Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 365-372, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
23 Jun 2016
E. N. Maderal, N. Valcarcel, J. Delgado, C. Sevilla, and J. C. Ojeda National Mapping Agency of Spain (IGN-ES)
Keywords: Lidar, DTM, water resources, fundamental datasets, IGN-ES, INSPIRE Abstract. National Geographic Institute of Spain (IGN-ES) has launched a new production system for automatic river network extraction for the Geospatial Reference Information (GRI) within hydrography theme. The goal is to get an accurate and updated river network, automatically extracted as possible. For this, IGN-ES has full LiDAR coverage for the whole Spanish territory with a density of 0.5 points per square meter. To implement this work, it has been validated the technical feasibility, developed a methodology to automate each production phase: hydrological terrain models generation with 2 meter grid size and river network extraction combining hydrographic criteria (topographic network) and hydrological criteria (flow accumulation river network), and finally the production was launched. The key points of this work has been managing a big data environment, more than 160,000 Lidar data files, the infrastructure to store (up to 40 Tb between results and intermediate files), and process; using local virtualization and the Amazon Web Service (AWS), which allowed to obtain this automatic production within 6 months, it also has been important the software stability (TerraScan-TerraSolid, GlobalMapper-Blue Marble , FME-Safe, ArcGIS-Esri) and finally, the human resources managing. The results of this production has been an accurate automatic river network extraction for the whole country with a significant improvement for the altimetric component of the 3D linear vector.

This article presents the technical feasibility, the production methodology, the automatic river network extraction production and its advantages over traditional vector extraction systems.

Conference paper (PDF, 1222 KB)

Citation: Maderal, E. N., Valcarcel, N., Delgado, J., Sevilla, C., and Ojeda, J. C.: AUTOMATIC RIVER NETWORK EXTRACTION FROM LIDAR DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 365-372,, 2016.

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