Automatic analysis of high-resolution planetscope data: Development of python-based open-source software

In this thesis study, an open-source software was developed using the Python programming language to facilitate the analysis of high-resolution satellite imagery. The developed system provides automatic access to PlanetScope images via the Planet API, downloading data according to user-defined filters (e.g., date, cloud cover). The software automatically performs spectral analyses, such as the Normalized Difference Vegetation Index (NDVI), through libraries like rasterio and numpy, and visualizes the results as time-series graphs and in CSV format using the plotly library. To test the effectiveness of the developed software, NDVI changes for the periods of April-May and October- November 2024 were examined for designated coordinates in Mahya Mountain, Kırklareli. The analyses successfully revealed the seasonal changes in vegetation. This study offers an efficient, flexible, and accessible analysis tool for researchers by automating the access to and processing of remote sensing data.