LiDAR, GIS, and multivariate , ,statistical analysis to assess landslide 

risk Horseshoe Run watershedrisk, Horseshoe Run watershed, West Virginia

Kory Konsoer

Spring 2008

West Virginia University

Dept. of Geology and Geography

Outline• Introduction• Study area – Horseshoe Run watershed

d b• Purpose and Objectives• Methodology

– Mapping and modeling landslidesMapping and modeling landslides– LiDAR‐derived imagery

• ResultsS fi i l G l d d lid– Surficial Geology Map and Landslide Inventory Map

– Statistical Analysis of landslides– Landslide Susceptibility Mapp y p

• Conclusions and Discussion– Classifications of landslidesAreas within Horseshoe of concern– Areas within Horseshoe of concern

IntroductionIntroduction• Why study landslides

– In U S $1‐2 billion in damages and 25 deaths per yearIn U.S., $1 2 billion in damages and 25 deaths per year (U.S.G.S., Landslide Hazards Program). 

– West VirginiaT h• Topography

• Climate• Anthropogenic alterations of landscapeff l l d– Effects to natural landscape• Stream morphology• Biological communities• Stream restoration

– In mountainous regions, mass‐wasting integral part of erosion

Canaan Valley Institute (CVI) –hHorseshoe Run

• Non‐profit organization concerned withNon profit organization concerned with serious impacted water resources (CVI)

• Horseshoe Run• Horseshoe Run– Channel instability seen by time‐series photos for at least 100 yearsat least 100 years

– Lateral channel migration causing erosion of local residents landsresidents lands

CVI – Natural Channel Design RestorationCVI  Natural Channel Design Restoration

• Cause of instability unknown (possibly related to logging inCause of instability unknown (possibly related to logging in early 1900’s)

• CVI conducted assessments (Rosgen) prior to implementing restoration techniques (lower 6.1 km)

• Determined instability = heavy sediment loads from upstream

• Did not perform hillslope assessment – OR geomorphic assessment of watershed

Importance of Hillslope ProcessesImportance of Hillslope Processes

• Vital to incorporate information for entire watershedVital to incorporate information for entire watershed

• Direct relationship between large amounts of ll l d d h l b lcolluvial sediments and channel instability 

(Cenderelli and Kite, 1998)

• Target areas of concern

• Overlooking geomorphic processes may have adverse effects on restoration projects

Study Area – Horseshoe Run Watershedy

• Tucker and Preston Counties

• 27 km, 144 km2

Hi t f l i ithi• History of logging within watershed (Fansler, 1962)

• Clearing of floodplain for g pgrazing and farmland (Fansler, 1962)

B tif l l d• Beautiful landscape

Horseshoe Run WatershedHorseshoe Run Watershed

• 4th largest tributary to Cheat RiverCheat River

• Max. relief – 640 m 

M tl l• Mostly rural landscape

• Steep and rugged• Steep and rugged terrain

• Heavily vegetated• Heavily vegetated

Photograph of Horseshoe Run watershed showing steep terrain and heavyPhotograph of Horseshoe Run watershed showing steep terrain and heavy vegetation.

Horseshoe Run watershed – Bedrock Geology

• Pottsville ss – Pennsylvanian, very resistant to erosion

• Mauch Chunk Group –pMississippian shaley units

• Greenbrier Limestone –relatively thin

• Pocono ss – resistant to erosion

• Hampshire Formation –pDevonian red‐beds of interbedded ss and shale

• Chemung Group –interbedded sandstones and shales, thick (82%)

Reger, 1923

Horseshoe Run watershed ‐ Structural geology

Horseshoe Run Horseshoe RunBackbone Mountain

Horseshoe Run Drainage Boundary

Reger, 1923

Purpose and ObjectivesPurpose and Objectives

• Determine whether slope failures are effectingDetermine whether slope failures are effecting channel instability of Horseshoe Run

• Create a surficial geology map a landslide• Create a surficial geology map, a landslide inventory  map, and a landslide susceptibility map for the watershedmap for the watershed

• Determine statistical differences between f ltypes of slope movements

MethodologyMethodology

• Surficial geology map/ Landslide inventory mapSurficial geology map/ Landslide inventory map

– 144 km2 drainage area – map scale 1:12,000144 km drainage area  map scale 1:12,000

– Steep and rugged terrain, heavily vegetated

– Use Light Detection and Ranging (LiDAR) to map remotely

– Field check remotely mapped surficial features

LiDAR‐derived imageryLiDAR derived imagery• data acquisition in 2006 during leaf-off conditionsconditions•Altitude = 3500 m•Air speed = 135 knots•Pulse frequency = 35 q ykHz•Half scan angle = 20o

•50% overlap•Maximum 4 returns•Maximum 4 returns•(Kinder, 2008)

LiDAR data for Horseshoe Run h dwatershed

• CVI post‐processed LiDAR data to create aCVI post processed LiDAR data to create a digital elevation model (DEM) (Bennett, 2007)

• High resolution aerial photography• High‐resolution aerial photography

• LiDAR‐derived DEM – 0.5 m resolution and 35 i l>35 cm vertical accuracy

• From LiDAR‐derived DEM, shaded relief maps

LiDAR‐derived shaded relief 

mapsmaps

•Sun azimuth and sun angle•Change sun azimuth t ill i t fi i lto illuminate surficial features on different hillslopeshillslopes

Sun azimuth ‐ 315 Reference scale – landslide roughly 125 m in width

Sun azimuth ‐ 135

Aerial Photography

Debris Fans180 m from end of tail to distal 

d f fend of fan

Debris Flows Failed material 55 m in width

Debris SlidesBottom of slide roughly 205 m in width

Rotational Slump330 m from head scarp to toe

Planar Slide Slide approximately 110 m in width

450 meters!!!

Statistical Analysis of LandslidesStatistical Analysis of Landslides

• Seven variables chosen for study:Seven variables chosen for study: – elevation

• slope angle slope aspectslope angle, slope aspect 

• plan curvature, profile curvature 

– distance from roads, distance from streams• Straight line distance

Initiation PointsInitiation Points

• Mapped landslide material – not sourceMapped landslide material  not source region

• Estimate initiation points upslope• Estimate initiation points upslope– Breaks in slope, cross‐sections within ArcGIS 9.2

• After initiation points chosen, buffered to 4m radius– Capture average landscape characteristics

Resampling of the LiDAR‐derived DEMResampling of the LiDAR derived DEM

• 144 km2 0 5m resolution = extremely large144 km , 0.5m resolution = extremely large data set!

• Also, 0.5m cell size may bias variables

• DEM resampled using ArcGIS 9.2 to create a p g2m DEM

Failed Slopes ‐ Data CollectionFailed Slopes  Data Collection

• 4m buffered initiation points 2m DEM4m buffered initiation points, 2m DEM

• Use zonal statistics to collect data for each initiation point on each variableinitiation point on each variable– Zonal statistics averages all cells within 4m buffered pointbuffered point

• Data set created using Microsoft Excel

Unfailed Slopes – Data CollectionUnfailed Slopes  Data Collection

• Failed slopes need to be compared to unfailedFailed slopes need to be compared to unfailed slopes to determine difference in landscape characteristicscharacteristics

• Random Sample generated using Hawth’s Tools in ArcGIS 9 2Tools in ArcGIS 9.2– 450 points, buffered to 4m

D ll i i l i i– Data collection using zonal statistics

• Unfailed data combined with failed data

D t T blValue Status Type

Slope(˚ )

Aspect(˚)

F il d b i lid 24 80 243 94Data Table 1 Fail debris slide 24.80 243.94

2 Fail debris slide 38.40 240.43

3 Fail slump 29.07 284.96

4 Fail landslide 18.48 110.48

5 Fail slump 17.86 134.82

6 Fail slump 22.24 93.18

7 Fail slump 16.68 114.60

8 Fail landslide 33.35 107.59

9 Fail debris fan 12.14 38.50

10 Fail debris flow 32.37 295.24

11 Fail debris flow 30.41 333.18

12 Fail active slope 27.05 210.7812 a act ve s ope

13 Fail landslide 29.35 142.65

14 Fail landslide 17.49 158.24

15 Fail landslide 36.17 89.96

16 Fail slump 7.12 105.7116 Fail slump 7.12 105.71

17 Fail active slope 36.13 285.13

18 Fail slump 19.05 157.94

19 Fail slump 14.32 114.90

30 00 35 3720 Fail debris slide 30.00 35.37

Statistical Analysis – Discriminant lAnalysis

• Discriminant Analysis– A procedure forA procedure for identifying boundaries between groups of bj t b d thobjects based on the 

characteristics that define them (Kachigan, 1986)

• Failed vs. Unfailed• Types of slope movements

Surficial Geology Map – Horseshoe Run watershed

Statistical Analyses of landslidesStatistical Analyses of landslides

• Discriminant AnalysisDiscriminant Analysis– Failed vs. Unfailed (71.3%)

– Types of failed vs. Unfailed (59.2%)

– Only types of failed slopes (76.3%)

Discriminant FunctionDiscriminant Function) pl 0.275( -0.107(el) 0.014(as) ) 0.492(sl F ++=

pr) 0.163( 0.02(ds) -0.031(dr) ++

•sl = slope angle

•as = slope aspect

•dr = dist. from roads

•ds = dist. from streams

•el = elevation

•pl = plan curvature

•pr = profile curvature

pl   plan curvature

Landslide Susceptibility Map

Landslide Proportion ofLandslide susceptibility risk

Proportion of area in Horseshoe Run watershed (%)wa e s ed (%)

low 7.43low-medium 24.46medium 41.62medium-high 20.48high risk 6.09

Discussion: Landslide Susceptibility dMap and Stream Restoration

• Use landslide susceptibility map to assessUse landslide susceptibility map to assess locations for stream restoration

• Visually inspect landslide susceptibility map to id if fidentify areas of concern

Benda et al., 2004Benda et al., 2004

• Larger tributaries have greater impact on mainLarger tributaries have greater impact on main stem channel

• threshold ratio – 1 km2 : 50 km2

– tributary drainage area : main stem drainage area (at confluence)

Wolf Run•Wolf Run drainage area = 20.12 km2area   20.12 km•Horseshoe Run at confluence = 48.65 km2

•ratio = 0.414•Potential for influencing Horseshoe Run•concentration of high risk upstream

Maxwell Run

• Maxwell Run drainage area = 9.4drainage area   9.4 km2

• ratio = 0.08

• Potential for• Potential for influence on Horseshoe Run

• Large concentratedLarge concentrated areas of high risk for failure

CVI – stream restorationCVI  stream restoration

• locations for streamlocations for stream restoration seems acceptable 

• good correlation between restoration and areas of concern reassuring b habout the susceptibility model

ReferencesReferences• Benda, Lee, Poff, N. Leroy, Miller, Daniel, Dunne, Thomas, Reeves, Gordon, Pess, George, and Pollock, Michael, 2004, The 

network dynamics hypothesis: How channel networks structure riverine habitats, BioScience, vol. 54, no. 5, p. 413‐427.y yp p• Bennett, Janette, 2007, Canaan Valley Institute, manager of GIS program, Science and Technology Division, personal 

communication, June, 2007.• Canaan Valley Institute, Horseshoe Run in Tucker County, West Virginia, www.canaanvi.org, data taken February, 

2007.• Cenderelli, Daniel A., and Kite, J. Stephen, 1998, Geomorphic effects of large debris flows on channel morphology p p ff f g f p gy

at North Fork Mountain, Eastern West Virginia, USA, Earth Surface Processes and Landforms, vol. 23, p. 1‐19.• Fansler, Homer Floyd, 1962, History of Tucker County, West Virginia: Parsons, W. Va., McClain Printing Company, 

702 p.• Greene, Angela, 2008, Canaan Valley Institute, Natural Resources Conservation Services liaison, personal phone 

conversation, March 24, 2008.• Hack, John T., and Goodlett, John C., 1960, Geomorphology and forest ecology of a mountain region in the central 

Appalachians, United States Geological Survey, Professional Paper 347, 66 p.• Jacobson, Robert B., Cron, Elizabeth D., and McGeehin, John P., 1989, Slope movements triggered by heavy rainfall, 

November 3‐5, 1985, in Virginia and West Virginia, U.S.A., Geological Society of America Special Paper 236: Landslide processes of the eastern United States and Puerto Rico, GSA 1989.

• Kachigan, Sam Kash, 1986, Statistical Analysis: An interdisciplinary introduction to univariate and multivariate methods, Radius Press, New York, 589 p.

• United States Geological Survey, Landslide Hazards Program, landslides.usgs.gov.• Reger, David B., West Virginia Geologic Survey, 1923, County Reports, Tucker County.• West Virginia GIS Technical Center West Virginia state GIS data available for downloading wvgis wvu eduWest Virginia GIS Technical Center, West Virginia state GIS data available for downloading, wvgis.wvu.edu.