Semi-automated recognition of planation surfacesand other flat landforms: a case study from theAggtelek Karst, Hungary

Veselský, Michal; Bandura, Peter; Burian, Libor; Harciníková, Tatiana; Bella, Pavel

doi:10.1515/geo-2015-0063

Cited by 10 publications

(8 citation statements)

References 13 publications

(15 reference statements)

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“…The ceiling channels were also delineated as areal landforms by the means of geomorphometric classification using the approach of Jasiewicz and Stepinsky (2013), which is implemented in GRASS GIS by the module r.geomorphon. The method was successfully used to identify planation surfaces related to Domica-Baradla cave system in the Aggtelek Karst in Hungary by Veselský et al (2015). The classification of the DEM_CH surface delineated the channels as ridges and some parts as summits.…”

Section: Identification Of Ceiling Channels Using 2-d Geomorphometrymentioning

confidence: 99%

Geomorphometric analysis of cave ceiling channels mapped with 3-D terrestrial laser scanning

Gallay

Hochmuth

Kaňuk

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. The change of hydrological conditions during the evolution of caves in carbonate rocks often results in a complex subterranean geomorphology, which comprises specific landforms such as ceiling channels, anastomosing half tubes, or speleothems organized vertically in different levels. Studying such complex environments traditionally requires tedious mapping; however, this is being replaced with terrestrial laser scanning technology. Laser scanning overcomes the problem of reaching high ceilings, providing new options to map underground landscapes with unprecedented level of detail and accuracy. The acquired point cloud can be handled conveniently with dedicated software, but applying traditional geomorphometry to analyse the cave surface is limited. This is because geomorphometry has been focused on parameterization and analysis of surficial terrain. The theoretical and methodological concept has been based on twodimensional (2-D) scalar fields, which are sufficient for most cases of the surficial terrain. The terrain surface is modelled with a bivariate function of altitude (elevation) and represented by a raster digital elevation model. However, the cave is a 3-D entity; therefore, a different approach is required for geomorphometric analysis. In this paper, we demonstrate the benefits of high-resolution cave mapping and 3-D modelling to better understand the palaeohydrography of the Domica cave in Slovakia. This methodological approach adopted traditional geomorphometric methods in a unique manner and also new methods used in 3-D computer graphics, which can be applied to study other 3-D geomorphological forms.

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Section: Identification Of Ceiling Channels Using 2-d Geomorphometrymentioning

confidence: 99%

Geomorphometric analysis of cave ceiling channels mapped with 3-D terrestrial laser scanning

Gallay

Hochmuth

Kaňuk

et al. 2016

Hydrol. Earth Syst. Sci.

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“…To address the misclassification problem due to topography effects, we accounted for complex landforms in mountainous areas based on the geomorphon concept [21][22][23][24] to circumvent the ill-posed calculus of taking spatial derivatives from noisy topography data. We employed the SRTM digital elevation model (DEM) [25] derived from satellite SAR data to calculate geomorphons.…”

Section: Mappingmentioning

confidence: 99%

“…Regarding the advances in the new method, we highlight (1) the advantage of utilizing the two-dimensional (2-D) space of VV-VH for building structure mapping to robustly resolve the issue of radar backscatter incidence and azimuth angle dependence; (2) the ability of consistent time-series Sentinel-1 SAR to identify persistent buildings in a multitude of environmental conditions across the rural-urban continuum; (3) the utilization of multiple SAR signature interactions between trees and water surface to correct for building misclassification; (4) the use of the geomorphon concept and its applications [21][22][23][24] to account for effects of complex topography; and (5) the SAR ability to detect stationary structures or installations that are maintained on sea surfaces. Finally, in the discussion and conclusion, we note future research extensions with multiple international SAR datasets to support the Paris Agreement on climate change, through the potential improvement in the estimation of FFCO2 emission using data products of persistent building structures to represent true settlements.…”

Section: Introductionmentioning

confidence: 99%

Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam

et al. 2021

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Mapping building structures is crucial for environmental change and impact assessment, and is especially important to accurately estimate fossil fuel CO2 emissions from human settlements. In this regard, the objective of this study is to develop novel and robust methods using time-series data acquired from Sentinel-1 synthetic aperture radar (SAR) to identify and map persistent building structures from coastal plains to high plateaus, as well as on the sea surface. From annual composites of SAR data in the two-dimensional VV-VH polarization space, we determined the VV-VH domain for detecting building structures, whose persistence was defined based on the number of times that a pixel was identified as a building in time-series data. Moreover, the algorithm accounted for misclassified buildings due to water-tree interactions in radar signatures and due to topography effects in complex mountainous landforms. The methods were tested in five cities (Bạc Liêu, Cà Mau, Sóc Trăng, Tân An, and Phan Thiết) in Vietnam located in different socio-environmental regions with a range of urban configurations. Using in-situ data and field observations, we validated the methods and found that the results were accurate, with an average false negative rate of 10.9% and average false positive rate of 6.4% for building detection. The algorithm could also detect small houses in rural settlements and in small islands such as in Hòn Sơn and Hòn Tre. Over sea surfaces, the algorithm effectively identified lines of power poles connecting islands to the mainland, guard shacks in marine blood clam farms in Kiên Giang, individual wind towers in the off-shore wind farm in Bạc Liêu, and oilrigs in the Vũng Tàu oil fields. The new approach was developed to be robust against variations in SAR incidence and azimuth angles. The results demonstrated the potential use of satellite dual-polarization SAR to identify persistent building structures annually across rural–urban landscapes and on sea surfaces with different environmental conditions.

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“…Thus, these landforms have specific topographic attributes, which allows topographically landscape to be distinguished from the other [33][34][35]. These features as morphological indicators are sensitive to spatially and temporally variable morphological processes and thus allow us to identify the physical, chemical and biological processes occurring in landscapes [34][35][36]. Thus, they give a quantitative description of landforms and soil variabilities.…”

Section: Introductionmentioning

confidence: 99%

“…However, new techniques will be needed to extract these data for use in soil and land classification in urban environments. There are several approaches developed for the extraction of morphometric parameters from different DEMs such as those using combination of morphometric parameters [37,38], fuzzy logic and unsupervised classification [6,7,32,[36][37][38][39][40][41], supervised classification [42][43][44], probabilistic clustering algorithms [25,45,46], multivariate descriptive statistics [47][48][49] and double ternary diagram classification [50].…”

Section: Introductionmentioning

confidence: 99%

Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Mohamed

2020

Land

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In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.

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Semi-automated recognition of planation surfacesand other flat landforms: a case study from theAggtelek Karst, Hungary

Cited by 10 publications

References 13 publications

Geomorphometric analysis of cave ceiling channels mapped with 3-D terrestrial laser scanning

Geomorphometric analysis of cave ceiling channels mapped with 3-D terrestrial laser scanning

Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam

Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

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