Abstract
Landslide is one of the most devastating natural disasters across the world with serious negative impact on its inhabitants and the environs. Landslide is considered as a type of soil erosion which could be shallow, deep-seated, cut slope, bare soil, and so on. Distinguishing between these types of soil erosions in dense vegetation terrain like Cameron Highlands Malaysia is still a challenging issue. Thus, it is difficult to differentiate between these erosion types using traditional techniques in locations with dense vegetation. Light detection and ranging (LiDAR) can detect variations in terrain and provide detailed topographic information on locations behind dense vegetation. This paper presents a hierarchical rule-based classification to obtain accurate map of landslide types. The performance of the hierarchical rule set classification using LiDAR data, orthophoto, texture, and geometric features for distinguishing between the classes would be evaluated. Fuzzy logic supervised approach (FbSP) was employed to optimize the segmentation parameters such as scale, shape, and compactness. Consequently, a correlation-based feature selection technique was used to select relevant features to develop the rule sets. In addition, in other to differentiate between deep-seated cover under shadow and normal shadow, the band ration was created by dividing the intensity over the green band. The overall accuracy and the kappa coefficient of the hierarchal rule set classification were found to be 90.41 and 0.86%, respectively, for site A. More so, the hierarchal rule sets were evaluated using another site named site B, and the overall accuracy and the kappa coefficient were found to be 87.33 and 0.81%, respectively. Based on these results, it is demonstrated that the proposed methodology is highly effective in improving the classification accuracy. The LiDAR DEM data, visible bands, texture, and geometric features considerably influence the accuracy of differentiating between landslide types such as shallow and deep-seated and soil erosion types like cut slope and bare soil. Therefore, this study revealed that the proposed method is efficient and well-organized for differentiating among landslide and other soil erosion types in tropical forested areas.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Santangelo, M., Chang, K.-T.: Landslide inventory maps: new tools for an old problem. Earth-Sci. Rev. 112(1), 42–66 (2012)
Pradhan, B., Jebur, M.N., Shafri, H.Z.M., Tehrany, M.S.: Data fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and quickbird satellite imagery. IEEE Trans. Geosci. Remote Sens. 54(3), 1610–1622 (2016)
Van Westen, C.J., Castellanos, E., Kuriakose, S.L.: Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng. Geol. 102(3), 112–131 (2008)
Parker, R.N., Densmore, A.L., Rosser, N.J., De Michele, M., Li, Y., Huang, R., Petley, D.N.: Mass wasting triggered by the 2008 Wenchuan earthquake is greater than orogenic growth. Nat. Geosci. 4(7), 449–452 (2011)
Chen, R.F., Lin, C.W., Chen, Y.H., He, T.C., Fei, L.Y.: Detecting and characterizing active thrust fault and deep-seated landslides in dense forest areas of southern taiwan using airborne LiDAR DEM. Remote Sens 7(11), 15443–15466 (2015)
Guzzetti, F., Cardinali, M., Reichenbach, P., Cipolla, F., Sebastiani, C., Galli, M., Salvati, P.: Landslides triggered by the 23 November 2000 rainfall event in the Imperia Province, Western Liguria, Italy. Eng. Geol. 73(3), 229–245 (2004)
Tarolli, P., Arrowsmith, J.R., Vivoni, E.R.: Understanding earth surface processes from remotely sensed digital terrain classifiers. Geomorphology 113(1), 1–3 (2009)
McKean, J., Roering, J.: Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology 57(3), 331–351 (2004)
Whitworth, M., Giles, D., Murphy, W.: Airborne remote sensing for landslide hazard assessment: a case study on the Jurassic escarpment slopes of Worcestershire, UK. Q. J. Eng. Geol. Hydrogeol. 38(3), 285–300 (2005)
Schulz, W.H.: Landslide susceptibility revealed by LIDAR imagery and historical records, Seattle, Washington. Eng. Geol. 89(1), 67–87 (2007)
Anders, N.S., Seijmonsbergen, A.C., Bouten, W.: Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens. Environ. 115(12), 2976–2985 (2011)
Belgiu, M., Drǎguţ, L.: Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS J Photogramm Remote Sens. 96, 67–75 (2014)
Drǎguţ, L., Tiede, D., Levick, S.R.: ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci. 24(6), 859–871 (2010)
Esch, T., Thiel, M., Bock, M., Roth, A., Dech, S.: Improvement of image segmentation accuracy based on multiscale optimization procedure. IEEE Geosci. Remote Sens. Lett. 5(3), 463–467 (2008)
Zhang, Y., Maxwell, T., Tong, H., Dey, V.: Development of a supervised software tool for automated determination of optimal segmentation parameters for ecognition: na (2010)
Zêzere, J.L., Trigo, R.M., Trigo, I.F.: Shallow and deep landslides induced by rainfall in the Lisbon region (Portugal): assessment of relationships with the North Atlantic Oscillation. Nat. Hazard Earth Sys. 5(3), 331–344 (2005)
Deng, S., Shi, W.: Semi-automatic approach for identifying locations of shallow debris slides/flows based on lidar-derived morphological features. Int. J. Remote Sens. 35(10), 3741–3763 (2014)
Lin, C.-W., Tseng, C.-M., Tseng, Y.-H., Fei, L.-Y., Hsieh, Y.-C., Tarolli, P.: Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography. J. Asian Earth Sci. 62, 389–400 (2013)
Rau, J.Y., Jhan, J.P., Rau, R.J.: Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM. IEEE Trans. Geosci. Remote Sens. 52(2), 1336–1349 (2014)
Kasai, M., Ikeda, M., Asahina, T., Fujisawa, K.: LiDAR-derived DEM evaluation of deep-seated landslides in a steep and rocky region of Japan. Geomorphology 113(1), 57–69 (2009)
Van Den Eeckhaut, M., Poesen, J., Verstraeten, G., Vanacker, V., Moeyersons, J., Nyssen, J., Van Beek, L.P.H.: The effectiveness of hillshade maps and expert knowledge in mapping old deep-seated landslides. Geomorphology 67(3), 351–363 (2005)
Passalacqua, P., Tarolli, P., Foufoula Georgiou, E.: Testing space scale methodologies for automatic geomorphic feature extraction from lidar in a complex mountainous landscape. Water Resour. Res. 46(11) (2010)
Dou, J., Chang, K.-T., Chen, S., Yunus, A.P., Liu, J.-K., Xia, H., Zhu, Z.: Automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm. Remote Sens. 7(4), 4318–4342 (2015)
Li, X., Cheng, X., Chen, W., Chen, G., Liu, S.: Identification of forested landslides using LiDar data, object-based image analysis, and machine learning algorithms. Remote Sens. 7(8), 9705–9726 (2015)
Chen, W., Li, X., Wang, Y., Chen, G., Liu, S.: Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the Three Gorges, China. Remote Sens. Environ. 152, 291–301 (2014)
Stumpf, A., Kerle, N.: Object-oriented mapping of landslides using random forests. Remote Sens. Environ. 115(10), 2564–2577 (2011)
Borghuis, A., Chang, K., Lee, H.: Comparison between automated and manual mapping of typhoon-triggered landslides from SPOT-5 imagery. Int. J. Remote Sens. 28(8), 1843–1856 (2007)
Danneels, G., Pirard, E., Havenith, H.-B.: Automatic landslide detection from remote sensing images using supervised classification methods. In: Paper Presented at the 2007 IEEE International Geoscience and Remote Sensing Symposium (2007)
Moine, M., Puissant, A., Malet, J.-P.: Detection of landslides from aerial and satellite images with a semi-automatic method. In: Application to the Barcelonnette Basin (Alpes-de-Hautes-Provence, France). Paper Presented at the Landslide Processes-from Geomorphologic Mapping to Dynamic Classifierling (2009)
Kurtz, C., Stumpf, A., Malet, J.P., Gançarski, P., Puissant, A., Passat, N.: Hierarchical extraction of landslides from multiresolution remotely sensed optical images. ISPRS J. Photogramm. Remote Sens. 87, 122–136 (2014)
Miner, A., Flentje, P., Mazengarb, C., Windle, D.: Landslide recognition using LiDAR derived digital elevation classifiers-lessons learnt from selected Australian examples (2010)
Martha, T.R., Kerle, N., Van Westen, C.J., Jetten, V., Kumar, K.V.: Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans. Geosci. Remote 49(12), 4928–4943 (2011)
Pradhan, B., Lee, S.: Regional landslide susceptibility analysis using back-propagation neural network classifier at Cameron Highland, Malaysia. Landslides 7(1), 13–30 (2010)
Olaya, V.: Basic land-surface parameters. Dev. Soil Sci. 33, 141–169 (2009)
Mezaal, M.R., Pradhan, B., Sameen, M.I., Mohd Shafri, H.Z., Yusoff, Z.M.: Optimized neural architecture for automatic landslide detection from high resolution airborne laser scanning data. Appl. Sci. 7(7), 730 (2017)
Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65(1), 2–16 (2010)
Li, M., Ma, L., Blaschke, T., Cheng, L., Tiede, D.: A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. J. Appl. Earth Obs. Geoinf. 49, 87–98 (2016)
Hamedianfar, A., Shafri, H.Z.M.: Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data. J. Appl. Remote Sens. 10(2), 025001 (2016)
Pradhan, B., Mezaal, M.R.: Optimized rule sets for automatic landslide characteristic detection in a highly vegetated forests. Laser Scanning Applications in Landslide Assessment, pp. 51–68. Springer International Publishing, New York (2017)
Kursa, M.B., Rudnicki, W.R.: Feature selection with the Boruta package. J. Stat. Softw. 36, 1–3 (2010)
Barbarella, M., Fiani, M., Lugli, A.: Application of LiDAR-derived DEM for detection of mass movements on a landslide. Int. Arch. Photogramm. Remote Sense Spat. Inf. Sci. 1(3), 89–98 (2013)
Sameen, M.I., Pradhan, B., Shafri, H.Z., Mezaal, M.R., bin Hamid, H.: Integration of ant colony optimization and object-based analysis for LiDAR data classification. IEEE J-STARS 10, 2055 (2017)
Bartels, M., Wei, H.: Threshold-free object and ground point separation in LIDAR data. Pattern Recognit. Lett. 31(10), 1089–1099 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mezaal, M.R., Pradhan, B., Shafri, H.Z.M., Mojaddadi, H., Yusoff, Z.M. (2019). Optimized Hierarchical Rule-Based Classification for Differentiating Shallow and Deep-Seated Landslide Using High-Resolution LiDAR Data. In: Pradhan, B. (eds) GCEC 2017. GCEC 2017. Lecture Notes in Civil Engineering , vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-8016-6_60
Download citation
DOI: https://doi.org/10.1007/978-981-10-8016-6_60
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8015-9
Online ISBN: 978-981-10-8016-6
eBook Packages: EngineeringEngineering (R0)