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Related Experiment Video

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Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

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Implementing Geographic Information System (GIS) tools: Algorithm and Quantum Geographic Information System (QGIS)

Klaudia Kryniecka¹

¹Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology Nowowiejska 20St, Warsaw 00-653, Poland.

|December 3, 2024

Summary

The Sandbar Detector plugin automates riverbed form analysis using Sentinel-2 satellite data and Quantum Geographic Information System (QGIS). This tool enhances environmental monitoring and water resource management by providing efficient and accurate detection of riverbed changes.

Keywords:

QGIS plugin Riverbed forms Sandbar Detector plugin for QGIS Sandbar detection Sentinel 2 Water indices Water mask

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Area of Science:

Environmental Science
Geographic Information Science
Remote Sensing

Background:

Manual detection of riverbed forms is time-consuming and requires specialized expertise.
Existing methods for analyzing riverbed dynamics are often inefficient and prone to human error.
Accurate monitoring of riverbed changes is crucial for effective water resource management.

Purpose of the Study:

To introduce the Sandbar Detector plugin for Quantum Geographic Information System (QGIS).
To automate the detection and analysis of riverbed forms using remote sensing data.
To provide a user-friendly tool for environmental analysis without requiring advanced GIS knowledge.

Main Methods:

Development of a Python-based plugin for QGIS.
Integration of the Sentinel Water Mask (SWM) for water and land differentiation.
Utilization of high-resolution Sentinel-2 satellite imagery for analysis.

Main Results:

The Sandbar Detector plugin successfully automates the detection of riverbed forms.
The plugin enhances data analysis efficiency and consistency.
Testing on the Lower Vistula River demonstrated the plugin's effectiveness in analyzing dynamic riverbed forms.

Conclusions:

The Sandbar Detector plugin offers a robust and accessible tool for environmental monitoring.
Automation reduces human error, leading to more accurate riverbed analysis.
The plugin supports improved water resource management and conservation efforts.