Predictability of Forest Biodiversity Variables on Varying Spectral and Spatial Resolutions

Obtaining high spatial resolution hyperspectral data is often difficult and expensive, limiting its use in large-scale or long-term forest monitoring. To develop scalable and efficient methods for forest biodiversity assessment, it is essential to evaluate model performance on different resolutions. Our study focuses on the predictability and sensitivity of forest biodiversity variables to changes in spectral and spatial resolution. We used high-resolution hyperspectral remote sensing imagery and forest biodiversity variable reference data to train a CNN model on varying resolutions. Our data consisted image patches of 16m×16m with 48 channels and 0.5m pixel resolution. In our experiments, spectral dimensionality was progressively reduced from hyperspectral to multispectral, RGB, and panchromatic, while spatial resolution was progressively downscaled by factors of two. Our results show that the predictability of composition-based variables decreased when spectral resolution was reduced to near-RGB level. Reducing spatial resolution led to a gradual decline of all variables, with a significant effect on structural variables. The findings demonstrate that while high-resolution hyperspectral data offer the best predictability of forest biodiversity variables, lower-resolution data can be sufficient in most cases. Multispectral imagery with meaningful spectral bands can provide reliable predictions even at a low spatial level, encouraging the use of open-access remote sensing data for large-scale forest biodiversity monitoring and analysis.