<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Extreme hydrological events, such as floods and droughts, are becoming more frequent as a result of climate change, leading to negative impacts on various economic sectors. The Pannonian-Carpathian Basin is particularly affected by the increasing frequency of hazardous hydrological events. Agricultural production, which is a highly significant economic sector in the region, is particularly vulnerable to these unfavourable climatic conditions. Changes in precipitation patterns and soil moisture levels can lead to reduced crop yields, while floods can pollute water sources and erode fertile soil. Mapping of Inland Excess Water (IEW), also known as ponding water or waterlogged areas, is crucial for informed decision-making, damage compensation, risk management, and future prevention planning. Remote sensing technology and machine learning have been demonstrated to be valuable tools for the mapping of IEW. The 2014 floods in Southeastern and Central Europe serve as a reminder of the importance of effective flood risk management. This study used a Geographical Information System (GIS) and a Semi-automated Classification Processing (SCP) tool to process high-resolution RapidEye satellite images from the 2014 floods in the Srem region of Serbia. The Spectral Angle Mapping (SAM) classification model was used to produce a map of IEW. The SAM model achieved an overall accuracy of 92.68 %. The study found that IEW affected approximately 2.90 % or 99.59 km² of the territory in Srem. The obtained maps can be used by responsible water management agencies to prevent and control excessive inland water.</p>
</abstract>

Extreme hydrological events, such as floods and droughts, are becoming more frequent as a result of climate change, leading to negative impacts on various economic sectors. The Pannonian-Carpathian Basin is particularly affected by the increasing frequency of hazardous hydrological events. Agricultural production, which is a highly significant economic sector in the region, is particularly vulnerable to these unfavourable climatic conditions. Changes in precipitation patterns and soil moisture levels can lead to reduced crop yields, while floods can pollute water sources and erode fertile soil. Mapping of Inland Excess Water (IEW), also known as ponding water or waterlogged areas, is crucial for informed decision-making, damage compensation, risk management, and future prevention planning. Remote sensing technology and machine learning have been demonstrated to be valuable tools for the mapping of IEW. The 2014 floods in Southeastern and Central Europe serve as a reminder of the importance of effective flood risk management. This study used a Geographical Information System (GIS) and a Semi-automated Classification Processing (SCP) tool to process high-resolution RapidEye satellite images from the 2014 floods in the Srem region of Serbia. The Spectral Angle Mapping (SAM) classification model was used to produce a map of IEW. The SAM model achieved an overall accuracy of 92.68 %. The study found that IEW affected approximately 2.90 % or 99.59 km² of the territory in Srem. The obtained maps can be used by responsible water management agencies to prevent and control excessive inland water.

Mapping of Inland Excess Water Using Geographical Information System and High-Resolution Satellite Images: A Case Study of SREM, Serbia

Ecological Chemistry and Engineering S

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Extreme hydrological events, such as floods and droughts, are becoming more frequent as a result of climate change, leading to negative impacts on various...

Mapping of Inland Excess Water Using Geographical Information System and High-Resolution Satellite Images: A Case Study of SREM, Serbia

Online veröffentlicht: 05. Okt. 2023

Seitenbereich: 343 - 355

DOI: https://doi.org/10.2478/eces-2023-0037

Schlüsselwörter
inland excess water, high resolution satellite images, GIS, Semi-automated Classification Processing tool, Spectral Angle Mapping

© 2023 Andrea Salvai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mapping of Inland Excess Water Using Geographical Information System and High-Resolution Satellite Images: A Case Study of SREM, Serbia

Online veröffentlicht: 05. Okt. 2023

Seitenbereich: 343 - 355

DOI: https://doi.org/10.2478/eces-2023-0037

Schlüsselwörterinland excess water, high resolution satellite images, GIS, Semi-automated Classification Processing tool, Spectral Angle Mapping

© 2023 Andrea Salvai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
inland excess water, high resolution satellite images, GIS, Semi-automated Classification Processing tool, Spectral Angle Mapping