Diploma of Environmental Monitoring & Technology
Study module 2
Raster data
MSS025005A
Produce Site maps
(PSM)
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Produce site maps (PSM) SM 2
Raster Data
INTRODUCTION
What is raster?
Raster versus Vector
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CHARACTERISTICS OF RASTER DATA
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DIFFERENT USES OF RASTER DATA
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Photographic base maps
Surface maps
Thematic maps
Raster as attributes
GEOREFERENCING
World files
Geo PDF
SOURCES OF RASTER DATA
Legal ways
Illegal ways
Digital Elevation Model
RESOLUTION
Spatial Resolution
Spectral resolution
DATA CONVERSION & ANALYSIS TECHNIQUES
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Raster to vector conversion
Vector to raster conversion
Raster analysis
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ASSESSMENT & SUBMISSION
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Knowledge questions
Assessor feedback
Assessment & submission rules
References & resources
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Introduction
What is raster?
You learnt in module 1 that there are two types of data used in displaying visual elements in
GIS; raster and vector. This module deals with raster data.
Raster refers to a spatial data model that defines space as an array of equally sized cells
arranged in rows and columns, and composed of single or multiple bands (grids). Each cell
contains an attribute value and location coordinates. Unlike a vector structure, which stores
coordinates explicitly, raster coordinates are contained in the ordering of the matrix. Groups
of cells that share the same value represent the same type of geographic feature.
Raster’s are made up of a matrix of pixels (also called cells), each containing a value that
represents the conditions for the area covered by that cell. In this topic we are going to take
a closer look at raster data, when it is useful and when it makes more sense to use vector
data. Raster’s are usually digital aerial photographs, imagery from satellites, digital pictures,
or even scanned maps or other documents.
Figure 2.1 - A raster dataset is composed of rows (running across) and columns (running
down) of pixels (also known as cells). Each pixel represents a geographical region, and the
value in that pixel represents some characteristic of that region.
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Figure 2.2 – An applied example showing how the pixels fit together to make an image.
Raster versus Vector
Raster data is used in a GIS application when we want to display information that is
continuous across an area and cannot easily be divided into vector features. Point, polyline
and polygon features work well for representing some features on this landscape, such as
trees, roads and building footprints. Other features on a landscape can be more difficult to
represent using vector features.
For example, grasslands have many variations in colour and density of cover. It would be
easy enough to make a single polygon around each grassland area, but a lot of the
information about the grassland would be lost in the process of simplifying the features to a
single polygon. This is because when you give a vector feature attribute values, they apply
to the whole feature, so vectors aren’t very good at representing features that are not
homogeneous (entirely the same) all over. Another approach you could take is to digitise
every small variation of grass colour and cover as a separate polygon. The problem with that
approach is that it will take a huge amount of work in order to create a good vector dataset.
Sometimes there is no choice as to how data are stored, the imagery may only be available
as a raster. However, there are many other features (such as points) and measurements
(such as rainfall) that could be stored as either a raster or a feature (vector) data type.
Following is a list of the advantages of storing data as a raster:
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A simple matrix data structure of cells being a coordinate or linked to attributes
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A powerful format for advanced spatial and statistical analysis
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The ability to represent continuous surfaces and perform surface analysis
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The ability to uniformly store points, lines, polygons, and surfaces
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The ability to perform fast overlays with complex data sets
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Storage space must also be a consideration when working with raster’s as they can be
potentially very large data sets. Resolution increases as the size of the cell decreases;
however, cost normally also increases in both disk space and processing speeds.
For a given area, changing cells to one‐half the current size requires as much as four times
the storage space, depending on the type of data and storage techniques used.
There is also a loss of precision that accompanies restructuring data to a regularly spaced
raster‐cell boundary.
Characteristics of raster data
In raster data sets, each cell (or pixel) has a value. The cell values represent the
phenomenon portrayed by the raster data set such as a category, magnitude, height, or
spectral value. The category could be a land‐use class such as grassland, forest, or road. A
magnitude might represent gravity, noise pollution, or percent rainfall. Height (distance)
could represent surface elevation above mean sea level, which can be used to derive slope,
aspect, and watershed properties. Spectral values are used in satellite imagery and aerial
photography to represent light reflectance and color (discussed later).
Cell values properties can be either positive or negative, integer, or floating point (varying
decimal point location). Integer values are best used to represent categorical (discrete) data,
and floating‐point values to represent continuous surfaces. Cells can also have a ‘NoData’
value to represent the absence of data. A cell value applies to the center point of the cell
and to the entire area of the cell, as depicted below, depending on the raster application.
Figure 2.3 – Example of values (height) that apply to the center point of a cell.
Figure 2.4 – Example of values (colour) applied to the whole of a cell.
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The area (or surface) represented by each cell consists of the same width and height and is
an equal portion of the entire surface represented by the raster. For example, a raster
representing elevation (that is, digital elevation model) may cover an area of 100 square
kilometers. If there were 100 cells in this raster, each cell would represent one square
kilometer of equal width and height (that is, 1 km x 1 km).
The dimension of the cells can be as large or as small as needed to represent the surface
conveyed by the raster data set and the features within the surface, such as a square
kilometer, square meter, or even a square centimeter. The cell size determines how coarse
or fine the patterns or features in the raster will appear.
The relationship between the size of the image and the number of pixels is referred to as
resolution. High resolution has a high pixel density; low resolution has a low pixel density.
However, the greater the number of cells, the longer it will take to process, and it will
increase the demand for storage space. If a cell size is too large, information may be lost or
subtle patterns may be obscured. For example, if the cell size is larger than the width of a
road, the road may not exist within the raster data set. In the diagram below, you can see
how this simple polygon feature will be represented by a raster data set at various cell sizes.
Figure 2.5 – The relationship between pixels and definition is termed resolution.
While the structure of raster data is simple, it is exceptionally useful for a wide range of
applications including the four main categories of base map, surface map, and thematic map
or as an attribute image.
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Different uses of raster data
Many people use raster data as a backdrop to be used behind vector layers in order to
provide more meaning to the vector information. The human eye is very good at
interpreting images and so using an image behind vector layers, results in maps with a lot
more meaning, and they are also good for representing more abstract ideas. For example,
raster’s can be used to show rainfall trends over an area, or to depict the fire risk on a
landscape. In these kinds of applications, each cell in the raster represents a different value
such as the risk of fire on a scale of one to ten.
Photographic base maps
By far, the most common use of raster data that you will use is by way of aerial or satellite
imagery. This is literally photo’s, the very same that you would take with your own camera,
their just taken from a plane or from a satellite. This type of raster data is used in a GIS is as
a background display for other feature layers. For example, ortho-photographs
(geometrically corrected) displayed underneath other layers provide the map user with
confidence that map layers are spatially aligned and represent real objects, as shown in the
image below. Three main sources of raster base maps are;
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ortho-photos from aerial photography
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satellite imagery
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scanned maps
Figure 2.6 – Example of an ortho-photo used as a base map in GIS
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Surface maps
Raster’s are well suited for representing data that changes continuously across a landscape,
which, in GIS (or geometry) we call a surface. They provide an effective method for storing
the continuity as a surface. They also provide a regularly spaced representation of surfaces.
Elevation values measured from the Earth’s surface are the most common application of
surface maps, as depicted in the figure below. Other values, such as rainfall, temperature,
concentration, and population density, can also define surfaces that can be spatially
analyzed.
Figure 2.7 – Example of a surface map. In this case, the surface is height data for the
continent of Australia.
Surfaces represent phenomena that have values at every point across their extent. The
values at the infinite number of points across the surface are derived from a limited set of
sample values. These may be based on direct measurement, such as height values for an
elevation surface, or temperature values for a temperature surface; between these
measured locations, values are assigned to the surface by interpolation (constructing new
data between two existing data points).
Surfaces can also be mathematically derived from other data, such as slope and aspect
surfaces derived from an elevation surface, a surface of distance from bus stops in a city, or
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surfaces showing concentration of criminal activity or probability of lightning strikes. In
modern GIS platforms, surfaces can be constructed using the following formats;
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Contours
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Points
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Triangular Irregular Network (TINs)
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Surfaces
Thematic maps
Rasters representing thematic data can be derived from analyzing other data. A common
analysis application is the classification of a satellite image by land‐cover categories.
Basically, this activity groups the values of multispectral data into classes and assigns a
categorical value.
Thematic maps can also result from geo-processing operations that combine data from
various sources, such as vector, raster, and terrain data. For example, a user can process
data through a geo-processing model to create a raster data set that maps suitability for a
specific activity.
Figure 2.8 – Example of a thematic map showing the biogeography of Australia.
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Raster as attributes
This is not something that you are likely to use, but raster’s can be used as attributes of a
feature which may be digital photographs, scanned documents, or scanned drawings related
to a geographic object or location.
A parcel layer may have scanned legal documents identifying the latest transaction for that
parcel, or a layer representing cave openings may have pictures of the actual cave. One
example seen when using NSW LPI is found with parish information.
Georeferencing
Georeferencing is the process of defining exactly where on the Earth’s surface an image or
raster dataset was created. This positional information is stored with the digital version of
the aerial photo. When the GIS application opens the photo, it uses the positional
information to ensure that the photo appears in the correct place on the map. Normally this
positional information consists of;
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a coordinate for the top left pixel in the image
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the size of each pixel in the X direction
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the size of each pixel in the Y direction
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the amount (if any) by which the image is rotated
With these few pieces of information, the GIS application can ensure that raster data are
displayed in the correct place.
World files
The georeferencing information for a raster is often provided in a small text file
accompanying the raster called a world file.
Geo PDF
Another common type of georeferencing is found with Adobe GeoPDF documents which are
the PDF equivalent of world files for images.
Sources of raster data
Legal ways
Raster data can be obtained in a number of ways. Two of the most common ways are;
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aerial photography
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satellite imagery
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Scanned maps or documents
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In aerial photography, a plane flies over an area with a camera mounted underneath it. The
photographs are then imported into a computer and georeferenced.
Satellite imagery is created when satellites orbiting the earth point special digital cameras
towards the earth and then take an image of the area on earth they are passing over. Once
the image has been taken it is sent back to earth using radio signals to special receiving
stations.
The process of capturing raster data from a plane or satellite is called remote sensing.
In other cases, raster data can be computed. For example meteorologists might generate a
local level raster showing average temperature, rainfall and wind direction using data
collected from weather stations. In these cases, they will often use raster analysis
techniques such as interpolation or kriging.
Sometimes raster data are created from vector data because the data owners want to share
the data in an easy to use format. For example, a company with road, rail, cadastral (land
tenure) and other vector datasets may choose to generate a raster version of these datasets
so that employees can view these datasets in a web browser. This is normally only useful if
the attributes, that users need to be aware of, can be represented on the map with labels or
symbology. If the user needs to look at the attribute table for the data, providing it in raster
format could be a bad choice because raster layers do not usually have any attribute data
associated with them. Common sources of raster based data in Australia include the
following.
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Geoscience Australia
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Find environmental data portal
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Data.gov.au
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NSW Land & Property Information
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NSW natural resource atlas
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CANRI
Illegal ways
Obviously this is wrong, and you will never do it in the workplace as they will pay for legal
copies of imagery, but while you’re a student, sometimes you need to just get things done
so that you can understand how the whole thing works.
In short, find an image provider online, such as Google Earth, take a screenshot, load into a
GIS application and georeference it using the built in tools. Be aware that you will always be
in breach of copyright when you do this!
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Digital Elevation Model
Digital Elevation Models (DEMs) are a type of raster GIS layer. Raster GIS represents the
world as a regular arrangement of locations. In a DEM, each cell has a value corresponding
to its elevation. The fact that locations are arranged regularly permits the raster GIS to infer
many interesting associations among locations:
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Which cells are upstream from other cells?
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Which locations are visible from a given point?
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Where are the steep slopes?
One of the most powerful applications of DEMs is adding synthetic hill shading to maps so
that the map reader may see the relationship between terrain and other things you may be
mapping.
Geoscience Australia have a section called the Digital Elevation Data page which allows
users to obtain DEM data for Australia.
Resolution
Spatial Resolution
Every raster layer in a GIS has pixels (cells) of a fixed size that determine its spatial
resolution. This becomes apparent when you look at an image at a small scale and then
zoom in to a large scale.
Figure 2.9 - This satellite image looks good when using a small scale
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Figure 2.10 - When viewed at a large scale you can see the individual pixels that the image is
composed of.
Several factors determine the spatial resolution of an image. For remote sensing data,
spatial resolution is usually determined by the capabilities of the sensor used to take an
image. For example SPOT5 satellites can take images where each pixel is 10m x 10m. Other
satellites, for example MODIS take images only at 500m x 500m per pixel. In aerial
photography, pixel sizes of 50cm x 50cm are not uncommon.
Images with a pixel size covering a small area are called ’high resolution’ images because it is
possible to make out a high degree of detail in the image. Images with a pixel size covering a
large area are called ’low resolution’ images because the amount of detail the images show
is low.
In raster data that is computed by spatial analysis (such as the rainfall map we mentioned
earlier), the spatial density of information used to create the raster will usually determine
the spatial resolution. For example if you want to create a high resolution average rainfall
map, you would ideally need many weather stations in close proximity to each other.
One of the main things to be aware of with raster’s captured at a high spatial resolution is
storage requirements. Making the pixel size smaller would greatly increase the amount of
storage needed.
Sometimes using a low spatial resolution is useful when you want to work with a large area
and are not interested in looking at any one area in a lot of detail. On the other hand, using
low resolution raster data can be problematic if you are interested in a small region because
you probably won’t be able to make out any individual features from the image.
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Spectral resolution
If you take a colour photograph with a digital camera or camera on a mobile phone, the
camera uses electronic sensors to detect red, green and blue light. When the picture is
displayed on a screen or printed out, the red, green and blue (RGB) information is combined
to show you an image that your eyes can interpret. While the information is still in digital
format though, this RGB information is stored in separate colour bands.
Whilst our eyes can only see RGB wavelengths, the electronic sensors in cameras are able to
detect wavelengths that our eyes cannot. Of course in a hand held camera it probably
doesn’t make sense to record information from the non-visible parts of the spectrum since
most people just want to look at pictures of their dog or what have you. Raster images that
include data for non-visible parts of the light spectrum are often referred to as multispectral images. In GIS recording the non-visible parts of the spectrum can be very useful.
For example, measuring infra-red light can be useful in identifying water bodies.
Because having images containing multiple bands of light is so useful in GIS, raster data are
often provided as multi-band images. Each band in the image is like a separate layer. The
GIS will combine three of the bands and show them as red, green and blue so that the
human eye can see them. The number of bands in a raster image is referred to as its
spectral resolution.
If an image consists of only one band, it is often called a grayscale image. With grayscale
images, you can apply false colouring to make the differences in values in the pixels more
obvious. Images with false colouring applied are often referred to as pseudo-colour images.
Data conversion & analysis techniques
Raster to vector conversion
It was explained that often raster data are used as a backdrop layer, which is then used as a
base from which vector features can be digitised.
Another approach is to use advanced computer programs to automatically extract vector
features from images. Some features such as roads show in an image as a sudden change of
colour from neighbouring pixels. The computer program looks for such colour changes and
creates vector features as a result. This kind of functionality is normally only available in
specialised (and often expensive) GIS software.
Vector to raster conversion
Sometimes it is useful to convert vector data into raster data. One side effect of this is that
attribute data (that is attributes associated with the original vector data) will be lost when
the conversion takes place. Having vectors converted to raster format can be useful though
when you want to give GIS data to non GIS users. With the simpler raster formats, the
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person you give the raster image to can simply view it as an image on their computer
without needing any special GIS software.
Raster analysis
There are a great many analytical tools that can be run on raster data which cannot be used
with vector data. For example, raster’s can be used to model water flow over the land
surface. This information can be used to calculate where watersheds and stream networks
exist, based on the terrain.
Raster data are also often used in agriculture and forestry to manage crop production. For
example with a satellite image of a farmer’s lands, you can identify areas where the plants
are growing poorly and then use that information to apply more fertilizer on the affected
areas only. Foresters use raster data to estimate how much timber can be harvested from
an area.
Raster data is also very important for disaster management. Analysis of Digital Elevation
Models can then be used to identify areas that are likely to be flooded. This can then be
used to target rescue and relief efforts to areas where it is needed the most.
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Assessment & Submission
This section provides formative assessment of the theory. Answer all questions by typing
the answer in the boxes provided. Speak to your teacher if you are having technical
problems with this document.
Knowledge questions
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Type brief answers to each of the questions posed below.
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All answers should come from the theory found in this document only unless the
question specifies other.
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Marks shown next to the question should act as a guide as to the relative length or
complexity of your answer.
1. Generally speaking, what is a raster? 2mk
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2. List three advantages of raster data. 1mk
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3. Every pixel in a raster has a value.
a. Provide three examples of possible value categories. 1mk
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b. Provide three examples of possible numerical properties of values. 1mk
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4. What is the relationship between the pixel width and height and the real world? 4mk
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5. What are four common uses of raster data? 4mk
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6. What is meant by the term ‘georeferencing’? Why is it needed? 2mk
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7. List the two types of georeferencing techniques used (by us). 2mk
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8. Where can we (legally) acquire raster data in Australia? 1mk
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9. What is a DEM? 2mk
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10. What is the difference between ‘spatial’ and ‘spectral’ resolution? 6mk
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Assessor feedback
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Assessment & submission rules
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Attempt all questions and tasks
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Write answers in the text-fields provided
Submission
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Use the documents ‘Save As…’ function to save the document to your computer using
the file name format of;
Yourname-PSM-SM-2
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email the document back to your teacher
Penalties
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If this assessment task is received greater than seven (7) days after the due date, it may
not be considered for marking without justification.
Results
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Your submitted work will be returned to you within 3 weeks of submission by email fully
graded with feedback.
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You have the right to appeal your results within 3 weeks of receipt of the marked work.
Problems
If you are having study related or technical problems with this document, make sure you
contact your assessor at the earliest convenience to get the problem resolved. The contact
details can be found at;
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www.cffet.net/env/contacts
References & resources
Resources
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www.mapwindow.org/
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www.esri.com/
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http://resources.arcgis.com/en/help/
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www.qgis.org/
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www.gislounge.com/learn-gis-for-free/
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References
Note that some of these resources might be available from your teacher or library
Bailey, D. W. (2003). Practical SCADA for Industry. Sydney: Newnes, Elsevier.
Brimicombe, A. (2010). GIS, Environmental Modelling & Engineering. 2nd Ed. Boca Raton:
CRC press.
Burden, F. E. (2002). Environmental Monitoring Handbook. McGraw-Hill Professional.
Ferrier, R. C. (2010). Handbook of Catchment Management. Oxford: Wiley-Blackwell.
Newton, A. (2007). Forest Ecology and Conservation. Oxford: Oxford University Press.
Schneider, R. R. (2011). MapWindow: Quick Start Tutorial. MapWindow 4.8.6. Edmonton:
Free Software Foundation.
Sutherland, W. (2006). Ecological Census Techniques. 2nd Ed. Cambridge: Cambridge
University Press.
Sutton, T. E. (2009). A Gentle Introduction to GIS. Eastern Cape, South Africa: Chief
Directorate: Spatial Planning & Information, Department of Land Affairs, Eastern
Cape.
.
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