon how the data are scaled. Thus a value greater than 1 or greater than 10,000 is likely caused by an error in either data collection or processing.

Find Bad Data Values

Sometimes a raster’s metadata will tell us the range of expected values for a raster. Values outside of this range are suspect and we need to consider that when we analyze the data. Sometimes, we need to use some common sense and scientific insight as we examine the data - just as we would for field data to identify questionable values.

Plotting data with appropriate highlighting can help reveal patterns in bad values and may suggest a solution. Below, reclassification is used to highlight elevation values over 400m with a contrasting colour.

Create A Histogram of Raster Values


We can explore the distribution of values contained within our raster using the geom_histogram() function which produces a histogram. Histograms are often useful in identifying outliers and bad data values in our raster data.

R

ggplot() +
    geom_histogram(data = DSM_HARV_df, aes(HARV_dsmCrop))

OUTPUT

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Notice that a warning message is thrown when R creates the histogram.

stat_bin() using bins = 30. Pick better value with binwidth.

This warning is caused by a default setting in geom_histogram enforcing that there are 30 bins for the data. We can define the number of bins we want in the histogram by using the bins value in the geom_histogram() function.

R

ggplot() +
    geom_histogram(data = DSM_HARV_df, aes(HARV_dsmCrop), bins = 40)

Note that the shape of this histogram looks similar to the previous one that was created using the default of 30 bins. The distribution of elevation values for our Digital Surface Model (DSM) looks reasonable. It is likely there are no bad data values in this particular raster.

Challenge: Explore Raster Metadata

Use describe() to determine the following about the NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_DSMhill.tif file:

  1. Does this file have the same CRS as DSM_HARV?
  2. What is the NoDataValue?
  3. What is resolution of the raster data?
  4. How large would a 5x5 pixel area be on the Earth’s surface?
  5. Is the file a multi- or single-band raster?

Notice: this file is a hillshade. We will learn about hillshades in the Working with Multi-band Rasters in R episode.

R

describe("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_DSMhill.tif")

OUTPUT

 [1] "Driver: GTiff/GeoTIFF"
 [2] "Files: data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_DSMhill.tif"
 [3] "Size is 1697, 1367"
 [4] "Coordinate System is:"
 [5] "PROJCRS[\"WGS 84 / UTM zone 18N\","
 [6] "    BASEGEOGCRS[\"WGS 84\","
 [7] "        DATUM[\"World Geodetic System 1984\","
 [8] "            ELLIPSOID[\"WGS 84\",6378137,298.257223563,"
 [9] "                LENGTHUNIT[\"metre\",1]]],"
[10] "        PRIMEM[\"Greenwich\",0,"
[11] "            ANGLEUNIT[\"degree\",0.0174532925199433]],"
[12] "        ID[\"EPSG\",4326]],"
[13] "    CONVERSION[\"UTM zone 18N\","
[14] "        METHOD[\"Transverse Mercator\","
[15] "            ID[\"EPSG\",9807]],"
[16] "        PARAMETER[\"Latitude of natural origin\",0,"
[17] "            ANGLEUNIT[\"degree\",0.0174532925199433],"
[18] "            ID[\"EPSG\",8801]],"
[19] "        PARAMETER[\"Longitude of natural origin\",-75,"
[20] "            ANGLEUNIT[\"degree\",0.0174532925199433],"
[21] "            ID[\"EPSG\",8802]],"
[22] "        PARAMETER[\"Scale factor at natural origin\",0.9996,"
[23] "            SCALEUNIT[\"unity\",1],"
[24] "            ID[\"EPSG\",8805]],"
[25] "        PARAMETER[\"False easting\",500000,"
[26] "            LENGTHUNIT[\"metre\",1],"
[27] "            ID[\"EPSG\",8806]],"
[28] "        PARAMETER[\"False northing\",0,"
[29] "            LENGTHUNIT[\"metre\",1],"
[30] "            ID[\"EPSG\",8807]]],"
[31] "    CS[Cartesian,2],"
[32] "        AXIS[\"(E)\",east,"
[33] "            ORDER[1],"
[34] "            LENGTHUNIT[\"metre\",1]],"
[35] "        AXIS[\"(N)\",north,"
[36] "            ORDER[2],"
[37] "            LENGTHUNIT[\"metre\",1]],"
[38] "    USAGE["
[39] "        SCOPE[\"Engineering survey, topographic mapping.\"],"
[40] "        AREA[\"Between 78°W and 72°W, northern hemisphere between equator and 84°N, onshore and offshore. Bahamas. Canada - Nunavut; Ontario; Quebec. Colombia. Cuba. Ecuador. Greenland. Haiti. Jamica. Panama. Turks and Caicos Islands. United States (USA). Venezuela.\"],"
[41] "        BBOX[0,-78,84,-72]],"
[42] "    ID[\"EPSG\",32618]]"
[43] "Data axis to CRS axis mapping: 1,2"
[44] "Origin = (731453.000000000000000,4713838.000000000000000)"
[45] "Pixel Size = (1.000000000000000,-1.000000000000000)"
[46] "Metadata:"
[47] "  AREA_OR_POINT=Area"
[48] "Image Structure Metadata:"
[49] "  COMPRESSION=LZW"
[50] "  INTERLEAVE=BAND"
[51] "Corner Coordinates:"
[52] "Upper Left  (  731453.000, 4713838.000) ( 72d10'52.71\"W, 42d32'32.18\"N)"
[53] "Lower Left  (  731453.000, 4712471.000) ( 72d10'54.71\"W, 42d31'47.92\"N)"
[54] "Upper Right (  733150.000, 4713838.000) ( 72d 9'38.40\"W, 42d32'30.35\"N)"
[55] "Lower Right (  733150.000, 4712471.000) ( 72d 9'40.41\"W, 42d31'46.08\"N)"
[56] "Center      (  732301.500, 4713154.500) ( 72d10'16.56\"W, 42d32' 9.13\"N)"
[57] "Band 1 Block=1697x1 Type=Float64, ColorInterp=Gray"
[58] "  Min=-0.714 Max=1.000 "
[59] "  Minimum=-0.714, Maximum=1.000, Mean=0.313, StdDev=0.481"
[60] "  NoData Value=-9999"
[61] "  Metadata:"
[62] "    STATISTICS_MAXIMUM=0.99999973665016"
[63] "    STATISTICS_MEAN=0.31255246777216"
[64] "    STATISTICS_MINIMUM=-0.71362979358008"
[65] "    STATISTICS_STDDEV=0.48129385401108"                                                                                                                                                                                                                                        
  1. If this file has the same CRS as DSM_HARV? Yes: UTM Zone 18, WGS84, meters.
  2. What format NoDataValues take? -9999
  3. The resolution of the raster data? 1x1
  4. How large a 5x5 pixel area would be? 5mx5m How? We are given resolution of 1x1 and units in meters, therefore resolution of 5x5 means 5x5m.
  5. Is the file a multi- or single-band raster? Single.

Key Points

  • The GeoTIFF file format includes metadata about the raster data.
  • To plot raster data with the ggplot2 package, we need to convert it to a dataframe.
  • R stores CRS information in the Proj4 format.
  • Be careful when dealing with missing or bad data values.