Morphological Study of The Liliba River Utilizing Remote Sensing System

Avilla Martha Anmuni, Onisius Loden, Jusuf Wilson Meynerd Rafael

Submitted : 2024-02-10, Published : 2024-05-31.

Abstract

The branch of natural sciences called river morphology focuses on the study of the characteristics and dynamics of rivers, including their structure, classification, and changes on spatial and temporal scales. Two main factors influence river configurations. The first is natural factors, such as floods and landslides, and the second is human factors, such as human activities that alter river morphology. Cyclone Seroja caused landslides on the banks of the Liliba River in Kupang, East Nusa Tenggara in April 2022. This mainly occurred at Naimata Bridge in Liliba Village, Oebobo District. River geometry, especially the channel and bed elevation, can be significantly influenced by landslides occurring on the riverbanks. Therefore, a study of the morphology of the Liliba River was conducted using remote sensing systems. To conduct a detailed analysis, this study incorporated these photos. In this review, changes in the river channel were examined by extracting the river's course from Landsat image data. From 2009 to 2022, the Liliba River experienced an average shift of 1.60 meters westward and eastward. The research results indicate the need for reforestation along the riverbanks, and residents should be encouraged to reduce the disposal of plastic waste into the river. Future research should also further investigate the geological characteristics of the Liliba River, such as rock types, and conduct hydrological analyses to comprehensively understand the factors influencing changes in the riverbed.

References

S. Baniya, R. Deshar, R. Chauhan, and S. Thakuri, “Assessment of channel migration of Koshi River in Nepal using remote sensing and GIS,” Environ. Challenges, vol. 11, no. January, 2023, doi: 10.1016/j.envc.2023.100692.

S. Rajakumari, M. Meenambikai, V. Divya, K. J. Sarunjith, and R. Ramesh, “Morphological changes in alluvial and coastal plains of Kandaleru river, Andhra Pradesh using RS and GIS,” Egypt. J. Remote Sens. Sp. Sci., vol. 24, no. 3, pp. 1071–1081, 2021, doi: 10.1016/j.ejrs.2021.10.008.

A. R. M. T. Islam, “Assessment of Fluvial Channel Dynamics of Padma River in Northwestern Bangladesh,” Univers. J. Geosci., vol. 4, no. 2, pp. 41–49, 2016, doi: 10.13189/ujg.2016.040204.

K. Chohan, S. R. Ahmad, A. Ashraf, M. Kamran, and R. Rasheed, “Remote sensing based innovative solution of river morphology for better flood management,” Int. J. Appl. Earth Obs. Geoinf., vol. 111, no. December 2021, p. 102845, 2022, doi: 10.1016/j.jag.2022.102845.

A. Koohizadeh Dehkordi, R. Fatahi Nafchi, H. Samadi-Boroujeni, M. Khastar Boroujeni, and K. Ostad–Ali–Askari, “Assessment of morphological changes of river bank erosion using landsat satellite time-series images,” Ain Shams Eng. J., vol. 15, no. 3, p. 102455, 2024, doi: 10.1016/j.asej.2023.102455.

I. N. . Soetedjo, P. De Rozari, and N. Leo, “Studi Penutupan Lahan Hulu dan Hilir Dareah Aliran Sungai Liliba Terhadap Kuantitas Air,” J. Ilmu Lingkung., vol. 19, no. 3, pp. 630–637, 2021, doi: 10.14710/jil.19.3.630-637.

X. Pons, L. Pesquer, J. Cristóbal, and O. González-Guerrero, “Automatic and improved radiometric correction of landsat imageryusing reference values from MODIS surface reflectance images,” Int. J. Appl. Earth Obs. Geoinf., vol. 33, no. 1, pp. 243–254, 2014, doi: 10.1016/j.jag.2014.06.002.

S. Widjojo, A. Rusmanto, and S. Suharjo, “Pengenalan Proses Citra secara Digital,” Forum Geogr., vol. 6, no. 1, p. 55, 2016, doi: 10.23917/forgeo.v6i1.4698.

A. Vyas, S. Yu, and J. Paik, “Image enhancement,” Signals Commun. Technol., no. Bagian 1, pp. 199–231, 2018, doi: 10.1007/978-981-10-7272-7_6.

X. Chen, S. Xu, S. Li, H. He, Y. Han, and X. Qu, “Identification of architectural elements based on SVM with PCA: A case study of sandy braided river reservoir in the Lamadian Oilfield, Songliao Basin, NE China,” J. Pet. Sci. Eng., vol. 198, no. 66, p. 108247, 2021, doi: 10.1016/j.petrol.2020.108247.

A. K. Taloor, Drinder Singh Manhas, and G. Chandra Kothyari, “Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data,” Appl. Comput. Geosci., vol. 9, no. December 2020, p. 100051, 2021, doi: 10.1016/j.acags.2020.100051.

Q. S. Du et al., “Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China,” Res. Cold Arid Reg., vol. 15, no. 4, pp. 202–209, 2023, doi: 10.1016/j.rcar.2023.08.002.

H. J. Jumaah, M. H. Ameen, G. H. Mohamed, and Q. M. Ajaj, “Monitoring and evaluation Al-Razzaza lake changes in Iraq using GIS and remote sensing technology,” Egypt. J. Remote Sens. Sp. Sci., vol. 25, no. 1, pp. 313–321, 2022, doi: 10.1016/j.ejrs.2022.01.013.

P. Lemenkova, “ISO Cluster classifier by ArcGIS for unsupervised classification of the Landsat TM image of Reykjavík,” Bull. Nat. Sci. Res., vol. 11, no. 1, pp. 29–37, 2021, doi: 10.5937/bnsr11-30488.

L. Lian and J. Chen, “Research on segmentation scale of multi-resources remote sensing data based on object-oriented,” Procedia Earth Planet. Sci., vol. 2, no. 1, pp. 352–357, 2011, doi: 10.1016/j.proeps.2011.09.055.

Article Metrics

Abstract view: 118 times
Download     : 20   times Download     : 7   times

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

  • There are currently no refbacks.