Abstract
The use of satellite sensor data can be used to detect discrete slope processes and landforms with a high degree of accuracy. Whereas previous attempts to classify slope features using per pixel spectral response patterns have provided classification accuracies that are less than 60%, it is demonstrated that a combination of high resolution optical imagery, image segmentation and ancillary data derived from a digital elevation model can discriminate some types of mass wasting processes with accuracies of 80% or higher. The spatial resolution of the imagery is critical to the successful classification of such features both in terms of information derived from textural analysis and in the ability to successfully segment landslide features. Furthermore, the data generated in this manner can be used for geomorphic research in terms of characterizing the occurrence of mass wasting within the bounds of the image scene.
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References
Baatz M, Schape A, 2000, Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation, In: Strobl J., Blaschke T., Griesebner G. (eds) Angewandte Geographische Informationsverarbeitung XII. Wichmann-Verlag, Heidelberg, pp 12–23.
Baatz M, Benz U, Dehghani S, Heymen M, Holtje A, Hofmann P, Ligenfelder I, Mimler M, Sohlbach M, Weber M, Willhauck G, 2001, eCognition Users Guide, Definiens Imaging GmbH, Munich.
Baeza C, Corominas J, 2001, Assessment of shallow landslide susceptibility by means of multivariate statistical techniques, Earth Surface Processes and Landforms 26: 1251–1263.
Barlow J, Franklin S, 2007, Mapping snow avalanche shoots in the Canadian Rockies using digital data, Arctic, Antarctic and Alpine Research. Submitted
Barlow J., Martin Y., Franklin S., 2007, Characterizing Debris Slide Occurrence using Digital Data, Chilliwack Valley, British Columbia, Canadian Journal of Earth Sciences, Submitted.
Barlow J, Franklin S, and Martin Y, 2006, High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes, Photogrammetric Engineering and Remote Sensing 72(6):687–692.
Barlow, J, Martin Y, and Franklin S, 2003. Detecting translational landslide scars using segmentation of Landsat ETM+ and DEM data in the northern Cascade Mountains, British Columbia, Canadian Journal of Remote Sensing 29(4): 510–517.
Brardinoni F, Slaymaker O, Hassan M, 2003, Landslide inventory in a rugged forested watershed: a comparison between air photo and field survey data, Geomorphology 54: 179–196.
Connery D, 1992, Avalanche vegetation classification using Landsat imagery and a digital model in southwestern Alberta. MSc Thesis, University of Calgary.
Connors K, Gardner T., 1987, Classification of geomorphic features and landscape stability in Northwestern New Mexico using simulated SPOT imagery, Remote Sensing of Environment 22: 187–207.
Cruden D, Varnes D, 1996, Landslide types and processes, In: Turner K, Schuster R, (eds) Landslides Investigation and Mitigation, Transportation Research Board Special Report 247, National Academy Press, Washington D.C., pp. 36–75.
Epp, H, Beaven L, 1988. Mapping slope failure tracks with digital Thematic Mapper data. IGARSS’88, Proceedings of the International Geoscience and Remote Sensing Symposium, 13–15 September 1988, Edinburgh, Scotland, IEEE New York, vol. 3, pp 1649–1652.
Gao J, 1993, Identification of topographic settings conducive to landsliding from DEM in Nelson County, Virginia, USA, Earth Surface Processes and Landforms 18: 579–591.
Gardner J, 1970, Geomorphic significance of avalanches in the Lake Louise Area, Alberta, Canada, Arctic and Alpine Research 2(2): 135–144.
Hovius N, Stark C, Allen P, 1997, Sediment flux from a mountain belt derived by landslide mapping, Geology 25: 231–234.
Haralick R, Shanmugam K, Itshak D, 1973, Textural features for image classification, IEEE Transactions on Systems, Man, and Cybernetics 6: 610–621.
Jensen J, 2005, Introductory digital image processing: a remote sensing perspective (3rd ed). Pearson Prentice Hall, Upper Saddle River.
Johnson E, 1987, The Relative Importance of Snow Avalanche Disturbance and Thinning on Canopy Plant Populations, Ecology 68(1): 43–53.
Kidwell K, 1990, Global Vegetation Index User’s Guide, U.S. Department of Commerce/National Oceanic and Atmospheric Administration/National Environmental Data and Information Service/National Climactic Data Centre/Satellite Data Services Division.
Luckman B, 1978, Geomorphic work of snow avalanches in the Canadian Rocky Mountains, Arctic and Alpine Research 10(2): 261–276.
Malamud B, Turcotte D, Guzzetti F, Reichenbach P, 2004, Landslide inventories and their statistical properties, Earth Surface Processes and Landforms 29: 687–711.
Manly B, 1994, Multivariate Statistical Methods, A Primer. Chapman & Hall, New York.
Martin Y, 2000, Modelling hillslope evolution: linear and nonlinear transport relations, Geomorphology 34: 1–21.
Martin Y, Church M, 1997, Diffusion in landscape development models: on the nature of basic transport relations, Earth Surface Processes and Landforms 22: 273–279.
Martin Y, Franklin S, 2005, Classification of soil-and bedrock-dominated landslides in British Columbia using segmentation of satellite imagery and DEM data, International Journal of Remote Sensing 26(7): 1505–1509.
McClung D, Schaerer P, 1993, The Avalanche Handbook. The Mountaineers, Seattle, Wa.
McDermid G, Franklin S, 1995, Remote sensing and geomorphic discrimination of slope processes, Zeitschrift fur Geomorphologie 101: 165–185.
McKean J, Buechel S, Gaydos L, 1991, Remote sensing and landslide hazard assessment, Photogrammetric Engineering and Remote Sensing 57(9): 1185–1193.
Nevatia R, 1986, Image segmentation, In: T. Young & K. Fu (Eds) Handbook of Pattern Recognition and Image Processing. Academic Press, New York, pp 215–231.
Pohl C, Van Genderen J, 1998, Multisensor image fusion in remote sensing: concepts, methods, and applications, International Journal of Remote Sensing 19(5): 823–854.
Rood K, 1984, An aerial photograph inventory of the frequency and yield of mass wasting on the Queen Charlotte Islands, British Columbia, BC Ministry of Forests, Land Management Report 34.
Sauchyn, D, Trench N, 1978. Landsat applied to landslide mapping. Photogrammetric Engineering and Remote Sensing 44: 735–741.
Saunders I, Clague J, Robets M, 1987, Deglaciation of Chilliwack River valley, British Columbia, Canadian Journal of Earth Sciences 24: 915–923.
Scheidegger A, 1991, Theoretical Geomorphology (3rd ed). Springer, Berlin.
Shih E, Schowengerdt R, 1983, Classification of arid geomorphic surfaces using Landsat spectral and textural features, Photogrammetric Engineering and Remote Sensing 49(3): 337–347.
Stark C, Hovius N, 2001, The characterization of landslide size distributions, Geophysical Research Letters 28(6): 1091–1094.
Suffling R, 1993, Induction of vertical zones in sup-alpine valley forests by avalanche-formed fuel breaks, Landscape Ecology 8(2): 127–138.
Walsh S, Butler D, Brown G, Bian L, 1990, Cartographic Modeling of Snow Avalanche Path Location within Glacier National Park, Montana, Photogrammetric Engineering and Remote Sensing 56(5): 615–621
Wills C, McCrink T, 2002, Comparing Landslide Inventories: The Map Depends on the Method, Environmental and Engineering Geoscience VIII(4): 279–29
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Barlow, J., Franklin, S.E. (2007). Mapping Hazardous Slope Processes Using Digital Data. In: Li, J., Zlatanova, S., Fabbri, A.G. (eds) Geomatics Solutions for Disaster Management. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72108-6_6
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DOI: https://doi.org/10.1007/978-3-540-72108-6_6
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