Assessment of Photogrammetry Structure-from-Motion Compared toTerrestrial LiDAR Scanning for Generating Digital Elevation Models.

Application to the Austre Lovenbreen Polar Glacier Basin, Spitsbergen 79oNJ.-M. Friedt1, E. Bernard2, A. Prokop3, F. Tolle2, M. Griselin2

jmfriedt@femto-st.fr, http://jmfriedt.free.fr1 FEMTO-ST/Time & Frequency, UMR CNRS 6174, Univ. Franche-Comte, Besancon, France

2 TheMA, UMR CNRS 6049, Univ. Franche-Comte, Besancon, France3 BOKU University of Natural Resources and Life Sciences, Vienna, Austria

Introduction: SfM in snow covered areas

• Area of interest ranging from a few 100 m2 to a few tens of km2

• Digital Elevation Model (DEM) = input for assessing mass balance• Sparse dataset (maps, aerial photography, satellite imagery)• Complemented by GPS-mapping (limited to accessible areas, exclud-

ing e.g. slopes)• Reference instrument: ground based LiDAR (high resolution and

insensitive to illumination conditions, but costly, high power con-sumption & heavy) Georeferenced pictures LiDAR & power supply setup

⇒ ground based georeferenced digital pictures for DEM generation using Structure from Motion (SfM)⇒ COTS digital camera: 4000 pixel wide image with an angular width of 60o ⇒ 1 m-wide pixel at 4 km

IGN MicMac (available at http://logiciels.ign.fr/?-Micmac,3-)

• Flexible opensource software implementing the vari-ous steps for 3D modelling:

– identify relevant features on each picture– identify optical properties of the lens & camera– identify camera position when pictures were taken– coarse pointcloud incl. camera position for validation– fine point cloud– remaining issue: point cloud to meshed surface conver-

sion (Meshlab, CloudCompare)

• user validates each processing step + assess resolution• registration based either on GCP or GPS camera position• detailed understanding of the processing algorithms

which can be updated by the user (lens, registration mod-els)

• alternative to Photoscan rejected as being a black boxDEM of Austre Loveenbreen from opportunistic records

Resolution: comparison of bird cliff point clouds• LiDAR v.s MicMac & MicMac v.s MicMac• birdcliff (45 m× 12 m, including snow covered areas)

scanned at the same time by Lidar (102 min. scanfor 2159000 points) & pictured (COTS camera)

• Manual overlap due to inconsistency in the meaning(X,Y, Z)+Lidar point cloud centered on instrument

• Transform matrix diagonal elements: 0.9926, 0.9997and 0.9926⇒ scale consistent to better than 1%

• Point cloud error assesment: 90% of the points lieat less than 32 cm error (LiDAR v.s MicMac) and22 cm error (MicMac v.s MicMac, typical: 6-10 cm)

Left: MicMac v.s MicMac. Right: LiDAR v.s MicMac

point cloud subtraction manual registration and subtraction

Conclusion: effect of snowcovered areas

• Avoid straight flight path (poor point cloudconstraint)

• Lack of relevant features on snow covered areas:any structure on the surface is usable, eg rocks,skidoo tracks

• Cast shadow effect on point cloud: holes in DEM• Cloudpoint resolution in the 30 cm range suffi-

cient for snow depth estimates• Actual DEM subtraction (October-April) re-

mains to be demonstrated −→Google Earth (top) v.s Micmac (bottom) Haav-imb to Slatto summit distance measurement

NN

Agisoft Photoscan generated DEM

J.-M Friedt, Reconstruction de structures tridi-mensionnelles par photographies : le logicielMicMac, OpenSilicium (Sept-Nov.2014): Mic-Mac tutorial translated to English available athttp://jmfriedt.free.fr/lm_sfm_en.pdf