Method of Analyzing the Optimal Composite Vegetation Index Based on Multisensor Information and Multivegetation Index Combinations

In this study, we investigated the variation in vegetation index in Hwado, South Korea, from 2022 to 2025.The analysis utilized time-series data collected from multiple sensors mounted on drones.The primary vegetation indices used for the analysis included the normalized difference vegetation index (NDVI) (V1), red-edge NDVI (RENDVI) (V2), normalized difference water index (NDWI) (V3), and photochemical reflectance index (PRI) (V4).These indices were specifically applied to assess vegetation health across various periods (A, B, C, and D).Notably, the indices V1 and V2 during Period A demonstrated a higher vitality than the other periods and indices.To overcome the limitations of single vegetation indices, we analyzed 44 unique combinations of multiple vegetation indices, together referred to as the composite vegetation index (CVI).In this study, we aimed to evaluate and assess areas of healthy vegetation both quantitatively and qualitatively.The results showed that combinations (1), ( 7), and (11) during Period A had the highest vegetation vitality, with more than 80% of the area covered by healthy vegetation.During all periods, combinations (1) and ( 7), each consisting of V1 and V2, consistently demonstrated a high percentage of healthy area.As a result, Combination (7) in Period A was identified as the optimal composite vegetation index (Optimal CVI) in this study.The proposed CVI accurately measures vegetation vitality by effectively reflecting the specific characteristics of the study area and integrating multisensor information.It can be utilized for health analysis in various types of forest.