AUTOMATION OF DAILY LANDSLIDE POTENTIAL INFORMATION BASED ON REMOTE SENSING SATELLITE IMAGERY USING OPEN-SOURCE SOFTWARE TECHNOLOGY

Authors

  • Ahmad Sutanto Remote Sensing Research Center – National Research and Innovation Agency (BRIN)
  • Anwar Annas Directorate of Laboratory Management, Research Facilities and Science Technology Area – National Research and Innovation Agency (BRIN)
  • Mohammad Ardha Remote Sensing Research Center – National Research and Innovation Agency (BRIN)
  • Taufik Hidayat Directorate of Laboratory Management, Research Facilities and Science Technology Area – National Research and Innovation Agency (BRIN)
  • Muhammad Rokhis Khomarudin Remote Sensing Research Center – National Research and Innovation Agency (BRIN)

DOI:

https://doi.org/10.30536/j.ijreses.2023.v20.a3836

Abstract

This automation system automatically generated landslide potential information based on daily precipitation data. This system is essential to replace the previous manual processing system with an automated and integrated system. The results of the developed system are the distribution of areas with landslide potential based on daily precipitation data. The system was built using geographic information systems and web service techniques. This allows the automation process to be performed quickly and accurately. The landslide susceptibility map used is from the National Disaster Management Agency, so the information is more reliable. Himawari-8 is used to determine the potential for extreme precipitation in 10 minutes because this satellite has a very high temporal resolution. The system is already in use and has proven to replace manual processing and is faster. Further development will be more challenging if the system can be connected to the sensors installed on site so that the sensors on site can issue a landslide warning in case of extreme precipitation so that the surrounding communities can respond immediately. Opportunities for future development of the system may also be incorporated into landslide potential prediction based on the precipitation forecast model

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Published

2025-11-25

Issue

Vol. 20 No. 1 (2023)

Section

Articles

License

Copyright (c) 2023 Author (s)

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