Airborne Hyperspectral Imaging in aerial photogrammetry and remote sensing is a new tool that can be used to map specific materials. As sunlight is reflected off the surface of materials it is received by the sensor in more than 100 different bands or channels. The combination of specific bands produces a unique signature for each material in the scene. These signatures are used to classify/identify the materials present at each location. It is therefore an excellent tool for environmental assessments, mineral mapping and exploration, vegetation communities/species and health studies, and general land management studies in aerial photogrammetry and remote sensing.
This imagery in aerial photogrammetry and remote sensing is especially powerful when combined with LIDAR points or the LIDAR generated surface. For example the extraction of forest canopy heights can be accomplished using a combination of hyperspectral classification and LIDAR based multiple return analysis techniques. The resultant hyperspectral vegetation classification could be used to generate polygons that exhibit the exact locations of forest canopy areas. It should be noted that these areas contain both forest canopy and ground elevation points.
The extraction of ground points within the heavy forest areas are conducted using multiple return analysis techniques. Such areas cannot be readily mapped using traditional photogrammetric techniques in aerial photogrammetry and remote sensing. Using a set of Boolean based decision rules, MRA techniques, cross-sectional and 3-D profiles, a generalized terrain model is created and used to identify non bare-earth points and retain points that represent the ground surface. The result is a bare-earth surface, and LIDAR points that are representative of the forest tree canopy and their associated heights.
LIDAR combined with additional imaging products is the emerging mapping technology of the new millennium. The products from these tools can be directly used in aerial photogrammetry and remote sensing environment to represent not only the terrain but manmade and natural features as well.
This imagery in aerial photogrammetry and remote sensing is especially powerful when combined with LIDAR points or the LIDAR generated surface. For example the extraction of forest canopy heights can be accomplished using a combination of hyperspectral classification and LIDAR based multiple return analysis techniques. The resultant hyperspectral vegetation classification could be used to generate polygons that exhibit the exact locations of forest canopy areas. It should be noted that these areas contain both forest canopy and ground elevation points.
The extraction of ground points within the heavy forest areas are conducted using multiple return analysis techniques. Such areas cannot be readily mapped using traditional photogrammetric techniques in aerial photogrammetry and remote sensing. Using a set of Boolean based decision rules, MRA techniques, cross-sectional and 3-D profiles, a generalized terrain model is created and used to identify non bare-earth points and retain points that represent the ground surface. The result is a bare-earth surface, and LIDAR points that are representative of the forest tree canopy and their associated heights.
LIDAR combined with additional imaging products is the emerging mapping technology of the new millennium. The products from these tools can be directly used in aerial photogrammetry and remote sensing environment to represent not only the terrain but manmade and natural features as well.
Posts
