Perubahan Tutupan Lahan pada DAS Bendung Kota Palembang dengan Metode NDVI

Vionadwiuchtia Idrat, Indrayani Indrayani, Akhmad Mirza, Tata Prayoga, Azzahra Ramadhanti, M. Rama Miftah Wijaya, Prasetya Ardhana

Abstract


Rapid urban development to accommodate population growth has led to significant land use changes. The Bendung Watershed, located in Palembang City, is one of the areas with a high potential for flooding. Several factors contribute to flooding in Palembang, including inadequate drainage system performance, extensive swamp reclamation over the years, low-lying topography, and land use changes. Land cover change is one of the factors affecting flood discharge; therefore, this study aims to analyze land cover changes in the Bendung Watershed using the Normalized Difference Vegetation Index (NDVI) method based on Landsat remote sensing imagery from 2005 and 2024. The research methodology included watershed boundary delineation using DEM SRTM data, processing of Landsat imagery through geometric and radiometric corrections, band composite generation, NDVI transformation, land cover classification, and classification accuracy assessment. The results indicate that between 2005 and 2024, the built-up area increased by 21.15%, while open space and water bodies decreased by 40.98% and 36.89%, respectively.

Keywords


DAS; Land Cover; NDVI; Flood; Drainage.

Full Text:

PDF

References


Gambo, J., Mohd Shafri, H. Z., Shaharum, N. S. N., Abidin, F. A. Z., & Rahman, M. T. A. (2018). Monitoring and Predicting Land Use-Land Cover (Lulc) Changes Within and Around Krau Wildlife Reserve (Kwr) Protected Area in Malaysia Using Multi-Temporal Landsat Data. Geoplanning: Journal of Geomatics and Planning, 5(1), 17. https://doi.org/10.14710/geoplanning.5.1.17-34

Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). Ndvi: Vegetation Change Detection Using Remote Sensing and Gis - A Case Study of Vellore District. Procedia Computer Science, 57, 1199–1210. https://doi.org/10.1016/j.procs.2015.07.415

Hariyono, M. I., & Dewi, R. S. (2023). LAND USE AND LAND COVER (LULC) CLASSIFICATION WITH MACHINE LEARNING APPROACH USING ORTHOPHOTO DATA. Majalah Ilmiah Globe, 25(1), 87-96.

Indrayani, Buchari, E., Putranto, D. D. A., & Saleh, E. (2018). The analysis of land use weights on road traces selection. MATEC Web of Conferences, 195, 1–7. https://doi.org/10.1051/matecconf/201819504018

Indrayani, I., & Saputra, R. H. (2024). Pemetaan Sebaran Genangan Dan Banjir Kota Palembang. Pustaka Aksara.

Lyu, L., Wang, X., Sun, C., Ren, T., & Zheng, D. (2019). Quantifying the effect of land use change and climate variability on greenwater resources in the Xihe River Basin, Northeast China. Sustainability (Switzerland), 11(2). https://doi.org/10.3390/su11020338

Mandala, M., Hakim, F. L., Indarto, I., & Kurnianto, F. A. (2024). Land Use and Land Cover Change in East Java Indonesia from 1972 to 2021: Learning from Landsat. Environmental Research, Engineering and Management, 80(3), 57–69. https://doi.org/10.5755/j01.erem.80.3.35362

Mubarok, Z., Murtilaksono, K., & Wahjunie, E. D. (2015). Response of Landuse Change on Hydrological Characteristics of Way Betung Watershed - Lampung. Jurnal Penelitian Kehutanan Wallacea, 4(1), 1. https://doi.org/10.18330/jwallacea.2015.vol4iss1pp1-10

Mulkal, Rizkya, P., Aflah, N., & Muchlis. (2024). Exploring the role of spectral indices on improving land cover classification accuracy based on Sentinel-2 satellite imagery in Banda Aceh City, Indonesia. Indonesian Journal of Environmental Sustainability, 2(2), 96–107. https://doi.org/10.22373/ijes.v2i2.5957

Nigar, A., Li, Y., Jat Baloch, M. Y., Alrefaei, A. F., & Almutairi, M. H. (2024). Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications. Frontiers in Environmental Science, 12, 1378443. https://doi.org/10.3389/fenvs.2024.1378443

Negash Seboka, G. (2016). Spatial assessment of NDVI as an indicator of desertification in Ethiopia using remote sensing and GIS. Master Thesis in Geographical Information Science.

Roy, D. P., Wulder, M. A., Loveland, T. R., Ce, W., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145, 154-172. https://doi.org/10.1016/j.rse.2014.02.001

Saputra, R. H., Mirza, A., Najib, A., Prakoso, M. A., & Anjani, W. L. (2024, February). Identification of Flood Distribution Per District Based on GIS in Palembang City. In 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023) (pp. 104-117). Atlantis Press. https://doi.org/10.2991/978-94-6463-386-3_13

S Sudarmadji, S., Indrayani, I., Praditya, N., Idrat, V., & Siahaan, Y. (2024, February). The identification of retention ponds for flood management in Palembang City. In 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023) (pp. 177-185). Atlantis Press.Thakur, J. K. (2015). Optimizing groundwater monitoring networks using integrated statistical and geostatistical approaches. Hydrology, 2(3), 148–175. https://doi.org/10.3390/hydrology2030148

WWF Nepal. (2012). Water Poverty of Indrawati Basin, Analysis and Mapping. 63.

U.S. Geological Survey. (2024). Landsat 8–9 Collection 2 Level-2 science products. U.S. Department of the Interior.




DOI: http://dx.doi.org/10.33087/talentasipil.v9i2.1530

Refbacks

  • There are currently no refbacks.


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

Jurnal Talenta Sipil, Faculty of Engineering, Batanghari University
Adress: Fakultas Teknik, Jl.Slamet Ryadi, Broni-Jambi, Kec.Telanaipura, Kodepos: 36122, email: talentasipil.unbari@gmail.com

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