NUPO Data Research

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Develop leading-edge data processing techniques that use state-of-the-art computer concepts to utilize the increased resolutions and accuracies made available by UAS low-altitude data collection.

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Graphic showing platform heights and associated data resolution

UAS abilities to carry sophisticated, high resolution sensors offer incredible enhancement opportunities relative to the amount, resolution, persistence, and analytics applied to remotely collected data. Some of the UAS mounted sensors currently operated by the DOI have provided image resolution improvements of 1,200% over the Landsat 8 satellite and 400% better than manned aircraft acquired data. 

Making use of this higher resolution raw imagery requires new techniques to generate the types of leading-edge geospatial products needed to best support DOI scientific research. New processes have already been developed that combine UAS collected imagery with a platforms built-in Global Navigation Satellite System (GNSS) data to generate products with an average geospatial accuracy of 1-3 meters, which additional ground control can improve to centimeters. 

Geospatial products that can be generated from UAS collected imagery include:

Orthophotos

Raw imagery captured from sensors can be converted into orthophotos using a process called orthorectification that removes the effects of topography and any sensor tilt to produce a distortion free aerial photograph with a uniform scale. Orthorectification for imagery captured from low-altitude sUAS utilizes the precise GPS data acquired during the flight to calibrate the imagery in structure from motion (SfM) software to produce high-resolution orthophotos with ground sample distances of less than 5 centimeters. And combining a series of high resolution orthophotos into a seamless orthomosaic is one of the best and most common ways to produce the base maps needed to support geospatial analysis.

Natural color (RGB) imagery from any number of commercially available low-cost digital single-lens reflex (SLR) or point-and-shoot cameras can create the orthophotos used to produce the most common type of base map, a natural color orthomosaic.

Orthomosaic of the Northeast Indiana study site

Orthomosaic of the Northeast Indiana study site generated from Ricoh GR natural color images

Thermal imagery from sensors such as the FLIR Vue Pro R captures non-contact temperature measurements that can be used to generate thermal orthophotos. And orthophotos generated from raw 16-bit radiometrically calibrated thermal imagery can produce a geospatial raster dataset where each pixel location has an associated absolute surface temperature.

Color infrared orthophotos and orthomosaics are made from multispectral imagery acquired from sensors like the MicaSense RedEdge that detect the visible through near-infrared wavelength range 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 using structure from motion (SfM) processing techniques on UAS collected natural color imagery or collected directly by UAS mounted Light Detection And Ranging (LiDAR) scanners. True-color point clouds can also be generated by processing the natural color imagery in SfM software or combining this imagery with LiDAR data collected over the same area. Point cloud data overlaid on natural color orthophotos or other types of georeferenced orthophotos generates highly accurate 3D or physical models.

Closeup of the 3D model of Devils Tower in Wyoming

Closeup of the 3D model that was generated for Devils Tower in Wyoming

Contours

Elevation values derived from orthomosaics created from UAS acquired imagery are an ideal data source for generating contour lines, a series of joined points of equal elevation above a given level. The contour interval used when generating the lines represents the elevation difference between successive contours. Contour maps are often used for terrain visualizations showing valleys and hills, and the steepness of slopes.

Contours over the Piute Valley in southern California

Contours 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) can be accurately derived from ground positions sampled at regularly spaced horizontal intervals by UAS mounted sensors. Highly accurate digital surface models (DSMs), a form of DEM that contains reflective surface elevations of natural terrain features in addition to vegetation and cultural features, can be produced from the point clouds generated from UAS-mounted LiDAR sensors. And UAS acquired LiDAR point cloud data can also be used to generate digital terrain models (DTMs) by removing the elevation signals of features such as vegetation and buildings, leaving only the elevation of the terrain or ground.

Digital Surface Model of the West Fork Mine in Missouri

Digital 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 features with feature extraction, an automated process of recognizing spatial and spectral patterns within an image and outlining or classifying those features into a newly defined dataset.

Extracted bird locations at the Chase Lake National Wildlife Refuge

Extracted 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 red and near infrared range can be used to generate Normalized Difference Vegetation Index (NDVI) maps. NDVI calculations create a standardized index utilizing the amount of infrared light reflected from a plant where the bright red display of the color ramp indicates healthy or highly reflective plants and the blue color indicates the lower reflectivity and possibly less healthy vegetation.

NDVI over the Sycan River in the Klamath Basin in Oregon

Normalized 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

NUPO data processing techniques can be found at Other Resources