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Talk · IEEE International Conference on Smart Information Systems and Technologies · May 15, 2024

Logical Sum of NDWI & NDPI Data for Detecting Flooded Houses: A Case of Atbasar Town, 2017

Marzhan Kusainova, Zhanat Toleubekova, Asset Akhmadiya, Kanshaim Nurmukhanova, Nabi Nabiyev, Aizada Kapassova

Asset Akhmadiya
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Abstract

In this article, NDWI and NDPI data were considered to detect flooded houses hit by a spring flood in Atbasar town, in 2017. This study used remote sensing data Sentinel-2A and Landsat-8 to compute water radiometric indices and formation binary images. Binary images are flood maps generated by threshold values from NDWI and NDPI data and comprise two classes (legend): water body (black color); non-waterbody (white color). Various water indices data were visually analyzed, and the choice was made to Normalized Difference Water Index (NDWI) and Normalized Difference Pond Index (NDPI) as better. NDWI and NDPI data of Sentinel-2A and Landsat-8 were fused by logical summation. After that, this result was imposed on GIS data of Atbasar Town to define locations and number of flooded houses.

Flood Risk Assessment and ManagementRemote-Sensing Image ClassificationRemote Sensing and Land UseNDWINDPIspring floods

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