LiDAR data processing
GEOMATRIX UAB has developed an original automated work-flow for processing of "raw" air-born LiDAR datasets (point clouds in LAS format) sampled with ~2 points/m2 density and delivered in low level of thematic classification (only "land", "water" and "other" classes). The solution is designed for automated parallel processing on a cluster of servers and is completely based on open source software (GRASS GIS version 7.0 with libLAS version 1.7). Except of the original LAS datasets, the solution does not require any other external data inputs for masking and clean-up of the results. LiDAR data processing work-flow currently is capable of performing the following operations:
Images on the left side show examples of our LiDAR processing work-flow. Top row - examples of LiDAR point cloud separation and surface interpolation: left - DTM+DSM raster surface (0.5 m resolution) of absolute elevation values and right - DTM raster surface (0.5 m resolution) produced from filtered LiDAR sampling points. Bottom row - DSM-DTM offsets raster surface (volume of surface objects) product displayed on top of a panchromatic aerial photo image. Pixels below 1m (low vegetation, errors, "noise") are removed from the DSM. Middle row - examples of masking and flattening of water surfaces: left - panchromatic aerial photo image of a small lake in Sweden with poor coverage of LiDAR points, right - corrected DEM with masked and flattened water surface.
Technology behind the LiDAR processing system is rather simple: it does not include any proprietary software code or custom processing functions, except those provided by a standard GRASS GIS software package. External libLAS software library is used ony for reading and filtering of the original LiDAR points cloud provided in LAS file format. Filtering and processing of LiDAR data is based on standard masking, smoothing, interpolation functions, as well as a powerful raster algebra system provided by the GRASS GIS software. Due to extremely high density and measurement precision of the original sample of points, LiDAR DEM raster datasets by default are produced in 64-bit float data type and have very large file sizes. It is possible to generate datasets of lower data depth on a special request.
Processing algorithm is optimised for speed, completely automated and very flexible. It allows easy manipulation of processing masks, resampling parameters and methods, pixel resolution and data type of the output raster products, as well as intervals and clean-up parameters of vector isolines. Parallel processing of multiple LiDAR sampling units makes the production relatively fast and efficient. It also enables step-wise processing approach, as well as distributed processing of large datasets on distributed productioon systems - even installed as robotic "boxes" within the production centers of our clients - in order to avoid delays and save money on download/delivery of large LiDAR datasets. This could be a valuable asset for the companies dealing with large amounts of LiDAR data on a regular basis, or providing emergency services.
Analysis of interpolated LiDAR raster datasets is based on a broad range of raster algebra functions, resampling statistics, moving window operations, buffering, cross products and other functions of raster manipulations available in the GRASS GIS software. Statistical analysis layers are stored in a GRASS database, allowing easy automated post-processing, combining with external data layers, automated production of digital maps, and even direct broadcasting as OGC-compliant WMS and CSW services. Due to a wide range of possible data analysis options, we did not develop a strictly defined statistical analysis work-flow for the interpolated LiDAR raster datasets. However, algorithms for automated computing of some "standard" derivative products and the main statistical analysis functions were implemented in the main LiDAR data processing work-flow.
A pilot testing use-case related to extraction of certain ranges of the forest cover, statistical resampling and assessmentt of the forest cover show some of the typical post-processing and statistical analysis capabilities of our automated LiDAR processing system. Left column of images show complete forest cover displayed on top of panchromatic aerial photo image: top - forest cover above 1 m, middle - forest cover above 10 m and bottom - forest cover above 20 m height. Middle column of images show forest cover resampled into a 10m resolution grid (specifically for fusion with Sentinel 1/2 products) and displayed on top of panchromatic aerial photo image: top - forest cover above 1 m, middle - forest cover above 10 m and bottom - forest cover above 20 m height. Right column of images show forest cover resampled into a 100m resolution grid (specifically for a European-scale statistics): top - forest cover above 1 m, middle - forest cover above 10 m and bottom - forest cover above 20 m height.