Detection of River Boundary Edges from Remotely Sensed Image
J. Environ. Nanotechnol., Volume 6, No 4 (2017) pp. 12-16
Abstract
Detection of river boundary from remotely sensed imagery plays avitalrole in space-based river studies. Procuring of river attributes such as length, width, branching pattern, boundaries and temporal variation are very useful in several applications such as surface water supply, transport, distribution, and dynamics. Typically, field surveying is commonly used method to study of these river characteristics.It is commonly time-consuming, labor intensive and expensive method to gather river attributes in the field. In particular, it is unsafe and not feasible to measure rivers in certain environments such as ice sheets, tidal flats, and floodplains.Therefore, satellite imageriesof the earth surface are playing critical roles in river studies.The detection of linear and curvilinear components is a classic subject in image processing studies. In present study a method for the detection of river boundary is described. Image of Linear Imaging and Self Scanning Sensor (LISS-III) of Resourcesat-2 satellite is used. Method includes three basic steps i.e. mosaicking of different tiles of images, edge detection and connected component analysis.
Full Text
Reference
Benstead, J. P. and Leigh, D. S., An expanded role for river networks, Nat. Geosci., 5(10), 678–67(2012).
https://doi.org/10.1038/ngeo1593
Canny, J., A computational approach to edge detection, Surveying Engineering, IEEE Trans. Pattern Analysis Machine Intelligence PAMI, 8, 679–698 (1986).
Dillabaugh, C. R., Niemann, K. O., Richardson, D. E., Semi-automated extraction of rivers from digital imagery, GeoInformatica., 6(3), 263-284(2002).
https://doi.org/10.1023/A:1019718019825
Güneralp, İ., Filippi, A.M., Hales, B.U., River-flow boundary delineation from digital aerial photography and ancillary images using support vector machines, Gisci. Remote Sens., 50, 01– 25(2013).
https://doi.org/10.1080/15481603.2013.778560.
Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J. and Xiao, T., An automated method for extracting rivers and lakes from Landsat imagery, Remote Sens., 6(6), 5067-5089(2014).
https://doi.org/10.3390/rs6065067
Klemenjak, S., Waske, B., Valero, S. and Chanussot, J., Automatic detection of rivers in high-resolution SAR data, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 5(5), 1364-1372(2012).
https://doi.org/10.1109/JSTARS.2012.2189099
Lau, T. and Franklin, W. R., River network completion without height samples using geometry-based induced terrain, Cartogr. Geogr.Inf. Sci., 40(4), 316-325(2013).
https://doi.org/10.1080/15230406.2013.780785
Liu, Z., Khan, U. and Sharma, A., A new method for verification of delineated channel networks,Water Resour. Res., 50(3), 2164–2175(2014).
https://doi.org/10.1002/2013WR014290
Pai, N. and Saraswat, D., A geospatial tool for delineating streambanks, Environ. Modell. Softw., 40, 151-159(2013).
https://doi.org/10.1016/j.envsoft.2012.08.012
Pavelsky, T. M. and Smith, L.C., RivWidth: A software tool for the calculation of river widths from remotely sensed imagery, IEEE Geosci. Remote Sens. Lett.,5(1), 70-73(2008).
https://doi.org/10.1109/LGRS.2007.908305
Trigg, M.A., Bates, P.D., Wilson, M.D., Schumann, G., Baugh, C., Floodplain channel morphology and networks of the middle amazon river, Water Resour. Res. 48(10), W10504(2012).
https://doi.org/10.1029/2012WR011888
Yang, K.; Smith, L. C., Supraglacial streams on the greenland ice sheet delineated from combined spectral-shape information in high-resolution satellite imagery, IEEE Geosci. Remote Sens. Lett., 10(4), 801–805(2013).