Tree Health Assessment Using UAV-Based Multispectral Imagery and NDVI Analysis

Abstract

Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors enable the capture of imagery across various spectral bands, facilitating the use of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to assess plant health. This capability is increasingly important in forestry, agriculture, and environmental monitoring. This study investigates the effectiveness of UAV-based multispectral imagery for tree health assessment within the Universiti Teknologi MARA (UiTM) campus in Malaysia, using both conventional NDVI validation methods and ArcMap-based NDVI analysis. A DJI Phantom 4 drone with a Parrot Sequoia multispectral sensor was deployed, configured with auto capture mode, 1.5-second timelapse, 17.5-meter GPS interval, and 80% image overlap. Tree RGB images were processed in Pix4D, while NDVI images were analyzed using ArcMap’s raster calculator. NDVI values obtained via conventional methods ranged from 0.720 to 0.842, indicating healthy vegetation based on comparisons with prior studies. In contrast, ArcMap-derived values ranged from 0.536 to 0.774. The root mean square error (RMSE) between both methods was 0.159, with a standard deviation of 0.201, indicating a consistent underestimation by ArcMap NDVI values. Notably, significant discrepancies were observed at points P1 and P5, while smaller differences occurred at P6. Overall, the findings support the reliability of UAV multispectral data and its integration into operational vegetation health monitoring. The results suggest that UAV and GIS-based workflows can provide cost-effective, scalable alternatives to traditional methods. For enhanced precision, future applications should consider high-resolution sensors with optimized parameters such as faster shutter speeds and appropriate sensor orientation.