Obtaining morphometric variables from gullies using two methods of interpolation laser scanner data: the case study of Vassouras, Brazil

Oliveira, Carlos Magno Moreira de; Francelino, Márcio Rocha; Mendonça, Bruno Araújo Furtado de; Ramos, Isabela Queiroz

doi:10.1007/s11629-020-6278-x

Cited by 6 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the evaluation of DEM data applicability, DEM error model can solve the accuracy evaluation problems of various applications, so the model accuracy can provide decision support for users in the evaluation of DEM applicability: in the evaluation of DEM interpolation model, DEM error model generates DEM accuracy fields of various interpolation models, and finally, the advantages and disadvantages of DEM interpolation model can be found according to comparison and statistics. In the evaluation of inverse distance-weighted interpolation algorithm, because DEM error model can generate accuracy field, it can evaluate the advantages and disadvantages of IDW algorithms more scientifically and objectively [32].…”

Section: Discussionmentioning

confidence: 99%

[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

Liu

Cheng

et al. 2021

Journal of Sensors

View full text Add to dashboard Cite

Although DEM occupies an important basic position in spatial analysis, so far, the quality of DEM modeling has still not reached a satisfactory accuracy. This research mainly discusses the influence of interpolation parameters in the inverse distance-weighted interpolation algorithm on the DEM interpolation error. The interpolation parameters to be studied in this paper are the number of search points, the search direction, and the smoothness factor. In order to study the optimization of IDW parameters, the parameters that have uncertain effects on DEM interpolation are found through analysis, such as the number of search points and smoothing factor. This paper designs an experiment for the optimization of the interpolation parameters of the polyhedral function and finds the optimal interpolation parameters through experimental analysis. Of course, the “optimum” here is not the only one, but refers to different terrain areas, which makes the interpolation results relatively good. The selection of search points will be one of the research focuses of this article. After determining the interpolation algorithm, the kernel function is also one of the important factors that affect the accuracy of DEM. The value of the smoothing factor in the kernel function has always been the focus of DEM interpolation research. Different terrains, different interpolations, and functions will have different optimal smoothing factors. The search direction is to ensure that the sampling points are distributed in all directions when the sampling points are sparse and to improve the contribution rate of the sampling points to the interpolation points. The selection of search shape is to improve computing efficiency and has no effect on DEM accuracy; the search radius is mainly controlled by the number of search points, and there are two methods: adaptive search radius and variable length search radius. When the weight coefficient k = 1 , 2 , 3 , 4 , the number of sampling points involved in the interpolation calculation is different, and the error in the residual varies greatly, and both increase with the increase of the number of sampling points in the parameter interpolation calculation. This research will help improve the quality evaluation of DEM.

show abstract

Section: Discussionmentioning

confidence: 99%

[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

Liu

Cheng

et al. 2021

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…Terrestrial LiDAR scanning (TLS) has in preceding years emerged as one of the most accurate and reliable geospatial methods for creation of very-high resolution (VHR) models over complex geomorphic features [1][2][3][4][5]. TLS is an indirect measuring technique that actively emits laser beams from a terrestrial, tripod-based station while simultaneously measuring the distance to intersecting surfaces from received laser returns and forming a dense 3D point cloud [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology

et al. 2022

View full text Add to dashboard Cite

Terrestrial LiDAR scanning (TLS) has in preceding years emerged as one of the most accurate and reliable geospatial methods for the creation of very-high resolution (VHR) models over gullies and other complex geomorphic features. Rough terrain morphology and rapid erosion induced spatio-temporal changes (STCs) can lead to significant challenges in multi-temporal field TLS surveys. In this study, we present a newly developed systematic framework for the optimization of multi-temporal terrestrial LiDAR surveys through the implementation of thorough systematic pre-survey planning and field preparation phases. The developed systematic framework is aimed at increase of accuracy and repeatability of multi-temporal TLS surveys, where optimal TLS positions are determined based on visibility analysis. The whole process of selection of optimal TLS positions was automated with the developed TLS positioning tool (TPT), which allows the user to adjust the parameters of visibility analysis to local terrain characteristics and the specifications of available terrestrial laser scanners. Application and validation of the developed framework were carried out over the gully Santiš (1226.97 m2), located at Pag Island (Croatia). Eight optimal TLS positions were determined by the TPT tool, from which planned coverage included almost 97% of the whole gully area and 99.10% of complex gully headcut morphology. In order to validate the performance of the applied framework, multi-temporal TLS surveys were carried out over the gully Santiš in December 2019 and 2020 using the Faro Focus M70 TLS. Field multi-temporal TLS surveys have confirmed the accuracy and reliability of the developed systematic framework, where very-high coverage (>95%) was achieved. Shadowing effects within the complex overhangs in the gully headcut and deeply incised sub-channels were successfully minimalized, thus allowing accurate detection and quantification of erosion induced STCs. Detection of intensive erosion induced STCs within the observed one-year period was carried out for the chosen part of the gully headcut. Most of the detected STCs were related to the mass collapse and gradual uphill retreat of the headcut, where in total 2.42 m2 of soil has been eroded. The developed optimization framework has significantly facilitated the implementation of multi-temporal TLS surveys, raising both their accuracy and repeatability. Therefore, it has great potential for further application over gullies and other complex geomorphic features where accurate multi-temporal TLS surveys are required for monitoring and detection of different STCs.

show abstract

“…The vegetation cover of the soil (assessed by NDVI) seems to be an important factor in mapping gullies in the basin, regardless of the used model. Evidence links between land use and gullies expansion have been studied in many regions; these include case studies from Spain (Lesschen et al, 2007), Brazil (De Oliveira et al, 2020), Ethiopia (Nyssen et al, 2006), DRC (Chuma et al, 2021a) and South Africa (Podwojewski et al, 2020). Many studies showed the relation between NDVI and land use (Griffith et al, 2002;Mekonnen et al, 2018).…”

Section: Conditioning Factors Related To Gully Erosion Susceptibility...mentioning

confidence: 99%

Gully erosion susceptibility mapping using four machine learning methods in Luzinzi watershed, eastern Democratic Republic of Congo

Chuma

Mugumaarhahama²,

Jean³

et al. 2023

Physics and Chemistry of the Earth, Parts A/B/C

View full text Add to dashboard Cite

Obtaining morphometric variables from gullies using two methods of interpolation laser scanner data: the case study of Vassouras, Brazil

Cited by 6 publications

References 42 publications

[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology

Gully erosion susceptibility mapping using four machine learning methods in Luzinzi watershed, eastern Democratic Republic of Congo

Contact Info

Product

Resources

About