Extracting Shallow-Water Bathymetry from Lidar Point Clouds Using Pulse Attribute Data: Merging Density-Based and Machine Learning Approaches

Bibliography

• Agrafiotis, P., D. Skarlatos, A. Georgopoulos, and K. Karantzalos. 2019a. Depthlearn: Learning to correct the refraction on point clouds derived from aerial imagery for accurate dense shallow water bathymetry based on SVMs-fusion with LiDAR point clouds. Remote Sensing 11 (19):2225.
   Web of Science ®Google Scholar
• Agrafiotis, P., D. Skarlatos, A. Georgopoulos, and K. Karantzalos. 2019b. Shallow water bathymetry mapping from UAV imagery based on machine learning. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W10:9–16. DOI:.
   Google Scholar
• American Society for Photogrammetry and Remote Sensing. 2013. LAS Specification Version 1.3-R13 (15 July 2013).
   Google Scholar
• Andersen, M., A. Gergely, Z. Al-Hamdani, F. Steinbacher, L. Larsen, and V. Ernstsen. 2017. Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment. Hydrology and Earth System Sciences 21 (1):43–63.
   Web of Science ®Google Scholar
• Birkebak, M., F. Eren, S. Pe’eri, and N. Weston. 2018. The effect of surface waves on airborne lidar bathymetry (ALB) measurement uncertainties. Remote Sensing 10 (3):453–19.
   Web of Science ®Google Scholar
• Brzank, A., C. Heipke, J. Goepfert, and U. Soergel. 2008. Aspects of generating precise digital terrain models in the Wadden Sea from lidar-water classification and structure line extraction. ISPRS Journal of Photogrammetry and Remote Sensing 63 (5):510–28.
   Web of Science ®Google Scholar
• Calder, B., and L. Mayer. 2003. Automatic processing of high-rate, high-density multibeam echosounder data. Geochemistry, Geophysics, Geosystems 4 (6):n/a–/a.
   Web of Science ®Google Scholar
• Calder, B., and G. Rice. 2017. Computationally efficient variable resolution depth estimation. Computers & Geosciences 106:49–59.
   Web of Science ®Google Scholar
• Calvert, J., J. A. Strong, M. Service, C. McGonigle, and R. Quinn. 2015. An evaluation of supervised and unsupervised classification techniques for marine benthic habitat mapping using multibeam echosounder data. ICES Journal of Marine Science 72 (5):1498–513.
   Web of Science ®Google Scholar
• Collin, A., P. Archambault, and B. Long. 2008. Mapping shallow water seabed habitat with the SHOALS. IEEE Transactions on Geoscience and Remote Sensing 46 (10):2947–55.
   Web of Science ®Google Scholar
• Dietrich, J. 2017. Bathymetric structure-from-motion: Extracting shallow stream bathymetry from multi-view stereo photogrammetry. Earth Surface Processes and Landforms 42 (2):355–64.
   Web of Science ®Google Scholar
• Eren, F., S. Pe'eri, Y. Rzhanov, and L. Ward. 2018. Bottom characterization by using airborne lidar bathymetry (ALB) waveform feature obtained from bottom return residual analysis. Remote Sensing of Environment 206:260–74.
   Web of Science ®Google Scholar
• Fernandez-Diaz, C., C. L. Glennie, W. Carter, R. Shrestha, M. Sartori, A. Singhania, C. Legleiter, and B. Overstreet. 2014. Early results of simultaneous terrain and shallow water bathymetry mapping using a single-wavelength airborne LiDAR sensor. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (2):623–35.
   Web of Science ®Google Scholar
• Friedman, J. 2001. Greedy function approximation: A gradient boosting machine. Annals of Statistics 29 (5):1189–232.
   Web of Science ®Google Scholar
• Heritage, G., and D. Hetherington. 2007. Towards a protocol for laser scanning in fluvial geomorphology. Earth Surface Processes and Landforms 32 (1):66–74.
   Web of Science ®Google Scholar
• Gholamalifard, M., T. Kutser, A. Esmaili-Sari, A. Abkar, and B. Naimi. 2013. Remotely sensed empirical modelling of bathymetry in the Southeastern Caspian Sea. Remote Sensing 5 (6):2746–62.
   Web of Science ®Google Scholar
• Hickman, G., and J. Hogg. 1969. Application of an airborne pulsed laser for near shore bathymetric measurements. Remote Sensing of Environment (1)1:47–58.
   Google Scholar
• Höfle, B., and M. Rutzinger. 2011. Topographic airborne LiDAR in geomorphology: A technological perspective. Zeitschrift Für Geomorphologie, Supplementary Issues 55 (2):1–29. (in English).
   Google Scholar
• Jawak, S., S. Vadlamani, and A. Luis. 2015. A synoptic review on deriving bathymetry information using remote sensing technologies: Models, methods, comparisons. Advances in Remote Sensing 04 (02):147–62.
   Google Scholar
• Kashani, A., M. Olsen, C. Parrish, and N. Wilson. 2015. A review of LIDAR radiometric processing: From Ad Hoc intensity correction to rigorous radiometric calibration. Sensors 15 (11):28099–128.
   PubMed Web of Science ®Google Scholar
• Kerr, J., and S. Purkis. 2018. An algorithm for optically deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data. Remote Sensing of Environment 210:307–24.
   Web of Science ®Google Scholar
• Kinzel, P., C. Legleiter, and P. Grams. 2021. Field evaluation of a compact, polarizing topo-bathymetric lidar across a range of river conditions. River Research and Applications (4)37:531–43. pp.
   Web of Science ®Google Scholar
• Kinzel, P., C. Legleiter, and J. Nelson. 2013. Mapping river bathymetry with a small footprint green LiDAR: Applications and challenges. JAWRA Journal of the American Water Resources Association 49 (1):183–‐204.
   Web of Science ®Google Scholar
• Kogut, T., and M. Weistock. 2019. Classifying airborne bathymetry data using the Random Forest algorithm. Remote Sensing Letters 10 (9):874–82.
   Web of Science ®Google Scholar
• Kutser, T., J. Hedley, C. Giardino, C. Roelfsema, and V. Brando. 2020. Remote sensing of shallow waters – a 50-year retrospective and future directions. Remote Sensing of Environment 240:111619–8. pp.
   Web of Science ®Google Scholar
• Lecours, V., M. Dolan, A. Micallef, and V. Lucieer. 2016. A review of marine geomorphometry, the quantitative study of the sea floor. Hydrology and Earth System Sciences (8)20:3207–44. DOI: 10.5194/hess-20-3207-2016.
   Web of Science ®Google Scholar
• Li, J., D. Knapp, S. Schill, C. Roelfsema, S. Phinn, M. Silman, J. Mascaro, and G. Asner. 2019. Adaptive bathymetry estimation for shallow coastal waters using Planet Dove satellite. Remote Sensing of Environment 232:111302.
   Web of Science ®Google Scholar
• Liu, S., Y. Gao, W. Zheng, and X. Li. 2015. Performance of two neural network models in bathymetry. Remote Sensing Letters 6 (4):321–30.
   Web of Science ®Google Scholar
• Lowell, K., B. Calder, and A. Lyons. 2021. Measuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning. International Journal of Geographical Information Science. doi: 10.1080/13658816.2020.1867147
   PubMed Web of Science ®Google Scholar
• Lyzenga, D., N. Malinas, and F. Tanis. 2006. Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing 44 (8):2251–9.
   Web of Science ®Google Scholar
• Maas, H.-G., D. Mader, K. Richter, and P. Westfeld. 2019. Improvements in lidar bathymetry data analysis. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W10:113–7–16.
   Google Scholar
• Mahalanobis, P. 1936. On the generalized distance in statistics. Proceedings of the National Institute of Sciences of India 2 (1):49–55.
   Google Scholar
• Mandlburger, G., Pfennigbauer, M. Schwarz, S. Mikolka-Flory, and L. Nussbaumer. 2020. Concept and performance evaluation of a novel UAV-borne topo-bathymetric LiDAR sensor. Remote Sensing 12 (6):986.
   Web of Science ®Google Scholar
• McFadden, D. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, ed. P. Zarembka, 105–42. New York, NY: Academic Press.
   Google Scholar
• McQueen, J. 1967. Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 281–97. Oakland, CA: University of California Press.
   Google Scholar
• Misra, A., Z. Vojinovic, B. Ramakrishnan, A. Luijendijk, and R. Ranasinghe. 2018. Shallow water bathymetry mapping using support vector machine (SVM) technique and multispectral imagery. International Journal of Remote Sensing 39 (13):4431–50.
   Web of Science ®Google Scholar
• Mitchell, S., and J. Thayer. 2014. Ranging through shallow semitransparent media with polarization lidar. Journal of Atmospheric and Oceanic Technology (3)31:681–97.
   Web of Science ®Google Scholar
• Nagle, D., and C. Wright. 2016. Algorithms used in the Airborne Lidar Processing System (ALPS). United States Dept. of the Interior/United States Geological Survey, Open File Report 2016-1046, 45.
   Google Scholar
• Niroumand-Jadidi, M., A. Vitti, and D. Lyzenga. 2018. Multiple optimal depth predictors analysis (MODPA) for river bathymetry: Findings from spectroradiometry, simulations, and satellite imagery. Remote Sensing of Environment 218:132–47.
   Web of Science ®Google Scholar
• Okhrimenko, M., and C. Hopkinson. 2020. A simplified end-user approach to lidar very shallow water bathymetric correction. IEEE Geoscience and Remote Sensing Letters 17 (1):3–7.
   Web of Science ®Google Scholar
• Pacheco, A., J. Horta, C. Loureiro, and O. Ferreira. 2015. Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters. Remote Sensing of Environment 159:102–16.
   Web of Science ®Google Scholar
• Pe'eri, S., and W. Philpot. 2007. Increasing the existence of very shallow LIDAR measurements using the red-channel waveforms. IEEE Transactions on Geoscience and Remote Sensing 45 (5):1217–23.
   Web of Science ®Google Scholar
• Pittman, S., B. Costa, and T. Battista. 2009. Using lidar bathymetry and boosted regression trees to predict the diversity and abundance of fish and corals. Journal of Coastal Research 10053:27–38.
   Web of Science ®Google Scholar
• Schmidt, A., F. Rottensteiner, and U. Soergel. 2012. Classification of airborne laser scanning data in Wadden sea areas using conditional random fields. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences XXIX-B3:161–6. DOI:.
   Google Scholar
• Schwarz, R., G. Mandlburger, M. Pfennigbauer, and N. Pfeifer. 2019. Design and evaluation of a full-wave surface and bottom-detection algorithm for LiDAR bathymetry of very shallow waters. ISPRS Journal of Photogrammetry and Remote Sensing 150:1–10.
   Web of Science ®Google Scholar
• Steinhaus, H. 1957. Sur la division des corps matériels en parties. (English: “On the division of body materials in parts.”). Bulletin de l’Académie Polonaise des Sciences 4 (12):801–4. (In French.)
   Google Scholar
• Su, D., F. Yang, Y. Ma, K. Zhang, J. Huang, and M. Wang. 2019. Classification of coral reefs in the South China Sea by combining airborne LiDAR bathymetry bottom waveforms and bathymetric features. IEEE Transactions on Geoscience and Remote Sensing 57 (2):815–28.
   Web of Science ®Google Scholar
• Tulldahl, H., and S. Wikström. 2012. Classification of aquatic macrovegetation and substrates with airborne lidar. Remote Sensing of Environment 121:347–57.
   Web of Science ®Google Scholar
• Wang, C., Q. Li, Y. Liu, G. Wu, P. Liu, and X. Ding. 2015. A comparison of waveform processing algorithms for single-wavelength LiDAR bathymetry. ISPRS Journal of Photogrammetry and Remote Sensing 101:22–35.
   Web of Science ®Google Scholar
• Wang, L., H. Liu, H. Su, and J. Wang. 2019. Bathymetry retrieval from optical images with spatially distributed support vector machines. GIScience & Remote Sensing 56 (3):323–37.
   Web of Science ®Google Scholar
• Westfeld, P., H.-G. Maas, K. Richter, and R. Weiß. 2017. Analysis and correction of ocean wave pattern induced systematic coordinate errors in airborne LiDAR bathymetry. ISPRS Journal of Photogrammetry and Remote Sensing 128:314–25.
   Web of Science ®Google Scholar
• Xing, S., D. Wang, Q. Xu, Y. Lin, P. Li, L. Jiao, Z. Zhang, and C. Liu. 2019. A depth-adaptive waveform decomposition method for airborne LiDAR bathymetry. Sensors 19:5065.
   PubMed Web of Science ®Google Scholar
• Yang, A., Z. Wu, F. Yang, D. Su, Y. Ma, D. Zhao, and C. Qi. 2020. Filtering of airborne LiDAR bathymetry based on bidirectional cloth simulation. ISPRS Journal of Photogrammetry and Remote Sensing 163:49–61.
   Web of Science ®Google Scholar
• Zhao, J., X. Zhao, H. Zhang, and F. Zhou. 2017. Shallow water measurements using a single green laser corrected by building a near water surface penetration model. Remote Sensing 9 (5):426.
   Web of Science ®Google Scholar