Comparison and Synthesis of Image Analysis Algorithms: NDVI and RGB Processing for UAV-Based Environmental Monitoring

Abstract

Vegetation health assessment is a fundamental task in precision agriculture, ecological monitoring, and environmental risk management. The Normalized Difference Vegetation Index (NDVI) uses red and near-infrared (NIR) reflectance data to function as the primary index that enables vegetation health assessment and stress detection. However, the practical deployment of such systems can be limited by the requirement for multispectral sensors that include a near-infrared (NIR) channel. This study evaluates three algorithmic approaches: (i) the traditional NDVI method, (ii) an RGB-based channel transformation approach, and (iii) a synthesized hybrid algorithm that applies NDVI-like normalization to green-dominance patterns in standard RGB imagery. The analysis includes mathematical formulations, radiometric interpretation, calibration requirements, and error-propagation analysis for all investigated approaches. A complete UAV processing workflow is presented, including geometric correction, radiometric normalization, noise filtering, and illumination compensation. The system becomes more useful through discussions about UAV implementation scenarios, flight-planning constraints, and geospatial post-processing workflows. The text includes forest-fire-related use cases as examples of rapid-assessment situations in which vegetation stress maps help with post-event choices, yet these examples do not represent the main focus. The results indicate that traditional NDVI provides higher reliability for quantitative biophysical analysis, whereas RGB-based methods remain effective for qualitative vegetation segmentation when hardware resources are limited. The developed algorithm delivers superior vegetation identification results compared with RGB transformation methods and functions as a useful solution for organizations that lack NIR-equipped systems. The document presents recommendations that help users choose methods based on their specific requirements regarding accuracy levels, budget constraints, deployment time, and system operational difficulties.