IndexVeg a tool for simplifying vegetation monitoring in forest ecosystems

Abstract The increasing frequency of droughts, wildfires, and forest degradation underscores the urgent need for reliable monitoring tools accessible to non-specialists. In response, this study presents a Shiny application developed in R that enables the analysis of drone-acquired multispectral imagery for assessing forest stand conditions without requiring prior coding experience. The application automatically computes 57 vegetation indices, including widely used metrics such as NDVI, GNDVI, and SAVI, as well as indices related to water stress (e.g., NDWI), pigment composition (e.g., MCARI, TCARI), providing a standardized and reproducible analytical framework. This functionality enables comprehensive analyses of vegetation dynamics across multiple spatial and temporal scales, supporting detailed forest monitoring and management. Users can execute predefined analytical routines without modifying the underlying code, simply by inputting multispectral images and selecting the desired indices. The spatial and temporal extent of the imagery depends on each user’s mission, allowing flexible application across diverse forest contexts. While IndexVeg ensures standardized and reproducible index computation, the interpretation and validation of index–biophysical relationships should be conducted using mission-specific field data. This approach substantially lowers the technical barriers associated with integrating advanced geomatic techniques into educational programs, applied research, and land manageme nt practices, particularly in resource-limited settings. Overall, the tool provides a valuable resource for adaptive forest management, empowering researchers, practitioners, and policymakers to monitor ecological processes and make informed decisions based on scientifically robust evidence.