Modeling the morphology of gully cross sections in the Yuanmou Dry-hot Valley

Feng, Yu; Mu, Hongli; Qin, Fanglu; Deng, Qi; Liu, Hui; Zhang, Bin; Luo, Mingliang; Shou-jiang, Liu; Liu, Gangcai

doi:10.1080/02723646.2017.1306386

Cited by 4 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total mean absolute error (MAE) was 3.37 cm for Survey A and 3. 25 [45,87], it can be concluded that the accuracy of created VHR models is sufficient for conducted spatio-temporal changes analysis.…”

Section: Spatial Resolution and Accuracy Of Created Interval Vhr Modelsmentioning

confidence: 97%

“…On the other hand, the measurement of such a large number of GCs and height samples would be hardly achievable with any traditional field measurement technique. Studies that have used profilometer measured significantly less GCs per one gully (e.g., 1 [78], 3 [24,25], 5 [27], 28 [30]) and less height samples within particular GCs (e.g., 46 [78], 50 [26], 100 [27,34]) (Table 1. ).…”

Section: Advantages Of Vertical Over the Profilometermentioning

confidence: 99%

“…Active gullies tend to grow as long as predisposing factors, such as lithology, vegetation cover, land use, terrain attributes, and climatic factors, sustain erosion processes and soil removal [16][17][18]. Research about gully erosion is mostly aimed at quantification of different aspects of spatio-temporal changes in gully geometry, for instance, gully headcut retreat rate [4,15,[19][20][21] or changes within gully cross-sections (GCs) [22][23][24][25]. Within this research, we concentrated on the accurate quantification of spatio-temporal changes in GCs as indicators of overall gully evolution.…”

Section: Introductionmentioning

confidence: 99%

“…The choice of the most appropriate measurement method for the detection and quantification of spatio-temporal changes depends primarily on the aim and spatial scope of the research, as well as on the desirable and achievable accuracy of the measurements [26,27]. A variety of traditional sampling methods for field study of changes in gully cross-sections exist (e.g., pole [27], tape and ruler [26,28,29], total station [27,30], profilometer [22,[24][25][26][27][31][32][33][34]). These methods differ in terms of precision and accuracy, time and cost-effectiveness, spatial coverage, complexity, and required expertise of researchers [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the main research objective (1) was to develop the pixel-based (interval VHR DSMs) methodological approach that will allow automated measurement of vertical spatio-temporal changes in a large number of GCs. A conceptual basis for the development of the VERTICAL method was based on the principle of profilometer, as one of the most commonly used devices for manual field measurement of vertical spatio-temporal changes within chosen cross-sections [22,[24][25][26][27][31][32][33][34]79]. A profilometer is a simple device that allows interval field measurement of depth at point samples distributed within identical distance intervals along the chosen cross-section [26,80,81].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

New Method for Automated Quantification of Vertical Spatio-Temporal Changes within Gully Cross-Sections Based on Very-High-Resolution Models

Šiljeg¹,

Domazetović²,

Marić³

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Gully erosion is one of the most prominent natural denudation processes of the Mediterranean. It causes significant soil degradation and sediment yield. Most traditional field methods for measurement of erosion-induced spatio-temporal changes are time and labor consuming, while their accuracy and precision are highly influenced by various factors. The main research question of this study was how the measurement approach of traditional field sampling methods can be automated and upgraded, while satisfying the required measurement accuracy. The VERTICAL method was developed as a fully automated raster-based method for detection and quantification of vertical spatio-temporal changes within a large number of gully cross-sections (GCs). The developed method was tested on the example of gully Santiš, located at Pag Island, Croatia. Repeat unmanned aerial vehicle (UAV) photogrammetry was used, as a cost-effective and practical method for the creation of very-high-resolution (VHR) digital surface models (DSMs) of the chosen gully site. A repeat aerophotogrammetric system (RAPS) was successfully assembled and integrated into one functional operating system. RAPS was successfully applied for derivation of interval (the two-year research period) DSMs (1.9 cm/pix) of gully Santiš with the accuracy of ±5 cm. VERTICAL generated and measured 2379 GCs, along the 110 m long thalweg of gully Santiš, within which 749 052 height points were sampled in total. VERTICAL proved to be a fast and reliable method for automated detection and calculation of spatio-temporal changes in a large number of GCs, which solved some significant shortcomings of traditional field methods. The versatility and adaptability of VERTICAL allow its application for other, similar scientific purposes, where multitemporal accurate measurement of spatio-temporal changes in GCs is required (e.g., river material dynamics, ice mass dynamics, tufa sedimentation and erosion).

show abstract

Section: Spatial Resolution and Accuracy Of Created Interval Vhr Modelsmentioning

confidence: 97%

Section: Advantages Of Vertical Over the Profilometermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

New Method for Automated Quantification of Vertical Spatio-Temporal Changes within Gully Cross-Sections Based on Very-High-Resolution Models

Šiljeg¹,

Domazetović²,

Marić³

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

A GIS focal approach for characterizing gully geometry

Castillo

Momm

Wells

et al. 2021

Earth Surf Processes Landf

View full text Add to dashboard Cite

In this work we examined the explanatory factors of gully geometry in the Campiña landscape in southern Spain using a geographic information system (GIS) focal approach with regional topographic products (1.5 m LiDAR [light detecton and ranging] digital elevation model [DEM] for elevation data and 0.5 m orthophotography for gully limit delineation). We evaluated the performance of a focal approach (based on neighbourhood calculations) using topographic factors for characterizing the gully linear extent and cross‐section (XS) morphology (i.e., depth, D, width, W, mean slope, S and width‐to‐depth ratio, WDR). As for the longitudinal analysis, focal variables (e.g., using the 11 × 11 mean thalweg slope and 75‐m downstream mean slope) outperformed the pixel‐based approach, with accurate spatial predictions for gully headcuts and mouth locations. The threshold between gullied and non‐gullied areas in a slope‐area diagram took the form of a break‐line rather than a straight limit. For estimating the main XS dimensions (D and W), the Half‐width method was applied by selecting the closest point to the thalweg at the gully limits. The tendency to underestimate XS depths was corrected by applying the equations derived from an assessment of the sampling errors in DEMs datasets. The 50 m adjacent slope to the gully was the best explanatory descriptor of both XS dimensions (due to energetic and geometric reasons) along with the channel density, drainage area and total gully length. The XS mean slope and WDR were controlled by the resistance of the materials (investigated through geotechnical and vane‐test surveys) where incision processes were dominant (V‐shaped XSs in upstream and middle reaches). As sedimentation started to become significant, both morphological parameters changed abruptly and XSs tended to show a trapezoid shape. An adaptative focal strategy with varying window sizes produced better results than the pixel‐based approach, requiring larger calculation scopes in areas of greater DEM uncertainty.

show abstract