Geovisualization for information extraction of shoreline changes in Padang city 2000–2020
Abstract
This study aims to create a system model that implements the concept of Geovisualization on shoreline changes in Padang city. This implementation is to make it easier to identify shoreline changes. The method used to detect changes is by interpreting satellite imagery with the Modified Normalized Difference Water Index (MNDWI) approach and the Digital Shoreline Analysis System (DSAS). The imagery used is Landsat 7 and Landsat 8 from 2000 to 2020. The model is designed with a Software Development Life Cycle (SDLC) approach. The results obtained are in the form of twenty shorelines per year as well as the amount of abrasion and accretion values from the interpretation. These results are visualized on an online-based map system that allows users to explore, synthesize, present and analyze the interpretation data. In conclusion, the Geovisualization system model is able to make serial data imagery presented dynamically to facilitate identification of shoreline changes.
Keyword : geovisualization, shoreline, Landsat, MNDWI, DSAS, SDLC, online-based map
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
BIG. (2018). Technical Guidelines for collecting and processing geospatial data on basic habitats of shallow sea waters. Geospatial Information – BIG, Indonesia.
Carr, M., & Verner, J. (1997). Prototyping and software development approaches. City University of Hongkong, Hongkong.
Fajrin, F. M., Muskananfola, M. R., & Hendrarto, B. (2016). Karakteristik Abrasi dan Pengaruhnya terhadap Masyarakat di Pesisir Semarang Barat. Management of Aquatic Resources Journal, 5(2), 43–50.
Gao, B.-C. (1996). NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Gautam, V. K., Gaurav, P. K., Murugan, P., & Annadurai, M. (2015). Assessment of surface water dynamics in Bangalore using WRI, NDWI, MNDWI, supervised classification and K-T transformation. Aquatic Procedia, 4, 739–746. https://doi.org/10.1016/j.aqpro.2015.02.095
Haryani. (2016). Model Mitigasi Bencana di Wilayah Pesisir dengan Pemberdayaan Masyarakat. Tataloka, 14(3), 201–212.
Himmelstoss, E. A., Henderson, R. E., Kratzmann, M. G., & Farris, A. S. (2018). Digital Shoreline Analysis System (DSAS) version 5.0 user guide (U.S. geological survey open-file report 2018-1179). https://doi.org/10.3133/ofr20181179
Isaías, P., Kommers, P., & Issa, T. (2015). The evolution of the internet in the business sector: web 1.0 to web 3.0. IGI Global, USA. https://doi.org/10.4018/978-1-4666-7262-8
Kraak, M.-J., & Ormeling, F. (2010). Cartography: Visualization of geospatial data (3rd ed.). Pearson Education Limited.
Laurini, R. (2017). 11 – Geovisualization and Chorems. In R. Laurini (Ed.), Geographic knowledge infrastructure (pp. 223–246). Elsevier. https://doi.org/10.1016/B978-1-78548-243-4.50011-6
Miles, R., & Hamilton, K. (2006). Learning UML 2.0. O’Reilly Media, Inc.
Murasugi, K. (2019). Linguistic cybercartography: Expanding the boundaries of language maps. In D. R. F. Taylor, E. Anonby, & K. Murasugi (Eds.), Modern Cartography Series (Vol. 9, pp. 389–412). Academic Press. https://doi.org/10.1016/B978-0-444-64193-9.00022-1
PEMKOPADANG. (2020). Gambaran Umum Kota Padang. https://padang.go.id/gambaran-umum-kota-padang
Schroeder, L., Hvingel, L., Hansen, H. S., & Jensen, B. H. (2011). Towards connected governance. In Urban and regional data management: UDMS annual 2011 (pp. 151–164). CRC Press.
United Nations. (2015). The 2030 agenda for sustainable development. https://sdgs.un.org/
Wazlawick, R. S. (2014). Object-oriented analysis and design for information systems: Modeling with UML, OCL, and IFML. Elsevier. https://doi.org/10.1016/B978-0-12-418673-6.00002-8
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Yulfa, A., Aditya, T., & Sutanta, H. (2019). Pengayaan Infrastruktur Data Spasial Menggunakan Data dari Crowd Untuk Tanggap Darurat Bencana. Majalah Ilmiah Globe, 21(2), 95–104. https://doi.org/10.24895/MIG.2019.21-2.939