Implementing Geographic Information System (GIS) tools: Algorithm and Quantum Geographic Information System (QGIS) plugin for automatic detection of riverbed forms.

This paper introduces the Sandbar Detector plugin for Quantum Geographic Information System (QGIS), designed to streamline the detection and analysis of riverbed forms, previously hindered by time-consuming manual methods requiring extensive expertise in remote sensing and Geographic Information System (GIS). The Sandbar Detector plugin, developed in Python, leverages the Sentinel Water Mask (SWM), a reliable remote sensing water index, for precise differentiation between water and land. By integrating SWM with QGIS, the plugin utilises high-resolution data from Sentinel-2 satellites, offering a robust tool for environmental analysis.•Automation of detection: The plugin automates detecting riverbed forms, enhancing data analysis efficiency and consistency.•User-friendly interface: It makes the plugin accessible to users without advanced remote sensing and GIS knowledge.•High-resolution data: The plugin uses Sentinel-2 satellite data, ensuring precise and reliable results. The plugin was tested on the Lower Vistula River, a central river system in Poland known for its dynamic riverbed forms shaped by natural and anthropogenic factors. The automation provided by the plugin reduces human error and supports more accurate environmental monitoring, which is crucial for better water resource management and conservation efforts. The Sandbar Detector plugin is freely available on GitHub, making it easy to access and use for collaborative research (Kryniecka, 2024).