Introducing a learning tool (QSVI): A QGIS plugin for computing vegetation, chlorophyll, and thermal indices with remote sensing images

Abstract

Remote sensing technology can be used to monitor environmental changes using satellite imagery. However, to obtain a more precise model, it is necessary to process high-resolution and multilayered data, which require high-capacity software. Commercial software is often difficult to access by students and researchers because of its high cost and complex interface. This paper introduces a plugin designed in open-source Quantum GIS (QGIS) software using Python, called QGIS Sentinel Vegetation Indices (QSVI). QSVI can quickly process and automatically calculate many environmental indices on a single platform. These included the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospheric Resilient Vegetation Index (ARVI), Leaf Area Index (LAI), Chlorophyll Vegetation Index (CVI), Urban Thermal Field Variation Index (UTFVI), and Thermal Disturbance Index (TDI). The performance of QSVI was tested in the Sarlyer District of Istanbul, Türkiye. The results indicate that, for Sentinel-2 data, QSVI reduces processing time by an average of 2.1 min compared to common commercial software, such as ArcGIS, GRASS, and SAGA GIS. Sentinel-3 data were processed 13.6 s quicker than with the same software. The findings indicate that QSVI can be an alternative tool for researchers and students because of its easy accessibility and low cost. Because of its speed and simple interface, it can provide practical solutions for both researchers and students.