Remote Sensing Terminology
The Landsat program is a series of American satellites that use the visible and infrared parts of the spectrum to record images of the Earth's surface. It is the longest running enterprise for acquisition of satellite imagery, and started back in 1972. The most recent, Landsat 8, was launched in 2013.
Landsat satellites are located in a polar orbit, which allows them to provide images of almost all of the Earth's geography. As the satellite orbits the Earth from pole to pole, it appears to move from east to west because of the Earth’s rotation. This apparent movement allows the satellite to view a new area with each orbit.
Determining land cover has become one of the most common uses of Landsat Imagery and remotely sensing generated images all around the world.
The LiDAR sensor produces a series of point measurements that consists of geographic location (X & Y) and height (Z) of both natural and man-made features, and can be further processed to produce several different products and integrated into a Geographic Information System (GIS).
Click here to learn more about LiDAR
The amount of energy returning to the sensor (known as backscatter) is dependent upon the topography, roughness, and dielectric properties (moisture). Areas of an image with low backscatter appear dark (such as water), while areas of high backscatter appear as light gray levels approximating white shades. By interpreting the various gray tones, textures and patterns, the user can detect information regarding to the regions geologic lithology and structure.
In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest. This is exemplified by the use of imaging systems where the following seven elements are involved. Note, however that remote sensing also involves the sensing of emitted energy and the use of non-imaging sensors. Click here to learn more about Remote Sesning
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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%.
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.
Color Shaded Relief (CSR) image generated from Bald Earth LIDAR points on the left, Middle image is of a Digital Surface Model generated with “All the LIDAR points” and on the right is an air photo for the same area. The area shown in the data samples are of Bouctouche in New Brunswick at a scale of 1:6000. Data sets were part of my gradute research work at the AGRG / COGS in 2003.