Communal roosts are essential stopover sites for migratory birds. Monitoring the diurnal activity patterns of birds in communal roosts (DAPBCR) is crucial for understanding their migratory behavior and ecological needs. This information is crucial for guiding habitat conservation and management strategies and assessing the impact of environmental changes on bird populations. Traditional methods for extracting DAPBCR often rely on detecting high reflectivity factor arc features generated by birds collectively leaving or returning to communal roosts using weather radar data and deep learning target detection algorithms. However, many bird echoes do not produce these high reflectivity factor arc features, making pixel-level extraction of DAPBCR challenging. To address this, we propose a method for DAPBCR extraction based on the differences in the probability distribution function (PDF) of differential backscattering phase between birds and insects. This method first removes non-biological echoes from polarimetric weather radar data, retaining only biological echoes. By calculating the differential backscattering phase using the differential phase and system differential phase, we obtain the probability distribution function of the differential backscattering phase for biological echoes. We fit this PDF to a mixed Von-Mises distribution to obtain the PDFs for birds and insects. Using posterior probabilities for birds and insects, we estimate the bird-insect mixing ratio and further estimate the number of birds by combining the reflectivity factor and mean radar cross-section of birds. Applying the proposed method, we extracted DAPBCR data in the midsection of the Huai River Basin near Fuyang City from June to October 2021. We found that bird activity peaked in August and September. Based on normalized cumulative bird activity, we estimated the start, peak, and end times of DAPBCR to be July 6, August 19, and September 30, respectively.

Accurately extracting biological echoes is a fundamental prerequisite for weather radar aeroecology monitoring. However, the concurrent presence of meteorological echoes and biological echoes greatly restricts the extraction accuracy. Traditional neural network-based echo extraction algorithms rely on the spatial continuity feature of the echoes. But the concurrent presence of multiple types of echoes will lead to the invalidation of the spatial feature and the error of boundary identification of biological echoes. To address this challenge, this study proposes a weak biological echo extraction algorithm using superpixel technique, aimed at preserving richer biological details in adverse weather conditions. To amplify the imaging distinctions between biological and meteorological components, we design 8-dimensional differential features for each superpixel patch on the CIELAB color space. The gradient boosting tree model is trained for biology classification in handling complex data scenarios. Trained trees exhibit strong generalization capabilities and imbalanced testing data that reflect real weather conditions. To mitigate the limitations posed by the lack of publicly available datasets, we establish a trainable weather radar image dataset encompassing typical weather conditions across national weather radar stations. Experimental results validated that the algorithm retains over 98% of biological data by under adverse weather conditions.

Weather radar holds the capability to monitor the extensive migration of bird and insect species. In particular, polarimetric weather radar can enhance aerial ecological monitoring by quantifying target shape through the measurement of polarization moments. This paper introduces an intelligent algorithm to classify bird and insect migration using polarimetric weather radar data. A radar image dataset was formed by intentionally curating typical migratory data of birds and insects captured by the polarimetric weather radar. Next, point features and spatial texture features were extracted from the radar images in the dataset for training a classifier using a supervised learning approach, resulting in a classification accuracy of 93.56%. Furthermore, the importance of the features was analyzed, uncovering that the most influential attribute was the reflectivity factor at 33.83%, surpassing the cumulative influence of other dual-polarization moments. In addition, spatial textures also played an essential role for the classifier, collectively weighing 35.65%. Lastly, the proposed method was validated with bird radar data, attaining an accuracy level of 95.36%.

Weather radar serves as a crucial tool for monitoring aeroecology by enabling the observation of migrating birds and insects. Although dual-polarization weather radar offers the possibility of classifying echoes, extracting migration echoes of birds and insects from historical single-polarization weather radar data remains challenging. The current deep-learning methods have been successfully extracting aerial migrations from single-polarization weather radar data. However, it still faces challenges in distinguishing between birds and insects at the pixel level, primarily due to the absence of distinct semantic features for each. To tackle this challenge, we propose a semi-supervised radar data processing framework, which generates a large number of single polarization training datasets from a small amount of dual polarization truth data, and trains the image segmentation network of single polarization data to distinguish between bird and insect echoes. The framework comprises three components: an image classifier, an image generator, and an image segmentation model. Specifically, the image classifier and image generator leverage a small set of manually annotated dual-polarization radar data to generate the pixel-level single-polarization dataset for training the image segmentation model. The well-trained image segmentation model extracts migration echoes of birds and insects from radar images. Experimental results demonstrate that the proposed method achieves a mean Intersection over Union of 97% for segmenting precipitation, bird, and insect targets. The proposed framework can utilize historical archived single-polarization weather radar data to provide large-scale, long-term, and repeatable monitoring data for birds and insects.

Migratory insects constitute a valuable component of atmospheric and terrestrial biomass, and their migratory behavior provides abundant information for insect management and ecological effect assessment. Effective monitoring of migratory insects contributes to the evaluation and forecasting of catastrophic migration events, such as pest outbreaks. With a large-scale monitoring technique using S-band weather radar, the insect density is estimated based on the linear relationship between radar reflectivity and the average radar cross-section (RCS) of the insects. However, the average RCS model neglects the morphological and observation parameters of the insects, which reduces the estimation accuracy. In this paper, we established an insect-equivalent RCS model based on the joint probability distribution of “body length–incident angle”. Then, we observed and extracted the morphological and observational parameters of the migratory insects by conducting a 69-day field experiment, using a Ku-band fully polarimetric entomological radar, in Dongying, Shandong Province, China. Finally, combined with the experimental results and the simulated scattering characteristics of individual insects with different body lengths, the typical insect-equivalent RCS model was established. The RCS of the model fluctuates between 0.233 mm² and 0.514 mm², with different incident angles. Our results lay a data foundation for the quantitative analysis of insects by weather radar.

Weather radar plays an important role in monitoring aerial animal migration, providing a stable data source for biological studies with large-scale coverage and consecutive-time samples. The accurate estimation of bird density from weather radar echoes is fundamental for quantitative biological studies. We analyzed the bird observation model in weather radar, and proposed a method to build the bird quantification model by jointly utilizing dual-polarization Doppler weather radar and scanning bird radar. We designed a detailed process to remove tracks or echoes from non-bird targets, ensuring the effectiveness of bird observations. The field experiments validated the quantification method, showing that the average radar cross section of birds in Jinan was 19.09 dBscm (i.e., 81.19 cm²; 95% confidence interval, CI: 18.92–19.27 dBscm) for the S-band weather radar, with an R² of 0.79. In addition, through the correlation analysis, we found that the ground terrain may affect the distribution pattern of aerial bird density.