Automatic mapping of valley networks on Mars

Molloy, Ian; Stepinski, T. F.

doi:10.1016/j.cageo.2006.09.009

Cited by 57 publications

(56 citation statements)

References 20 publications

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“…The plan curvature is a popular terrain parameter that seems relevant, as it should directly inquire about the presence of a thalweg. Since the plan curvature and the tangential curvature have the same spatial distribution of convex and concave areas (Molly and Stepinski, 2007), these two parameters have the same impact in this work. The CI showed great potential to reveal the gully floor features in a previous work (Bretar et al, 2009).…”

Section: Detection Of Significant Convergence Areasmentioning

confidence: 94%

“…For both studies, the core idea is to flag convergence cells; however, they each compute convergence differently. One uses a proxy of curvature stemming from the Peuker and Douglas (1975) algorithm (Tarboton and Ames, 2001), and the other uses the tangential curvature (Molly and Stepinski, 2007). The curvature parameter integration aims to improve the ground-truth of the delineated thalwegs and introduce variability in the drainage density of a catchment.…”

Section: Detection Of Significant Convergence Areasmentioning

confidence: 99%

“…Drainage algorithms allowed for upstream-downstream reconnection, as in Tarboton and Ames (2001) and Molly and Stepinski (2007). Most of the drainage algorithms implemented are based on local analyses and follow the same steps: (i) determination of flow direction; (ii) computation of flow accumulation; (iii) and delineation of the network using a unique contributing area threshold (CA T ).…”

Section: Calculation Of a Tree-structured Gully Floor Areamentioning

confidence: 99%

See 2 more Smart Citations

Extraction of thalweg networks from DTMs: application to badlands

Thommeret¹,

Bailly

Puech³

2010

Hydrol. Earth Syst. Sci.

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Abstract. To study gully spatial patterns in the badlands using a continuous thalweg vector network, this paper presents methods to extract the badlands' thalweg network from a regular grid digital terrain model (DTM) by combining terrain morphology indices with a drainage algorithm. This method will delineate a thalweg only where the DTM denotes a significant curvature with respect to DTM accuracy and relies on three major steps. First, discontinuous concave areas were detected from the DTM using morphological criteria, either the plan curvature or the convergence index. Second, the concave areas were connected using a drainage algorithm, which provides a continuous, thick, tree-structured scheme. We assumed that these areas were physically significant and corresponded to a gully floor. Finally, the thick path was reduced to its main course and vectorised to obtain a thalweg network. The methods were applied to both virtual and actual DTM cases. The actual case was a LiDAR DTM of the Draix badlands in the French Alps. The obtained networks were quantitatively compared, both with a network obtained using the usual drainage area criteria and with a reference network mapped in the field. The CI-based network showed the great potential for thalweg network extraction.

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Section: Detection Of Significant Convergence Areasmentioning

confidence: 94%

Section: Detection Of Significant Convergence Areasmentioning

confidence: 99%

Section: Calculation Of a Tree-structured Gully Floor Areamentioning

confidence: 99%

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Extraction of thalweg networks from DTMs: application to badlands

Thommeret¹,

Bailly

Puech³

2010

Hydrol. Earth Syst. Sci.

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“…The referenced studies dealt with a prior assessment of the input data (Thommeret et al, 2010), calibration of the kernel size by interactively testing its effectiveness related to the investigated features (Pirotti and Tarolli, 2010), fixed arbitrarily chosen scales to evaluate topographic parameters (Tarboton and Ames, 2001;Molloy and Stepinski, 2007;Tarolli and Dalla Fontana, 2009;Passalacqua et al, 2010a,b). Some open questions still remain, as in how to identify thresholds that are not data-driven and how to objectively select scale without calibrating it on results and without considering a previous analysis of the data and of the study area.…”

Section: Introductionmentioning

confidence: 99%

An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification

Sofia

Tarolli

Cazorzi

et al. 2011

Hydrol. Earth Syst. Sci.

116

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Abstract.A statistical approach to LiDAR derived topographic attributes for the automatic extraction of channel network and for the choice of the scale to apply for parameter evaluation is presented in this paper. The basis of this approach is to use distribution analysis and statistical descriptors to identify channels where terrain geometry denotes significant convergences. Two case study areas with different morphology and degree of organization are used with their 1 m LiDAR Digital Terrain Models (DTMs). Topographic attribute maps (curvature and openness) for various window sizes are derived from the DTMs in order to detect surface convergences. A statistical analysis on value distributions considering each window size is carried out for the choice of the optimum kernel. We propose a three-step method to extract the network based (a) on the normalization and overlapping of openness and minimum curvature to highlight the more likely surface convergences, (b) a weighting of the upslope area according to these normalized maps to identify drainage flow paths and flow accumulation consistent with terrain geometry, (c) the standard score normalization of the weighted upslope area and the use of standard score values as non subjective threshold for channel network identification. As a final step for optimal definition and representation of the whole network, a noise-filtering and connection procedure is applied. The advantage of the proposed methodology, and the efficiency and accurate localization of extracted features are demonstrated using LiDAR data of two different areas and comparing both extractions with field surveyed networks.

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“…In network mapping, this is usually carried out using only geographical overlap criteria (Molly and Stepinski, 2007;Heipke et al, 1997). However, this method does not permit the assessment of how realistic the morphology of the reconstructed network is.…”

Section: Introductionmentioning

confidence: 99%

A spatial stochastic algorithm to reconstruct artificial drainage networks from incomplete network delineations

Bailly

Levavasseur²,

Lagacherie³

2011

International Journal of Applied Earth Observation and Geoinfor

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A spatial stochastic algorithm that aims to reconstruct an entire artificial drainage network of a cultivated landscape from disconnected reaches of the network is proposed here. This algorithm uses random network initialisation and a simulated annealing algorithm, both of which are based on random pruning or branching processes, to converge the multi-objective properties of the networks; the reconstructed networks are directed tree graphs, conform to a given cumulative length and maximise the proportion of reconnected reaches. This algorithm runs within a directed plot boundaries lattice, with the direction governed by elevation. The proposed algorithm was applied to a 2.6-km 2 catchment of a Languedocian vineyard in the south of France.The 24-km-long reconstructed networks maximised the reconnection of the reaches obtained either from a hydrographic database or remote sensing data processing. The distribution of the reconstructed networks compared to the actual networks was determined using specific topographical and topological metrics on the networks. The results show that adding data on disconnected * Corresponding author Email addresses: bailly@teledetection.fr (J.S. Bailly), levavasseur@supagro.inra.fr (F. Levavasseur), lagacherie@supagro.inra.fr (P. Lagacherie) Preprint submitted to International Journal of Applied Earth Observation and GeoinformationJune 7, 2011Author-produced version of the article published in International Journal of Applied Earth Observation and Geoinformation, 2011, 13(6) Doi : 10.1016Doi : 10. /j.jag.2011 reaches to constrain reconstruction, while increasing the accuracy of the reconstructed network topology, also adds biases to the geometry and topography of the reconstructed network. This network reconstruction method allows the mapping of uncertainties in the representation while integrating most of the available knowledge about the networks, including local data and global characteristics. It also permits the assessment of the benefits of the remote sensing partial detection process in drainage network mapping., 853-862. The original publication is available at http://www.sciencedirect.com/

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Automatic mapping of valley networks on Mars

Cited by 57 publications

References 20 publications

Extraction of thalweg networks from DTMs: application to badlands

Extraction of thalweg networks from DTMs: application to badlands

An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification

A spatial stochastic algorithm to reconstruct artificial drainage networks from incomplete network delineations

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