Mapping tree species diversity in temperate montane forests using Sentinel-1 and Sentinel-2 imagery and topography data

Detailed information on spatial patterns of tree species diversity (TSD) is essential for biodiversity assessment, forest disturbance monitoring, and the management and conservation of forest resources. TSD mapping ap-proaches based on the Spectral Variability Hypothesis (SVH) could provide a reliable alternative to image classification methods. However, such methods have not been tested in large-scale TSD mapping using Sentinel-1 and Sentinel-2 images. In this study, we developed a new workflow for large-scale TSD mapping in an approximately 4000 km2 temperate montane forest using Sentinel-1 and Sentinel-2 imagery-based heterogeneity metrics and topographic data. Through a systematic comparison of model performance in 24 prediction scenarios with different combinations of input variables, and a correlation analysis between six image heterogeneity metrics and two in-situ TSD indicators (species richness S and Shannon-Wiener diversity H '), we assessed the effects of vegetation phenology, image heterogeneity metrics, and sensor type on the accuracy of TSD pre-dictions. Our results show that (1) the combination of Sentinel-1 and Sentinel-2 imagery produced higher ac-curacy of TSD predictions compared to the Sentinel-2 data alone, and that the further inclusion of topographic data yielded the highest accuracy (S: R2 = 0.562, RMSE = 1.502; H ': R2 = 0.628, RMSE = 0.231); (2) both Multi -Temporal and Spectral-Temporal-Metric data capture phenology-related information of tree species and signif-icantly improved the accuracy of TSD predictions; (3) texture metrics outperformed other image heterogeneity metrics (i.e., Coefficient of Variation, Rao's Q, Convex Hull Volume, Spectral Angle Mapper, and the Convex Hull Area), and the enhanced vegetation index (EVI) derived image heterogeneity metrics were most effective in predicting TSD; and (4) the spatial distribution of TSD showed a clear decrease trend along the altitudinal gradient (r = -0.61 for S and -0.45 for H ') and varied significantly among forest types. Our results suggest a good potential of the SVH-based approaches combined with Sentinel-1 and Sentinel-2 imagery and topographic data for large-scale TSD mapping in temperate montane forests. The TSD maps generated in our study will be valuable for forest biodiversity assessments and for developing management and conservation measures.

Automated summary

Detailed information on spatial patterns of tree species diversity (TSD) is crucial for biodiversity assessment and forest management. This study developed a new workflow for large-scale TSD mapping using Sentinel-1 and Sentinel-2 imagery and topographic data, and found that the combination of these data sources yielded the highest accuracy in TSD predictions.