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 main goal of this project was to generate flood maps and DEMs with better than 30 cm vertical accuracy for the coastal area of southeastern New Brunswick.
I created and presented the following poster that summarized my Flood Simulation Modeling with High Resolution LIDAR project as part of my Applied Geomatics graduate work at COGS in 2004. The areas in the poster are of Shediac & Parlee Beach, New Brunswick
The above two images were created for my LIDAR flood modeling graduate research project. The first image is before the flood scenario; featuring a color shaded relief perspective view pointing south east from the Northumberland Strait landwards across the Pointe Du Chene wharf. The second image is of the same color shaded relief perspective view but features a 2.55 m flood level super imposed on top of it.
The 2.55 m flood level was an actual recorded storm surge water level that effected this area during a winter storm on January 2001. The two images below show the same flood level and area but from an overhead aerial view. The first image is with an orthophoto and the second image is with the color shaded relief.
The following co-authored paper featuring my graduate LiDAR research work at the AGRG was published in the Canadian Journal of Remote Sensing in 2006. Airborne light detection and ranging (LiDAR) has the spatial density and vertical precision required to map coastal areas at risk of flooding from water levels typically 1–2 m higher than predicted tides during storm surges. In this study …