Introducing a Learning Tool (QSVI): A QGIS Plugin for Computing Vegetation, Chlorophyll, and Thermal Indices with Remote Sensing Images

Abstract. Recent advances in remote sensing technology have increased the demand for software that supports educational and research activities. However, commercial software often comes with high costs and complex interfaces, presenting challenges for users. In contrast, open-source software offers a more accessible and cost-effective solution, making it increasingly popular for remote sensing and image processing applications. This study introduces a new computational approach for widely used vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Atmospherically Resistant Vegetation Index (ARVI). It also presents new tools for assessing chlorophyll, specifically the Leaf Area Index (LAI) and Chlorophyll Vegetative Index (CVI), as well as thermal indices like the Urban Thermal Field Variation Index (UTFVI) and Thermal Discomfort Index (TDI). Developed using Python, a popular programming language, within QGIS, the QSVI plugin features rapid processing capabilities and a user-friendly interface, making it particularly accessible for both researchers and educators. The effectiveness of the application was evaluated in the Sarıyer district of Istanbul using remote sensing data from the European Space Agency's Sentinel-2 and Sentinel-3 satellites. The results indicate that the QSVI plugin significantly reduces computation time compared to popular geographic information system (GIS) software, including ArcGIS, GRASS GIS, and SAGA GIS. For Sentinel-2 datasets, QSVI is, on average, 2.1 minutes faster than these applications. Additionally, for Sentinel-3 datasets, QSVI performs approximately 13.6 seconds faster than the others. These time savings highlight QSVI's efficiency in handling large datasets and demonstrate its advantages in environmental monitoring and analysis.