Land use and land cover change analysis of flood prone area using remote sensing data and machine learning in Malang Raya, East Java, Indonesia

A natural disaster is one type of disaster that requires holistic handling. One of the natural disasters that often occurs is flooding. A flood is overflowing a large amount of water beyond its normal confines. Floods often cause an increased vulnerability of fatalities, so disaster mitigation effor...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2023-05, Vol.1173 (1), p.12051
Main Authors: Hariyono, M I, Ramdani, D, Silalahi, F E S, Kurniawan, A A, Indriasari, N, Buswari, M
Format: Article
Language:English
Subjects:
Classification
Data analysis
Disaster management
Disasters
Emergency preparedness
Environmental risk
Flooding
Floods
Image classification
Image processing
Land cover
Land use
Learning algorithms
Machine learning
Mitigation
Moisture content
Natural disasters
Remote sensing
Risk management
Risk reduction
Satellite imagery
Satellites
Water content
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Summary:A natural disaster is one type of disaster that requires holistic handling. One of the natural disasters that often occurs is flooding. A flood is overflowing a large amount of water beyond its normal confines. Floods often cause an increased vulnerability of fatalities, so disaster mitigation efforts are needed. To reduce flood risk, this study uses remote sensing data to analyze changes in land use and land cover. The research location is Malang Raya, one of the locations prone to flooding in East Java. The method analyzes the results of processing satellite image data with the machine learning tools available at Google Earth Engine (GEE). This method provides convenience in accelerating the object classification in satellite image data. The study results indicate that using satellite image data analysis and machine learning processes is beneficial in accelerating object classification and approaching the actual conditions, making it easier to make a policy in handling disaster risk reduction.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/1173/1/012051