A combined algorithm for automated drainage network extraction

Chorowicz, J.; Ichoku, Charles; Riazanoff, Serge; Kim, Youn-Jong; Cervelle, Bernard

doi:10.1029/91wr03098

Cited by 89 publications

(41 citation statements)

References 23 publications

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“…Chorowicz et al [47] proposed a method for the extraction of drainage networks of areal features. The method seeks to combine a threshold-based "profile scan" and the "hydrological flow routing" method to overcome the problem of hydrological flow routing yielding one-pixel wide channel networks.…”

Section: Delineating Valleysmentioning

confidence: 99%

Delineation of Valleys and Valley Floors

Straumann

Purves

2008

Geographic Information Science

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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

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Section: Delineating Valleysmentioning

confidence: 99%

Delineation of Valleys and Valley Floors

Straumann

Purves

2008

Geographic Information Science

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“…The errors vary between 0.1 m and 4.7 m in a typical 25 m DEM (Tarboton et al, 1991). Although many authors agree that sinks and peaks may actually represent the true nature of topography (Chorowicz et al, 1992), they may act as local barriers that trap water flow and cause a major problem for drainage network extraction. To avoid this problem and before performing any hydrologic analysis, sinks in the DEM were identified and eliminated using TerraStream software (Danner et al, 2007).…”

Section: Generation Of Digital Elevation Modelmentioning

confidence: 99%

The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data

Kheir

Bøcher

Greve

2010

Hydrol. Earth Syst. Sci.

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Abstract. Accurate information about organic/mineral soil occurrence is a prerequisite for many land resources management applications (including climate change mitigation). This paper aims at investigating the potential of using geomorphometrical analysis and decision tree modeling to predict the geographic distribution of hydromorphic organic landscapes in unsampled area in Denmark. Nine primary (elevation, slope angle, slope aspect, plan curvature, profile curvature, tangent curvature, flow direction, flow accumulation, and specific catchment area) and one secondary (steady-state topographic wetness index) topographic parameters were generated from Digital Elevation Models (DEMs) acquired using airborne LIDAR (Light Detection and Ranging) systems. They were used along with existing digital data collected from other sources (soil type, geological substrate and landscape type) to explain organic/mineral field measurements in hydromorphic landscapes of the Danish area chosen. A large number of tree-based classification models (186) were developed using (1) all of the parameters, (2) the primary DEM-derived topographic (morphological/hydrological) parameters only, (3) selected pairs of parameters and (4) excluding each parameter one at a time from the potential pool of predictor parameters. The best classification tree model (with the lowest misclassification error and the smallest number of terminal nodes and predictor parameters) combined the steady-state topographic wetness index and soil type, and explained 68% of the variability in organic/mineral field measurements. The overall accuracy of the predictive organic/inorganic landscapes' map produced (at 1:50 000 cartographic scale) using the best tree was estiCorrespondence to: R. Bou Kheir (rania.boukheir@agrsci.dk) mated to be ca. 75%. The proposed classification-tree model is relatively simple, quick, realistic and practical, and it can be applied to other areas, thereby providing a tool to facilitate the implementation of pedological/hydrological plans for conservation and sustainable management. It is particularly useful when information about soil properties from conventional field surveys is limited.

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“…the "profile scan" method of Chorowicz et al (1992) and the "GA (genetic algorithm)" of Nakayama (1997)). It seems that this type has the advantage of avoiding further information loss of the raw DEM data, but some parameters used in these two algorithms are subjective and have no physical basis.…”

Section: Existing Methods To Treat Pits and Flat Areas In Raw Demsmentioning

confidence: 99%

“…However, there is neither an established quantitative method nor a generally accepted criterion to express its accuracy (Chorowicz et al, 1992;Tarboton et al, 1991). In practice, visual agreement of main streams and principal tributaries is often the criterion for validation.…”

Section: Validation Of the Computed Drainage Networkmentioning

confidence: 99%

“…However, whatever the creating methods or data sources of available grid DEMs, and regardless of their resolutions, almost all distributed grid DEMs contain noisenatural and spurious pits (Chorowicz et al, 1992;Tribe, 1992)-defined as a point or a set of adjacent points surrounded by neighbours that have higher elevations (Tarboton et al, 1991). Hutchinson (1989) has proposed an algorithm to create pit free DEMs, but most grid DEMs have not been created by this method (Tribe, 1992).…”

Section: Dem Data Setsmentioning

confidence: 99%

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Development and application of a new algorithm for automated pit removal for grid DEMs

Ao¹,

Takeuchi

Ishidaira

et al. 2003

Hydrological Sciences Journal

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Pits and flat surfaces in raster digital elevation models (DEMs) have been unavoidable obstacles in the extraction of drainage networks. A new automatic method of "pits filling" is presented that eliminates all pits and flat areas simultaneously. Spatially distributed elevation increments of pits are computed. Based on this new approach, drainage networks of more than 10 medium-to large-sized basins have been extracted, including the Mekong River and the Yangtze River. Overlaying the computed drainage networks onto the digitized actual stream networks, good visual consistency was obtained. Besides, the calculated flow pattern over a flat area has been shown to be convergent. The results indicate that the proposed algorithm is easy to use and applicable to DEMs with a wide range of terrain relief and resolution.

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A combined algorithm for automated drainage network extraction

Cited by 89 publications

References 23 publications

Delineation of Valleys and Valley Floors

Delineation of Valleys and Valley Floors

The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data

Development and application of a new algorithm for automated pit removal for grid DEMs

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