Articles | Volume 24, issue 9
https://doi.org/10.5194/nhess-24-3075-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-3075-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Sep 2024
Research article |
| 13 Sep 2024
| 13 Sep 2024
AscDAMs: advanced SLAM-based channel detection and mapping system
Tengfei Wang
State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao SAR, People's Republic of China
Fucheng Lu
State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao SAR, People's Republic of China
Jintao Qin
State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao SAR, People's Republic of China
Taosheng Huang
State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao SAR, People's Republic of China
Hui Kong
CORRESPONDING AUTHOR
State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering, University of Macau, Macao SAR, People's Republic of China
State Key Laboratory of Internet of Things for Smart City and Department of Ocean Science and Technology, University of Macau, Macao SAR, People's Republic of China
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Xinlong Zhang, Jiayi Fang, Yue Qin, Weiping Wang, and Ping Shen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3799, https://doi.org/10.5194/egusphere-2024-3799, 2025
Preprint archived
Short summary
Short summary
Compound coastal extreme weather like strong winds and heavy rain can induce sea level rise. We studied global data and found that these extreme weather events are linked especially in colder regions. They happen more often and with greater impact than thought. The increased sea levels during these events heighten the risk of coastal flooding. Our research predicts these conditions will worsen throughout this century, emphasizing the need to prepare for more frequent and severe coastal weather.
Cited articles
Bailey, T. and Durrant-Whyte, H.: Simultaneous localization and mapping (SLAM): Part II, IEEE Robot. Autom. Mag., 13, 108–117, https://doi.org/10.1109/Mra.2006.1678144, 2006.
Barros, A. M., Michel, M., Moline, Y., Corre, G., and Carrel, F.: A Comprehensive Survey of Visual SLAM Algorithms, Robotics, 11, 11010024, https://doi.org/10.3390/robotics11010024, 2022.
Berger, C., McArdell, B. W., and Schlunegger, F.: Direct measurement of channel erosion by debris flows, Illgraben, Switzerland, J. Geophys. Res.-Earth, 116, W0502, https://doi.org/10.1029/2010jf001722, 2011a.
Berger, C., McArdell, B. W., and Schlunegger, F.: Sediment transfer patterns at the Illgraben catchment, Switzerland: Implications for the time scales of debris flow activities, Geomorphology, 125, 421–432, https://doi.org/10.1016/j.geomorph.2010.10.019, 2011b.
Berti, M., Genevois, R., Simoni, A., and Tecca, P. R.: Field observations of a debris flow event in the Dolomites, Geomorphology, 29, 265–274, https://doi.org/10.1016/S0169-555x(99)00018-5, 1999.
Blasone, G., Cavalli, M., Marchi, L., and Cazorzi, F.: Monitoring sediment source areas in a debris-flow catchment using terrestrial laser scanning, Catena, 123, 23–36, https://doi.org/10.1016/j.catena.2014.07.001, 2014.
Bonneau, D. A., Hutchinson, D. J., McDougall, S., DiFrancesco, P. M., and Evans, T.: Debris-Flow Channel Headwater Dynamics: Examining Channel Recharge Cycles With Terrestrial Laser Scanning, Front. Earth Sci., 10, 883259, https://doi.org/10.3389/feart.2022.883259, 2022.
Bradley, D. and Roth, G.: Adaptive thresholding using the integral image, J. Graph. Tools, 12, 13–21, https://doi.org/10.1080/2151237X.2007.10129236, 2007.
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., and Leonard, J. J.: Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age, IEEE T. Robot., 32, 1309–1332, https://doi.org/10.1109/Tro.2016.2624754, 2016.
Caduff, R., Schlunegger, F., Kos, A., and Wiesmann, A.: A review of terrestrial radar interferometry for measuring surface change in the geosciences, Earth Surf. Proc. Land., 40, 208–228, https://doi.org/10.1002/esp.3656, 2015.
Cao, C., Zhang, W., Chen, J. P., Shan, B., Song, S. Y., and Zhan, J. W.: Quantitative estimation of debris flow source materials by integrating multi-source data: A case study, Eng. Geol., 291, 106222, https://doi.org/10.1016/j.enggeo.2021.106222, 2021.
Chen, H. X. and Zhang, L. M.: EDDA 1.0: integrated simulation of debris flow erosion, deposition and property changes, Geosci. Model Dev., 8, 829–844, https://doi.org/10.5194/gmd-8-829-2015, 2015.
Chen, M., Tang, C., Xiong, J., Chang, M., and Li, N.: Spatio-temporal mapping and long-term evolution of debris flow activity after a high magnitude earthquake, Catena, 236, 107716, https://doi.org/10.1016/j.catena.2023.107716, 2024.
Cucchiaro, S., Cavalli, M., Vericat, D., Crema, S., Llena, M., Beinat, A., Marchi, L., and Cazorzi, F.: Geomorphic effectiveness of check dams in a debris-flow catchment using multi-temporal topographic surveys, Catena, 174, 73–83, https://doi.org/10.1016/j.catena.2018.11.004, 2019.
Durrant-Whyte, H. and Bailey, T.: Simultaneous localization and mapping: Part I, IEEE Robot. Autom. Mag., 13, 99–108, https://doi.org/10.1109/Mra.2006.1638022, 2006.
Fan, X. M., Juang, C. H., Wasowski, J., Huang, R. Q., Xu, Q., Scaringi, G., van Westen, C. J., and Havenith, H. B.: What we have learned from the 2008 Wenchuan Earthquake and its aftermath: A decade of research and challenges, Eng. Geol., 241, 25–32, https://doi.org/10.1016/j.enggeo.2018.05.004, 2018.
Fan, X. M., Scaringi, G., Korup, O., West, A. J., van Westen, C. J., Tanyas, H., Hovius, N., Hales, T. C., Jibson, R. W., Allstadt, K. E., Zhang, L. M., Evans, S. G., Xu, C., Li, G., Pei, X. J., Xu, Q., and Huang, R. Q.: Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts, Rev. Geophys., 57, 421–503, https://doi.org/10.1029/2018rg000626, 2019.
Guo, X. J., Cui, P., Li, Y., Zou, Q., and Kong, Y. D.: The formation and development of debris flows in large watersheds after the 2008 Wenchuan Earthquake, Landslides, 13, 25–37, https://doi.org/10.1007/s10346-014-0541-6, 2016.
Hu, T. and Huang, R. Q.: A catastrophic debris flow in the Wenchuan Earthquake area, July 2013: characteristics, formation, and risk reduction, J. Mt. Sci.-Engl., 14, 15–30, https://doi.org/10.1007/s11629-016-3965-8, 2017.
Huang, G. H., Lv, G. S., Zhang, S., Huang, D. L., Zhao, L. H., Ni, X. Q., Liu, H. W., Lv, J. H., and Liu, C. D.: Numerical analysis of debris flows along the Sichuan-Tibet railway based on an improved 3D sphere DDA model and UAV-based photogrammetry, Eng. Geol., 305, 106722, https://doi.org/10.1016/j.enggeo.2022.106722, 2022.
Imaizumi, F., Masui, T., Yokota, Y., Tsunetaka, H., Hayakawa, Y. S., and Hotta, N.: Initiation and runout characteristics of debris flow surges in Ohya landslide scar, Japan, Geomorphology, 339, 58–69, https://doi.org/10.1016/j.geomorph.2019.04.026, 2019.
Kinsey-Henderson, A., Hawdon, A., Bartley, R., Wilkinson, S. N., and Lowe, T.: Applying a Hand-Held Laser Scanner to Monitoring Gully Erosion: Workflow and Evaluation, Remote Sens.-Basel, 13, 4004, https://doi.org/10.3390/rs13194004, 2021.
Kukko, A., Kaijaluoto, R., Kaartinen, H., Lehtola, V. V., Jaakkola, A., and Hyyppa, J.: Graph SLAM correction for single scanner MLS forest data under boreal forest canopy, ISPRS J. Photogram., 132, 199–209, doi10.1016/j.isprsjprs.2017.09.006, 2017.
Li, J. P., Wu, W. T., Yang, B. S., Zou, X. H., Yang, Y. D., Zhao, X., and Dong, Z.: WHU-Helmet: A Helmet-Based Multisensor SLAM Dataset for the Evaluation of Real-Time 3-D Mapping in Large-Scale GNSS-Denied Environments, IEEE T. Geosci. Remote, 61, 5702016, https://doi.org/10.1109/Tgrs.2023.3275307, 2023.
Li, Z. H., Chen, J. P., Tan, C., Zhou, X., Li, Y. C., and Han, M. X.: Debris flow susceptibility assessment based on topo-hydrological factors at different unit scales: a case study of Mentougou district, Beijing, Environ. Earth Sci., 80, 365, https://doi.org/10.1007/s12665-021-09665-9, 2021.
Liang, W. J., Zhuang, D. F., Jiang, D., Pan, J. J., and Ren, H. Y.: Assessment of debris flow hazards using a Bayesian Network, Geomorphology, 171, 94–100, https://doi.org/10.1016/j.geomorph.2012.05.008, 2012.
Lin, J. and Zhang, F.: R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package, in: 2022 International Conference on Robotics and Automation, 23–27 May 2022, Philadelphia, https://doi.org/10.1109/ICRA46639.2022.9811935, 2022.
Liu, H. H., Zhao, Y. J., Wang, L., and Liu, Y. Y.: Comparison of DEM accuracies generated from different stereo pairs over a plateau mountainous area, J. Mt. Sci.-Engl., 18, 1580–1590, https://doi.org/10.1007/s11629-020-6274-1, 2021.
Liu, Y. and Zhong, R. F.: Buildings and Terrain of Urban Area Point Cloud Segmentation based on PCL, in: 35th International Symposium on Remote Sensing of Environment, 22–26 April 2013, Beijing, https://doi.org/10.1088/1755-1315/17/1/012238, 2014.
Luo, S. Y., Xiong, J. N., Liu, S., Hu, K. H., Cheng, W. M., Liu, J., He, Y. F., Sun, H. Z., Cui, X. J., and Wang, X.: New Insights into Ice Avalanche-Induced Debris Flows in Southeastern Tibet Using SAR Technology, Remote Sens.-Basel, 14, 2603, https://doi.org/10.3390/rs14112603, 2022.
Marotta, F., Teruggi, S., Achille, C., Vassena, G. P. M., and Fassi, F.: Integrated Laser Scanner Techniques to Produce High-Resolution DTM of Vegetated Territory, Remote Sens.-Basel, 13, 2504, https://doi.org/10.3390/rs13132504, 2021.
Mergili, M., Fischer, J.-T., Krenn, J., and Pudasaini, S. P.: r.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows, Geosci. Model Dev., 10, 553–569, https://doi.org/10.5194/gmd-10-553-2017, 2017.
Meyer, N. K., Schwanghart, W., Korup, O., Romstad, B., and Etzelmuller, B.: Estimating the topographic predictability of debris flows, Geomorphology, 207, 114–125, https://doi.org/10.1016/j.geomorph.2013.10.030, 2014.
Morino, C., Conway, S. J., Balme, M. R., Hillier, J., Jordan, C., Saemundsson, T., and Argles, T.: Debris-flow release processes investigated through the analysis of multi-temporal LiDAR datasets in north-western Iceland, Earth Surf. Proc. Land., 44, 144–159, https://doi.org/10.1002/esp.4488, 2019.
Mueting, A., Bookhagen, B., and Strecker, M. R.: Identification of Debris-Flow Channels Using High-Resolution Topographic Data: A Case Study in the Quebrada del Toro, NW Argentina, J. Geophys. Res.-Earth, 126, 006330, https://doi.org/10.1029/2021JF006330, 2021.
Pierzchala, M., Giguere, P., and Astrup, R.: Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM, Comput. Electron. Agr., 145, 217–225, https://doi.org/10.1016/j.compag.2017.12.034, 2018.
Ram, P. and Sinha, K.: Revisiting kd-tree for Nearest Neighbor Search, in: Proceedings of the 25th Acm Sigkdd International Conferencce on Knowledge Discovery and Data Mining, 4–8 August 2019, Anchorage, https://doi.org/10.1145/3292500.3330875, 2019.
Remaître, A., Malet, J. P., and Maquaire, O.: Morphology and sedimentology of a complex debris flow in a clay-shale basin, Earth Surf. Proc. Land., 30, 339–348, https://doi.org/10.1002/esp.1161, 2005.
Rusu, R. B. and Cousins, S.: 3D is here: Point Cloud Library (PCL), in: 2011 IEEE International Conference on Robotics and Automation, 9–13 May 2011, Shanghai, https://doi.org/10.1109/ICRA.2011.5980567, 2011.
Schurch, P., Densmore, A. L., Rosser, N. J., Lim, M., and McArdell, B. W.: Detection of surface change in complex topography using terrestrial laser scanning: application to the Illgraben debris-flow channel, Earth Surf. Proc. Land., 36, 1847–1859, https://doi.org/10.1002/esp.2206, 2011.
Shan, T. X., Englot, B., Meyers, D., Wang, W., Ratti, C., and Rus, D.: LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping, in: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, 24 October 2020–24 January 2021, Las Vegas, https://doi.org/10.1109/Iros45743.2020.9341176, 2020.
Shen, P., Zhang, L. M., Chen, H. X., and Fan, R. L.: EDDA 2.0: integrated simulation of debris flow initiation and dynamics considering two initiation mechanisms, Geosci. Model Dev., 11, 2841–2856, https://doi.org/10.5194/gmd-11-2841-2018, 2018.
Shen, P., Zhang, L. M., Fan, R. L., Zhu, H., and Zhang, S.: Declining geohazard activity with vegetation recovery during first ten years after the 2008 Wenchuan earthquake, Geomorphology, 352, 106989, https://doi.org/10.1016/j.geomorph.2019.106989, 2020.
Simoni, A., Bernard, M., Berti, M., Boreggio, M., Lanzoni, S., Stancanelli, L. M., and Gregoretti, C.: Runoff-generated debris flows: Observation of initiation conditions and erosion-deposition dynamics along the channel at Cancia (eastern Italian Alps), Earth Surf. Proc. Land., 45, 3556–3571, https://doi.org/10.1002/esp.4981, 2020.
Stock, J. D. and Dietrich, W. E.: Erosion of steepland valleys by debris flows, Geol. Soc. Am. Bull., 118, 1125–1148, https://doi.org/10.1130/B25902.1, 2006.
Sun, Q., Zhang, L., Hu, J., Ding, X. L., Li, Z. W., and Zhu, J. J.: Characterizing sudden geo-hazards in mountainous areas by D-InSAR with an enhancement of topographic error correction, Nat. Hazards, 75, 2343–2356, https://doi.org/10.1007/s11069-014-1431-x, 2015.
Tanduo, B., Martino, A., Balletti, C., and Guerra, F.: New Tools for Urban Analysis: A SLAM-Based Research in Venice, Remote Sens.-Basel, 14, 4325, https://doi.org/10.3390/rs14174325, 2022.
Tang, C., Zhu, J., Li, W. L., and Liang, J. T.: Rainfall-triggered debris flows following the Wenchuan earthquake, B. Eng. Geol. Environ., 68, 187–194, https://doi.org/10.1007/s10064-009-0201-6, 2009.
Tang, Y. M., Guo, Z. Z., Wu, L., Hong, B., Feng, W., Su, X. H., Li, Z. G., and Zhu, Y. H.: Assessing Debris Flow Risk at a Catchment Scale for an Economic Decision Based on the LiDAR DEM and Numerical Simulation, Front. Earth Sci., 10, 821735, https://doi.org/10.3389/feart.2022.821735, 2022.
Ullman, M., Laugomer, B., Shicht, I., Langford, B., Ya'aran, S., Wachtel, I., Frumkin, A., and Davidovich, U.: Formation processes and spatial patterning in a late prehistoric complex cave in northern Israel informed by SLAM-based LiDAR, J. Archaeol. Sci.-Rep., 47, 103745, https://doi.org/10.1016/j.jasrep.2022.103745, 2023.
Walter, F., Hodel, E., Mannerfelt, E. S., Cook, K., Dietze, M., Estermann, L., Wenner, M., Farinotti, D., Fengler, M., Hammerschmidt, L., Hansli, F., Hirschberg, J., McArdell, B., and Molnar, P.: Brief communication: An autonomous UAV for catchment-wide monitoring of a debris flow torrent, Nat. Hazards Earth Syst. Sci., 22, 4011–4018, https://doi.org/10.5194/nhess-22-4011-2022, 2022.
Wang, T. and Lu, F.: AscDAMs test data [dataset], figshare [code and data set], https://doi.org/10.6084/m9.figshare.c.7088278.v2, 2024.
Whipple, K. X.: Open-channel flow of Bingham fluids: Applications in debris-flow research, J. Geol., 105, 243–262, https://doi.org/10.1086/515916, 1997.
Xiong, J., Tang, C., Gong, L. F., Chen, M., Li, N., Shi, Q. Y., Zhang, X. Z., Chang, M., and Li, M. W.: How landslide sediments are transferred out of an alpine basin: Evidence from the epicentre of the Wenchuan earthquake, Catena, 208, 105781, https://doi.org/10.1016/j.catena.2021.105781, 2022.
Xu, Q., Zhang, S., Li, W. L., and van Asch, T. W. J.: The 13 August 2010 catastrophic debris flows after the 2008 Wenchuan earthquake, China, Nat. Hazards Earth Syst. Sci., 12, 201–216, https://doi.org/10.5194/nhess-12-201-2012, 2012.
Yang, Y., Tang, C. X., Cai, Y. H., Tang, C., Chen, M., Huang, W. L., and Liu, C.: Characteristics of Debris Flow Activities at Different Scales after the Disturbance of Strong Earthquakes-A Case Study of the Wenchuan Earthquake-Affected Area, Water, 15, 698, https://doi.org/10.3390/w15040698, 2023a.
Yang, Y., Tang, C. X., Tang, C., Chen, M., Cai, Y. H., Bu, X. H., and Liu, C.: Spatial and temporal evolution of long-term debris flow activity and the dynamic influence of condition factors in the Wenchuan earthquake-affected area, Sichuan, China, Geomorphology, 435, 108755, https://doi.org/10.1016/j.geomorph.2023.108755, 2023b.
Ye, H. Y., Chen, Y. Y., and Liu, M.: Tightly Coupled 3D Lidar Inertial Odometry and Mapping, in: 2019 International Conference on Robotics and Automation, 20–24 May 2019, Montreal, https://doi.org/10.1109/ICRA.2019.8793511, 2019.
Zhang, J. and Singh, S.: LOAM: Lidar odometry and mapping in real-time, Robotics, 2, 1–9, https://doi.org/10.15607/RSS.2014.X.007, 2014.
Zhang, S. and Zhang, L. M.: Impact of the 2008 Wenchuan earthquake in China on subsequent long-term debris flow activities in the epicentral area, Geomorphology, 276, 86–103, https://doi.org/10.1016/j.geomorph.2016.10.009, 2017.
Zhang, S., Zhang, L. M., and Chen, H. X.: Relationships among three repeated large-scale debris flows at Pubugou Ravine in the Wenchuan earthquake zone, Can. Geotech. J., 51, 951-965, https://doi.org/10.1139/cgj-2013-0368, 2014.
Zhang, W., Chen, J. Q., Ma, J. H., Cao, C., Yin, H., Wang, J., and Han, B.: Evolution of sediment after a decade of the Wenchuan earthquake: a case study in a protected debris flow catchment in Wenchuan County, China, Acta Geotech., 18, 3905–3926, https://doi.org/10.1007/s11440-022-01789-x, 2023.
Zhang, X. Z., Tang, C. X., Li, N., Xiong, J., Chen, M., Li, M. W., and Tang, C.: Investigation of the 2019 Wenchuan County debris flow disaster suggests nonuniform spatial and temporal post-seismic debris flow evolution patterns, Landslides, 19, 1935–1956, https://doi.org/10.1007/s10346-022-01896-6, 2022.
Zhang, Y. Y., Huang, C., Huang, C., and Li, M. Y.: Spatio-temporal evolution characteristics of typical debris flow sources after an earthquake, Landslides, 19, 2263–2275, https://doi.org/10.1007/s10346-022-01883-x, 2022.
Zheng, C. R., Zhu, Q. Y., Xu, W., Liu, X. Y., Guo, Q. Z., and Zhang, F.: FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry, in: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, 23–27 October 2022, Kyoto, https://doi.org/10.1109/Iros47612.2022.9981107, 2022.
Zhou, P., Tang, X. M., Wang, Z. M., Cao, N., and Wang, X.: Vertical Accuracy Effect Verification for Satellite Imagery With Different GCPs, IEEE Geosci. Remote Sens., 14, 1268–1272, https://doi.org/10.1109/Lgrs.2017.2705339, 2017.
Short summary
Harsh environments limit the use of drone, satellite, and simultaneous localization and mapping technology to obtain precise channel morphology data. We propose AscDAMs, which includes a deviation correction algorithm to reduce errors, a point cloud smoothing algorithm to diminish noise, and a cross-section extraction algorithm to quantitatively assess the morphology data. AscDAMs solves the problems and provides researchers with more reliable channel morphology data for further analysis.
Harsh environments limit the use of drone, satellite, and simultaneous localization and mapping...
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