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Optimized Rule Sets for Automatic Landslide Characteristic Detection in a Highly Vegetated Forests

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Laser Scanning Applications in Landslide Assessment

Abstract

The rapid expansion of cities and the continuously increasing population in urban areas lead to the establishment of settlements in mountainous areas.

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References

  • Agliardi, F., Crosta, G. B., Zanchi, A., & Ravazzi, C. (2009). Onset and timing of deep-seated gravitational slope deformations in the eastern Alps, Italy. Geomorphology, 103(1), 113–129.

    Article  Google Scholar 

  • Anders, N. S., Seijmonsbergen, A. C., & Bouten, W. (2011). Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sensing of Environment, 115(12), 2976–2985.

    Article  Google Scholar 

  • Bai, S., Wang, J., Zhang, Z., & Cheng, C. (2012). Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China. Catena, 99, 18–25.

    Article  Google Scholar 

  • Barlow, J., Martin, Y., & 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.

    Article  Google Scholar 

  • Belgiu, M., & Drǎguţ, L. (2014). Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 67–75.

    Article  Google Scholar 

  • Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.

    Article  Google Scholar 

  • Borghuis, A., Chang, K., & Lee, H. (2007). Comparison between automated and manual mapping of typhoon-triggered landslides from SPOT-5 imagery. International Journal of Remote Sensing, 28(8), 1843–1856.

    Article  Google Scholar 

  • Brunetti, M., Guzzetti, F., & Rossi, M. (2009). Probability distributions of landslide volumes. Nonlinear Processes in Geophysics, 16(2), 179–188.

    Article  Google Scholar 

  • Bugnion, L., Volkwein, A., & Denk, M. (2009). Artificial full scale shallow landslides. Paper presented at the EGU General Assembly Conference Abstracts.

    Google Scholar 

  • Chang, K.-T., Liu, J.-K., & Wang, C.-I. (2012). An object-oriented analysis for characterizing the rainfall-induced shallow landslide. Journal of Marine Science and Technology, 20(6), 647–656.

    Google Scholar 

  • Chen, R.-F., Lin, C.-W., Chen, Y.-H., He, T.-C., & Fei, L.-Y. (2015). Detecting and characterizing active thrust fault and deep-seated landslides in dense forest areas of Southern Taiwan Using Airborne LiDAR DEM. Remote Sensing, 7(11), 15443–15466.

    Article  Google Scholar 

  • Chen, W., Li, X., Wang, Y., Chen, G., & Liu, S. (2014). Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China. Remote Sensing of Environment, 152, 291–301.

    Article  Google Scholar 

  • Cruden, D. M., & Varnes, D. J. (1996). Landslides: investigation and mitigation. Chapter 3-Landslide types and processes. Transportation research board special report (247).

    Google Scholar 

  • Daniel, S. (2014). Predictive modeling of trust to Social Media content.

    Google Scholar 

  • Danneels, G., Pirard, E., & Havenith, H.-B. (2007). Automatic landslide detection from remote sensing images using supervised classification methods. Paper presented at the 2007 IEEE International Geoscience and Remote Sensing Symposium.

    Google Scholar 

  • Darwish, A., Leukert, K., & Reinhardt, W. (2003). Image segmentation for the purpose of object-based classification. Paper presented at the International Geoscience and Remote Sensing Symposium.

    Google Scholar 

  • Delgado, J., Vicente, F., García-Tortosa, F., Alfaro, P., Estévez, A., Lopez-Sanchez, J., et al. (2011). A deep seated compound rotational rock slide and rock spread in SE Spain: Structural control and DInSAR monitoring. Geomorphology, 129(3), 252–262.

    Article  Google Scholar 

  • Dey, V., Zhang, Y., & Zhong, M. (2010). A review on image segmentation techniques with remote sensing perspective: na.

    Google Scholar 

  • Đomlija, P., Bernat, S., Mihalić, S. A., & Benac, Č. (2014). Landslide inventory in the area of Dubračina River Basin (Croatia) Landslide science for a safer geoenvironment (pp. 837–842). Berlin: Springer.

    Google Scholar 

  • Dou, J., Chang, K.-T., Chen, S., Yunus, A. P., Liu, J.-K., Xia, H., et al. (2015a). Automatic case-based reasoning approach for landslide detection: Integration of object-oriented image analysis and a genetic algorithm. Remote Sensing, 7(4), 4318–4342.

    Article  Google Scholar 

  • Dou, J., Paudel, U., Oguchi, T., Uchiyama, S., & Hayakavva, Y. S. (2015). Shallow and deep-seated landslide differentiation using support vector machines: A case study of the Chuetsu Area, Japan. Terrestrial, Atmospheric & Oceanic Sciences, 26(2).

    Google Scholar 

  • Drǎguţ, L., Tiede, D., & Levick, S. R. (2010). ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6), 859–871.

    Article  Google Scholar 

  • Gao, J., & Maro, J. (2010). Topographic controls on evolution of shallow landslides in pastoral Wairarapa, New Zealand, 1979–2003. Geomorphology, 114(3), 373–381.

    Article  Google Scholar 

  • Goudie, A. (2004). Encyclopedia of geomorphology (Vol. 2). UK: Psychology Press.

    Google Scholar 

  • Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K.-T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112(1), 42–66.

    Article  Google Scholar 

  • Heleno, S., Matias, M., Pina, P., & Sousa, A. (2015). Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island. Natural Hazards & Earth System Sciences Discussions, 3(9).

    Google Scholar 

  • Hervás, J., & Rosin, P. L. (1996). Landslide mapping by textural analysis of ATM data. Paper presented at the Proceedings of the Thematic Conference on Geologic Remote Sensing.

    Google Scholar 

  • Hong, Y., He, X., Cerato, A., Zhang, K., Hong, Z., & Liao, Z. (2015). Predictability of a physically based model for rainfall-induced shallow landslides: Model development and case studies. In Modern technologies for landslide monitoring and prediction (pp. 165–178). New York: Springer.

    Google Scholar 

  • Kasai, M., Ikeda, M., Asahina, T., & Fujisawa, K. (2009). LiDAR-derived DEM evaluation of deep-seated landslides in a steep and rocky region of Japan. Geomorphology, 113(1), 57–69.

    Article  Google Scholar 

  • Kellerer-Pirklbauer, A., Proske, H., & Strasser, V. (2010). Paraglacial slope adjustment since the end of the Last Glacial Maximum and its long-lasting effects on secondary mass wasting processes: Hauser Kaibling, Austria. Geomorphology, 120(1), 65–76.

    Article  Google Scholar 

  • Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1), 273–324.

    Article  Google Scholar 

  • Korup, O. (2006). Effects of large deep‐seated landslides on hillslope morphology, western Southern Alps, New Zealand. Journal of Geophysical Research: Earth Surface, 111(F1).

    Google Scholar 

  • Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package: Journal.

    Google Scholar 

  • Li, M., Ma, L., Blaschke, T., Cheng, L., & Tiede, D. (2016). A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation, 49, 87–98.

    Article  Google Scholar 

  • Lin, C.-W., Tseng, C.-M., Tseng, Y.-H., Fei, L.-Y., Hsieh, Y.-C., & Tarolli, P. (2013). Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography. Journal of Asian Earth Sciences, 62, 389–400.

    Article  Google Scholar 

  • Ma, H.-R., Cheng, X., Chen, L., Zhang, H., & Xiong, H. (2016). Automatic identification of shallow landslides based on Worldview2 remote sensing images. Journal of Applied Remote Sensing, 10(1), 016008.

    Article  Google Scholar 

  • Malamud, B. D., Turcotte, D. L., Guzzetti, F., & Reichenbach, P. (2004). Landslide inventories and their statistical properties. Earth Surface Processes and Landforms, 29(6), 687–711.

    Article  Google Scholar 

  • Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J., & Kumar, K. V. (2010). Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology, 116(1), 24–36.

    Article  Google Scholar 

  • Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V., & Kumar, K. V. (2011). Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4928–4943.

    Article  Google Scholar 

  • McKean, J., & Roering, J. (2004). Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology, 57(3), 331–351.

    Article  Google Scholar 

  • Moine, M., Puissant, A., & Malet, J.-P. (2009). Detection of landslides from aerial and satellite images with a semi-automatic method. Application to the Barcelonnette basin (Alpes-de-Hautes-Provence, France): Paper presented at the Landslide processes-from geomorphologic mapping to dynamic modelling.

    Google Scholar 

  • Möller, M., Lymburner, L., & Volk, M. (2007). The comparison index: A tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation, 9(3), 311–321.

    Article  Google Scholar 

  • Mondini, A., Guzzetti, F., Reichenbach, P., Rossi, M., Cardinali, M., & Ardizzone, F. (2011). Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sensing of Environment, 115(7), 1743–1757.

    Article  Google Scholar 

  • Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277–1294.

    Article  Google Scholar 

  • Pontius, R. G., Jr., & Millones, M. (2011). Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429.

    Article  Google Scholar 

  • Pradhan, B., Jebur, M. N., Shafri, H. Z. M., & Tehrany, M. S. (2016). Data fusion technique using wavelet transform and taguchi methods for automatic landslide detection from airborne laser scanning data and quickbird satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 54(3), 1610–1622.

    Article  Google Scholar 

  • Radoux, J., & Bogaert, P. (2014). Accounting for the area of polygon sampling units for the prediction of primary accuracy assessment indices. Remote Sensing of Environment, 142, 9–19.

    Article  Google Scholar 

  • Rau, J.-Y., Chang, K.-T., Shao, Y.-C., & Lau, C.-C. (2012). Semi-automatic shallow landslide detection by the integration of airborne imagery and laser scanning data. Natural Hazards, 61(2), 469–480.

    Article  Google Scholar 

  • Rau, J.-Y., Jhan, J.-P., & Rau, R.-J. (2014). Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM. IEEE Transactions on Geoscience and Remote Sensing, 52(2), 1336–1349.

    Article  Google Scholar 

  • Segoni, S., Leoni, L., Benedetti, A., Catani, F., Righini, G., Falorni, G., et al. (2009). Towards a definition of a real-time forecasting network for rainfall induced shallow landslides. Natural Hazards and Earth System Sciences, 9(6), 2119–2133.

    Article  Google Scholar 

  • Stumpf, A., & Kerle, N. (2011). Object-oriented mapping of landslides using Random Forests. Remote Sensing of Environment, 115(10), 2564–2577.

    Article  Google Scholar 

  • Stumpf, A., Lachiche, N., Malet, J.-P., Kerle, N., & Puissant, A. (2014). Active learning in the spatial domain for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 52(5), 2492–2507.

    Article  Google Scholar 

  • Tarolli, P. (2014). High-resolution topography for understanding Earth surface processes: Opportunities and challenges. Geomorphology, 216, 295–312.

    Article  Google Scholar 

  • Tehrany, M. S., Pradhan, B., & Jebuv, M. N. (2014). A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International, 29(4), 351–369.

    Article  Google Scholar 

  • Tian, J., & Chen, D. M. (2007). Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition. International Journal of Remote Sensing, 28(20), 4625–4644.

    Article  Google Scholar 

  • van Asselen, S., & Seijmonsbergen, A. (2006). Expert-driven semi-automated geomorphological mapping for a mountainous area using a laser DTM. Geomorphology, 78(3), 309–320.

    Article  Google Scholar 

  • Van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3), 112–131.

    Article  Google Scholar 

  • Vennari, C., Gariano, S., Antronico, L., Brunetti, M., Iovine, G., Peruccacci, S., et al. (2014). Rainfall thresholds for shallow landslide occurrence in Calabria, southern Italy. Natural Hazards and Earth System Sciences, 14(2), 317–330.

    Article  Google Scholar 

  • Vohora, V., & Donoghue, S. (2004). Application of remote sensing data to landslide mapping in Hong Kong. Remote Sensing and Spatial Information Sciences: International Archives of Photogrammetry.

    Google Scholar 

  • Wiegand, C., Rutzinger, M., Heinrich, K., & Geitner, C. (2013). Automated extraction of shallow erosion areas based on multi-temporal ortho-imagery. Remote Sensing, 5(5), 2292–2307.

    Article  Google Scholar 

  • Yu, T.-T., Wang, T.-S., & Cheng, Y.-S. (2015). Analysis of factors triggering shallow failure and deep-seated landslides induced by single rainfall events. Journal of Disaster Research, 10(5), 966–972.

    Article  Google Scholar 

  • Zêzere, J. L., Trigo, R. M., & Trigo, I. F. (2005). Shallow and deep landslides induced by rainfall in the Lisbon region (Portugal): Assessment of relationships with the North Atlantic Oscillation. Natural Hazards and Earth System Science, 5(3), 331–344.

    Article  Google Scholar 

  • Zhang, Y., Maxwell, T., Tong, H., & Dey, V. (2010). Development of a supervised software tool for automated determination of optimal segmentation parameters for ecognition: na.

    Google Scholar 

  • Zizioli, D., Meisina, C., Bordoni, M., & Zucca, F. (2014). Rainfall-triggered shallow landslides mapping through Pleiades images In Landslide science for a safer geoenvironment (pp. 325–329). New York: Springer.

    Google Scholar 

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Correspondence to Biswajeet Pradhan .

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Pradhan, B., Mezaal, M.R. (2017). Optimized Rule Sets for Automatic Landslide Characteristic Detection in a Highly Vegetated Forests. In: Pradhan, B. (eds) Laser Scanning Applications in Landslide Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-55342-9_3

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