Sometimes commonly known as quantitative geomorphology, digital terrain modeling is thecomputer processing of raster grid arrays of elevation data. Using Geographic information system (GIS) technology we can further enable terrain-modeling results to be combined with non topographic spatial data creating several value added products.

Terrain Models are derived from data represented by digital elevation models (DEMs) and can include shaded relief models,
slope and aspect models, perspective scene generation, and drainage basin analysis (and other models).

The physiogeographic characteristics of a surface can often be determined by elevation, slope, and its orientation, or aspect. Together they can virtually define the surface plane completely, and provide valuable information for land use planning and other aspects of geomatics.

Traditional images in geomatics are often two dimensional, meaning that all data in the image can be referenced by X and Y coordinates. Three dimensional images (3-D) incorporate a third dimension (the Z component) which represents the elevation or depth aspect of the data. To incorporate it into an image requires creating special geomatics value added products that allow users to perceive the presence of the third dimension into a traditional two dimensional setting
(because most paper and computer screens are flat or two dimensional).

In GIS applications it is often beneficial to add a texture component to the spatial data that will help the user get a feeling for the vertical depth of the data by emphasizing the elevation. To do this you need to create a shaded relief model from the DEM to model into the data.

Shaded relief models use a defined light source at a fixed location it indicate terrain displacements using a shadow effect from evaluating the aspect and slope relative to the light’s azimuth angle and altitude achieved with varying grey scale tones resulting in the darkening of one side of terrain features, such as hills and ridges (the darker the shading, the steeper the slope).

The shadow direction is affected by the light’s azimuth setting and shadow length is affected by the altitude component. The models provide subtle shadings which we naturally perceive as depth, helping to make the image look three dimensional. A drawback with this type of model is that depending on the placement of the illumination source, the eye and brain often see different things. Adding color to the shaded relief images utilizes chromo stereoscopic techniques to help emphasize the depth of the Z dimension of the data.

Color shaded relief models (CSR) are usually graded with a pseudo color ramp from cooler (darker) colors representing
lower elevations to warmer (brighter) colors depicting greater elevations. Most imagery and data that we view in geomatics is typically viewed vertical downwards toward the map or image. Occasionally it is useful to change that default traditional view because additional topographic information can often be revealed by observing the same elevation data obliquely (commonly known as a three dimensional perspective view). Data integration and overlays are very common with perspective views because it allows traditional flat images to become new products by incorporating an elevation component and providing a new look at the same data.  It is also probably used more so for visual appeal then as another method of extracting data.

The image above and to the left is a perspective view of a color shaded relief created from high resolution LIDAR using PCI Geomatica software. The oblique angle view point looking down at the image allows the observer to easily identify many of the data’s features such as trees, cars and buildings.

The perspective scene in the image on the left is a representation of an urban terrain model including buildings and other various features. It was created using ESRI ArcScene software and high resolution LIDAR digital point data. The artificial oblique view allows the observer to obtain a unique glimpse from above looking down in a southerly direction towards City Hall of downtown Toronto, Ontario.

More information on Terrain modeling and examples

• MacKinnon E (2003) Surface Modeling and LIDAR Validation Middleton, Nova Scotia: Applied Geomatics Research Group, Centre of Geographic Sciences, 49 pages
• MacKinnon E (2004) Three Dimensional Flood Modeling with High Resolution LIDAR (Graduate Thesis) Middleton, Nova Scotia: Applied Geomatics Research Group, Centre of Geographic Sciences, 200 pages
• MacKinnon E (2005) Three Dimensional Flood Modeling with High Resolution LIDAR (2005 CIG Conference Proceeding) Ottawa, Ontario:Canadian Institute of Geomatics, 8 pages
• MacKinnon E, Sangster F & Hynes D (1999)  Geological Data Integration: Makkovik, Labrador Lawerncetown, Nova Scotia: Centre of Geographic Sciences, 12 pages
• MacKinnon E (2004)  3D Modeling with High Resolution LIDAR - presented at the GeoTec Conference in Toronto,Ontario and displayed at the Applied Geomatics Research Group in Middleton, Nova Scotia
• MacKinnon E (2004)  Bouctouche, NewBrunswick – Color Shaded Relief - presented at CCAF annual general meeting held at the University of Moncton in Moncton, New Brunswick, and displayed at the Applied Geomatics Research Group in Middleton, Nova Scotia
• MacKinnon E (2004) FLood Simulation Modeling with High Resolution LIDAR - presented at CCAF annual general meeting held at the University of Moncton in Moncton, New Brunswick, and displayed at the Applied Geomatics Research Group in Middleton, Nova Scotia
• McCurdy C, MacKinnon E & Lynds T (1999) Integration of Digital elevation Models and Imagery : Terrain Analysis of the Antigonish Highlands  - presented at the Center of Geographic Sciences in Lawrencetown, Nova Scotia

• Digital Elevation Models (DEMs)
• Shaded Relief Models
• Color Shaded Relief Models (CSR)
• Creating a CSR model in Geomatica v9.1
• Slope Models
• Aspect Models
• Perspective View Models

Basic overview of Spatial Data base design

The second part of the project was focused on generating a GIS spatial database of the vegetation found within each campsite that was collected during a Rapid Vegetation
Assessment (RVA) Survey.

If you do not fully understand the fundamentals of spatial database design and management, then you may never unleash the power of GIS. Behind almost all colorful maps that you see around is a complex data management framework and structured spatial data that required special attention to issues of scale, accuracy and projection issues.

A database can be defined as a collection of interrelated information or data, managed and stored together as a collective unit. A GIS spatial database is a database that includes collections of information about the spatial location, relationship and shape of topological geographic features and the data in the form of attributes.

The design of the spatial database is the formal process of analyzing facts about the real world into a structured model. Database design is characterized by the following phases: requirement analysis, logical design and physical design. In more common terms, you basically need a plan, a design layout and then the data to complete the process.

Having a solid well designed spatial database is the key to performing good Spatial Analysis. The database can be complex and designed with expensive sophisticated software or can be merely a simple well organized collection of data that can be utilized in a geographic form.

Three main categories of spatial modeling functions that can be applied to geographic features within a GIS are: (1) geometric models, such as calculating the Euclidean distance between features, generating buffers, calculating areas and perimeters, and so on; (2) coincidence models, such as topological overlay; and (3) adjacency models (path finding, redistricting, and allocation). All three model categories support operations on spatial data such as points, lines, polygons, tins, and grids. Functions are organized in a sequence of steps to derive the desired information for analysis.

Examples of GIS Spatial Database and Modeling

• MacKinnon E (2004)
  
  Spatial GIS Vegetation Database and GIS Spatial Modeling for the Jeremy’s
  Bay Campground of Kejimkujik National Park and Historic Site.

The slope or the gradient of a straight line within a Cartesian coordinate system is known as the measure of how steep a line is relative to the horizontal axis.

In calculations; it is generally represented by the letter m, and defined as the change in the Y coordinate divided by the corresponding change in the X coordinate, between two distinct points on the line (X1, Y1 and X2, Y2). Since the Y axis is vertical and the X axis is horizontal by convention, slope is often referred to as the rise over the run or the change in the vertical coordinates, divided by the change in the horizontal coordinates.

Basically, the larger the slope value, the steeper the line is. A horizontal line has a slope of 0, a 45 degree line has a slope of 1, and the slope of a vertical line is typically undefined. In trigonometry two lines are considered to be parallel if and only if their slopes are equal or if they both are vertical and therefore undefined. Two lines are considered to be perpendicular if and only if the product of their slopes is -1 or one has a slope of 0 and the other is vertical and undefined.

There are two common ways to describe slope. One method is to use the angle of the slope in degrees (0 to 90), and the other is to represent the slope as a percentage (0 to 100). Expressing slope as a percent is common but can be confusing because a percent slope can be greater then 100%. A 100% slope is actually only a 45 degree angle due to the fact that the rise and run of a 45 degree angle are equal and when divided always equals 1 and when multiplied by 100 will equal 100%.

In terrain modeling we generally model an entire surface and not just one line so we need to calculate the slope of a best fit surface plane (which is made of lines). Because the terrain model is usually continuous across the entire surface, it is important to be able to calculate how to represent grid cells (or pixels) when going from one elevation to the next. To do this we generally need to know the aspect or the direction that the surface plane is sloped as well. Together the slope combined with the aspect of the surface can virtually define the surface plane completely.

In the example shown to the left, a slope map of Lismore, Nova Scotia was derived from a digital elevation model (DEM) calculated using PCI Geomatica remote sensing software. It is represented with a grey scale color ramp therefore the color white represents a 0 slope and the shades of grey increase through to black which represents an undefined slope. The majority of slopes for this map do not exceed 17 degrees (except for vertical slopes) as this is a relative low lying area of Appalachian terrain.

The image above and to the right is of an actual bedrock cliff with some technical information embedded onto it so it may be used to help better understand slope. The black arrow represents the slope or the measured angle that the rock is dipping towards. The slope in the image would be 45 degrees approximately so the slope would be 1 or 100%. The rise and the run of a slope with a 45 degree angle will always equals 1, thus when multiplied by 100 to calculate percent slope will equal always equal 100%.

]]> http://tmackinnon.com/slope.php/feed 0 http://tmackinnon.com/slope-map-lismore-example.php http://tmackinnon.com/slope-map-lismore-example.php#comments Sat, 25 Feb 2006 19:10:27 +0000 tmackinnon http://tmackinnon.com/?p=911 The image below is a Slope Model that I derived from a digital elevation model (DEM) of Lismore, Nova Scotia. The values of the slopes of the DEM are represented by a 0-255 grey scale color ramp, therefore the color white represents a 0 slope and the shades of grey increase through to black which represents an undefined slope. The majority of slopes for this map do not exceed 17 degrees (except for vertical slopes) as this is a relative low lying area of Appalachian terrain.

Click here to learn a little more about Slope Models  and how the image below was created.