Intro to Raster Data (Advance)
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Questions
- What is a raster dataset?
- How can I handle missing or bad data values for a raster?
Objectives
- Explore raster attributes and metadata using R.
- Describe the difference between single- and multi-band rasters.
R
library(terra)
library(ggplot2)
library(dplyr)
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.
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.
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
nly() function.
R
nlyr(DSM_HARV)
OUTPUT
[1] 1However, 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. By default the raster() function only imports the
first band in a raster regardless of whether it has one or more bands.
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.
OUTPUT
The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
which was just loaded, will retire in October 2023.
Please refer to R-spatial evolution reports for details, especially
https://r-spatial.org/r/2023/05/15/evolution4.html.
It may be desirable to make the sf package available;
package maintainers should consider adding sf to Suggests:.
The sp package is now running under evolution status 2
(status 2 uses the sf package in place of rgdal)
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.
R
describe(sources(DSM_HARV))
OUTPUT
[1] "Driver: GTiff/GeoTIFF"
[2] "Files: /home/runner/work/DTRA_workshop_GIS/DTRA_workshop_GIS/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:
- Does this file have the same CRS as
DSM_HARV? - What is the
NoDataValue? - What is resolution of the raster data?
- How large would a 5x5 pixel area be on the Earth’s surface?
- 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" - If this file has the same CRS as DSM_HARV? Yes: UTM Zone 18, WGS84, meters.
- What format
NoDataValuestake? -9999 - The resolution of the raster data? 1x1
- 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.
- Is the file a multi- or single-band raster? Single.