Data Products from UAS collected imagery Active
New processing techniques, developed by NUSO researchers to utilize the higher resolution data collected by UAS, generate the leading-edge geospatial data products needed to best support DOI scientific research.
Geospatial products that can be generated from UAS collected imagery include:
Orthophotos
Raw imagery captured by camera sensors is converted into orthophotos using the orthorectification process. This process removes the effects of topography and any sensor tilt to produce a distortion-free aerial photograph with a uniform scale. Orthorectification of UAS imagery utilizes Structure from Motion (SfM) software and precise GNSS data acquired during the flight to produce high-resolution orthophotos. These orthophotos may have ground sample distances of less than 5 centimeters. A series of high-resolution orthophotos can be combined into a seamless orthomosaic; a common technique used to produce base-maps supporting geospatial analysis.
Natural color (RGB) imagery from commercially available low-cost digital single-lens reflex cameras, mirrorless interchangeable-lens cameras, or point-and-shoot cameras can generate orthophotos used to produce the most common type of base map, a natural color orthomosaic.
Thermal imagery from sensors such as the FLIR Vue Pro R captures non-contact temperature measurements that generate thermal orthophotos. Thermal orthophotos contain 16-bit radiometrically calibrated raster data; each pixel has an associated absolute surface temperature.
Color infrared orthophotos and orthomosaics are generated from multispectral sensors like the MicaSense RedEdge and MicaSense Altum. These sensors detect the visible and near-infrared wavelengths (in individual bands) needed to support vegetative analysis.
Point Clouds and 3D Models
Point clouds are a set of geographic data points in a three-dimensional coordinate system derived by either SfM processing techniques (photogrammetrically from overlapping raster images) or collected directly by LiDAR scanners. True-color point clouds are generated by processing the natural color imagery in SfM software or combining this imagery with LiDAR data collected at the same location.
Contours
Elevation values derived from UAS point clouds are an ideal data source for generating contour lines. Contour lines represent equal elevations of a surface and contour intervals represent the elevation difference between successive contours. Contour maps are used for terrain visualizations showing valleys, hilltops, and the steepness of slopes.
Elevation Models (DEMs, DSMs, DTMs)
A digital elevation model (DEM) is a generic term for digital topographic and bathymetric data. A DEM or a Digital Terrain Model (DTM) implies an x, y coordinate system and z values of bare-earth terrain, i.e. void of vegetation and man-made features. Digital surface models (DSMs) are a form of DEM that contains surface elevations of natural terrain features in addition to vegetation and man-made features. Both UAS acquired LiDAR data and photogrammetry derived point cloud data can be used to generate accurate DEMs/DTMs and DSMs.
Extracted Features
A high-resolution orthomosaic generated from imagery collected on low-altitude UAS flights provides an ideal source for accurately identifying small-scale (<1 m) to larger-scale objects (>1 m) with feature extraction. This automated process recognizes spatial and spectral patterns within an image and can outline or classify those features into a newly defined dataset. Examples of objects identified using feature extraction include outlining individual tree crowns and identifying birds or other animals for population estimates.
Normalized Difference Vegetation Index (NDVI)
Orthomosaics made from UAS collected multispectral imagery with bands in the visible red and near-infrared (NIR) range can be used to generate Normalized Difference Vegetation Index (NDVI) maps. NDVI calculations create a standardized index utilizing the amount of red light compared to NIR light reflected from a plant. The bright red display of an NDVI color ramp indicates areas with high NIR reflectance associated with healthy plants. The blue shades of the color ramp indicate lower NIR reflectivity and possibly less healthy vegetation.
- Overview
New processing techniques, developed by NUSO researchers to utilize the higher resolution data collected by UAS, generate the leading-edge geospatial data products needed to best support DOI scientific research.
Geospatial products that can be generated from UAS collected imagery include:
Orthophotos
Raw imagery captured by camera sensors is converted into orthophotos using the orthorectification process. This process removes the effects of topography and any sensor tilt to produce a distortion-free aerial photograph with a uniform scale. Orthorectification of UAS imagery utilizes Structure from Motion (SfM) software and precise GNSS data acquired during the flight to produce high-resolution orthophotos. These orthophotos may have ground sample distances of less than 5 centimeters. A series of high-resolution orthophotos can be combined into a seamless orthomosaic; a common technique used to produce base-maps supporting geospatial analysis.
Natural color (RGB) imagery from commercially available low-cost digital single-lens reflex cameras, mirrorless interchangeable-lens cameras, or point-and-shoot cameras can generate orthophotos used to produce the most common type of base map, a natural color orthomosaic.
Thermal imagery from sensors such as the FLIR Vue Pro R captures non-contact temperature measurements that generate thermal orthophotos. Thermal orthophotos contain 16-bit radiometrically calibrated raster data; each pixel has an associated absolute surface temperature.
Color infrared orthophotos and orthomosaics are generated from multispectral sensors like the MicaSense RedEdge and MicaSense Altum. These sensors detect the visible and near-infrared wavelengths (in individual bands) needed to support vegetative analysis.
Sources/Usage: Public Domain. View Media DetailsOrthomosaic of the Northeast Indiana study site generated from Ricoh GR natural color images Point Clouds and 3D Models
Point clouds are a set of geographic data points in a three-dimensional coordinate system derived by either SfM processing techniques (photogrammetrically from overlapping raster images) or collected directly by LiDAR scanners. True-color point clouds are generated by processing the natural color imagery in SfM software or combining this imagery with LiDAR data collected at the same location.
Sources/Usage: Public Domain. View Media DetailsCloseup of the 3D model that was generated for Devils Tower in Wyoming Contours
Elevation values derived from UAS point clouds are an ideal data source for generating contour lines. Contour lines represent equal elevations of a surface and contour intervals represent the elevation difference between successive contours. Contour maps are used for terrain visualizations showing valleys, hilltops, and the steepness of slopes.
Sources/Usage: Public Domain. View Media DetailsContours over the Piute Valley in southern California derived on a ground sample distance (GSD) of 1.4 inch Elevation Models (DEMs, DSMs, DTMs)
A digital elevation model (DEM) is a generic term for digital topographic and bathymetric data. A DEM or a Digital Terrain Model (DTM) implies an x, y coordinate system and z values of bare-earth terrain, i.e. void of vegetation and man-made features. Digital surface models (DSMs) are a form of DEM that contains surface elevations of natural terrain features in addition to vegetation and man-made features. Both UAS acquired LiDAR data and photogrammetry derived point cloud data can be used to generate accurate DEMs/DTMs and DSMs.
Sources/Usage: Public Domain. View Media DetailsDigital Surface Model of the West Fork Mine in Missouri generated from high-resolution imagery (5-10 cm pixel size) and elevation data (6-10 cm vertical and 2-4 cm horizontal resolution) Extracted Features
A high-resolution orthomosaic generated from imagery collected on low-altitude UAS flights provides an ideal source for accurately identifying small-scale (<1 m) to larger-scale objects (>1 m) with feature extraction. This automated process recognizes spatial and spectral patterns within an image and can outline or classify those features into a newly defined dataset. Examples of objects identified using feature extraction include outlining individual tree crowns and identifying birds or other animals for population estimates.
Sources/Usage: Public Domain. View Media DetailsExtracted bird locations at the Chase Lake National Wildlife Refuge; pelican nests (red), cormorant nests (blue), gull/snowy egret non-nesting (black) Normalized Difference Vegetation Index (NDVI)
Orthomosaics made from UAS collected multispectral imagery with bands in the visible red and near-infrared (NIR) range can be used to generate Normalized Difference Vegetation Index (NDVI) maps. NDVI calculations create a standardized index utilizing the amount of red light compared to NIR light reflected from a plant. The bright red display of an NDVI color ramp indicates areas with high NIR reflectance associated with healthy plants. The blue shades of the color ramp indicate lower NIR reflectivity and possibly less healthy vegetation.
Sources/Usage: Public Domain. View Media DetailsNormalized Difference Vegetation Index (NDVI) derived from an orthomosaic of near infrared imagery taken from approximately 400 feet AGL over the Sycan River in the Klamath Basin in Oregon