Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian forests

High-resolution satellite imagery, such as Quickbird data, significantly outperforms medium-resolution options (e.g., ASTER and Landsat-TM) for estimating forest structural attributes like stand volume, basal area, and tree stem density in the Hyrcanian broadleaf forests of northern Iran. Despite its higher cost, Quickbird provided more accurate models with lower RMSE and bias compared to ASTER and TM data, which yielded slightly similar but less precise results. The study employed a random cluster sampling method across 112 plots (0.09 ha each) and used the CART algorithm for statistical modeling, highlighting the potential of high-resolution remote sensing in forest management without compromising cost-free medium-resolution alternatives like ASTER or TM imagery.

Nourian, N., Shataee-Joibary, S., & Mohammadi, J. (2016). Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian forests. Forest Systems, 25(3), e074. https://doi.org/10.5424/fs/2016253-08682