GRASS Remote Sensing Slides
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Transcript of GRASS Remote Sensing Slides
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Processing aerial survey data using open source GIS software
John A Stevenson, Neil Mitchell, Harry Pinkerton
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Outline
Nesjahraun Data source: ARSF GRASS Higher level products
Digital Elevation Model Multispectral infrared Orthophotos Field data maps Google Earth map
Conclusions
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Lava flow emplaced 1.8 kyr Flowed into Thingvallavatn and continued 1 km underwater Interesting flow features: aa and pahoehoe; tension cracks,
platy-ridge zone, rootless cones
Nesjahraun, ingvellir, Iceland
Figure prepared using GMT
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http://arsf.nerc.ac.uk
Sensors 39 megapixel digital camera Leica ALS50 LiDAR 11-channel Airborne Thematic Mapper (Daedalus ATM)
Data source: NERC ARSFAirborne Research and Survey Facility
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http://www.osgeo.org/grass
Processing: GRASS GISFully-featured GIS
Raster Vector NVIZ
Compatible with many formatsModular structure
r.shaded.relief v.digit
GUI and CLILinux, Mac or WindowsScripts allow batch processing
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Data 13 text files containing 70 million
points Time, position, intensity (for first
and last returns)Processing
Initial binning of data onto 10 m grid
r.in.xyz
From point cloud to DEM
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Higher resolution DEMsLoad individual points
v.in.ascii r.in.xyz
Additional lidar processing v.lidar.*
Surface interpolation (1 m grid) v.surf.rst r.surf.rst
Problems with misregistration of lines Use individual lines 2 m grid spacing kriging via gstathttp://www.gstat.org
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DEM-derived products
Shaded relief maps show surface textures: r.shaded.relief Slope maps highlight edges such as flow margins: r.slope.aspect Sun's denoising algorithm improves clarity on slope maps
http://www.cs.cf.ac.uk/meshfiltering
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Data require rectification from flight-line projection Import as 11-band geotiff files
r.in.gdal 11 band images can be recombined in different combinations
r.composite red=... green=... blue=... Batch-processing saves huge amounts of time
Visible Near IR Short Wave IR Thermal IR
Multispectral infrared
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An orthophoto is an aerial photo that has been warped to give a constant scale over the whole image.
Requirements to orthorectify an aerial photo:
Aerial photograph Camera geometry
parameters Ground Control Points
(GCPs) Topographic model
(DEM)
High-resolution LiDAR DEMs allow very precise orthorectification
Orthorectification
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Orthorectification
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Orthophotos can be used as base maps during field mapping
The photographic background allows precise navigation on complex terrain
Features such as cracks in the lava and prominent boulders are clearly visible
Fieldwork - orthophotos
gmt.soest.hawaii.edu
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Fieldwork - orthophotos
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Incorporating field dataGPS data imported via GPS Babel:http://www.gpsbabel.org/
KML export also available
Clickable map of photo locations via GRASS HTMLMAP driver
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Geospatial Data Abstraction Libraryhttp://www.gdal.org
gdal_translate: Convert between different GIS raster file formats
Geotiff IMG NetCDF (GMT) ESRII Grid ASCII
ogr2ogr: Convert between different GIS vector formats
Shapefile KML
gdal_warp: Reproject data into different projections (uses EPSG codes) gdal2tiles.py: Export raster files for viewing in Google Earth
Windows binaries available as part of FWTools: http://fwtools.maptools.org/
Export to Google Earth (GDAL)
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Conclusions Open source software is a cost-effective way to
process NERC ARSF data GRASS GIS provides most of the necessary tools Integration with other software extends
capabilities Scripts and batch processing hugely speed up
repetitive tasks
As of November 2009 I will be available for subcontracting and / or training. Contact me on: [email protected]
Slide 1OutlineNesjahraun, ingvellir, IcelandData source: NERC ARSFProcessing: GRASS GISFrom point cloud to DEMHigher resolution DEMsDEM-derived productsMultispectral infraredOrthorectificationSlide 11Fieldwork - orthophotosSlide 13Incorporating field dataExport to Google Earth (GDAL)Conclusions