Vegetation mapping is essential for monitoring conservation efforts in national parks and can be performed
remotely using remote sensing and GIS technologies. However, the process is often complex and requires
technical expertise. This study explores the use of AI, specifically ChatGPT, to simplify and support
vegetation mapping workflows. We monitored monthly vegetation changes in Merapi Mountain National
Park (TNGM) from 2017 to 2023 using the Normalized Difference Vegetation Index (NDVI) derived from
Sentinel-2 satellite data. The workflow combined Google Earth Engine (GEE) for satellite image processing
and Python in Jupyter Notebook for time series analysis, with ChatGPT assisting in code editing. Our results
show NDVI patterns are significantly influenced by volcanic activity, particularly eruptions and pyroclastic
clouds, and about one-third of images were affected by cloud cover, especially during the rainy season.
ChatGPT performed well in non-coding queries with a 79% satisfaction rate, but only 53% of generated
code prompts were correct without modification. We conclude that while AI tools like ChatGPT have strong
potential to enhance accessibility and efficiency in remote vegetation mapping, human oversight and
foundational knowledge in geospatial analysis remain essential for accurate results.