A drainage system analysis evaluation of, and comparison between, Landsat-3 RBV, Landsat-5 TM and SPOT PA imageries covering the Central Macedonia district, Greece

Astaras, Theodoros; Lambrinos, Nikolaos; Soulakellis, Nikolaos

doi:10.1080/01431169008955113

Cited by 13 publications

(3 citation statements)

References 7 publications

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“…A semi-automatic extraction of the drainage network from a JERS-1 radar mosaic image was carried out, combining an automatic procedure with manual delineation or correction. Manual delineation of a drainage network from aerial photographs or satellite images has often been performed (for example, Astaras, 1985;Astaras et al, 1990). Drainage networks have been partly resolved by automatic extraction from Landsat or SPOT images in arid regions (Gardner et al, 1989;Ichoku et al, 1996), and this technique could theoretically be extended to other climatic zones (Ichoku et al, 1996).…”

Section: Digital Drainage Network 3: Extraction From Jers Sar Images mentioning

confidence: 99%

Watershed delineation for the Amazon sub‐basin system using GTOPO30 DEM and a drainage network extracted from JERS SAR images

Seyler¹,

Muller²,

Cochonneau

et al. 2009

Hydrological Processes

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Abstract:In this study, we used a geographic information system (GIS), remote sensing products and a digital elevation model (DEM) to prepare and to test the existing data sources and algorithms for a distributed, physically based, hydrological model, incorporating the gauging stations of the national networks of the Amazon basin. Watershed delineation for the Amazon sub-basin system is a necessary first step in distributed hydrological modelling. The DEMs currently available for the Amazon basin are GTOPO30, which has a grid spacing of about 1 km, and SRTM, which can be freely obtained from the internet at a resolution of about 90 m. Each of these DEMs has different sources and consequently different kinds of uncertainties. We have tested the two DEMs, comparing the results obtained using both of them on different sub-basins within the Amazonian basin. The delineation of the sub-basins for the entire Amazonian basin, which is currently available on our project site (http://www.mpl.ird.fr/hybam/), has been obtained from the GTOPO30. With GTOPO30 alone, the D8 algorithm (which determines the direction of flow in eight neighbouring cells) does not always give the correct delineation of sub-basins corresponding to the gauging stations. This problem occurs mainly in the very flat areas of the region. One way to overcome this problem is to burn-in the DEM with a river network. The spatial precision of this river network must be compatible with that of the DEM, and homogeneous throughout the basin. We tested three river networks on the Negro River sub-basins. We found that a suitable river network could be extracted by digitising a JERS-1 mosaic image. The DEM GTOPO30 burned-in with the JERS-1 extracted river network made the correct distributed modelling of the Amazon gauged sub-basins possible by determining water transfer times within the basin.

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Section: Digital Drainage Network 3: Extraction From Jers Sar Images mentioning

confidence: 99%

Watershed delineation for the Amazon sub‐basin system using GTOPO30 DEM and a drainage network extracted from JERS SAR images

Seyler¹,

Muller²,

Cochonneau

et al. 2009

Hydrological Processes

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“…To extend stream protection efforts into small streams compromising conflicts between stream protection and timber production, their positions must be represented very accurately on maps, which are one of the most basic and most important materials in water and/or forest management planning (Shepard 2006). Therefore, numerous stream mapping algorithms based on remote sensing data have been developed to produce better stream map products or higher positional accuracy of their products in the past several decades (Astaras 1985;Astaras et al 1990;Chorowicz et al 1992;Tarboton 1997;Vogt et al 2003;Mouton 2005;Wilson et al 2007). The problem in introducing stream mapping algorithms is that statistically sound quantitative methods have not been established yet for the evaluation of the positional accuracy of stream network maps -the products of stream mapping algorithms (Chorowicz et al 1992;Heine et al 2004).…”

Section: Introductionmentioning

confidence: 99%

A suggestion for the evaluation of maps of small streams in forests

Park

2013

Forest Science and Technology

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Numerous remotely sensed stream-mapping methods have been developed over the last several decades to extend small stream protection. While the accuracy assessment of the final products has been considered to be very essential, the estimation of the positional accuracy of stream map products has not been well addressed because it is not easy to acquire the field reference data, especially for streams located in remote forest areas. More importantly, it is also because there does not exist any prominent statistical method to estimate the positional accuracy of stream maps. Since most positional accuracy assessment methods for the remote sensing data product are designed for at least two dimensional features, the linear feature of streams does not fit with those methods. As a result of this challenge, the previously introduced estimation methods for stream maps' accuracy have not dealt with the positional accuracy with regard to two error aspects, commission and omission errors. In this study, I suggest a road-crossing stream points (RCSPs) sampling method, which is the practical version of the contour line-based line transect sampling method to collect field stream position, and review the previously introduced estimation methods and the feasibility of the application of the error matrix, which has not been inspected before. I found that the error matrix has good reliability in evaluating stream network positions even if it has the pseudo-infinite problem. Among the error matrix-based accuracy estimates, the producer accuracy, user accuracy, andK 1 (KHAT-inf) are feasible to evaluate the positional accuracy of stream maps, but Congalton's overall accuracy cannot be applied to stream maps. The sampling method and the accuracy assessment method that are newly introduced in this study were applied to evaluate three D8 stream maps, and it was proved that these new methods are feasible.

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“…The manual procedure for channel network extraction is accurate (Astaras et al 1990) but labour intensive and, therefore, impracticable for application over large areas. As a solution to this problem, several automatic digital elevation model (DEM)-based procedures have been developed over the last two decades, using the eight flow direction matrix method (D8; O'Callaghan and Mark 1984).…”

Section: Introductionmentioning

confidence: 99%

Integrating automatically processed SPOT HRV Pan imagery in a DEM-based procedure for channel network extraction

Svoray

2004

International Journal of Remote Sensing

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Abstract. The contribution of automatically processed Système Pour l'Observation de la Terre (SPOT) high resolution visible (HRV) Pan data as an ancillary source of information to a digital elevation model (DEM)-based method for channel network extraction is introduced. The image processing stage included an application of a Laplacian filter for edge detection. Edge pixels that were not contiguous with the main channel network were eliminated and the channel network was buffered and finally skeletonized to create channels with one-pixel width. A DEM-based approach was implemented for an overlapping area using the terrain analysis using digital elevation models (TauDEM) procedure based on the principles of the flow direction matrix method. The channel network that was extracted by the use of the two methods in fusion was tested against the imagery, and the DEM-based, channel networks and conformed to the reference data more accurately in terms of coverage of channels, network connectivity and location of extracted channels. A disadvantage of the data fusion is the additional, few, artificial channels. IntroductionChannel network maps are essential for studying watershed processes, river basin geomorphology and drainage network development (Rodriguez-Iturbe and Rinaldo 1997). The manual procedure for channel network extraction is accurate (Astaras et al. 1990) but labour intensive and, therefore, impracticable for application over large areas. As a solution to this problem, several automatic digital elevation model (DEM)-based procedures have been developed over the last two decades, using the eight flow direction matrix method (D8; O'Callaghan and Mark 1984). Other automatic methods for channel network extraction have focused on the description of local surface morphology (e.g. Haralick 1983, Wood 1998.In general, D8 methods are implemented through two main steps: (i) calculating a flow direction matrix from elevation data and then, (ii) measuring the area that contributes to each DEM cell and delineating the channel network based on an empirical threshold value that should be determined according to landscape characteristics.The problem with DEM-based methods is that the accuracy of their predictions depends on the accuracy of the source DEM. DEMs suffer from both random and

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A drainage system analysis evaluation of, and comparison between, Landsat-3 RBV, Landsat-5 TM and SPOT PA imageries covering the Central Macedonia district, Greece

Cited by 13 publications

References 7 publications

Watershed delineation for the Amazon sub‐basin system using GTOPO30 DEM and a drainage network extracted from JERS SAR images

Watershed delineation for the Amazon sub‐basin system using GTOPO30 DEM and a drainage network extracted from JERS SAR images

A suggestion for the evaluation of maps of small streams in forests

Integrating automatically processed SPOT HRV Pan imagery in a DEM-based procedure for channel network extraction

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