Mapping forest successional stages with UAVs: comparing RGB and multispectral imagery in seasonal tropical forests

Understanding forest successional dynamics is crucial for conservation and ecosystem management. Increasing anthropogenic pressure on tropical forest fragments highlights the need for efficient, scalable, and accessible monitoring methods. Remote sensing with Unmanned Aerial Vehicles (UAVs) has emerged as a promising tool, offering high spatial resolution and operational flexibility for tracking forest succession. This study assessed the potential of RGB and multispectral sensors mounted on UAVs to classify successional stages in Seasonal Semi-deciduous Tropical Forest fragments. Successional groups were defined through spectral, textural, and structural attributes extracted from high-resolution orthomosaics, combined with the K-means algorithm. Results showed that both RGB and multispectral data successfully distinguished early, intermediate, and advanced stages. While multispectral sensors enhanced differentiation of intermediate stages, RGB sensors alone proved capable of monitoring forest regeneration. This finding underscores the applicability of lower-cost RGB sensors, making UAV-based monitoring more accessible. The use of UAVs represents a significant advance for conservation and forest management, enabling detailed monitoring of vegetation dynamics, detection of structural changes, and support for ecological restoration policies. Moreover, this approach aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 15 (Life on Land) and SDG 13 (Climate Action), by promoting forest conservation through technological innovation and sustainable, cost-effective monitoring solutions.