Morphological approach to extract ridge and valley connectivity networks from Digital Elevation Models

Sagar, B. S. Daya; Murthy, M. B. R.; Rao, C. Babu; Raj, Baldev

doi:10.1080/01431160304983

Cited by 35 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sagar et al [48] studied the extraction of what they term ridge and valley connectivity networks (RCN and VCN). The authors use multi-scale opening and closing, as well as erosion and dilation of the DEM to extract these networks.…”

Section: Delineating Valleysmentioning

confidence: 99%

Delineation of Valleys and Valley Floors

Straumann

Purves

2008

Geographic Information Science

View full text Add to dashboard Cite

Methods to automatically derive landforms have typically focused on pixel-based, bottom-up approaches and most commonly on the derivation of topographic eminences. In this paper we describe an object-based, top-down algorithm to identify valley floors. The algorithm is based on a region growing approach, seeded by thalwegs with pixels added to the region according to a threshold gradient value. Since such landforms are fiat we compare the results of our algorithm for a particular valley with a numberof textual sources describing that valley. In a further comparison, we computed a pixel-based six-fold morphometric classification for regions we classified as either being, or not being, valley floor. The regions classified as valley floor are dominated by pla nar slopes and channels,though the algorithm is robust enough to allow local convexities to be classified as within the valley floor. Future work will explore the delineation of valley sides, and thus complete valleys. Abstract. Methods to automatically derive landforms have typically focused on pixel-based, bottom-up approaches and most commonly on the derivation of topographic eminences. In this paper we describe an object-based, top-down algorithm to identify valley floors. The algorithm is based on a region growing approach, seeded by thalwegs with pixels added to the region according to a threshold gradient value. Since such landforms are fiat we compare the results of our algorithm for a particular valley with a number of textual sources describing that valley. In a further comparison, we computed a pixel-based sixfold morphometric classification for regions we classified as either being, or not being, valley floor. The regions classified as valley floor are dominated by planar slopes and channels, though the algorithm is robust enough to allow local convexities to be classified as within the valley floor. Future work will explore the delineation of valley sides, and thus complete valleys. Delineation of Valleys and Valley Floors

show abstract

Section: Delineating Valleysmentioning

confidence: 99%

Delineation of Valleys and Valley Floors

Straumann

Purves

2008

Geographic Information Science

View full text Add to dashboard Cite

show abstract

“…These transformations are recursively performed on M by increasing the size of structuring element to obtain various orders of channel subsets. The possible channel network exists within F‐DEM referred to as fractal‐skeletal based channel network (F‐SCN) follows Horton's laws [ Sagar et al , 1998b; Sagar and Murthy , 2000; Sagar et al , 2001; Sagar et al , 2003]. The ridge connectivity network subsets of n th order is defined as

where ridge network subsets of nth order ( RID n ( M )) are obtained by {( CH c ⊖S n )}\{[( CH c ⊖S n )⊖S]⊕ S } in which the complement of CH ( M ) is used to generate ridge connectivity network.…”

Section: Fractal Dem and Unique Connectivity Networkmentioning

confidence: 99%

“…These transformations are recursively performed on M by increasing the size of structuring element to obtain various orders of channel subsets. The possible channel network exists within F-DEM referred to as fractal-skeletal based channel network (F-SCN) follows Horton's laws [Sagar et al, 1998b;Sagar and Murthy, 2000;Sagar et al, 2001;Sagar et al, 2003]. The ridge connectivity network subsets of nth order is defined as…”

Section: Fractal Dem and Unique Connectivity Networkmentioning

confidence: 99%

Allometric power‐law relationships in a Hortonian fractal digital elevation model

Sagar

Tay

2004

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

[1] We provide a topologically viable model that is geomorphologically realistic from the point of its Hortonity and general allometric scaling laws. To illustrate this, we consider a fractal binary basin, generated in such a way that it follows certain postulates, and decompose it into various coded topologically prominent regions the union of which is defined as geomorphologically realistic Fractal-DEM. We derive two unique topological networks from this Hortonian fractal DEM based on which we derive allometric power-law relationships among the basic measures of decomposed sub-basins of all orders ranging from w = 1 to w = W. Our results are in good accord with optimal channel networks and natural river basins.

show abstract

“…Applications of geospatial technology have produced many topographic features in the past decades, particularly from digital elevation models (DEMs) (e.g., Sagar et al, 2003;Yang et al, 2011;Martha et al, 2012). Although ac curacy assessment of DEMs has been well documented (e.g., Muller, 1999;Reusser and Bierman, 2007;Aguilar and Mills, 2008;Höhle and Höhle, 2009), few studies have been carried out for automated assessment of linear topo graphic features such as ridges and valleys, which can be derived from DEMs or mapped using other methods.…”

Section: Introductionmentioning

confidence: 99%

Automated accuracy assessment for ridge and valley polylines using high-resolution digital elevation models

Dong

2017

Geosphere

View full text Add to dashboard Cite

For better understanding of geologic and geomorphic processes responsible for the formation of ridges and valleys widely distributed over the Earth's surface, accurate delineation of the features is very important. This paper intro duces an automated method for accuracy assessment of ridge and valley polylines using high-resolution digital elevation models (DEMs) derived from light detection and ranging (LiDAR) data and software implementation of a Python add-in toolbar for Esri's ArcGIS 1. Compared with existing methods that use two input polyline layers for comparison, the new method automatically extracts reference points from the DEM on-the-fly. In addition to producing statistical results of positional accuracy following the National Standard for Spatial Data Accuracy, the method also identifies locations where inaccuracies occur, so that the polylines can be edited and reevaluated. Two data sets from New Mexico and Colorado are also used as samples to demonstrate the application of the geographic information system (GIS) add-in. The results suggest that the free GIS add-in can be widely used by geoscientists.

show abstract

Morphological approach to extract ridge and valley connectivity networks from Digital Elevation Models

Cited by 35 publications

References 6 publications

Delineation of Valleys and Valley Floors

Delineation of Valleys and Valley Floors

Allometric power‐law relationships in a Hortonian fractal digital elevation model

Automated accuracy assessment for ridge and valley polylines using high-resolution digital elevation models

Contact Info

Product

Resources

About