Forest Biomass Estimation through the Integration of UAV Imagery and Vegetation Indices: Toward Accurate and Efficient Monitoring - Fadilah, Vira Hasna; Hadiana, Asep Id; Komarudin, Agus (Department of Informatics, Siliwangi University, Indonesia, 2025)
Forest biomass estimation method using drone imagery and vegetation index, focusing on the effectiveness and efficiency of the approach. Using high-resolution drone imagery, this study analyzes vegetation structure and density, and supports the development of a more accurate biomass estimation model compared to traditional methods. Drone imagery has the advantage of collecting data quickly and in real time, especially in areas that are difficult to access manually. Vegetation indices, such as NDVI, are used to assess vegetation health and density, which are closely related to biomass estimation. The combination of drone imagery and vegetation indices can produce more detailed data, support 3D vegetation modeling, and help estimate biomass volume over time. This study is expected to produce data and biomass estimation models that support sustainable forest management as well as technical recommendations for the use of drones for vegetation monitoring. The findings of this study show that the proposed method produces an estimation accuracy of 85.2% based on field validation data calculated using simple linear regression. The findings of this study are expected to make a significant contribution to the development of drone-based technology for efficient and environmentally friendly natural resource management.