Monitoring Public Bird Roosts with Saliency-Constrained Multi-Peak Doppler Spectra from Weather Radar

Highlights: What are the main findings? A saliency-constrained multi-peak Doppler spectrum decomposition and classification method is developed to separate mixed Doppler power spectra into independent subpeaks, and each subpeak is classified using polarimetric features to identify bird motion subpeaks. The proposed Bird Roost Index (BRI), integrating the number of bird motion subgroups and their radial velocity dispersion, enables reliable identification of communal bird roosting activity. What are the implications of the main findings? The proposed method provides a multi-peak Doppler spectral analysis framework that breaks through the limitations of classifying mixed biological echoes within a single weather radar resolution cell. The BRI derived from sub-spectral features enables robust identification of bird roosts under weak echo intensity or incomplete structures, supporting the monitoring of public bird roosts in the Dongting Lake Basin. Monitoring bird activity at public roosts is essential for understanding stopover behavior during migration, assessing ecological change, and supporting conservation strategies. Existing weather radar-based roost detection methods primarily rely on high-reflectivity ring-shaped echoes, which can lead to missed detections when roost-related echo structures are weak or indistinct. To address this limitation, this study proposes a saliency-constrained multi-peak spectral approach for monitoring and identifying public bird roosts using weather radar. At the radar resolution-cell scale, a saliency-constrained multi-peak Doppler spectrum decomposition and classification method is developed. Mixed Doppler power spectra are decomposed into multiple independent subpeaks through spectral peak saliency detection, and spectral polarimetric features are utilized to identify bird-related subpeaks, yielding a set of bird motion subgroups within each resolution cell. On this basis, a Bird Roost Index (BRI) is introduced, which couples the number of bird subgroups with their radial velocity dispersion to quantitatively characterize the complexity of bird motion modes in local airspace. Finally, the proposed method is applied to operational S-band weather radar observations collected over the Dongting Lake Basin roosts region during the spring season. The results demonstrate that the BRI exhibits strong spatial consistency and coherent temporal evolution, enabling robust characterization of communal roosting activity. This confirms the robustness of the proposed approach and highlights its potential for operational monitoring of migratory bird communal roosts using weather radar spectral data.