Airborne LiDAR point cloud in mapping of fluvial forms: a case study of a Hungarian floodplain

Buró

et al. 2020

Water

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The spatial complexity of floodplains is a function of several processes: hydrodynamics, flow direction, sediment transportation, and land use. Sediments can bind toxic elements, and as there are several pollution sources, the risk of heavy metal accumulation on the floodplains is high. We aimed to determine whether fluvial forms have a role in metal accumulations. Topsoil samples were taken from point bars and swales in the floodplain of the Tisza River, North-East Hungary. Soil properties and metal concentrations were determined, and correlation and hypothesis testing were applied. The results showed that fluvial forms are important drivers of horizontal metal patterns: there were significant differences (p < 0.05) between point bars and swales regarding Fe, K, Mg, Mn, Cr, Cu, Ni, Pb, and Zn. Vertical distribution also differed significantly by fluvial forms: swales had higher metal concentrations in all layers. General Linear Models had different results for macro and micro elements: macro element concentrations were determined by the organic matter, while for micro elements the clay content and the forms were significant explanatory variables. These findings are important for land managers and farmers because heavy metal concentration has a direct impact on living organisms, and the risk of bioaccumulation can be high on floodplains.

Section: Study Sitementioning

confidence: 99%

Geomorphology as a Driver of Heavy Metal Accumulation Patterns in a Floodplain

Buró

et al. 2020

Water

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“…Furthermore, traditional statistical analysis or machine learning can provide valuable data for all types of geographical analyses (e.g. Allen, C. et al 2016;Szabó, Z. et al 2017;Balázs, B. et al 2018;Enyedi, P. et al 2018). Our study focused on image classification, but the procedure also works with tabular data.…”

Section: The Linear Type Of Discriminant Analysismentioning

confidence: 99%

Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery – a comparison

et al. 2018

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Identification of roofing material is an important issue in the urban environment due to hazardous and risky materials. We conducted an analysis with Discriminant Function Analysis (DFA) and Random Forest (RF) on WorldView-2 imagery. We applied a three-and a six-class approach (red tile, brown tile and asbestos; then dividing the data into shadowed and sunny roof parts). Furthermore, we applied pan-sharpening to the image. Our aim was to reveal the efficiency of the classifiers with a different number of classes and the efficiency of pan-sharpening. We found that all classifiers were efficient in roofing material identification with the classes involved, and the overall accuracy was above 85 per cent. The best results were gained by RF, both with three and with six classes; however, quadratic DFA was also successful in the classification of three classes. Usually, linear DFA performed the worst, but only relatively so, given that the result was 85 per cent. Asbestos was identified successfully with all classifiers. The results can be used by local authorities for roof mapping to build registers of buildings at risk.

“…Point bars are always in a higher terrain position in relation to swales; accordingly, swales' water coverage lasts longer, and the vegetation's water supply is relevantly better. In our previous study [50], we revealed that the vegetation density is significantly higher in swales; therefore, the number of ground points per m 2 was significantly smaller (5.88 vs. 4.91). NDVI differed significantly (F = 1567, p < 0.001), which was a relevant background factor which influenced the model accuracy when investigating these fluvial forms: removing the dense vegetation from the surface cannot be as accurate for swales as point bars.…”

Section: Discussionmentioning

confidence: 67%

“…Thus, larger cloths (larger kernel windows) have more relating points where the algorithm can validate the threshold setting. An important result is that point density was 4 points/m 2 ; furthermore, calculating with multiple echoes, it can even reach 10 points/m 2 [50]. Accordingly, a finer cloth size would have been reasonable, the recommendation of the developer is one-third of the point spacing (http://ramm.bnu.edu.cn/researchers/wumingzhang/english/default_contributions.htm), but according to the mean difference between the two settings, the 5 m cloth was 0.012 m better (for the neighborhood-related filter (Figure 4)).…”

Section: Discussionmentioning

confidence: 99%

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Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Tóth

Holb

et al. 2020

Sensors

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Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the separation of the ground points. In this study, we intended to reveal the efficiency of different denoising approaches and an easy-to-use ground point classification technique in a floodplain with fluvial forms. We analyzed a point cloud from the perspective of the efficiency of noise reduction, parametrizing a ground point classifier (cloth simulation filter, CSF), interpolation methods and resolutions. Noise filtering resulted a wide range of point numbers in the models, and the number of points had moderate correlation with the mean accuracies (r = −0.65, p < 0.05), indicating that greater numbers of points had larger errors. The smallest differences belonged to the neighborhood-based noise filtering and the larger cloth size (5) and the smaller threshold value (0.2). The most accurate model was generated with the natural neighbor interpolation with the cloth size of 5 and the threshold of 0.2. These results can serve as a guide for researchers using point clouds when considering the steps of data preparation, classification, or interpolation in a flat terrain.