Migration of the Western Marsh Harrier to the African wintering quarters along the Central Mediterranean flyway:a 5-year study

Background: The Western Marsh Harrier(Circus aeruginosus) is a partial migrant with the populations from Eastern and Northern Europe migrating south to sub-Saharan Africa. During the autumn migration, that is peaking in Septem ber, harriers move on a broad front heading SW and undertake long sea-crossings en route to their wintering quarters, passing in substantial numbers through Italy and Malta with the highest concentrations recorded at the Strait of Messina. Most of the individuals migrating across the Strait are heading for the wintering quarters in Africa, while fewer spend the winter in Sicily.Methods: In a 5-year study(2011-2015), between 26 August and 30 September, we determined age and sex of autumn migrating harriers through this flyway. In 2014 we determined, by marine radar and optical range finder, the flight altitude of migrating harriers.Results: A total of 10,261 Western Marsh Harriers were counted during the whole study, with an average of 2052 per autumn season. Adults outnumbered juveniles and males outnumbered females. Harriers flew at lower altitudes during the morning while large flocks flew lower than single birds or small flocks.Conclusions: Our observations are consistent with previous surveys and confirm that adult males have a tendency to migrate over a long distance, while substantial numbers of adult females and juveniles do not head for the wintering quarters in Africa. Finally, flight patterns recorded can be explained by a more pronounced flapping flight of Western Marsh Harriers during migration.

A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3

Crop type mapping using remote sensing is critical for global agricultural monitoring and food security.However,the complexity of crop planting patternsand spatial heterogeneity pose significant challenges to field data collection,thereby limiting the accuracy of remotely sensed crop mapping.This study proposed a new approach for rapidly collecting field crop data by integrating unmanned aerial vehicle(UAV)images with the YOLOv3(You Only Look Once version 3)algorithm.The impacts of UAV flight altitude and the number of training samples on the accuracy of crop identification models were investigated using peanut,soybean,and maize as examples.The results showed that the average Fl-score for crop type detection accuracy reached 0.91 when utilizing UAV images captured at an altitude of 20 m.In addition,a positive correlation was observed between identification accuracy and the number of training samples.The model developed in this study can rapidly and automatically identify crop types from UAV images,which significantly improves the survey efficiency and provides an innovative solution for acquiring field crop data in large areas.