DOGAMI Special Paper 42, Protocol for Inventory Mapping of Landslide

Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (lidar) Imagery by William J. Burns and Ian P. Madin

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SPECIAL PAPER 42

2009

G E O L O G Y F A N O D T N M I E N M E T R

R A

A L

I E

R E S Oregon Department of Geology and Mineral Industries O

1937 Cover image: (left) Hillshade image showing area around Oregon Highway 213 and Newell Creek Canyon in Oregon City, Oregon, and (right) same image draped with landslide polygons mapped using the protocol developed in this paper. State of Oregon Department of Geology and Mineral Industries Vicki S. McConnell, State Geologist

Special Paper 42

Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (lidar) Imagery

William J. Burns and Ian P. Madin Oregon Department of Geology and Mineral Industries, 800 NE Oregon Street #28, Suite 965, Portland, Oregon 97232-21622

e-mail: [email protected]; [email protected]

G E O L O G Y F A N O D T N M I E N M E T R

R A

A L

I E

R E

S O

1937

2009 NOTICE

The Oregon Department of Geology and Mineral Industries is publishing this paper because the subject matter is consistent with the mission of the Department. The paper is not intended to be used for site specific planning. The protocol described in this paper cannot serve as a substitute for site-specific investigations by qualified practitioners. Site-specific data may give results that differ from those which would result from use of the protocol described in this paper. The hazards of an individual site should be assessed through geotechnical or engineering geology investigation by qualified practitioners.

Oregon Department of Geology and Mineral Industries Special Paper 42 Published in conformance with ORS 516.030

For copies of this publication or other information about Oregon’s geology and natural resources, contact:

Nature of the Northwest Information Center 800 NE Oregon Street #5, Suite 177 Portland, Oregon 97232 (503) 872-2750 http://www.naturenw.org

or these DOGAMI field offices: Baker City Field Office Grants Pass Field Office 1510 Campbell Street 5375 Monument Drive Baker City, OR 97814-3442 Grants Pass, OR 97526 Telephone (541) 523-3133 Telephone (541) 476-2496 Fax (541) 523-5992 Fax (541) 474-3158

For additional information: Administrative Offices 800 NE Oregon Street #28, Suite 965 Portland, OR 97232 Telephone (971) 673-1555 Fax (971) 673-1562 http://www.oregongeology.com http://egov.oregon.gov/DOGAMI/ Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Table of Contents

1.0 EXECUTIVE SUMMARY ...... 1 2.0 INTRODUCTION ...... 2 2.1 Intended Audience and DOGAMI Role ...... 2 2.2 Background ...... 2 3.0 PREVIOUS LANDSLIDE INVENTORIES ...... 5 4.0 LANDSLIDE TYPES ...... 7 5.0 METHODOLOGY ...... 9 5.1 Acquisition and Visualization of Base Data ...... 9 5. 2 Mapping Landslides: Spatial Data and Tabular Data ...... 12 5.2.1 Spatial Data ...... 12 5.2.2 Tabular Data ...... 14 5.2.2.1 Type of movement ...... 14 5.2.2.2 Classification of material and movement types ...... 14 5.2.2.3 Confidence of landslide identification ...... 14 5.2.2.4 Estimated age or time of landslide activity ...... 16 5.2.2.5 Known date of movement and landslide name ...... 17 6.2.2.6 Slope angle, head scarp, and fan depth measurements ...... 17 5.2.2.7 Classification of deep or shallow ...... 18 5.2.2.8 Horizontal distance between scarps ...... 20 5.2.2.9 General movement direction and size ...... 20 5.2.2.10 Area and volume ...... 21 5.2.2.11 No data ...... 21 6.0 LANDSLIDE INVENTORY MAP TEMPLATE ...... 21 7.0 LIMITATIONS OF MAPS PRODUCED USING THIS PROTOCOL ...... 23 8.0 POTENTIAL USES OF THE DATA AND MAPS PRODUCED USING THIS PROTOCOL ...... 24 9.0 ACKNOWLEDGMENTS ...... 24 10.0 REFERENCES ...... 25 11.0 APPENDIX A: Geodatabase Tabular Field Data ...... 27 12.0 APPENDIX B: Guidelines for working with the Landslide Geodatabase Template ...... 28 12.1 Editing Guidelines ...... 28 12.2 Workflow ...... 28 12.3 Tips and Tricks for Editing ...... 28 12.4 Attributes ...... 29 12.5 Checking Your Work ...... 29 12.6 Geodatabase Topologies ...... 30

Oregon Department of Geology and Mineral Industries Special Paper 42 iii Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

List of Figures (COVER) Hillshade image showing Oregon Highway 213 and Newell Creek Canyon in Oregon City, Oregon, and draped with landslide polygons mapped using the protocol developed in this paperi Figure 1. Risk diagram shows the overlap of landslide hazard and vulnerable population ...... 2 Figure 2. Comparison of five remote-sensing data sets...... 3 Figure 3. Map of the landslide database consolidated after severe storms in 1996-1997...... 5 Figure 4. The Statewide Landslide Information Layer for Oregon (SLIDO) release 1...... 6 Figure 5. Types of landslide movements...... 7 Figure 6. Block diagram of a slump-earth flow showing common features...... 8 Figure 7. Lidar-derived DEM viewed as a hillshade in grayscale and with a color profile...... 9 Figure 8. Color hillshade draped on slope map and with contour lines...... 10 Figure 9. Color hillshade-slope map and orthophoto showing three landforms resembling landslide morphology...... 11 Figure 10. Previously mapped landslide points from Special Paper 34 and landslide extents from SLIDO...... 11 Figure 11. Screenshot showing landslide geodatabase template structure developed for this protocol ...... 12 Figure 12. Block diagram and map view of landslide deposit polygon, head scarp and flanks, top of head scarp, and internal scarps...... 13 Figure 13. Block diagrams and map views of channelized debris-flow fan and rockfall/topple talus...... 13 Figure 14. Example of a grid layer used to keep track of mapped areas...... 14 Figure 15. Examples of low- and high-confidence examples for debris flow fans...... 16 Figure 16. Geomorphic changes in surface morphology of a landslide with time...... 16 Figure 17. Example location to measure adjacent slope angle...... 17 Figure 18. Example location to measure estimated head scarp height ...... 17 Figure 19. Example location to measure estimated maximum debris flow fan depth ...... 18 Figure 20. Calculation of estimated slope normal thickness or depth to failure...... 18 Figure 21. Example of shallow and deep-seated landslides...... 19 Figure 22. Example of horizontal distance measurements between two scarps...... 20 Figure 23. Example of measurement of direction of movement in azimuth...... 20 Figure 24. Example map using protocol of inventory mapping described in this paper...... 22 Figure B1. Edit all features connected to the node...... 30 Figure B2. Edit shape of all features under edge...... 30

List of Tables Table 1. Tabular data fields with brief descriptions ...... 15 Table 2. Simplified classification of landslides ...... 15 Table 3. Confidence of landslide identification points and scale...... 15

iv Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

1.0 EXECUTIVE SUMMARY

Landslides are one of the most widespread and damag- was selected to compare remote sensing data/images ing natural hazards in Oregon. To reduce losses from for effectiveness (Burns, 2007). Two key findings from landslides, areas of landslide hazard must first be iden- this pilot study were: 1) the use of the light detection tified. The initial step in landslide hazard identification and ranging (lidar) data resulted in identification of 3 is to create an inventory of past (historic and prehis- to 200 times the number of landslides found with the toric) landslides. The inventory can be used to create other data sets, and 2) the accuracy of the spatial extent susceptibility maps that display areas likely to have of the landslides identified was greatly improved with landslides in the future. After landslide hazards have lidar data. Thus, lidar-derived digital elevation models been identified on inventory and susceptibility maps, were selected as the base from which to create the land- the risk can be quantified and mitigation projects can slide inventory described in this paper. The inventory be prioritized and implemented. mapping protocol consists of six steps: To create a consistent landslide inventory for Oregon the Oregon Department of Geology and Mineral Indus- 1. Acquire and visualize base data tries (DOGAMI) developed the protocol described in 2. Map spatial and tabular data in a geodatabase this paper. We developed the protocol using the best 3. Verify mapped data in the field available data (lidar imagery) to ensure the most accu- 4. Display landslide inventory data using a template rate results. The Oregon Lidar Consortium plans to map continue collecting lidar data so that mapping using 5. Understand the limitations of the data and rec- this protocol can continue throughout Oregon. An ommendations for use additional benefit of creating the protocol and its asso- 6. Use the landslide inventory data products ciated map template and geodatabase is expeditious mapping and publication of landslide inventory data. The protocol and products produced using this pro- To begin the extensive undertaking of mapping exist- tocol can be used to help Oregon communities become ing landslides throughout Oregon, a pilot project area more resilient to the impacts of landslide hazards.

Oregon Department of Geology and Mineral Industries Special Paper 42 1 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

2.0 INTRODUCTION

Landslides are one of the most devastating natural, and 2.1 Intended Audience and DOGAMI Role sometimes human-induced, disasters. Worldwide, they cause billions of dollars in property damage and thou- This protocol was developed so that DOGAMI and sands of deaths every year (Hong and others, 2007). others can produce consistent lidar-based landslide Landslides in the United States cause an average of inventory maps quickly and consistently. The existing 25–50 deaths and over $2 billion in economic losses information is not comprehensive, but future efforts annually (Turner and Schuster, 1996; Spiker and Gori, can build on and refine the data and protocol. 2003). The intended audience of this paper is primar- In Oregon, landslides pose significant threats to ily DOGAMI scientists but also includes government, people and infrastructure. As population growth con- industry, and university scientists who are interested in tinues to expand and as development on landslide sus- producing standardized landslide inventory maps, and ceptible terrain increases, greater losses are likely to end users of maps. DOGAMI encourages the use of result (Figure 1). Most of Oregon’s landslide damage this protocol and intends to have it established as the has been associated with severe winter rain storms — Oregon standard for geographic information system landslide losses exceed $100 million in direct damage (GIS) based landslide inventory through the state Geo- (such as the February 1996 event) (Wang and others, spatial Enterprise Office (GEO; http://www.oregon. 2002). However, landslides are also a chronic hazard in gov/DAS/EISPD/GEO/index.shtml) Framework pro- Oregon; annual average maintenance and repair costs gram. DOGAMI plans to publish landslide inventory for landslides in Oregon are estimated at over $10 mil- maps that are developed using this protocol. Publi- lion (Wang and others, 2002). Many parts of Oregon cation of maps produced by non-DOGAMI staff will are susceptible to landslides induced by earthquake likely require detailed review by a DOGAMI employee shaking, and losses associated with sliding in moder- to ensure consistency. ate-to-large earthquakes are likely to be significant. In addition to maps for specific areas, data published Volcano-induced landslide hazards are also potential by DOGAMI using this protocol will be used to update threats to parts of Oregon. data in the Statewide Landslide Information Layer for DOGAMI recently researched the best remote sens- Oregon (SLIDO) (http://www.oregongeology.org/sub/ ing data set (photos, photogrammetric elevation data, slido/index.htm). lidar elevation data) to use as a primary tool for system- atic mapping of landslides in Oregon (Figure 2; Burns, 2.2 Background 2007). The conclusion of this pilot study was that lidar was overwhelmingly better than other available remote In 2005, DOGAMI began a collaborative landslide sensing data sets (e.g., 30-m shuttle data, 10-m national research program with the U.S. Geological Survey elevation data set, aerial photos). (USGS) Landslide Hazards Program to identify and understand landslides in Oregon. A pilot project area was selected in order to compare remote-sensing data sets for effectiveness. The data sets compared included (Burns, 2007): • 30-m (98 ft) digital elevation model (DEM) from Expanding Landslide Landslide Vulnerable the Shuttle Radar Topography Mission (Figure 2a) Hazard Risk Population • 10-m (33 ft) DEM derived from USGS topographic quadrangles (Figure 2b) • photogrammatic and ground-based 7-m (23 ft) interval contour data (Figure 2c) • stereo aerial photographs from 1936 to 2000 Figure 1. Risk diagram shows the overlap of landslide hazard and vulnerable population (modified after Wood, 2007).

2 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

30-m SRTM DEM 10-m USGS DEM A B

7-m City of Portland DEM 1-m lidar DEM C D

1-m DEM + aerial photo to simulate stereo pair E

Figure 2. Comparison of five remote-sensing data sets for the same area. SRTM is Shuttle Radar Topography Mission; USGS is U.S. Geological Survey. The aerial photo (e) is draped over a digital elevation model (DEM) so that it simulates the three-dimensional view provided by a stereo-pair photograph.

Oregon Department of Geology and Mineral Industries Special Paper 42 3 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

• lidar imagery with an average of one data point 1. All previously identified landslides from geologic per square meter (3.2 ft2) and with a vertical accu- maps, landslide studies, and other local sources racy of about 15 cm (6 in) (Figure 2d) should be compiled. 2. The mapper should have experience identifying Two key findings of the pilot project were: 1) use of all types and ages of landslides within the area lidar data resulted in identification of 3 to 200 times being studied. the number of landslides identified using the other data 3. Lidar data should be used to identify landslides sets, and 2) mapping the spatial extents of landslides and accurately locate the extents of previously identified from lidar data was easier and more accurate mapped landslides (from step 1). compared to other mapping methods. 4. An orthophoto of similar age to the lidar data When examining the results of the comparison of should be used to minimize misidentification of remote-sensing data, several debris flow fans at the man-made cuts and fills as landslides. mouths of channels or potential channelized debris 5. The mapper should use at least one set of histori- flow deposits were identified with serial stereo-pair cal stereo-pair aerial photography to locate land- aerial photos, which did not get identified on the lidar- slides in the area being studied. derived DEMs. Dense development has taken place in 6. Nonspatial data should also be collected at the Oregon in the last 40 years, which can mask landslide time of the mapping so that a comprehensive features, especially if major earthwork has taken place. database can be formed. Nonspatial data should In most of the populated areas of Oregon, if historic generally include, for example, confidence of air photos are available, at least one review of (greater interpretation, movement class, and direction of than 40 years old) photos should be undertaken (Burns, movement. The nonspatial data gathered for this 2007). protocol are described in detail in section 5.2.2 of When developing accurate large-scale landslide this paper. inventory maps, we recommend the following minimal 7. A comprehensive check, including technical steps: review of mapped landslides and field checks where possible, of spatial (map) and nonspatial data should be developed and implemented.

4 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

3.0 PREVIOUS LANDSLIDE INVENTORIES

The first statewide database of landslides for Oregon tified landslides from geologic maps, previous landslide was prepared after the severe storm events in 1996 and studies, and other local sources. SLIDO includes digital 1997 and was published as DOGAMI Special Paper landslide mapping derived from 257 published geolog- 34 (Hofmeister, 2000). The database incorporated ic reports and geologic hazard studies primarily by the information compiled by a number of federal, state, USGS and DOGAMI, along with regional studies by the and local data sources; hence, the quality of the data U.S. National Forest Service (USFS) and theses studies varies considerably. The database provides locations for in the state. Most of the maps used to compile SLIDO approximately 9,500 landslides (Figure 3) that occurred were geologic maps that have widely variable quality during 1996-1997. Most mapped locations are points, and completeness in regard to mapping of landslides so in many cases the spatial extent of the landslides is and related features. This is apparent in the numerous unknown. Five deaths are directly attributed to land- map boundary faults on the full-size map. slides during 1996 (Hofmeister, 2000). A third statewide inventory of landslides from the As shown in Figure 3, almost all of the landslides in Oregon Department of Transportation (ODOT) is now the database are located west of the Cascade Moun- partially available in a GIS (K. Castelli and C. Mohney, tains. This is because most of these landslides were unpublished data, unstable slopes inventory program, triggered by intense rain events and/or rain-on-snow ODOT, 2008). This inventory of historic landslides is events that generally did not impact the eastern portion limited to state and interstate highways; however, it of the state (Burns and others, 1998). includes detailed information at each location. From research performed by DOGAMI and the Other landslide data that should be acquired and USGS during 2005-2006 and from the recommenda- reviewed during the landslide inventory process is from tion of Burns (2007), DOGAMI compiled the State- the local county and city in which the mapping is being wide Landslide Information Database of Oregon, done. These data are often very useful for identifying release 1 (SLIDO-1; Figure 4) (Burns and others, 2008) historical landslides. a statewide database of the majority of previously iden-

Figure 3. Map of a landslide database (blue dots) consolidated after severe storms in 1996-1997 (Hofmeister, 2000).

Oregon Department of Geology and Mineral Industries Special Paper 42 5 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Figure 4. The Statewide Landslide Information Layer for Oregon (SLIDO) release 1 (Burns and others, 2008) is a database of the roughly 15,000 Oregon landslide deposits identified in published literature. A data map shows fans (red polygons), debris flow fans (orange polygons), and landslide-related deposits (yellow polygons) including colluvium and talus.

6 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

4.0 LANDSLIDE TYPES

The general term “landslide” refers to a range of mass All landslides can be classified into six types of move- movements including rock falls, debris flows, earth ment (Figure 5): 1) falls, 2) topples, 3) slides, 4) spreads, slides, and other mass movements (Varnes, 1978). Dif- 5) flows, and 6) complex. Most slope failures are com- ferent types of landslides have different frequencies of plex combinations of these distinct types, but the gen- movements, triggering conditions, and very different eralized groupings provide a useful means for framing resulting hazards. discussion of the type of hazard associated with the

Falls are near-vertical, rapid movements of masses of materials, such as rocks or boulders. The rock debris sometimes accumulates as talus at the base of a cliff.

Topples are distinguished by forward rotation about some pivotal point, below or low in the mass.

Slides are downslope movement of soil or rock on a surface of rupture (failure plane or shear-zone). • Rotational slides move along a surface of rupture that is curved and concave. • Translational slides displace along a planar or undulating surface of rupture, sliding out over the original ground surface.

Spreads are commonly triggered by earthquakes, which can cause liquefaction of an underlying layer and extension and subsidence of commonly cohesive materials overlying liquefied layers.

Channelized Debris Flows commonly start on a steep, concave slope as a small slide or earth flow into a channel. As this mixture of landslide debris and water flows down the channel, it pick ups more debris, water, and speed, and deposits in a fan at the outlet of the channel.

Earth Flows commonly have a characteristic “hourglass” shape. The slope material liquefies and runs out, forming a bowl or depression at the head.

Complex landslides are combinations of two or more types. A common complex landslide is a slump-earth flow, which usually exhibit slump features in the upper region and earth flow features near the toe.

Figure 5. Types of landslide movements (modified after Highland, 2004).

Oregon Department of Geology and Mineral Industries Special Paper 42 7 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

landslide, the landslide characteristics, identification ing fans. Debris flows are also commonly mobilized methods, and potential mitigation alternatives. by other types of landslides that occur on slopes near Movement type should be combined with other a channel. They can also initiate within channels from landslide characteristics such as type of material, rate of accelerated erosion during heavy rainfall or snow melt. movement, depth of failure, and water content in order Hill slope areas that have failed often remain in a to more fully understand the landslide behavior. Many weakened state, and many of these areas tend to fail landslides exhibit common features as shown in Figure repeatedly over time. A channel with a debris flow fan 6. For a more complete description of the different at its mouth indicates a history of debris flows in that types of landslides, see U.S. Transportation Research channel. The formation of talus slopes indicates that Board Special Report 247 (Turner and Schuster, 1996), numerous rock falls have occurred. Large landslide which has an extensive chapter on landslide types and complexes may have moved dozens of times over thou- processes. sands of years, with long periods of stability punctuated One type of landslide that is often life threatening is by episodes of movement. Thus previously failed areas the channelized debris flow or “rapidly moving land- are particularly important to identify, as they maybe slide.” Debris flows often initiate on a steep slope, move susceptible to future instability. In some cases, areas into a steep channel (or drainage), increase in volume that have previously failed have subtle topographic by incorporating channel materials, and then deposit morphology, making them difficult to identify. material, usually at the mouth of the channel on exist-

Tension cracks (crown cracks) Internal scarps Crown

Flank (minor scarp)

Compression ridge (transverse ridge) Head scarp

Failure plane Toe (surface of rupture) Landslide deposit (main body)

Figure 6. Block diagram of a slump-earth flow showing common features (modified from Highland, 2004).

8 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

5.0 METHODOLOGY

The method employed to identify landslide areas in this 5.1 Acquisition and Visualization of Base study is divided into four main steps: Data 1. Acquire and visualize base data including: ◦◦ DEM derived from lidar data, In the last half decade or so, very high resolution, ◦◦ Slope map derived from lidar data, high-accuracy DEMs developed using lidar data have ◦◦ Orthorectified aerial photo of similar age to become available for some parts of Oregon. These the lidar data, new data give us a much better image of the surface ◦◦ Previous landslide inventories or other data of Earth, allowing the identification of topographic fea- on landslides within the proposed mapping tures associated with landslides, such as concave slope area, and (closed) depressions, steep or vertical scarps, shear ◦◦ Geologic map. zones located along the flanks of a landslide, and trans- 2. Map landslide spatial data and tabular data by verse ridges, snouts, and toes (Turner and Schuster, ◦◦ Setting up the computer workspace neces- 1996). Recognizing these topographic features allows sary for mapping and identification of landslides with a high level of certainty ◦◦ Mapping at multiple scales. and accurate mapping. In the past, most of the highly 3. Review and field verify spatial and tabular data. accurate landslide maps have been created by combin- 4. Create one-quarter-quadrangle landslide inven- ing aerial photography and extensive field surveys. For tory maps. this mapping protocol, bare-earth DEMs derived from lidar data are the essential tool. The lidar-derived DEM should be first viewed as a shaded relief map or hillshade map with a sun azimuth of 315° and altitude of 45° as shown in Figure 7. Because the grayscale DEM can become confusing when a mapper is scanning the map at various scales, we recommend adding an elevation color profile to help the mapper visualize the upslope and downslope direction easily at all scales (Figure 7).

Figure 7. Lidar-derived digital elevation model (DEM) viewed as (left) a hillshade in grayscale with a sun azimuth of 315° and altitude of 45° and (right) with a color profile (blue for lower elevations and green for higher elevations).

Oregon Department of Geology and Mineral Industries Special Paper 42 9 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Previous mapping of landslides using lidar imagery In addition to lidar-derived imagery, an orthophoto in Seattle, Washington, by the USGS found that hill- of similar age to the lidar data should be used to help shade images had to be viewed with at least three sun differentiate between man-made and natural land- azimuths to provide suitable lighting conditions for dif- forms (Figure 9, top). In the lower images in Figure 9, ferent slope orientations (Schulz, 2004). Rather than three features that could be mistaken as landslides are viewing the same area at the same scale with three dif- identified (ovals); however, upon review of the ortho- ferent hillshades, we recommend the following: photo (lower right) all three features appear to be man- made. The man-made features are cuts and fills that 1. Apply 50% transparency to the colored hillshade can resemble the head scarp and toe morphology of a (the original colored hillshade before transpar- landslide. ency is applied is shown in Figure 7, right). Additional base data layers that should be compiled 2. Create a slope map with all slopes greater than and added to the mapping project are previous land- 45° in a dark gray. slide inventories or other data on landslides within the 3. Layer the slope map beneath the colored hill- proposed mapping area. As a minimum this should shade and apply 10% transparency to the slope include any landslides contained in DOGAMI Special map. Paper 34 (Hofmeister, 2000) and the latest version of SLIDO (release 1, Burns and others, 2008) (Figure 10). The resulting image is shown in Figure 8 (left). In We found that to zoom and pan easily at varying some instances, it may be advantageous to eliminate scales in a GIS environment using the lidar-derived the hillshade and just use the combined slope map and images and the other recommended layers, the lidar- DEM with color. We also found that adding colors to derived images had to be separated into sections the slope map at particular slope intervals helped iden- roughly one quarter the size of a 7.5-minute quadran- tify slope breaks and other topographic features. gle. Section size is a function of computer power and To further enhance the morphology and to assist type of GIS software, and the appropriate size may vary in locating and accurately defining landslide features, for each user environment. topographic contours derived from the lidar DEM The final base layer that should be examined is geo- can be overlaid on the hillshade/slope map (Figure 8, logic map. The Oregon Geologic Database Compilation right). Contours are particularly helpful in identify- is an assemblage of the best available geologic maps for ing the subtle morphology associated with debris-flow the state of Oregon in GIS (Jenks and others, 2008). The fans. The images in Figure 8 are the two main views that project is over 75% finished and expected to be com- should be used to identify topographic features that plete for the entire state by 2009. define and outline landslides.

Figure 8. (left) Colored hillshade draped on slope map and (right) with contour lines.

10 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Figure 9. (upper left) Color hillshade-slope map and (upper right) orthophoto. (lower left) Example of three landforms resembling landslide morphology identified by brown circles. (lower right) The same three areas identified as man-made features on an orthophoto. (Note that the lower images are not from the same area as upper images.)

Figure 10. Previously mapped landslide points from Special Paper 34 (Hofmeister, 2000) (red points) and landslide extents from SLIDO-1 (Burns and others, 2008) (yellow polygons).

Oregon Department of Geology and Mineral Industries Special Paper 42 11 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

5. 2 Mapping Landslides: Spatial Data and All four of these items may not be present at every Tabular Data landslide. For example, many debris flow fan deposits are commonly mapped without the other spatial data. The method employed to identify landslide areas in this These polygons and lines are illustrated as block dia- study uses two kinds of data: 1) spatial data and 2) tabu- grams with a corresponding map view in Figure 12. The lar data. Spatial data are data that can be mapped as examples shown in Figure 12 are applicable to most points, lines, or polygons. Tabular data are descriptive landslide types, except channelized debris flows and data, usually in text or numeric form, stored in rows rock fall/topples, which are illustrated in Figure 13. and columns in a database and linked to spatial data. These landslides and landslide features should be digi- To facilitate data collection, a geodatabase template tized/mapped using GIS software and either an inter- was developed as part of this protocol. The template active pen display monitor or mouse. An orthophoto of includes empty feature classes for deposits, scarp flanks, similar age to the lidar data should be inspected during scarps, and photos. A screenshot of the structure as set the digitizing/mapping process to assure that man- up in Environmental Systems Research Institute, Inc. made features such as cuts and fills are not misidenti- (ESRI) ArcCatalog™ is shown in Figure 11. The geoda- fied as landslides and landslide features (Figure 9). tabase includes relationship classes between the feature It is very helpful to use a grid layer to help keep track classes. The template includes all fields for the tabular of areas already mapped (Figure 14). While digitiz- data (Table 1 and Appendix A), located in the Deposits ing/mapping an area, the previously mapped landslide feature class. Individual mappers who use this template inventories available in the area should also be inspect- can then easily transfer their data into a master geoda- ed and confirmed or corrected. tabase. Appendix B is a guide for setting up and using Landslides and landslide features vary in size, so the geodatabase template. mapping should be done at several different scales. We recommend scanning the area at the following scales: 5.2.1 Spatial Data • 1:24,000 (the native scale of standard printed 7.5- Spatial data that should be compiled includes the fol- minute topographic quadrangles) lowing: • 1:10,000 • Polygon (outline) of the mapped landslide deposit • 1:4,000 (including debris flow fans and talus extent) After landslides have been mapped at the different • Polygon (outline) of the landslide head scarp and scales, all lines should be relocated at a scale of 1:4,000 flanks to ensure that all spatial data (lines) have been mapped • Line of the uppermost extent of the head scarp consistently. • Lines of internal scarps

Figure 11. Screenshot of Environmental Systems Research Institute, Inc. (ESRI) ArcCatalog™ interface showing landslide geodatabase template structure developed for this protocol.

12 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Landslide Landslide Deposit Head Scarp and Flanks

Top of Landslide Internal Head Scarp Scarps

Figure 12. Block diagrams and map views showing the four kinds of spatial data compiled for each landslide.

Figure 13. Block diagrams and map views of (top) channelized debris-flow fan and (bottom) rockfall/topple talus.

Oregon Department of Geology and Mineral Industries Special Paper 42 13 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Landslide mapping using DEMs derived from lidar (DEM grid size). The limitations of the maps produced in the Seattle, Washington, area was done systemati- following this protocol are discussed in section 7. cally at several scales (Schulz, 2004). If historic air photographs are available for the mapping area, at least one 5.2.2 Tabular Data review of photographs more than 40 years old should Each kind of spatial data should also have several attri- be undertaken (Burns, 2007). As a last step in compiling butes (tabular data) linked to the polygons or lines spatial data, all GIS data should be processed to remove (Table 1). Appendix A lists the tabular data fields. redundant or unneeded data and topological errors. Related to mapping scale and confidence (discussed 5.2.2.1 Type of movement later in this section) is the accuracy and precision of the Each landslide should be classified into one the fol- mapped landslide deposits and features. Analysis of the lowing types of movement (Figure 5): 1) slides 2) flows, accuracy and precision is very difficult, especially when 3) spreads, 4) topples, 5) falls, 6) complex. there is no definitive map for comparison. Therefore, we assume that the areas mapped following this proto- 5.2.2.2 Classification of material and movement types col are landslide deposits and features. Landslides should be differentiated by the kinds of As previously discussed, accuracy of mapped land- material involved and the mode of movement. A clas- slides identified from lidar data is greatly improved sification system based on these parameters was devel- when compared with other mapping methods, espe- oped by Varnes (1978) and is shown in Table 2. To cially if the recommendations for visualization of base assist the mapper, block diagram examples and detailed data in this protocol are followed (Burns, 2007). How- descriptions of some of the most common types of ever, the accuracy will still vary with the skill and care of landslides in Oregon are given in Figure 5. the individual mapper and the quality of the lidar data. Precision will also be improved if the recommenda- 5.2.2.3 Confidence of landslide identification tions in this protocol are followed. However, precision Each landslide should be classified according to is a function of the visual strength and sharpness (or a “confidence” the mapper assigns based on the like- clarity) of the topographic features used to identify lihood that the landslide actually exists (Irvine and the landslides and the related quality of the lidar data others, 2007). Landslides are mapped based characteristic topographic features, and the confidence of the interpretation is based on the visual clarity of the features. As a landslide ages, weathering (primarily through erosion) degrades the topographic features produced by landsliding. With time, landslide features may become so subtle that they resemble features produced by geologic processes and conditions unrelated to landsliding. Most landslides have several different types of topographic features associated with them (Figure 6). A good way to define certainty is through a simple point system (Table 3) associated these features. The point system in this protocol is based on a ranking of four primary landslide features with 0 to 10 points per feature, with zero points for an unidentifiable feature and 10 points for a very clearly identifiable feature. For example, if the head scarp and toe of a landslide were clearly identifiable in the lidar DEM, the mapper would apply 10 points for the head scarp and 10 points for the Figure 14. Example of a grid layer (green) used to toe, equaling 20 points, which would be associated with keep track of mapped areas. a moderate certainty of identification.

14 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Table 1. Tabular data fields in landslide geodatabase. Field Name Brief Description Field Name Brief Description QUADNAME 7.5 minute quadrangle name FAN_DEPTH estimated and/or UNIQUE_ID “QUADNAME”_”ID” * calculated fan depth TYPE_MOVE type of movement DEEP_SHAL deep or shallow seated MOVE_CLASS movement classification name HS_IS1 horizontal distance from head scarp to internal scarp no. 1 MOVE_CODE movement classification code IS1_IS2 horizontal distance from internal scarp no. 1 to CONFIDENCE confidence of internal scarp no. 2 identification IS2_IS3 horizontal distance from internal scarp no. 2 to AGE estimated age internal scarp no. 3 DATE_MOVE date of last known movement IS3_IS4 horizontal distance from internal scarp no. 3 to NAME landslide name internal scarp no. 4 GEOL geologic unit HD_AVE Average horizontal distance between internal SLOPE adjacent slope angle scarps: calculated average horizontal distance HS_HEIGHT Head scarp height: change in elevation from between scarps bottom to top of head scarp DIRECT direction of movement FAIL_DEPTH Failure depth, estimated and/or calculated slope AREA area of landslide deposit normal thickness of failure depth VOL volume of landslide deposit FAN_HEIGHT change in elevation from top to toe of fan *Identification numbers (IDs) are sequential numbers (starting at 1 for the first mapped landslide) for each landslide mapped in each 7.5- minute quadrangle. The UNIQUE_ID is a concatenation of the QUADNAME and ID fields. UNIQUE_ID result in a unique code for every landslide mapped in the state of Oregon. An example of a unique ID is given below (in bold), with the corresponding reference info: Portland_1 is the first landslide mapped within the Portland quadrangle.

Table 2. Simplified classification of landslides (Varnes, 1978).

Type of Type of Material Movement Rock Debris Soil Fall RF rock fall DF debris fall EF earth fall Topple RT rock topple DT debris topple ET earth topple Slide-rotational RS-R rock slide-rotational DS-R debris slide-rotational ES-R earth slide-rotational Slide-translational RS-T rock slide-translational DS-T debris slide-translational ES-T earth slide-translational Lateral spread RSP rock spread DSP debris spread ESP earth spread Flow RFL rock flow DFL debris flow EFL earth flow Complex C complex or combinations of two or more types (for example, ES-R + EFL)

Table 3. Confidence of landslide identification points and scale.

Landslide Feature Points Confidence Total Points Head scarp 0-10 High > 30 Flanks 0-10 Moderate 11–29 Toe 0-10 Low ≤ 10 Internal scarps, sag ponds or closed 0-10 * depressions, compression ridges, etc. *Applied only once so that total points do not exceed 40.

Oregon Department of Geology and Mineral Industries Special Paper 42 15 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

For debris-flow fans, the confidence scale should movement, a landslide will begin to undergo geomor- be used directly (e.g. low, moderate, high) based on phic changes similar to that displayed in Figure 16. We the distinctness of the fan, as these types of landslide recommend two age groups: historic (<150 years) and deposits do not have the landslide features associated prehistoric (>150 years). The cutoff point of 150 years is with other landslides (Figure 15). used in Oregon, because the state was brought into the Some known landslides may not exhibit enough fea- union as an official state in 1858. tures (and thus enough points) to result in a moderate • Historic or active (movement <150 years): The or high confidence. In these cases the scoring system landslide appears to be currently moving or to should be overridden and a high confidence assigned. have moved within historic time or historic data has identified the landslide as having moved in the 5.2.2.4 Estimated age or time of landslide activity last 150 years. Landslide features generally sharp Estimation of the age of a landslide can be very diffi- and clear (Figure 16, example A). cult. However, as age is often an important attribute for • Prehistoric or ancient (movement >150 years): hazard assessments, an estimate should be attempted. Landslide features are slightly to strongly eroded In general, if a landslide has had recent activity (with the or covered with younger deposits. Features may exception of channelized debris flows, rock falls, and be subdued and indistinct.(Figure 16, examples B topples), it will display topographic features as shown and C). in Figure 16. In western Oregon, if there is no renewed

Figure 15. Examples of confidence for debris flow fans: (left) low and (right) high. The low-confidence example is in a highly developed area that has had earth movement, which can mask the original debris flow deposit.

A. Landslide featurs are B. Landslide features are less defined. C. Landslide features are very subtle. sharply defined. Slopewash and shallow mass Drainage follows rifts and sags inside movements modify sharp edges, but slide mass, internal blocks are dissected, no drainage lines established. material is eroded from slide mass.

Figure 16. Geomorphic changes in surface morphology of a landslide with time (McCalpin, 1974).

16 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

5.2.2.5 Known date of movement and landslide name Head scarp vertical height should be measured. This If there is a known date of last movement or succes- measurement will be used to calculate an estimated sive movements, these dates should be recorded. Also, slope normal thickness or depth of failure (Harp and if the landslide has a common name (for example, the others, 2006; Burns and others, 1998). Because the Washington Park Slide), the name should be entered height of the head scarp will vary horizontally, the into the tabular database. height should be measured at several locations along the head scarp and average height recorded (Figure 18). 6.2.2.6 Slope angle, head scarp, and fan depth In the case of a debris-flow fan, the estimated maxi- measurements mum depth should be measured on the DEM. This To best estimate the slope angle of the ground prior measurement will be used to calculate an estimated fan to the landslide, the slope directly adjacent to the land- volume. In general, debris-flow fans tend to be shaped slide should be measured on the DEM. This measure- like a semi-circle in map view and fan volume is similar ment will serve as an estimated prefailure slope angle to half a cone. To calculate the volume of half a cone (Figure 17). The slope angle will vary due to the accu- and thus to estimate the volume of the fan, the height racy of the DEM and variations in natural slope con- of the cone (fan) is necessary. The height of the fan is ditions, so the slope angle should be averaged. Slope the difference in elevation from the bottom to the top angle should be recorded in degrees (0°–90°). of the fan. An imaginary line should be drawn between

Figure 17. Example location to measure adjacent slope angle (purple line on the block diagram and map view).

Figure 18. Example location to measure estimated head scarp height (yellow line on block diagram and map view).

Oregon Department of Geology and Mineral Industries Special Paper 42 17 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

the flat area where the fan is deposited and the base of or depth of failure should be calculated as shown in the hills (dashed line in Figure 19). This imaginary line Figure 20. acts as the centerline of the cone (dividing the cone in This calculation is done to reduce the overestima- half) and should be used to measure the highest ele- tion of thickness for relatively thin landslides on steep vation of the fan (yellow dot in Figure 19). The lowest slopes. With an estimated slope normal thickness or elevation of the fan can then be measured perpendicu- depth of failure, the landslide can be classified as deep lar to the dashed line and along the lower edge of the seated or shallow seated. This differentiation is - nec mapped fan. The lower elevation should be subtracted essary because different models are used to estimate from the higher elevation and the height recorded. regional stability or susceptibility for different landslide depths. There is no widely accepted boundary value 5.2.2.7 Classification of deep or shallow between deep and shallow landslides. We selected 4.5 After the slope angle and head scarp height have m (15 ft) as the boundary based on the combination of been measured, the estimated slope normal thickness several factors and results from other studies.

Figure 19. Example location to measure estimated maximum debris flow fan depth (elevation difference between two points – yellow dots).

Figure 20. Calculation of estimated slope normal thickness or depth to failure.

18 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Most geologists and engineers classify landslides as Excavations of up to 4.5 m (15 ft) have become rou- shallow or deep from the materials at the failure sur- tine (standard practice) in the construction industry. face. If the basal failure surface is at or above the con- For example, an excavation for a typical residential tact between surficial materials (colluvium, residual house with a basement is generally between 3 m (10 soil, etc.), then the landslide has traditionally been clas- ft) and 4.5 m (15 ft) deep. Because excavation and con- sified as “shallow.” If the basal failure surface is below struction of structural entities such as retaining walls this contact (within the bedrock) then the landslide has at this depth range has become standard practice, it is been classified as “deep” (Figure 21). This is logical from assumed that this practice would be used to potentially a stability analysis and mitigation method perspective. mitigate landslides or landslide areas with these depths. However, this classification method results in a variable On the other hand, areas that have deep-seated land- depth depending on how thick the surfical materials slides or the potential for deep-seated landslides would are at any given location. likely require special types of mitigation that might Sidle and Ochiai (2006) note that shallow landslides include dewatering or construction of very large retain- are characteristically less than 2 m (6.5 ft) in depth and ing structures. that deep landslides are generally greater than 5 m (16.5 On the basis of these factors and other studies, we ft) in depth. Burns (1998) found a bimodal distribution selected a boundary value of 4.5 m (15 ft) between shal- for depth to failure surfaces in a study area in Oregon low-seated and deep-seated landslides. City, Oregon. He concluded a cutoff value of 4.5 m (15 ft) was appropriate.

Upper-surficial soils or highly weathered bedrock

Weathered to unweathered bedrock

Figure 21. Example of shallow and deep-seated landslides.

Oregon Department of Geology and Mineral Industries Special Paper 42 19 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

5.2.2.8 Horizontal distance between scarps 5.2.2.9 General movement direction and size The horizontal distance between all identified scarps, The last value that should be collected is the gener- including the head scarp, should be measured on the alized movement direction. This value should be col- DEM. This measurement may serve as an estimate of lected as an azimuth (0° to 360°) in increments of 22.5° future retrogressive failure distance behind the upper- so that a single number, in degrees, is recorded (Figure most head scarp (Figure 22). The horizontal distance 23). The value should be recorded as one of the values should always be measured from the top of one scarp shown in Figure 23. The direction should be measured to the base of the upslope scarp (red line to red line in from the approximate center of the uppermost head Figure 22), not from top of scarp to top of scarp. scarp to the approximate center of the toe. After all the horizontal distances have been collected, an average distance should be calculated.

HS-IS1 HS-IS1 IS1-IS2 IS1-IS2

Figure 22. Example of horizontal distance measurements between two scarps (red lines). HS is the head scarp; IS1 and IS2 are internal scarps.

225

360 337.5 -0 22.5 315 45 292.5 67.5

270 90

247.5 112.5 225 135 202.5 180 157.5 AZIMUTH

Figure 23. Example of measurement of direction of movement in azimuth (black line from ball to arrow).

20 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

5.2.2.10 Area and volume 5.2.2.11 No data The area of each landslide deposit should be esti- In some cases, a mapper will not input data in the mated using a GIS program after the landslide depos- database, because there is not enough information to it (polygon) has been drawn. Once the area has been make a choice or the data are simply not available. found, the volume should be calculated by multiplying the area and depth. In the case of debris-flow fans, the area should be multiplied by one third of the estimated maximum depth, on the assumption that the volume of the fan is shaped like a half cone.

6.0 LANDSLIDE INVENTORY MAP TEMPLATE

A map template was developed as part of this proto- DEM using the “hillshade” tool in the Spatial Analysis col to display the data. The template was produced by extension of Environmental Systems Research Institute, building upon the Inventory Map Series developed by Inc. (ESRI) ArcGIS™. The DEM was first multiplied by the California Geological Survey (Wiegers, 2006; Irvine 5 times (vertical exaggeration) prior to the hillshade and others, 2007) and an engineering geology map by image creation to enhance slope areas. The settings in Burns (1999) An example of the landslide protocol the “hillshade” tool included a sun angle at 315° and map template with the landslide inventory data from at 45° from the horizontal. A transparency of 40% was the northwest quarter of the Oregon City quadrangle is applied to this layer. shown in Figure 24. The map template was developed The aerial photograph image was created using 2005 to expedite publication of the data in a form that allows statewide orthorectified images. The image was changed people without GIS software to quickly and easily view from RGB composite (multi-color) to a stretched color the data. ramp from white to black (i.e., grayscale). A transpar- Because the “base map” on the map template is ency of 45% was applied to this layer. unique, a short description of how it was created is Finally, the two layers were grouped, and a brightness included here. The base consists of two layers: a hill- of 20% was applied. In the group, the hillshade image shade image and an aerial photograph image. The hill- was placed on top of the orthophoto image. shade image was created by transforming the original

Oregon Department of Geology and Mineral Industries Special Paper 42 21 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

G E O L O G Y F A N O D IMS-26 T N M I E N M E T R R A Landslide Inventory Map of the Northwest Quarter of the Oregon City Quadrangle, INTERPRETIVE MAP SERIES A L P STATE OF OREGON I E

U N

S DEPARTMENT OF GEOLOGY AND MINERAL INDUSTRIES O

I Landslide Inventory Map of the Northwest Quarter of the R

E VICKI S. MCCONNELL, STATE GEOLOGIST S O Clackamas County, Oregon Oregon City Quadrangle, Clackamas County, Oregon

1937 2009 by William J. Burns and Ian P. Madin Partial funding provided by the U.S. Geologic Survey,

000 000 000 122°37'30"W 530 532 534 122°33'45"W Landslide Hazards Program (Grant 00320340) 45°22'30"N 45°22'30"N EXPLANATION This map is an inventory of existing landslides in this quarter quadrangle. The landslide inventory is one of the essential data layers EFL used to delineate regional landslide susceptibility. This landslide inventory is not regulatory, and revisions can happen when new information regarding landslides is found or when future (new) landslides occur. Therefore, it is possible that landslides within the ES-R + EFL 5 ES-R + EFL 0 mapped area were not identified or occurred after the map was prepared. 2 te This inventory map was prepared by following the Protocol for Inventory Mapping of Landslide Deposits from Light Detection and DFL ta Ranging (Lidar) Imagery developed by Burns and Madin (2009). The three primary tasks included compilation of previously mapped DFL s EFL r landslides (including review of DOGAMI Special Paper 34 [Hofmeister, 2000] and the Statewide Landslide Information Layer for Oregon, release 1 [Burns and others, 2008]), lidar-based morphologic mapping of landslide features, and review of aerial photographs. ES-R + EFL Clackamas River Inte Landslides identified by these methods were digitally compiled into a GIS database at varying scales. The recommended map scale for these data is 1:8,000, as displayed on this map. Each landslide was also attributed with classifications for activity, depth of failure, movement type, and confidence of interpretation. The landslide data are displayed on top of a base map that consists of an aerial photograph (orthorectified) overlaid on the lidar-derived hillshade image.

ES-R + EFL This landslide inventory map is intended to provide users with basic information regarding landslides within the quarter quadrangle. The geologic, terrain, and climatic conditions that led to landslides in the past may provide clues to the locations and conditions of H future landslides, and it is intended that this map will provide useful information to develop regional landslide susceptibility maps, to i g guide site-specific investigations for future developments, and to assist in regional planning and mitigation of existing landslides.

DFL w

Highway 99E

a Willamette Drive (Highway - 43) y LANDSLIDE CLASSIFICATION EFL 2 1 Each landslide shown on this map has been classified according to a number of specific characteristics identified at the time recorded in 3 ES-R + EFL 4000 4000 the GIS database. The classification scheme was developed by the Oregon Department of Geology and Mineral Industries (Burns and

502 502 Madin, 2009). Several significant landslide characteristics recorded in the database are portrayed with symbology on this map. The DFL EFL rd ES-R + EFL specific characteristics shown for each landslide are the activity of landsliding, landslide features, deep or shallow failure, type of va le landslide movement, and confidence of landslide interpretation. These landslide characteristics are determined primarily on the basis of RS-T EFL u geomorphic features, or landforms, observed for each landslide. The symbology used to display these characteristics is explained below. b Bo EFL m EFL co EFL l LANDSLIDE ACTIVITY: Each landslide has been classified according to the relative age of last movement. This map display uses Ho color to show the activity. EFL EFL W E S T L I N N EFL HISTORIC and/or ACTIVE (movement less than 150 years ago): The landslide appears to have moved within EFL EFL historic time or is currently moving (active). DFL EFL EFL EFL PREHISTORIC or ANCIENT (movement greater than 150 years ago): Landslide features are slightly eroded and there is no evidence of historic movement. In some cases, the observed landslide features have been greatly eroded and/or covered with deposits that result in smoothed and subdued morphology.

LANDSLIDE FEATURES: Because of the high resolution of the lidar-derived topographic data, some additional landslide features EFL were identified. These include: EFL DFL HEAD SCARP ZONE and FLANK ZONE(S): The head scarp or uppermost scarp, which in many cases exposes EFL the primary failure plane (surface of rupture), and flanks or shear zones. EFL EFL EFL EFL EFL DFL R EFL HEAD SCARP LINE and INTERNAL SCARP LINES: Uppermost extent of the head scarp and internal scarps EFL edl within the body of the landslide. Hatching is in the down-dropped direction. EFL EFL ES-R an EFL d Willamette River R EFL oa DFL d DEPTH OF FAILURE: The depth of landslide failure was estimated from scarp height. Failures less than 4.5 m (15 ft) deep are EFL EFL classified as shallow seated and failures greater than 4.5 m (15 ft) deep are classified as deep seated. EFL

EFL ES-R + SHALLOW-SEATED LANDSLIDE: Estimated failure plane depth is less than 4.5 m (15 ft). EFL EFL DFL EFL EFL ES-R + DEEP-SEATED LANDSLIDE: Estimated failure plane depth is greater than 4.5 m (15 ft). EFL EFL EFL EFL

EFL

A CONFIDENCE OF INTERPRETATION: Each landslide should be classified according to the confidence that the mapper assigns

EFL b based on the likelihood that the landslide actually exists. Landslides are mapped on the basis of characteristic morphology, and the

EFL e r confidence of the interpretation is based on how clearly visible that morphology is. As a landslide ages, weathering (primarily through

n erosion) degrades the characteristic morphologies produced by landsliding. With time, landslide morphologies may become so subtle

EFL EFL e t that they resemble morphologies produced by geologic processes and conditions unrelated to landsliding. h

05 EFL y Landslides may have several different types of morphologies associated with them, and we define confidence through a simple point 2 OREGON CITY e C system (see table below) associated with these features. The point system is based on a ranking of four primary landslide features with at et r a ranking of 0 to 10 points per feature. For example, if during mapping, the head scarp and toe of a landslide were identifiable and t N e s e ES-R + clearly visible, the mapper would apply 10 points for the head scarp and 10 points for the toe, equaling 20 points, which would be r tr EFL EFL e e te EFL w k n S EFL EFL DFL associated with a moderate confidence of identification. I e on ll Cr The visual display of this landslide characteristic is through the use of different line styles as shown below. t 7 g th EFL EFL in S EFL e EFL tre EFL sh e ek a t EFL W EFL Landslide Feature Points EFL DFL HIGH CONFIDENCE (≥30 points) EFL Head scarp 0-10 EFL DFL EFL Flanks 0-10 MODERATE CONFIDENCE (11-29 points) EFL EFL Toe 0-10

Internal scarps, sag ponds, compression ridges, etc. 0-10* EFL ES-T EFL LOW CONFIDENCE (�10 points) EFL EFL EFL EFL *Applied only once so that total points do not exceed 40. RS-R EFL EFL ES-R EFL EFL EFL 2000 EFL EFL 2000 EFL CLASSIFICATION OF MOVEMENT: Each landslide was classified with the type of landslide movement. There are five types of

502 EFL EFL EFL 502 landslide movement: slide, flow, fall, topple, and spread. These movement types are combined with material type to form the landslide EFL classification. Not all combinations are common in nature, and not all are present in this quadrangle. EFL DFL DFL EFL EFL ES-R + EFL EFL RS-R+EFL EFL – Earth Flow – Abbreviation for class of slope movement. The table below displays the types (Varnes, 1978). RS-R + EFL DFL EFL EFL EFL Generalized diagrams (some modified from Highland, 2004) showing types of movement are displayed below the DFL table. EFL EFL DFL ES-R + ES-R + EFL EFL Type of Material ES-R + EFL RS-T RS-R+ Type of EFL EFL Movement EFL EFL ES-T EFL EFL Rock Debris Soil EFL EFL Fall RF rock fall DF debris fall EF earth fall EFL Topple RT rock topple DT debris topple ET earth topple EFL EFL EFL Slide-rotational RS-R rock slide-rotational DS-R debris slide-rotational ES-R earth slide-rotational RS-R + EFL EFL EFL EFL Slide-transitional RS-T rock slide-transitional DS-T debris slide-transitional ES-T earth slide-transitional EFL Lateral spread RSP rock spread DSP debris spread ESP earth spread EFL Flow RFL rock flow DFL debris flow EFL earth flow EFL DFL ES-R + EFL Complex C complex or combinations of two or more types (for example, ES-R + EFL) RS-R + EFL DFL EFL RS-R+ ES-R + DFL EFL EFL Falls are near-vertical, rapid movements of masses of materials, such as rocks or boulders. The rock DFL DFL debris sometimes accumulates as talus at the base of a cliff. EFL DFL RS-R +

EFL DFL Topples are distinguished by forward rotation about some pivotal point, below or low in the mass. M EFL ol RS-R+EFL a Highwa l la Ho Ave ES-R + RS-R+ ES-R + l DFL Slides are downslope movements of soil or rock on a surface of rupture (failure plane or shear zone). EFL ly EFL EFL ES-R + y 2 L n a u EFL n e 13 e EFL RS-R+EFL RS-R+EFL Rotational slides move along a surface of rupture that is curved and concave. EFL

Linn Avenue Translational slides displace along a planar or undulating surface of rupture, sliding out over the EFL original ground surface. DFL

DFL EFL EFL Spreads are commonly triggered by earthquakes, which can cause liquefaction of an underlying layer and extension and subsidence of commonly cohesive materials overlying liquefied layers. EFL

DFL Initiation Transport EFL DFL RS-R+ RS-R+EFL EFL Channelized Debris Flows commonly start on a steep, concave slope as a small slide or earth flow into a EFL channel. As this mixture of landslide debris and water flows down the channel, the mixture picks up more Warner Milne Road debris, water, and speed, and deposits in a fan at the outlet of the channel. Deposition RS-R+ EFL RS-R+EFL Earth Flows commonly have a characteristic “hourglass” shape. The slope material liquefies and runs out, forming a bowl or depression at the head.

Complex landslides are combinations of two or more types. An example of a common complex landslide is a slump-earth flow, which usually exhibits slump features in the upper region and earth flow features near the toe. 0000 0000 502 502

LIMITATIONS

This landslide inventory was developed with the best available data, using the protocol of Burns and Madin (2009). However there are inherent limitations as discussed below. These limitations underscore that this map is designed for regional applications and should not B e be used as an alternative to site-specific studies in critical areas. a v e 1. Every effort has been made to ensure the accuracy of the GIS and tabular database, but it is not feasible to completely verify rc all original input data. re d e 2. Burns and Madin (2009) recommend a protocol to develop landslide inventories that is based on four primary tasks: 1) a k interpretation of lidar-derived topographic data, 2) compilation and review of previously mapped landslides, 3) review of R Ro oa historic air photos, and 4) limited field checking. These tasks can affect the level of detail and accuracy of the landslide int d inventory. We expect the lidar data quality to improve in the future, which will likely result in the identification of more landslides with greater accuracy and confidence. Due to time limitations some previously mapped landslides have likely been Po l missed. In some locations, historic air photos may not be available. Because field work is time consuming and therefore a r expensive, field checking may be extensive in some locations and very limited in other locations. nt Ce 3. The lidar-based mapping is a “snapshot” view of the current landscape that may change as new information regarding landslides becomes available and as new landslides occur. 4. Because of the resolution of the lidar data and air photos, landslides that are smaller than 100 square meters (1,075 square feet) may not be identified. Some small landslides were included if they were reported by a local governmental agency, a site- specific study, a regional study report, or a local area landslide expert, and are found to be accurately located by the mapper. 5. Even with high-quality lidar-derived topographic data, it is possible that some existing landslides will be missed, overlooked, or misinterpreted by the map author. This database and map were prepared in accordance with a published protocol (Burns and Madin, 2009) and were reviewed to minimize these problems. 6. Earthwork related to development on hillsides can remove the geomorphic expressions of past landsliding. This can result in landslides being missed in the inventory. Earthwork on hillsides can also create geomorphic expressions that mimic past landsliding; for example, a cut and fill can look like a landslide scarp and toe. This limitation can sometimes be addressed by viewing aerial photographs that predate development in the area being mapped. Therefore, to ensure that past landslides have been adequately identified, if a landslide was identified on the predevelopment air photos, it was included in the landslide inventory, whether or not surface expression was located in the lidar-derived mapping. 7. Some landslides have been mitigated. Because it is not feasible to collect detailed site-specific information on every landslide, for example if it has been mitigated and what level of mitigation was implemented, mitigation has been omitted. Again, because of these limitations this map is intended for regional purposes only and cannot replace site-specific investigations. However, the map can serve as a useful tool for estimating the regional landslide hazard and as a starting place for future detailed landslide site-specific maps. Please contact DOGAMI if errors and/or omissions are found so that they can be corrected in future versions of this map. Mey e Lel rs R a n o d R ad ACKNOWLEDGMENTS H o ad ig h We thank the people at the U.S. Geological Survey Landslide Hazard Program who contributed to the protocol and map template w through discussions and suggestions, especially Jeff Coe, who provided a detailed review that significantly improved the protocol used ay to map this quarter quadrangle. We also thank DOGAMI staff who helped with this project through technical assistance, review, and 2 general support, especially Rob Witter, Yumei Wang, and Deb Schueller. 13

REFERENCES ES-R RS-R + EFL Burns, W. J., and Madin, I. P., 2009, Protocol for inventory mapping of landslide deposits from light detection and ranging (lidar) RS-R + EFL imagery: Portland, Oreg., Oregon Department of Geology and Mineral Industries Special Paper 42, 30 p. Burns, W. J., Madin, I. P., and Ma, L., 2008, Statewide landslide information layer for Oregon (SLIDO), release 1: Portland, Oreg., Oregon Department of Geology and Mineral Industries Digital Data Series SLIDO-1, 45 p., 1 pl., scale 1:500,000. Highland, L., compiler, 2004, Landslide types and processes, U.S. Geological Survey Fact Sheet 2004-3072 (ver. 1.1), 4 p. Hofmeister, R. J., 2000, Slope failures in Oregon: GIS inventory for three 1996/97 storm events: Portland, Oreg., Oregon Department of Geology and Mineral Industries Special Paper 34, 20 p. Varnes, D. J., 1978, Slope movement types and processes, in Schuster, R. L., and Krizek, R. J., eds., Landslides—Analysis and control: RS-R + EFL Washington, D. C., Transportation Research Board Special Report 176, p. 11–33. RS-R + EFL 8000 RS-R + EFL 8000 RS-R + 501 501 EFL LOCATION MAP RS-R +

EFL

Æ ¤£30 Ä Washougal ¨¦§5 EFL 213 RS-R + Hillsboro Linnton Mount Tabor RS-R + EFL ¤£30BY Camas ¨¦§84

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Cartography by William J. Burns, Oregon Department of Geology and Mineral Industries 210 43 Ä 213 212

Outside agency review by Nancy Kraushaar, City of Oregon City 99W Ä

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Lidar-derived elevation data are from The City of Oregon City, 2005. Digital elevation

5 Estacada Æ ¨¦§ Ä

model (DEM) consists of a 3-foot square elevation grid that was converted into a 1,300 650 0 1,300 2,600 3,900 5,200 Canby Redland

H Newberg Sherwood Oregon City 211

Ä Æ

Æ hillshade image with sun angle at 315 degrees at a 45 degree angle from horizontal. Ä R Feet Æ Yamhill Ä O 551

The DEM is multiplied by 5 (vertical exaggeration) to enhance slope areas. 219 For copies of this publication contact:

N IMPORTANT NOTICE 224 Clackamas Æ Ä Nature of the Northwest Information Center C

T 0.25 0.125 0 0.25 0.5 0.75 1 551 Æ Ä 800 NE Oregon Street, #5, Ste. 177 Orthophoto is from Oregon Geospatial Enterprise Office, 2005 and consists of 2005 E This map depicts an inventory of existing landslides based on 213 TRUE NORTH Portland, Oregon 97232 orthophoto draped over DEM with transparency. GN Miles published and unpublished reports and interpretation of

Marion telephone (503) 872-2750

topography derived from lidar data and air photos. The inventory Molalla Colton Elwood Æ

OREGON SaintÄ Paul Woodburn Yoder http://www.naturenw.org

Æ Projection: North American Datum 1983, UTM zone 10 north. was created following the protocol defined by Burns and Madin 219 Ä Æ APPROXIMATE MEAN Ä 0.25 0.125 0 0.25 0.5 0.75 1 211 DECLINATION, 2006 (2009). This map cannot serve as a substitute for site-specific 99E Software: ESRI ArcMap 9.3, Adobe Illustrator CS2. Kilometers investigations by qualified practitioners. Site-specific data may give results that differ from those shown on this map. U.S. Geological Survey 7.5-minute quadrangles and counties are Source File: Rocks\Publications\IMS-26\IMS-26.mxd. labeled. This map extent shown as gray rectangle.

Figure 24. Example map displaying results based on the inventory mapping protocol described in this paper (DOGAMI Interpretive Map Series IMS-26, Burns and Madin, 2009).

22 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

7.0 LIMITATIONS OF MAPS PRODUCED USING THIS PROTOCOL

Several limitations are attached to the databases and specific study, a regional study report, or a local maps created using this protocol. These limitations area landslide expert, and are found to be accu- underscore that the databases and maps are designed rately located by the mapper. for community-scale applications and should not be 5. Even with high-quality lidar-derived topographic used as an alternative to site-specific studies in critical data, it is possible that some existing landslides areas. The following list of limitations is included on will be missed, overlooked, or misinterpreted by the map template (see Figure 24 for example): the map author. This database and map were prepared in accordance with a published protocol 1. Every effort has been made to ensure the accuracy (Burns and Madin, 2009) and were reviewed to of the GIS and tabular database, but it is not fea- minimize these problems. sible to completely verify all original input data. 6. Earthwork related to development on hillsides 2. Burns and Madin (2009) recommend a protocol to can remove the geomorphic expressions of past develop landslide inventories that is based on four landsliding. This can result in landslides being primary tasks: 1) interpretation of lidar-derived missed in the inventory. Earthwork on hillsides topographic data, 2) compilation and review of can also create geomorphic expressions that previously mapped landslides, 3) review of histor- mimic past landsliding; for example, a cut and fill ic air photos, and 4) limited field checking. These can look like a landslide scarp and toe. This limi- tasks can affect the level of detail and accuracy of tation can sometimes be addressed by viewing the landslide inventory. We expect the lidar data aerial photographs that predate development in quality to improve in the future, which will likely the area being mapped. Therefore, to ensure that result in the identification of more landslides with past landslides have been adequately identified, greater accuracy and confidence. Due to time lim- if a landslide was identified on the predevelop- itations some previously mapped landslides have ment air photos, it was included in the landslide likely been missed. In some locations, historic air inventory, whether or not surface expression was photos may not be available. Because field work located in the lidar-derived mapping. is time consuming and therefore expensive, field 7. Some landslides have been mitigated. Because checking may be extensive in some locations and it is not feasible to collect detailed site-specific very limited in other locations. information on every landslide, for example if it 3. The lidar-based mapping is a “snapshot” view of has been mitigated and what level of mitigation the current landscape that may change as new was implemented, mitigation has been omitted. information regarding landslides becomes avail- Again, because of these limitations this map is able and as new landslides occur. intended for regional purposes only and cannot 4. Because of the resolution of the lidar data and air replace site-specific investigations. However, the photos, landslides that are smaller than 100 square map can serve as a useful tool for estimating the meters (1,075 square feet) may not be identified. regional landslide hazard and as a starting place Some small landslides were included if they were for future detailed landslide site-specific maps. reported by a local governmental agency, a site-

Oregon Department of Geology and Mineral Industries Special Paper 42 23 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

8.0 POTENTIAL USES OF THE DATA AND MAPS PRODUCED USING THIS PROTOCOL

The primary purpose of this protocol is to explain how • Estimating potential losses from specific landslide the mapping is done so that many maps can be cre- events, ated with consistent content and without generating a • Emergency management applications: A poten- detailed, unique technical explanation for each map. tial use of the databases and maps is as an aid in Landslide inventory databases and maps created emergency management activities such as devel- using this protocol are intended to provide users with oping and refining emergency response plans and basic information regarding landslides within the estimating resource impacts from future landslide quadrangle mapped. The geologic, terrain, and climat- movement. ic conditions that led to slope failures in the past may • Land-use planning: Common applications of provide clues to the locations and conditions of future landslide databases in land-use planning include slope failures. input to comprehensive planning and develop- Besides providing inventory data for SLIDO, spa- ment of hazard ordinances with attached zoning tial information in these landslide databases and maps and regulations. should serve as useful tools for differentiating areas of • Prioritizing mitigation measures to reduce future higher and lower hazards. The data can also be used losses. for applications such as those listed below. It is likely We reiterate that the databases and maps devel- that individual communities will find unique and new oped using this protocol are not definitive enough for applications to suit particular needs. site-specific evaluations; however, they are valuable • Regional landslide susceptibility maps: The data for regional screening for landslides and selection of gathered from inventory mapping using this pro- appropriate areas on which to focus further site-specif- tocol should be used to help create susceptibility ic studies. The databases and maps produced using this maps. Susceptibility maps aid in estimating the protocol are particularly suitable for incorporation into potential for future landslides. state, county, and city development ordinances. • Identifying vulnerable areas that may require special planning considerations,

9.0 ACKNOWLEDGMENTS

Funding for this project was provided by the State of Series, upon which we built to create our landslide Oregon and the U.S. Geologic Survey Landslide Haz- inventory map template. These CGS staff include Allan ards Program (CRGR0002 and CRGR0009). We thank Barrows, Chris Wills, Tim McCrink, Kevin Clahan, the people at the USGS Landslide Hazards Program Terilee McGuire, Barbara Wanish, Diane Vaughan, who contributed to this protocol through discussions and, especially, Pam Irvine. and suggestions including Bill Schulz, Rex Baum, Jona- Finally, we thank all the people at DOGAMI who than Godt, Jon McKenna, and especially Jeff Coe, who helped with this project through technical assistance, provided a detailed review that improved this paper review, and general assistance, especially Sarah Robin- significantly. son, Rob Witter, Yumei Wang, and Deb Schueller. We also thank the people at the California Geologic Survey (CGS) who worked on the CGS Inventory Map

24 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

10.0 REFERENCES

Burns, W. J., 1999, Engineering geology and relative sta- Irvine, P. J., McCrink, T. P., and Wills, C. J., 2007, Land- bility of the southern half of Newell Creek canyon, slide mapping by the California Geological Survey: Oregon City, Oregon: Portland, Oreg., Portland Tools for assessing landslide hazards in California, State University, M.S. thesis, 143 p., 3 pl. AEG Special Publication 23. Conference Presenta- Burns, W. J., 2006, Landslide hazards in Oregon: tions, 1st North American Landslide Conference, Oregon Department of Geology and Mineral Vail, Colo. Industries fact sheet, 2 p. Jenks, M., Wiley, T. J., Ferns, M., Staub, P., Ma, L., Madin, Burns, W. J., 2007, Comparison of remote sensing data- I. P., Niewendorp, C. A., Watzig, R. J., Taylor, E. M., sets for the establishment of a landslide mapping and Mertzman, S. A., 2008, Oregon geologic data protocol in Oregon. AEG Special Publication 23: compilation (southwest, central, southeast, and Vail, Colo., Conference Presentations, 1st North northeast Oregon), OGDC-4: Oregon Depart- American Landslide Conference. ment of Geology and Mineral Industries. Burns, W. J., and Madin, I. P., 2009, Landslide inventory Madin, I. P., and Burns, W. J., 2006, Map of Landslide map of the northwest quarter of the Oregon City Geomorphology of Oregon City, Oregon, and quadrangle: Oregon Department of Geology and vicinity interpreted from lidar imagery and aerial Mineral Industries Interpretive Map Series IMS- photographs: Oregon Department of Geology and 26, 1 pl., scale 1:8,000. Mineral Industries Open-File Report O-06-27. Burns, S. F., Burns, W. J., James, D. H., and Hinkle, J. C., McCalpin, J., 1974. Preliminary age classification of 1998, Landslides in the Portland, Oregon, metro- landslides for inventory mapping: 21st Annual politan area resulting from the storm of February Symposium on Engineering Geology and Soils 1996: Inventory map, database, and evaluation: Engineering, Proceedings: Moscow, Ida., Univer- Portland State University, Department of Geology, sity of Idaho, p. 99–111. report to Portland Metro Regional Government, Schlicker, H. G., and Finlayson, C., 1979, Geology and contract 905828, 68 p. geologic hazards of northwestern Clackamas Burns, W. J., Madin, I. P., and Ma, L., 2008, Statewide County, Oregon: Oregon Department of Geology landslide information layer for Oregon (SLIDO), and Mineral Industries Bulletin B-99, 79 p., 10 pl., release 1: Portland, Oreg., Oregon Department scale 1:24,000. of Geology and Mineral Industries Digital Data Schulz, W., H., 2004, Landslides mapped using lidar Series SLIDO-1, 45 p., 1 pl., scale imagery, Seattle, Washington: U.S. Geological Harp, E. L., Michael, J. A., and Laprade, W. T., 2006, Survey Open-File Report 2004-1396, 11 p. Shallow-landslide hazard map of Seattle, Wash- Sidle, R. C., Ochiai, H., 2006, Landslides processes, ington: U.S. Geological Survey Open-File Report prediction, and land use, Water Resources Mono- 2006-1136, 20 p. graph 18: Washington, D.C., American Geophysi- Highland, L., compiler, 2004, Landslide types and pro- cal Union, 312 p. cesses, U.S. Geological Fact Sheet 2004-3072 (ver. Spiker, E. C., and Gori, P. L., 2003. National landslide 1.1), 4 p. hazards mitigation strategy – A framework for loss Hofmeister, R. J., 2000, Slope failures in Oregon: GIS reduction, U.S. Geological Survey Circular 1244. inventory for three 1996/97 storm events: Port- Turner, A. K., and Schuster, R. L., eds., 1996, Land- land, Oreg., Oregon Department of Geology and slides—Investigation and mitigation: Washing- Mineral Industries Special Paper 34, 20 p. ton, D.C., Transportation Research Board Special Hong, Y., Adler, R. F., and Huffman, G. J., 2007, Satellite Report 247, 673 p. remote sensing for global landslide monitoring, Varnes, D. J., 1978, Slope movement types and process- Eos Trans. AGU, v. 88, no. 37. es, in Schuster, R. L., and Krizek, R. J., eds., Land- slides—Analysis and control: Washington, D.C., Transportation Research Board Special Report 176, p. 11–33.

Oregon Department of Geology and Mineral Industries Special Paper 42 25 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

Wang, Y., Summers, R. D., and Hofmeister, R. J., 2002, Wood, N., 2007, Variations in city exposure and sensi- Landslide loss estimation pilot project in Oregon: tivity to tsunami hazards in Oregon: U.S. Geologi- Oregon Department of Geology and Mineral cal Survey Scientific Investigations Report 2007- Industries Open-File Report O-02-05, 23 p. 5283: 43 p. Wiegers, M. O., 2006, Landslide inventory map of the Morgan Hill quadrangle, Santa Clara County, Cal- ifornia: California Geological Survey Landslide Inventory Map Series, Morgan Hill quadrangle.

26 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

11.0 APPENDIX A: Geodatabase Tabular Field Data

Gray-shaded boxes are field names in the landslide geo- per field. Variable type, field description, units (where database template. Unshaded boxes are allowed values applicable), and example data are also provided.

QUADNAME TYPE_MOVE MOVE_CLASS MOVE_CODE Text; 7.5-minute quadrangle name. Text; type of Text; Text; movement Example: Oregon City movement. movement classification. classification code. Debris Slide - Rotational DS-R UNIQUE_ID Debris Slide - Translational DS-T Text; unique identification number: Earth Slide - Rotational ES-R Slide concatenation of QUADNAME_ID. * Earth Slide - Translational ES-T Example: Oregon City_1 Rock Slide - Rotational RS-R Rock Slide - Translational RS-T CONFIDENCE Debris Flow DFL Text; confidence of identification. Flow Earth Flow EFL High (≥30) Rock Flow RFL Moderate (20-30) Debris Spread DSP Low (≤20) Spread Rock Spread RSP Rock Spread RSP AGE Debris Fall DF Text; estimated age. Fall Rock Fall RF Historic (<150 years) Rock Fall RF Pre-Historic (>150 years) Debris Topple DT Topple Earth Topple ET DATE_MOVE Rock Topple RT Text; date of last known movement or Complex C movements. Complex Complex Earth Slide - Rotational ES-R>EFL Examples: 10/6/1996, 2/12/1997 & Earth Flow DEEP_SHAL FAN_DEPTH DIRECT Text; deep or shallow seated; 4.5 m (15 ft) is the boundary value. Float; estimated calculated fan depth. Float; direction of movement, in Units: feet. Example: 33 increments of 22.5. Units: degrees. Deep Shallow 0 HS_IS1 22.5 NAME Float; horizontal distance from head scarp 45 Text; landslide name. (HS) to internal scarp no. 1 (IS1). Units: feet. 76.5 Example: Spady Landslide Example: 5 90 112.5 GEOL IS1_IS2 135 Text; geologic unit. Float; horizontal distance from internal 157.5 Example: Troutdale Formation scarp no. 1 (IS1) to internal scarp no. 2 180 (IS2). Units: feet. Example: 5 202.5 SLOPE 225 Float; adjacent slope angle, 0 to 90. IS2_IS3 247.5 Units: degrees. Example: 32 Float; horizontal distance from internal 270 scarp no. 2 (IS2) to internal scarp no. 3 292.5 HS_HEIGHT (IS3). Units: feet. Example: 5 315 Float; change in elevation from bottom to 337.5 top of head scarp. Units: feet. Example: 16 IS3_IS4 360 Float; horizontal distance from internal FAIL_DEPTH scarp no. 3 (IS3) to internal scarp no. 4 AREA Float; estimated calculated depth of (IS4). Units: feet. Example: 5 Float; size of landslide deposit. Units: failure. Units: feet. Example: 14 square feet. Example: 500 HD_AVE FAN_HEIGHT Float; calculated average horizontal VOL Float; change in elevation from bottom to distance between scarps. Units: feet. Float; volume of landslide deposit. Units: top of fan. Units: feet. Example: 35 Example: 5 cubic feet. Example: 7000

* Identification numbers (IDs) are sequential numbers (starting at 1 for the first mapped landslide) for each landslide mapped in each 7.5-minute quadrangle.

Oregon Department of Geology and Mineral Industries Special Paper 42 27 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

12.0 APPENDIX B: Guidelines for 12.3 Tips and Tricks for Editing working with the Landslide Geodatabase Template • Change your sticky-move tolerance to something large, like 50 (Editor > Options > General tab). Note: An ArcInfo or ArcEditor license is necessary to • Set your Snapping tolerance to some number edit a geodatabase that contains relationship classes or between about 7 and 12 (Editor > Options > Gen- topologies. If you do not have access to these licenses, use eral tab). the ArcView license level geodatabase provided. • Auto-Complete will fill in the areas adjacent to existing polygons if you snap to vertex on both 12.1 Editing Guidelines sides of the area in which you want a new polygon (Editor toolbar > Task dropdown menu). • Feature classes must always be named Deposits, • Other options under Topology Tasks include Scarp_Flanks, Scarps, and Photos. Use the empty Reshape Edge and Modify Edge. These tools will feature classes provided you in the geodatabase. simultaneously edit features that have a shared These feature classes are already named, and they edge. They are accessible through a geodatabase have the correct fields and domains built in. topology but are also accessible through a Map • Relationship classes exist between these four Topology, for which you don’t need an ArcInfo feature classes. The relationship classes assume license. (See Geodatabase Topologies section, that the Deposits features are the parent features. Figures B1 and B2.) During editing, if a Deposits feature is deleted, ◦◦ To build a map topology, choose Editing> any Scarps, Flanks, or Photos features will also be Start. Turn on the Topology toolbar and deleted, given the Unique_ID that connects the click the Map Topology button (just to the features is present in all related features. right of the Map Layers dropdown menu). • Domains are established to decrease the amount Choose the layers you want to participate in attribute entry you need to do and to ensure that the topology and click OK. The map topol- typographic errors do not sneak into the data. ogy only lasts for your edit session; you must These domains appear via dropdown menus in build a new one each time you start editing. the Attributes dialog box or in the Attribute table. • Other methods for editing that will ensure coincident features include the Trace tool (same location 12.2 Workflow as the sketch tool, but the bottom right choice). • Be careful of Clip! It will clip through all visible • Because Deposits are considered the parent fea- layers you are currently editing. Because of this, ture to which all other features are related, it is I will frequently keep a junk clip feature class recommended that your workflow be as follows: handy into which I can copy and paste features draw and attribute the Deposits feature, then to do a quick clip until they are topologically draw its associated Scarp_Flanks, then create coincident — making sure, of course, that I have Scarps and Photos features from the geometry of turned off all other layers! — and then copy them these features. back into my main feature class. I then can use the • Because the Scarps lines should be exactly coinci- Attribute Transfer Tool (Spatial Adjustment tool- dent with Scarp_Flanks edges, these must match bar) to copy all the attributes from the old feature vertex for vertex. A simple way to do this is to to the newly-clipped feature (see below). draw the Scarp_Flanks polygon, then copy and paste it into Scarps. You can then clip the line to the extent of the Scarp and you will have an iden- tical feature.

28 Oregon Department of Geology and Mineral Industries Special Paper 42 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

12.4 Attributes 12.5 Checking Your Work

• The key attribute field for these feature classes is • If you have ArcInfo or ArcEditor, you can create the Unique_ID field. This is a string field that is a geodatabase topology that will establish rules the Quad name concatenated with a unique ID between feature classes. See ArcMap Help for number (1–n, where n is the last feature you cre- more information. ated). This field is what the relationship classes • Two additional tools can help check your work, and the error-checking tools below rely on, and once you are entirely finished editing: Find it is very important that for the Deposits feature Unmatched Features and Unjoin Layers. These class, the Unique_ID has no duplicates. simple models can be run in ArcGIS. Add the pro- • If you have ArcInfo license, you can use the vided toolbox to ArcMap (right-click in the white Frequency tool to check to see if any value of area of your Toolbox Window and choose Add) Unique_ID has more than one occurrence (Anal- and expand to see the two models. ysis Tools > Statistics). This is similar to a pivot ◦◦ Find Unmatched Features locates Scarp_ table in Excel, so you could export the attribute Flanks, Scarps, or Photo features with table as a .dbf file and open it in Excel to check for Unique_IDs that do not have a matching duplicates. Access also provides a handy wizard to Unique_ID in Deposits. These will be select- check for duplicates. Be careful when accessing ed on your screen after you run the model. your geodatabase tables from Microsoft Access. You may scroll to all selected items in your • During editing, you can sort in ascending or data view to check them before deleting descending order in the Attribute table to find out them, or you may export the selected items what your next-highest unique ID number should as a temporary shapefile or feature class so be. Because this is an alphanumeric field, the that you have a guide for which features to format for the Unique_ID should be as follows: check. [Quad Name]_001 ◦◦ When you click on the Find Unmatched Fea- [Quad Name]_011 tures model, a dialog box appears with one [Quad Name]_111 drop-down option. Select Scarps, Photos, or • For this work, all attributes are carried in Depos- Scarp_Flanks, as these are the layers you are its, and smaller subsets of the same attributes are comparing to Deposits. carried in the Photo, Scarps, and Scarp_Flanks ◦◦ You can run this model three times (one feature classes. After all your features are drawn, for each feature class), and the features will and your Deposits feature is fully attributed, you remain selected in all three feature classes. can use the Attribute Transfer Mapping tool on However, you cannot run it again for the the Spatial Adjustment toolbar. same feature class until you run the second ◦◦ Click Spatial Adjustment and Attribute model, Unjoin Layers. Transfer Mapping. A dialog box appears. ◦◦ For your selections to appear, you must turn Specify your From layer—which will always the layer off and on. be Deposits—and your To layer—Scarp_ ◦◦ For your selections to disappear after you Flanks, Scarps, or Photo. Auto-Match will have run Unjoin Layers, you must scroll so automatically find the fields the two layers that the selected features go out of the edges have in common. Uncheck the Transfer of the data frame. Geometry option. Click Okay. In ArcMap your cursor will now be a round circle. Click in a From feature and then click a target To feature. For each target feature, you must again click in a From feature. You can have only one From layer and one To layer speci- fied at a time.

Oregon Department of Geology and Mineral Industries Special Paper 42 29 Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (Lidar) Imagery

12.6 Geodatabase Topologies ◦◦ Current topology rules: • Deposits must not overlap self • It is an optional step to add a topology to your • Deposits must not overlap Scarp_ Feature Dataset. This is useful for checking your Flanks work for various mismatches and editing errors in • Scarps must be covered by boundary the data, as well as easily repairing some topology of Scarp_Flanks errors. Some sample topology rules and editing • An added benefit of a topology is the topology tasks are described below. editing tools. Using the Topology Edit tool on the ◦◦ The topology can be modified with ArcCata- Topology toolbar, you can click an edge or a node log to fit certain needs, as the rules already (see Figures B1 and B2) and it will turn pink. You set for you are not true all the time. These can then selected the Topology edit tasks instead rules, however, will help to find certain of the standard ones and reshape or move several errors in your data. If a feature is marked as features at one time. a topology error and it is in fact not, you can mark it as an exception with the Fix Topol- ogy Error tool on the Topology toolbar. (For example, sometimes Deposits features really do overlap each other.)

Figure B1. Edit all features connected to the node. Dragging the node changes the shapes of both polygons.

Figure B2. Edit the shape of all features under edge (scarp is highlighted pink). Deleting a node and dragging the line changes the shapes of both the line and the polygon.

30 Oregon Department of Geology and Mineral Industries Special Paper 42