Automatised identification of vegetation types on a floodplain area based on airborne LiDAR survey
DOI:
https://doi.org/10.56617/tl.3490Keywords:
Decision Tree, scikit-learn, riparian vegetation, Python, point-cloud, cross-validationAbstract
In the last decades several environmental and anthropogenic effects altered the channels and floodplains of rivers, some of them resulted in increased riparian vegetation density and increased flood levels. Therefore, to provide sufficient flood safety proper floodplain management is needed, which must be based on up-to-date spatial data. The present paper aims to provide a method for the classification of riparian vegetation applying spatially continuous airborne LiDAR data and machine learning. The 3 km2 large floodplain area was divided into 15x15 m pixels. The statistical parameters of the LiDAR point-cloud representing the vegetation of these pixels were calculated, and resulted data were classified applying a decision tree. Based on the data the following vegetation types were identified: open surface/grassland, Amorpha thicket, young and older poplar plantations, riparian willow forest, and riparian poplar forest dominated by white poplar. The accuracy of the decision tree was tested by 10-fold cross-validation, and also by field-survey checking the results of the decision tree on 72 points. Based on the resulted confusion matrix the overall accuracy of the classification was 83%.
References
Abernethy, B., Rutherfurd, I.D. 1998: Where along a river’s length will vegetation most effectively stabilise stream banks? Geomorphology 23: 55–75. https://doi.org/10.1016/S0169-555X(97)00089-5
Bengio, Y., Grandvalet, Y. 2004: No unbiased estimator of the variance of K-Fold cross-validation. Journal of Machine Learning Research 5: 1089–1105.
Brooks, G.R. 2005: Overbank deposition along the concave side of the Red River meanders, Manitoba, and its geomorphic significance. Earth Surface Processes and Landforms 30: 1617–1632. https://doi.org/10.1002/esp.1219
Corenblit, D., Tabacchi, E., Steiger, J., Gurnell, A.M. 2007: Reciprocal interactions and adjustments between fluvial landforms and vegetation dynamics in river corridors: A review of complementary approaches. Earth-Science Reviews 84: 56–86. https://doi.org/10.1016/j.earscirev.2007.05.004
Geerling, G.W., Kater, E., van den Brink, C., Baptist, M.J., Regas, A.M.J., Smits, A.J.M. 2008: Nature rehabilitation by floodplain excavation: The hydraulic effect of 16 years of sedimentation and vegetation succession along the Waal River, NL. Geomorphology 99: 317–328. https://doi.org/10.1016/j.geomorph.2007.11.011
Grabmeier, J. L., Lambe, L. A. 2007: Decision trees for binary classification variables grow equally with the Gini impurity measure and Pearson’s chi-square test. International Journal of Business Intelligence and Data Mining 2(2): 213. https://doi.org/10.1504/IJBIDM.2007.013938
Heurich, M., Thoma, F. 2008: Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests. Forestry, 81: 645–661. https://doi.org/10.1093/forestry/cpn038
Hudak, A., Crookston, N., Evans, J., Hall, D., Falkowski, M. 2008: Nearest neighbour imputation of species-level, plot-scale forest structure attributes from lidar data. Remote Sensing of Environment 112: 2232–2245. https://doi.org/10.1016/j.rse.2007.10.009
Jalonen, J, Järvelä, J, Virtanen, J.P., Vaaja, M, Kurkela, M, Hyyppä, H. 2015: Determining characteristic vegetation areas by terrestrial laser scanning for floodplain flow modelling. Water 7(2): 420–437. https://doi.org/10.3390/w7020420
Jung, S.E., Kwak, S.A., Park, T., Lee, W.K, Yoo, S. 2011: Estimating crown variables of individual trees using airborne and terrestrial laser scanners. Remote Sensing 3: 2346–2363. https://doi.org/10.3390/rs3112346
Kiss, T., Fiala, K., Sipos, Gy., Szatmári, G. 2019a: Long-term hydrological changes after various river regulation measures: are we responsible for flow extremes? Hydrology Research 50(2): 417–430. https://doi.org/10.2166/nh.2019.095
Kiss, T., Nagy, J., Fehérváry, I., Vaszkó, Cs. 2019b: (Mis)management of floodplain vegetation: The effect of invasive species on vegetation roughness and flood levels. Science of the Total Environment 686: 931–945. https://doi.org/10.1016/j.scitotenv.2019.06.006
Kiss, T., Sándor, A. 2009: Land-use changes and their effect on floodplain aggradation along the Middle-Tisza River, Hungary. AGD Landscape and Environment 3(1): 1–10. https://doi.org/10.14232/jengeo-2008-43855
Kovács S., Váriné Szöllősi I. 2003: A Vásárhelyi Terv Továbbfejlesztését megalapozó hidrológiai és hullámtér hidraulikai vizsgálatok eredményei a Közép-Tiszán. MHT XXI. 2/12: 1–11.
Laes, D., Reutebuch, S., McGaughey, B., Maus, P., Mellin, T., Wilcox, C., Anhold, J., Finco, M., Brewer, K. 2008: Practical lidar acquisition considerations for forestry applications. RSAC-0111-BRIEF1. U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center, Salt Lake City, ÚT. 32 p.
Madsen, B., Treier, A. U., Zilinszky, A., Lucieer, A., Normand, S. 2020: Detecting shrub encroachment in seminatural grasslands using UAS LiDAR. Ecology and Evolution 10(11): 4876–4902. https://doi.org/10.1002/ece3.6240
Manners, R., Schmidt, J., Wheaton, M.J. 2013: Multiscalar model for the determination of spatially explicit riparian vegetation roughness. Journal of Geophysical Research: Earth Surface 118: 65–83. https://doi.org/10.1029/2011JF002188
McGaughey, R. 2018: Users Manual of Fusion/LDV: Software for LIDAR Data Analysis and Visualization. United States Department of Agriculture, Forest Service, Pacific Northwest Research Station
Michez, A., Piégay, H., Lisein, J., Claessens, H., Lejeune, P. 2016: Classification of riparian forest species and health conditionusing multi-temporal and hyperspatial imagery from unmanned aerial system. Environmental Monitoring and Assessment 188: 146. https://doi.org/10.1007/s10661-015-4996-2
Naesset, E., Gobakken, T., Holmgren, J., Hyyppa, J., Maltamo, M., Nilsson, M., Olsson, H., Persson, A., Doderman, U. 2004: Laser scanning of forest resources: the Nordic experience. Scandinavian. Journal of Forest Research 19: 482–499. https://doi.org/10.1080/02827580410019553
Nagy J., Kiss T., Fiala K. 2018: Hullámtér-feltöltődés vizsgálata az Alsó-Tisza mentén. II. Folyóhátak (parti hátak) feltöltődését befolyásoló tényezők. Hidrológiai Közlöny 98(1): 33–40.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É. 2011: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12(85): 2825–2830.
Rátky I., Farkas P. 2003: A növényzet hatása a hullámtér vízszállító képességére. Vízügyi Közlemények 85(2): 246–264.
Saarinen, N., Vastaranta, M., Vaaja, M., Lotsari, E., Jaakkola, A., Kukko, A., Kaartinen, H., Holopainen, M., Hyppä, H., Alho, P. 2013: Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning. Remote Sensing 5: 5285–5303. https://doi.org/10.3390/rs5105285
Schaffer, C. 1993: Overfitting Avoidance as Bias. Machine Learning 10: 153–178. https://doi.org/10.1007/BF00993504
Steiger, J, Gurnell, A.M., Ergenzinger, P., Snelder, D.D. 2001: Sedimentation in the riparian zone of an incising river. Earth Surface Processes and Landforms 26: 91–108. https://doi.org/10.1002/1096-9837(200101)26:1<91::AID-ESP164>3.0.CO;2-U
Vetter, M., Höfle, B., Hollaus, M., Gschöpf, C., Mandlburger, G., Pfeifer, N. 2011: Vertical vegetation structure analysis and hydraulic roughness determination using dense ALS point cloud data–a voxel based approach. International archives of the photogrammetry, remote sensing and spatial information sciences 38(5): 200–206. https://doi.org/10.5194/isprsarchives-XXXVIII-5-W12-265-2011
Zellei L., Sziebert J. 2003: Árvizi áramlásmérések tapasztalatai a Tiszán. In: Szlávik L. (szerk.) Elemző és módszertani tanulmányok az 1998-2001. évi ár- és belvizekről. Vízügyi Közlemények 4: 133–144.
Waldhauser, C., Hochreiter, R., Otepka, J., Pfeifer, N., Ghuffar, S., Korzeniowska, K., Wagner, G. 2014: Automated classification of airborne laser scanning point clouds, solving computationally expensive engineering problems. In: Koziel, S., Leifsson, L., Yang X. (eds.) Springer Proceedings in Mathematics & Statistics 97: 269–292. https://doi.org/10.1007/978-3-319-08985-0_12
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Fehérváry István, Kiss Tímea
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
A folyóirat Open Access (Gold). Cikkeire a Creative Commons 4.0 standard licenc alábbi típusa vonatkozik: CC-BY-NC-ND-4.0. Ennek értelmében a mű szabadon másolható, terjeszthető, bemutatható és előadható, azonban nem használható fel kereskedelmi célokra (NC), továbbá nem módosítható és nem készíthető belőle átdolgozás, származékos mű (ND). A licenc alapján a szerző vagy a jogosult által meghatározott módon fel kell tüntetni a szerző nevét és a szerzői mű címét (BY).
