Applications of Remote Sensing in GIS

As computing technology advances, engineers' access to high-quality and large-volume data increases. Geographic Information System (GIS) has become a critical tool in various industries to maximize the value of this data.

FREMONT, CA: Any location on the planet is associated with massive amounts of data, which include not only physical characteristics but also political, economic, and social data. GIS enables visualize, analyze, and comprehension of this data.

 Remote sensing is a technique that is frequently used to collect physical data for integration into GIS. Without making direct contact with the objects on the earth, remote sensors collect data. They accomplish this by detecting reflected energy from the earth and are typically mounted on satellites or aircraft. Recent years have seen a dramatic increase in the prevalence, accuracy, and accessibility of remote sensing technology, covering a broad range of engineering applications.

To begin, engineers use remote sensing to ascertain the topography of our planet. LiDAR, which stands for Light Detection and Ranging (LiDAR), can be used to create three-dimensional images of the earth's surface.

LiDAR sensors collect dense groups of elevation points called point clouds using pulsed lasers in conjunction with position and orientation data. These point clouds can then be processed to create contour maps and digital elevation models (DEMs) that accurately represent the earth's shape. Engineers can use a DEM to quickly and accurately estimate the amount of earthmoving required to develop a tract of land.

Additionally, remote sensing is used to monitor land use, which assists engineers and planners in making design decisions. Optical sensors determine the amount of solar radiation reflected from the earth's surface. The different wavelengths detected are combined to create an image resembling a camera photographing the earth's surface.

Finally, remote sensing is used in hydrologic applications, such as determining the impervious surface area. Impervious surface datasets are generated by classifying various bands in optical sensor images. This is possible because multiple materials reflect visible and infrared light differently.