Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data

Abstract Landslide inventories are routinely compiled by means of aerial photo interpretation (API). When examining photo pairs, the forest canopy (notably in old-growth forest) hides a population of “not visible” landslides. In the present study, we attempt to estimate how important is the contribution of landslides not detectable from aerial photographs to the global mass of sediment production from mass failures on forested terrain of the Capilano basin, coastal British Columbia. API was coupled with intensive fieldwork for identification and measurement of all landslides. A 30-year framework was adopted. We show that “not visible” landslides can represent up to 85% of the total number of failures and account for 30% of the volume of debris mobilised. Such percentages display high sub-basin variability with rates of sediment production varying by one order of magnitude between two sub-basins of the study area. This is explained qualitatively by GIS-based analysis of slope frequency distributions, drainage density, and spatial distribution of surficial materials. Such observations find further support in the definitions of transport-limited and supply-limited basins. As a practical consideration to land managers, we envisage that supplementary fieldwork for landslide identification is mandatory in transport-limited systems only. Fieldwork has demonstrated that gully-related failures have a greater importance than one could expect from API.

[1]  R. Sidle,et al.  Hillslope stability and land use , 1985 .

[2]  G. Wieczorek,et al.  Preparing a Detailed Landslide-Inventory Map for Hazard Evaluation and Reduction , 1984 .

[3]  Matthias Jakob,et al.  The role of debris supply conditions in predicting debris flow activity , 1999 .

[4]  Ashcroft Ranch,et al.  GREATER VANCOUVER REGIONAL DISTRICT , 2004 .

[5]  E. George Robison,et al.  Oregon Department of Forestry Storm Impacts and Landslides of 1996: Final Report , 1999 .

[6]  Q. Záruba,et al.  Landslides and their control , 1969 .

[7]  J. Armstrong,et al.  LATE WISCONSIN MARINE DRIFT AND ASSOCIATED SEDIMENTS OF THE LOWER FRASER VALLEY, BRITISH COLUMBIA, CANADA , 1954 .

[8]  J. Flageollet.  The time dimension in the study of mass movements , 1996 .

[9]  J. A. Roddick.  Vancouver North, Coquitlam, and Pitt Lake map Areas, British Columbia With Special Emphasis On the Evolution of the Plutonic Rocks , 1965 .

[10]  Fausto Guzzetti,et al.  Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard , 1999 .

[11]  F. Brardinoni,et al.  Complex mass wasting response of drainage basins to forest management in coastal British Columbia , 2003 .

[12]  M. Turrini,et al.  An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy) , 2002 .

[13]  T. Millard,et al.  Predicting Post-Logging Landslide Activity Using Terrain Attributes: Coast Mountains, British Columbia , 2001 .

[14]  Leslie M. Reid,et al.  Rapid Evaluation of Sediment Budgets , 1996 .

[15]  John W. Williams,et al.  Rates of Landsliding as Impacted by Timber Management Activities in Northwestern California , 1986 .

[16]  S. S. Holland.  Landforms of British Columbia : a physiographic outline , 1964 .

[17]  Investigation of the stability of the steepland forest soils in the coast mountains, southwest British Columbia , 1972 .

[18]  M. Kirkby,et al.  Hillslope Form and Process , 1972 .

[19]  Ramón V. León,et al.  BLOCKING MULTIPLE SOURCES OF ERROR IN SMALL ANALYTIC STUDIES , 2000 .

[20]  Theo van Asch,et al.  Deterministic Modelling in Gis-Based Landslide Hazard Assessment , 1995 .