Shoreline analysis using Landsat-8 satellite image

被引：13

作者：

Yadav A. ^{[1
]}

Dodamani B.M. ^{[1
]}

Dwarakish G.S. ^{[1
]}

机构：

[1] Dept. of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal

来源：

ISH Journal of Hydraulic Engineering | 2021年 / 27卷 / 03期

关键词：

ArcGIS; EPR and LRR; ERDAS imagine; Landsat-8; shoreline change;

D O I：

10.1080/09715010.2018.1556569

中图分类号：

学科分类号：

摘要：

The shoreline is a boundary between wet and dry part of the beach, and it is dynamic in nature. Natural and human factors are always influencing shoreline configuration. One of the important natural events which are responsible for the shoreline configuration along the Karnataka coast is southwest monsoon, and hence there is a change in shoreline position between pre- and post-monsoon. For the present research work, Karwar beach with two beaches, Rabindranath Tagore beach and Devabagh beach along Karnataka coast, West coast of India were selected as study area. Landsat-8 satellite images for the years 2013–2017 were used in the present study and processed for May and October of every year, using ERDAS imagine 2014 and ArcGIS 10.3 tools to generate shoreline configuration maps. Finally, the comparison was made between 2013 and 2017 years, and the results indicate that the Devbagh beach during pre-monsoon season has an average shoreline change rate of −7.54 m/yr (EPR) and −5.57 m/yr (LRR) and during post-monsoon season it is 0.34 m/yr (EPR) and −0.46 m/yr (LRR). Similarly, Rabindranath Tagore beach during pre-monsoon seasons has an average shoreline change rate of 0.004 m/yr (EPR) and 1.67 m/yr (LRR), and in post-monsoon season, it is −5.77 m/yr (EPR) and −6.55 m/yr (LRR) respectively. The total uncertainty error was estimated and found to be (Formula presented.) 5.00 m/yr. © 2018 Indian Society for Hydraulics.

引用

页码：347 / 355

页数：8

共 35 条

[1]

Abdulshahed A.M., Longstaff A.P., Fletcher S., The application of ANFIS prediction models for thermal error compensation on CNC machine tools, Appl. Soft Comput. J., 27, pp. 158-168, (2015)

[2]

Addo K.A., Shoreline morphological changes and the human factor. case study of accra ghana, J. Coastal Conserv., 17, 1, pp. 85-91, (2013)

[3]

Addo K.A., Jayson-Quashigah P.N., Kufogbe K.S., Quantitative analysis of shoreline change using medium resolution satellite imagery in Keta, Ghana, Mar. Sci., 1, pp. 1-9, (2011)

[4]

Addo K.A., Walkden M., Mills J.P., Detection, measurement and prediction of shoreline recession in Accra, Ghana, ISPRS J. Photogramm. Remote Sens., 63, 5, pp. 543-558, (2008)

[5]

Aedla R., Dwarakish G.S., Reddy D.V., Automatic shoreline detection and change detection analysis of netravati-gurpurRivermouth using histogram equalization and adaptive thresholding techniques, Aquat. Procedia, 4, pp. 563-570, (2015)

[6]

Almonacid-Caballer J., Sanchez-Garcia E., Pardo-Pascual J.E., Balaguer-Beser A.A., Palomar-Vazquez J., Evaluation of annual mean shoreline position deduced from landsat imagery as a mid-term coastal evolution indicator, Mar. Geol., 372, pp. 79-88, (2016)

[7]

Berger A.R., The geoindicator concept and its application: an introduction. Geoindicators: assessing rapid environmental changes in earth systems, AA Balkema, Rotterdam, 1, (1996)

[8]

Bhat U.G., Neelakantan B., Kusuma N., Naik U.G., Environmental characteristics of the marine and estuarine habitats of Karwar: an overview, J. Indian Fish. Assoc., 18, pp. 401-412, (1988)

[9]

Boak E.H., Turner I.L., Shoreline definition and detection: a review, J. Coastal Res., pp. 688-703, (2005)

[10]

Courtaud J., (2000)

← 1 2 3 4 →