Miet Van Den Eeckhaut 1 , Norman Kerle 2 , Jean Poesen 3 , Javier Hervás 1

1. Identification of vegetated landslides using only a LiDAR-based Terrain Model and derivatives in an object-oriented environment Miet Van Den Eeckhaut1, Norman Kerle2, Jean Poesen3, Javier Hervás1 1Climate Risk Management Unit, Institute for Environment and Sustainability, Joint Research Centre (JRC), European Commission (Italy), miet.van-den-eeckhaut@jrc.ec.europa.eu 2 Faculty of Geo-Information Science and Earth Observation, University of Twente, The Netherlands 3 Department Earth and Environmental Sciences, KU Leuven, Belgium

2. Content • Introduction • Objectives • Study area and expert-based landslide inventory • Workflow • 4.1 LiDAR data and derivatives • 4.2 Translation to OOA • 4.2 Results and accuracy assessment • Discussion • Conclusions

3. 1. Introduction (Airborne) LiDAR and Landslides Identification and mapping Characterisation (morphologic features) Monitoring (displacement) Modelling Expert-based (semi-)automatic

4. 1. Introduction (Airborne) LiDAR and Landslides Identification and mapping Expert-based (semi-)automatic Analysis of shaded relief, slope and contour lines maps (e.g. Schulz, 2004; Van Den Eeckhaut et al., 2007; 2011) (Van Den Eeckhaut et al., 2007)

5. 1. Introduction (Airborne) LiDAR and Landslides Identification and mapping Expert-based (semi-)automatic Pixel-based • McKean and Roering (2004): surface roughness filters • Glenn et al. (2006): stdev of elevation differences from a hypothetical spline plain, semivariograms, and fractal dimensions • Booth et al. (2009): Power spectra produced using the 2D discrete Fourier transform and the 2D continuous wavelet transform (accuracy 82%) • Kasai et al. (2009): combination of LiDAR-derived Eigenvalue Ratio filter and slope angle

6. Local Regional Booth et al. (2009) Glenn et al. (2006) An ~10–30 m blanket of loess covers many of the high, low-angle ridges (smooth in the hillshade map). - Individual landslides - No delineation, only characterization (Accuracy 82%)

7. 1. Introduction (Airborne) LiDAR and Landslides Identification and mapping Expert-based (semi-)automatic Pixel-based Object-based • DEMs and their derivatives have only been used for classification (Martha et al., 2010) • With decreasing pixel size, OOA might be more logical (first delineation then classification) • Lidar-derivatives used for more general geomorphological mapping (no specific landslide class) (Dragut et al., 2012/2012; Anders et al., 2011)

8. 2. Objectives • Objectives: To test OOA for landslide mapping using (only) LiDAR data for both the segmentation and classification steps: • to exploit the profound morphologic manifestation of vegetated landslides as geomorphic features consisting of different parts, • to test scale optimization methods developed for passive optical imagery, • to outline the pros and cons of the methodology (incl. applicability in different environments). • Relevance: • Passive optical remote sensing does not allow landslide identification and characterization under forest. • (Semi-)automatic landslide identification on a pixel-base using LiDAR has problems with delineating individual landslides.

9. 3. Study area and expert-based landslide inventory Flemish Ardennes (Belgium) • Hilly • Loose Tertiary lithology overlaid by loess • Affected by >200 old, deep-seated and recent shallow landslides Validation (50 km2) Calibration (10 km2) (Elevation exaggeration x1; @Google Earth) Study area and expert-based landslide inventory (LiDAR and field survey; Van Den Eeckhaut et al., 2007) Complex slide Rotational slide

10. 4. Workflow 1. LiDAR data and derivatives 2. Human perception conceptualization of landslides and possible false positives 3. Translation to OOA segmentation and optimal resolution (PoF) classification (SVM) 4. Accuracy assessment

11. 4.1. LiDAR data and derivatives • LiDARdata (@AGIV, 2001 / 2002) • Azimuth Aeroscan small footprint (30 cm), vertical accuracy of 4 cm, swath of 600 m, average pulse density of 1 per 4 m² • production of the bare earth DTM with Terrascan software and a manual check • available data (.txt) have a point density of at least 1 per 20 m², a horizontal accuracy >15 cm, vertical accuracy from 7 cm for freshly cut lawn to 20 cm for pastures and forests • TIN interpolation to produce 2 m DTM and other derivatives in ArcGIS

12. 4.1. LiDAR data and derivatives Diffuse analytical shaded relief Slope gradient Plan curvature Roughness Openness Multiple flow direction

13. 4.2. Conceptualization of a landslide • The ultimate benchmark of OOA is human perception • How do we recognize landslides in the field, from pictures, 3D block diagrams or LiDAR derivatives? • How do we replicate this subjective landslide recognition in an OOA using only LiDAR derivatives? (Van Den Eeckhaut et al., subm)

14. 4.3. Translation to OOA: segmentation • Segmentation • Optimal scale: Plateau Objective Function (Martha et al., 2011) • Segmentation types: Image binarization/ thresholding + multiresolution segmentation • Objective derivation of optimal scales around 13 and 33-35, but subjectivity remains: • - Which LiDAR derivative? • - How to combine objective functions from different derivatives? • - Which peak contributes to which landslide part or false positive?

15. 4.3. Translation to OOA: classification • Classification • Support Vector Machines (Cortes and Vapnic, 1995) Samples selected in calibration area (Van Den Eeckhaut et al., subm)

16. 4.3. Translation to OOA: classification • Important stages towards identification of landslides in OOA using SVM: • Segm of the slope gradient map with thresholding; • Multiresolutionsegm using scale factor 35 and classification of main scarps, earth banks and large cropland fields; • Multiresolutionsegm using scale factor 13 and classification of segments that remained unclassified in stage (B) into landslide body candidates and small cropland fields; • Intermediate cleaning; • Creation of landslide flanks (done for each potential landslide individually); • Delineation of landslide body as the landslide body candidates, downslope of the main scarp and enclosed by flanks; • Final cleaning of the delineated landslides. (Van Den Eeckhaut et al., subm)

17. 5. Results and accuracy assessment Expert Automatic (Van Den Eeckhaut et al., subm)

18. 5. Results and accuracy assessment (A) Large slides from which only the main scarp was identified, because the landslide body is affected by human interventions (B) and (C) 2 of 18 false positives: earth bank and valley head incorrectly classified as main scarp and grown into a landslide (Van Den Eeckhaut et al., subm)

19. 5. Discussion • First results in Flemish Ardennes, show that: • 1) For (semi)automatic mapping of vegetated landslides, OOA with LiDAR derivatives and using PoF and SVM can be used for landslide mapping. • Similar accuracies as previous studies on passive optical remote sensing data (e.g. Martha et al., 2010) (i.e. 71% of main scarps together with at least 50% of the landslide body). • The method is especially suited for extraction of landslide main scarps (i.e. TP 0.92) and well-defined deep-seated slides. • FP are steep valley heads or earth banks. • 2) Some subjectivity remains. • Selection of LiDAR derivatives. • Combination of objective functions of different derivatives. • Allocation of a certain landslide feature to a specific optimal scale. • Need for further investigation.

20. 5. Discussion • Use of LiDAR in an OOA in regions with different terrain characteristics • Soil covered areas (e.g. Flanders, and the study areas of e.g. Booth et al., 2009) • -> Landslides are characterized by a much higher surface roughness compared to the surrounding landslide-free areas • Mountain areas (e.g. Vorarlberg, Austria) • -> Presence of stable bed rock outcrops with high topographic roughness • -> Many landslides have subdued geomorphic signature • Transferability is even more difficult

21. 5. Discussion • Differences between using OOA with passive optical and active optical remote sensing data • Passive optical remote sensing data • -> Studies focus on fresh landslides • -> Landslides are consisting of one or a few segments • Active optical remote sensing data • -> Landslides are geomorphologically complex and consist of different segments with different geomorphological characteristics • (features such as main direction, aspect and flow direction, length/width can only be used once each landslide part consists of one single segment) • -> The aggregation of the segments is not straightforward

22. 6. Conclusions • Our visual sense of a landslide is a common experience, yet not always easy to communicate, and even more difficult to translate in rule sets. • For (semi)automatic mapping of densely vegetated landslides, OOA with LiDAR derivatives can be an alternative to passive optical sensors for production of landslide inventory maps. • Obtained accuracy results (i.e. 92% main scarps; 71% landslide body) prove that it is worthwhile to further exploit the possibilities of OOA with LiDAR data. • The downslope part of a landslide often has a poor geomorphometric signature. However, this problem has also been reported for expert-based landslide inventory mapping (e.g. Schulz, 2004).

23. Thank you for your attention More info: Van Den Eeckhaut, M., Kerle, N., Poesen, J., Hervás, J., subm. Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data. Geomorphology. Or www.itc.nl/OOA-group Contact: miet.van-den-eeckhaut@jrc.ec.europa.eu