The combination of attraction model and genetic algorithm to predict stream networks

Document Type : Original Article

Authors

1 Assistant Professor, Dept. of Remote Sensing & GIS, Tarbiat Modares University, Tehran, Iran

2 Associate Professor, Department of Range and Watershed Management, College of

Abstract

The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. In this method, the pixels were divided into a number of sub-pixels according to the values of the neighboring pixels. The results of the attraction model showed that Scale 2 with the Neighborhood model 2 is more accurate than other Neighborhoods for extracting DEM with higher resolution. The results showed that the predicted stream-network landscapes created using the GLE algorithm had the self-similar tree structure of natural stream networks. Also, the results of the genetic algorithm showed that a change in the degree of waterways in the study area over time compared to the current situation, so that the degree of number of first-class waterways in the future will change to grade 3 due to erosion in upper lands. Therefore, using these models, the condition of waterways can be predicted in the future and better management can be adopted for watersheds.

Keywords

References

Adams, J., Contemporary uplift and erosion of the Southern Alps, New Zealand, Geol. Soc. Am. Bull., Part II, vol. 91, no. 11, pp. 1–114, 1980.
Ardila, J.P., Tolpekin, V.A., Bijker, W. & Stein, A., 2011, Markov-random-field-basedsuper-resolution mapping for identification of urban trees in VHR images, ISPRS J. Photogram. Remote Sens. 66: 762–775.
Atkinson, P.M., 2005, Sub-pixel target mapping from soft-classified, remotely sensed imagery, Photogramm. Eng. Remote Sens. 71 (7): 839–846.
 
Banavar, J. R., Colaiori, F., Flammini, A., Amos Maritan, A., & Rinaldo, A., 2001, Scaling, optimality, and landscape evolution, Journal of Statistical Physics 104(1), 1-48
Boucher, A., Kyriakidis, P.C., 2006, Super-resolution land cover mapping with indicatorgeostatistics, Remote Sens. Environ. 104 (3): 264–282.
Bui, T.N., & Moon, B.R., 1995, On multi-dimensional encoding/crossover. In Proceedings of the 6th International Conference on Genetic Algorithms (pp. 56-49, Morgan Kaufmann Publishers Inc.
Chen, Z., Ye, F., Fu, W., Ke, Y. & Hong, H., 2020, The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China. Natural Hazards, 1-25
Hessari, B., Bonabi, O. & Jahangir, I., 2019, Effects of Digital Elevation Model (DEM) Spatial Resolution on the Recognition of Physiography Characteristics of the Basin (A Case Study of Shahrchai Watershed), jwmseir. 13 (45) :12-22
Jokar Sarhangi, A., Telang, A. & Lorestani, A.H., 2017, Investigation of morphometric changes of the river with emphasis on the twists. Case study: Chehelchai-Normab river, Journal of Spatial Planning, Scientific, 27(7), 17-30.
Kasetkasem, T., Arora, M.K. & Varshney, P.K., 2005, Super-resolution land cover mappingusing a Markov random field based approach, Remote Sens. Environ. 96 (3/4):302–314.
Knighton, D., Fluvial Forms and Processes: A New Perspective, London, U.K.: Arnold, 1998, p. 383.
Li, Y.-H., Denudation of Taiwan island since the Pliocene epoch, Geology, vol. 4, no. 2, pp. 105–107, 1976.
Mertens, K.C., Baets, B.D., Verbeke, L.P.C. & Wulf, R.D., 2006, A sub-pixel mappingalgorithm based on sub-pixel/pixel spatial attraction models, Int. J. Remote Sens. 27 (15): 3293–3310.
Mertens, J.C.E. & Chawla, N., 2014, A custom lab-scale high resolution x-ray computed tomography system for 4D materials science. Imaging performance modeling and characterization, In: Proceedings of SPIE. The International Society for Optical Engineering. http://dx.doi.org/ /12,2062638/10,1117
Mertens, K.C., Verbeke, L.P.C., Ducheyne, E.I. & Wulf, R.D., 2014, Using genetic algorithmsin sub-pixel mapping, Int. J. Remote Sens. 24 (21): 4241–4247.
Mokarram, M., Darvishi Blourani, A. & Neghaban, S., 2014, Relationship between morphometric features of watersheds and erosion at different altitudes using topographic position index (TPI) Case study: Nazlouchai watershed, Quarterly Journal of Geographical Information. 26(101), 142-131.
Mokarrama, M., & Hojati, M., 2018, Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM), The Egyptian Journal of Remote Sensing and Space Science, 21(1), 111-120
Nigussie, D., Milla, R.Z., Clevers, J.G.P.W.,  Possibilities and limitations of artificialneural networks for subpixel mapping of land cover, Int. J. Remote Sens. 32 (22) (2011) 7203–7226.
Paik, K., 2011, Optimization Approach for 4-D Natural Landscape Evolution, IEEE Trans, Evol. Comput. 15,684-691.
Paik, K. & Kumar, P., Emergence of self-similar tree network organization, Complexity, vol. 13, no. 4, pp. 30–37, 2008.
Rodríguez‐Iturbe, I., Rinaldo, A., Rigon, R., Bras, R.L., Marani, A. & Ijjász‐Vásquez, E., 1992, Energy dissipation, runoff production, and the three‐dimensional structure of river basins, Water Resources Research, 28(4), 1095-1103.
Shayan, S.; Mullah Mehralizadeh F., Jannati, M., 2006, Performance data of remote sensing (RS) in mapping landforms and its role in environmental planning, The Journal of Spatial Planning. Volume 9, Issue 4, Pages 111-148.
Samal, D. R., Gedam, S.S. & Nagarajan, R., 2015, GIS based drainage morphometry and its influence on hydrology in parts of Western Ghats region, Maharashtra, India, Geocarto International, 730,778-755
Tatem, A,J., Lewis, H.G., Atkinson, P.M. & Nixon, M.S., 2001, Super-resolution targetidentification from remotely sensed images using a Hopfield neural network,IEEE Trans, Geoscience Remote Sensing. 39 (4):781–796.
Tolpekin, V.A. & Stein, A., 2009, Quantification of the effects of land-cover-class spectralreparability on the accuracy of markov-random-field-based superresolutionmapping, IEEE Trans. Geosci. Remote Sens. 47 (9): 3283–3297.
Tarbuck, E.J. & Lutgens, F.K., 2008, Earth : an introduction to physical geology, Pearson Prentice Hall.
Verhoeye, J., De W. R., 2002, Land cover mapping at sub-pixel scales using linearoptimization techniques, Remote Sens. Environ. 79 (1): 96–104.
Wu, K., Zhang, L.P., Niu, R.Q., Du, B. & Wang, Y., 2011, Super-resolution land-covermapping based on the selective endmember spectral mixture model inhyperspectral imagery, Opt. Eng. 50 (12) 126201.
Xu, X., Zhong, Y. & Zhang, L., 2014, A sub-pixel mapping method based on an attraction modelfor multiple shifted remotely sensed images, Neurocomputing 134 (2014) 79–91.
Yatsu, E., 1955, On the longitudinal profile of the graded river, Trans. Am. Geophys. Union 36, 655. https://doi.org/ 10.1029/TR036i004p00655
Zhang, L.P., Wu, K., Zhong, Y.F. & Li, P.X., A new sub-pixel mapping algorithm basedon a BP neural network with an observation model, Neurocomputing 71 (2008) 2046–2054.

Files

Share

How to cite

Statistics