University of Twente Student Theses
Mapping of landslides under forest using high resolution LIDAR data: Integration of airborne and terrestrial laser scan data for the assessment of tree characteristics in relation to landslide processes. Bois Noir, French Alps, France
Suprijatna, Syams Nashrullah (2011) Mapping of landslides under forest using high resolution LIDAR data: Integration of airborne and terrestrial laser scan data for the assessment of tree characteristics in relation to landslide processes. Bois Noir, French Alps, France.
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| Abstract: | Mapping of landslide under forest is difficult and less effective with conventional remote sensing techniques such as aerial photo and high resolution optical imagery, since they cannot provide terrain information under vegetation. LiDAR as a recent technology can solve this problem with its capability to penetrate canopy in vegetated area and provide high quality of terrain representation. Nevertheless assessing landslide activities remain difficult particularly in the area where the mass movement is quite slow and unperceivable. Tree structure anomaly can physically indicate these kind of movement, lead to importance of tree characteristic analysis in landslide study. The aim of this study was to utilize airborne laser scanning (ALS) and terrestrial laser scanning (TLS) data for landslide under forest mapping and also extraction of tree characteristics as an indicator of landslide activity in Bois Noir, Southern French Alps. Visual interpretation of digital elevation model (DEM) derived from ALS data was conducted to map landslide type and morphology. The DEM as terrain representation was generated by Hierarchical Robust Interpolation (HRI) filtering that proposed suitable for landslide mapping and has a good quality of accuracy. For visual interpretation, openness map was presented because it can reveal details of morphology features of landslide. To assess landslide activity, this study also selected tree samples from ALS and TLS data overlaid with landslide map in order to extract tree characteristics from skeletonization and TreVaw. The extracted tree characteristics then validated with field data. |
| Item Type: | Essay (Master) |
| Faculty: | ITC: Faculty of Geo-information Science and Earth Observation |
| Programme: | Geoinformation Science and Earth Observation MSc (75014) |
| Link to this item: | https://purl.utwente.nl/essays/92761 |
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