A Canopy Height Model (CHM) is a regular gridded raster representing the height of vegetation above the ground. The first step in creating the CHM is classifying the vegetation as a separate feature. Significant additional work may be required to reclassify buildings and other structures so that the vegetation classification is accurate. The CHM is calculated as the difference between the DSM and the DEM, and therefore gives the actual height of vegetation above the ground.
Another parameter that can be used in conjunction with the Canopy Height Model is Foliage Density. These two datasets are both easy to use and combine to provide details of the height and density of vegetation. They are two very useful parameters for anyone carrying out environmental analysis of vegetation.
Canopy Height Model of a lowland coastal area which was used in conjunction with the Foliage Density Model to develop an environmental management plan for the area.
A LiDAR survey produces a large mass of points known as a Point Cloud. A Classified Point Cloud is simply the point cloud classified into feature types. At a minimum, the point cloud is classified into ground and non-ground. The data can then be further classified into features such as powerlines, buildings and vegetation.
Classified LiDAR points can be provided as LAS, DWG or ascii XYZ formats. If provided as ascii, the data can be provided as separate files for each class (eg ground / non-ground/) or an additional field can be included – x,y,z,c – where “c” is the classification field.
Classified High Definition LiDAR Point Cloud. The green points represent classified vegetation encroaching on the 11kV power line represented by the purple points. The yellow and red points represent a nearby building and car. The ground is represented by a regular DEM.
A Digital Elevation Model or DEM represents the ground as a regular grid of points. A DEM is an interpolation of a Digital Terrain Model or DTM. A DTM is the irregular network of LiDAR points which have hit the ground and is often a very large file. The DEM interpolated from this DTM is a much smaller file and is suitable for use in the majority of applications. There are some minor losses in accuracy when a DEM is interpolated from a DTM.
In summary a DEM is slightly less accurate than a DTM but has smaller file sizes and generally can be used by virtually all GIS, Design and CAD packages.
A DEM can also be viewed as a relief shaded image which gives the user a good visual interpretation of the terrain.
Relief shaded 1 meter resolution Digital Elevation Model (DEM) of a low lying coastal area. Despite having to penetrate thick vegetation the DEM shows the terrain details, including the foreshore, in intricate detail.
A Digital Surface Model or DSM is the surface created by the LiDAR pulse’s first return, that is, the tops of trees, buildings and sometimes the ground. Terranean can supply this surface as a regular grid.
Digital Surface Model of an urban area showing the tops of houses, infrastructure and vegetation.
The very dense irregular network of LiDAR ground points which make a terrain surface known as a Digital Terrain Model or DTM. A DTM produced from Terranean’s scanner is extremely detailed and is usually a very large file that can only be used by specialized software or by being broken into small files. Terranean can reduce the size of a DTM by up to 90% by generating a Filtered DTM or interpolating a Digital Elevation Model or DEM. In summary:
- Detailed DTM - comprises of very detailed and accurate ground points in large files which are generally usable by selected design packages over small areas only;
- Filtered DTM – up to 90% of points are removed but the integrity of the ground is maintained and the data is usable by most design packages over larger areas;
- Digital Elevation Model (DEM) – a regular grid that is interpolated from the DTM with a small loss of accuracy, but is very manageable and easy to use by virtually all GIS and engineering packages over large areas.
The Foliage Density Model is a grid that indicates the percentage of LiDAR points in the vegetation in comparison to those that hit the ground. It is an accurate measure of foliage density.
Another parameter that can be used in conjunction with Foliage Density is the Canopy Height Model (CHM). These two datasets are both easy to use and combine to provide details of the height and density of vegetation. They are two very useful parameters for anyone carrying out environmental analysis of vegetation.
Foliage Density Model of a forestry plantation that was used in conjunction with the Canopy Height Model to map the health of the trees.
Geo-coding is the process of taking a normal textual record from a database or spreadsheet and finding its position on a map. To do this, the record must have a “spatial key” such as a postcode number or street address. For instance, a listing of survey results that includes postcodes can be mapped to the position of those postcodes. If the same survey included street addresses, then the database records could be mapped to the position of the actual houses.
Geo-coding is often the first step in loading data into a GIS, so
that it can be integrated with other mapping data and spatially analysed.
Geo-PDF images are intelligent maps that can be viewed by the free software Adobe Reader. By using Adobe Reader to display Geo-PDF maps one can perform the following functions:
Display, pan, zoom, layer control, attribute attachment, query, search, geo-measure, geo-reference, GPS track, markup, redline annotate, comment, create forms and print.
Aligning spatial data to a known coordinate system, so that it can be viewed, queried, and analyzed in relation to other spatial data. Geo-referencing may involve shifting, rotating, scaling, skewing, and in some cases warping (or rubber sheeting) the data.
Geo-processing is a GIS operation used to manipulate spatial data. A typical geo-processing operation takes an input dataset, performs an operation on that dataset, and returns the result of the operation as an output dataset. Geo-processing allows for definition, management, and analysis of information used to form decisions.
Geo-spatial Modelling uses geo-processing to develop models that are:
- An abstraction and description of reality used to represent objects, processes, or events.
- A set of clearly defined analytical procedures used to derive new information from input data.
- A set of rules and procedures for representing a phenomenon or predicting an outcome.
In a generic sense, GIS is a "smart map" tool that allows users to create interactive queries, and analyse and edit map information. In a more rigorous sense a GIS can be described as a system for managing map data and associated attributes. It is a computer system capable of integrating, storing, editing, analyzing, and displaying geographically-referenced information.
A LiDAR (Light Detection And Ranging) system rapidly transmits laser pulses that reflect off the terrain and other landscape features such as infrastructure and vegetation. The return pulse is converted to electrical impulses and collected by a high-speed data recorder. Because the speed of light is known and the time interval of the pulse from transmission to return has been measured, the range or distance can be derived.
Based on positional information obtained from ground/aircraft GNSS receivers, and the on-board Inertial Measurement Unit (IMU) that continually records the attitude (pitch, roll, and heading) of the aircraft, this range data is then used to calculate the xyz terrestrial coordinates of the return laser pulses. LiDAR systems collect positional (x,y) and elevation (z) data at pre-defined intervals. The resulting LiDAR data is a very dense network or point cloud of x,y,z points that define the ground and the features on the ground.
Historically, LiDAR has returned only several discrete measurements from each laser pulse emitted from the scanner. The first return represents the first object that the pulse hits, for instance a tree, house or the ground. The last return is generally the ground. Terranean's high definition scanner has the ability to capture over five discrete returns from each pulse. More importantly, a full waveform digitisation of intensity for the entire time the pulse is in flight is also recorded. This full waveform representation of the complete data received for each pulse is thus able to give a much greater insight into the journey and features encountered by each pulse rather than the simplistic representation of several discrete returns. This powerful feature is called Full Waveform digitizing and results in highly detailed mapping of features on the ground. It is ultimately far more comprehensive than the limited data provided by traditional discrete LiDAR.
The irregular network of LiDAR points which hit the ground make a surface known as a Digital Terrain Model or DTM. A DTM produced from Terranean’s scanner is extremely detailed and is usually creates a very large file. A LiDAR DTM can be reduced in size by filtering or it can be reprocessed into a regular grid called a Digital Elevation Model or DEM.
As described above the entire mass of points produced by LiDAR, including the points between the first and last return, is known as the point cloud. All points above the ground or DTM are known as above-ground points. These points can be classified into different features such as vegetation and infrastructure.
There are a number of factors that influence the usefulness and applicability of LiDAR data. These include:
- Point density
- Target discrimination distance
- Relative accuracy
- Absolute accuracy
Point Density describes the number of points that hit the surface and is a function of the frequency of the laser scanner and flying height. As a general rule, the greater the number of points, the greater the definition of the surface being mapped. This is particularly true where vegetation exists because as few as one in 10 points will penetrate thick vegetation. Specifications for low accuracy/low detail surveys typically require 1 – 2 points/sq m. and high accuracy/high detail projects require up to 15 points/sq m.
It should be noted that point density is generally defined as the number of points per square meter in non-overlap areas between flight runs. If the double coverage of overlap areas between runs is included in the calculation of point density, this artificially raises the average number of points/sq meter and gives a deceptive indication of the accuracy/detail that will be achieved.
Target Discrimination Distance
Target discrimination distance has a critical effect on the ability to discriminate between objects. A low discrimination distance adds enormously to the quality and detail of data collected. For instance a LiDAR scanner with a discrimination distance of three meters will not distinguish a one meter fence on the ground.
Discrimination Distance is maximized with wave form digitizing and a short laser pulse length. Terranean’s scanner has both of these features and consequently has a very low discrimination distance of half a meter which results in extremely high definition. For instance one laser pulse from Terranean’s scanner that hits a 2.5 m tree will return up to four LiDAR returns within the tree and one for the ground. In contrast a scanner with a 3 m discrimination distance, which is the norm, will only return one point from the first point it hits on the tree.
A low discrimination distance facilitates the detailed modeling of vegetation and infrastructure
Relative and Absolute Accuracy
Relative accuracy is the accuracy between any two given points in the point cloud. Terranean’s high definition scanner has a relative accuracy of approximately 15mm. For example, in a powerline survey, that is not tied to the ground by survey control, one could still expect the accuracy between two points, for example vegetation and conductors, to be in the order of 15mm.
Absolute accuracy is the accuracy of any point in the LIDAR point cloud in relation to a real world coordinate system. Terranean’s survey procedures, without ground control, result in a vertical absolute accuracy of approximately 0.5 meter. This figure can be enhanced by survey control and flying extra LiDAR strips called cross strips. Depending on the quality of the control and the number of cross-strips, absolute vertical accuracy can be reduced to as little at 2.5cm.
Laser scanners are becoming increasingly specialized and each has it’s special characteristics and associated optimum set of applications. Terranean’s scanner has a high point density, short pulse length and wave form digitizing and is ideally suited to mapping terrain, infrastructure and vegetation in exceptional detail and accuracy.
Terranean can provide a range of LiDAR derived products which include:
A LiDAR Intensity Image is a grey-tone image showing the total intensity of the LiDAR return signal. The quality of the image is not high but it is sufficient to see vegetation, roads, buildings etc and can be used as a background image in the absence of aerial photography.
As a rule of thumb, the resolution of the intensity image should be about half the LiDAR point spacing. Therefore if the LiDAR point density is 4 pts/sq. m, the point spacing is 0.5m and the resolution of the intensity image will be 1 metre.
Intensity images can be provided as TIFF, JPEG or any common image format.
LiDAR Intensity image, which in the absence of aerial photography can be used as a useful background image that defines features, such as vegetation, buildings and roads.
Map Projections display the curved surface of the Earth as a flat surface. This generally requires a systematic mathematical transformation of the Earth's graticule of lines of longitude and latitude onto a plane. This can be visualized as a transparent globe with a central light bulb that casts lines of latitude and longitude onto a sheet of paper. Generally, the paper is either flat and placed tangential to the globe (a planar or azimuthal projection) or formed into a cone or cylinder and placed over the globe (cylindrical and conical projections). Every map projection distorts distance, area, shape, direction, or some combination thereof.
- Maps of adjacent areas with the same projection and scale, whose boundaries have been matched and dissolved.
- A raster dataset that is composed of two or more merged raster datasets, for example, one image created by merging several individual images or photographs of adjacent areas.
Infrared imaging is one method of using wavelengths other than visible light to gather information about Earth. Most satellites and airborne sensors measure energy at many wavelengths, which is called multi-spectral imaging. Images taken at different wavelengths can be combined to make composite images by displaying the image for each wavelength as red, green, or blue in the final image. These composite images result in color patterns that can be used to identify features invisible to the normal visible wavelengths of light.
A digital ortho-rectified image is a satellite or aerial photographic image that has been digitally corrected to ensure ground features are depicted in their correct geographic location. While satellite or aerial photographic images are produced as accurately as possible, misrepresentations do occur due to factors such as the position of the sensor or camera at the time of capture. These misrepresentations are rectified through this technique, resulting in a true depiction of the terrain that enables precise distance and area measurements to be taken from the image.
A digitally ortho-rectified image offers all the benefits of the original image, and delivers a feature-rich picture of the Earth's surface, combined with the highly accurate spatial data of a map. Many users overlay ortho-rectified images with traditional data, such as zoning, utilities or property ownership maps, in order to comprehensively view and analyse this data in the context of the surrounding landscape.
Photogrammetry is the science of making reliable measurements of physical objects and the environment, by measuring and plotting data from aerial photographs and remote-sensing systems against land features identified in ground control surveys. Generally, this is performed in order to produce planimetric, topographic, and contour maps.
Remote Sensing satellites carry two types of sensor systems known as “active” and “passive”. A “passive” system generally consists of an array of small sensors or detectors that record (as digital numbers) the amount of electro-magnetic radiation reflected and emitted from the Earth's surface. SPOT is an example of a passive system. An “active” system propagates its own electro-magnetic radiation and measures (as digital numbers) the intensity of the return signal. Synthetic Aperture Radar (SAR) is an example of an active system.
The smallest unit of information in a satellite image or raster map is a pixel, which is usually square or rectangular in shape. The term pixel is often used synonymously with the term, cell. Pixel size in a given image correlates to the degree of detail or resolution of that image.
Images with different resolution are complementary to one another. Some users require highly detailed imagery, while others need only large-area coverage. Higher resolution images, showing more detail, are ideal for applications such as transportation network mapping, disaster preparedness, urban planning, precision farming, and telecommunications. Lower resolution images are useful for environmental assessment, regional mapping, forestry management, widespread disaster assessment, and urban monitoring.
The following images show the San Francisco International Airport, and illustrate how much detail can be seen in satellite images with different resolutions.
As you look from left to right, these aerial and satellite images show a decreasing amount of detail, as the resolution reduces. That is, one-meter resolution imagery shows significantly more detail than 25-meter resolution imagery. In the one-meter image you can see planes on the tarmac and cars in the parking lot. In the lowest resolution image on the far right, you can only see the vague outline of the airport jutting out into San Francisco Bay.
Simulated resolution comparison of San Francisco Airport at the same scale. The images below show what you would see if you were looking at a single object, for instance an airplane, at exactly the same scale but at different resolutions.
One-meter This image was collected by an airplane camera and has a resolution of one meter. It simulates IKONOS satellite imagery.
Five-meter This image was collected by the Indian Remote Sensing (IRS) satellite and has a resolution of five meters.
25-meter This image was collected by the U.S. Landsat satellite and has a resolution of 25 meters.
One-Meter This one-meter resolution picture simulates an IKONOS satellite image. It is zoomed-in to focus on two airplanes parked at the concourse.
Five-Meter This is the same picture, but it has been altered to simulate what you would see in a five-meter resolution image. The airplanes appear blurry at this resolution.
25-Meter At 25-meter resolution, this same picture is unreadable. The airplanes are not recognisable; however, for large-area analysis, this level of detail is satisfactory.
Commonly expressed as a ratio or a number, scale refers to the ratio or relationship between a distance or area on a map and the corresponding distance or area on the ground. A map scale of 1:100,000 or 100K means that one unit of measure on the map equals 100,000 of the same unit on the earth i.e. 1cm on the map is equal to 100m on the ground.
The description of scale is somewhat counter-intuitive. A large-scale map is detailed and covers a relatively small area. A small-scale map covers a wide area and is quite general in the level of detail.
Detailed or large-scale mapping is typically in the range of 1:1,000 to 1:25,000 (25K).
Medium-scale mapping is typically in the range of 1:50,000 to 1:250,000 (250K).
Broad-coverage or small-scale mapping is generally described as smaller than 1:250,000 (250K), for example 1:1,000,000.
Spatial is a contemporary term used to describe the field of mapping; other terms with similar meaning include geo-spatial, location-based and geographic. Spatial Technologies is a generic term used to describe technologies that capture, process, store, access, and analyse spatial information.
Spatial data refers to any data that can be mapped. There are two fundamental types of digital spatial data, Raster and Vector.
Raster data is a digital image made up of pixels. Satellite images or scanned maps are examples of raster data.
Vector data represents spatial features as points, lines, and boundaries (polygons). Cadastral boundaries and road centerlines are examples of vector data.
Attribute data is tabular or textual data that describes the geographic characteristics of map features. In vector data, attributes are associated with each feature. For instance, the attribute data of a census boundary might include the population of people living within that boundary. In raster data, attributes are associated with each pixel. For instance a group of dark green pixels could signify an area of dense vegetation.
Meta data are the parameters that describe a dataset. Typical meta-data includes the source, scale, accuracy, and currency of a particular dataset.
See the Spatial Data Section for the wide range of off-the-shelf spatial data available from Terranean.