The use of multi-sensor satellite imagery to analyze flood events and land cover changes using change detection and machine learning techniques in the Barito watershed

Authors

  • Muhammad Priyatna Remote Sensing Research Center, Research Organization of Aeronautics and Space, The National Research and Innovation Agency (BRIN) http://orcid.org/0000-0003-2316-6444
  • Sastra Kusuma Wijaya Physics Department, Faculty of Mathematics and Natural Sciences, Universitas of Indonesia
  • Muhammad Rokhis Khomarudin Remote Sensing Research Center, Research Organization of Aeronautics and Space, The National Research and Innovation Agency (BRIN)
  • Fajar Yulianto Remote Sensing Research Center, Research Organization of Aeronautics and Space, The National Research and Innovation Agency (BRIN)
  • Gatot Nugroho Remote Sensing Research Center, Research Organization of Aeronautics and Space, The National Research and Innovation Agency (BRIN)
  • Pingkan Mayestika Afgatiani Remote Sensing Research Center, Research Organization of Aeronautics and Space, The National Research and Innovation Agency (BRIN)
  • Anisa Rarasati Remote Sensing Research Center, Research Organization of Aeronautics and Space, The National Research and Innovation Agency (BRIN)
  • Muhammad Arfin Hussein Instrumentation Elctronics Studi Program, Politeknik Teknik Nuklir Indonesia

DOI:

https://doi.org/10.15243/jdmlm.2023.102.4073

Keywords:

Landsat-8, land use/land cover (LULC), Otsu method, random forest, Sentinel-1

Abstract

Indonesia is one of the countries in the world that is frequently affected by floods. Flood disasters can have various negative impacts and therefore need to be analyzed to determine prevention and mitigation measures. This study examined land cover change, flood detection, and flood distribution using multitemporal Sentinel-1 and Landsat-8 satellite imagery in the Barito watershed. A combination of change detection and the application of the Otsu algorithm was used to detect floodplains from Sentinel-1 imagery. Land use/land cover (LULC) changes are detected using a combination of change detection and machine learning in the form of a random forest algorithm. The overlay technique was used to analyze the distribution of floodplains. In this study, the floodplain in the study area was mapped to 109,623 ha. The change detection method detects a decrease in the areas of primary forest, secondary forest, fields, rice fields, shrubs and ponds, respectively, by 13,020 ha, 116,235 ha, 259 ha, 146,696 ha, 47,308 ha, and 9,601 ha. Settlements, bare land, plantations and water bodies increase by 14,879 ha, 64,830 ha, 218,916 ha, and 34,768 ha, respectively. Flooding was mainly found in the classes of rice fields, water bodies and primary forests.

Author Biographies

Muhammad Priyatna, Remote Sensing Research Center, Research Organization of Aeronautics and Space, The National Research and Innovation Agency (BRIN)

Physics Department, Faculty of Mathematics and Natural Sciences, Universitas of Indonesia

Pingkan Mayestika Afgatiani, Remote Sensing Research Center, Research Organization of Aeronautics and Space, The National Research and Innovation Agency (BRIN)

Faculty of Science, Graduate School of Science and Engineering, University of the Ryukyus

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Submitted

26-06-2022

Accepted

10-10-2022

Published

01-01-2023

How to Cite

Priyatna, M., Wijaya, S. K., Khomarudin, M. R., Yulianto, F., Nugroho, G., Afgatiani, P. M., Rarasati, A., & Hussein, M. A. (2023). The use of multi-sensor satellite imagery to analyze flood events and land cover changes using change detection and machine learning techniques in the Barito watershed. Journal of Degraded and Mining Lands Management, 10(2), 4073–4080. https://doi.org/10.15243/jdmlm.2023.102.4073

Issue

Vol. 10 No. 2 (2023)

Section

Research Article

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