Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement ...Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement and forest resource surveys.The UAV–LiDAR flight altitude and forest canopy cover significantly impact the accuracy of understory terrain estimation.However,since no research examined their combined effects,we aimed to investigate this relationship.This will help optimize UAV–LiDAR flight parameters for understory terrain estimation and forest surveys across various canopy cover.This study analyzed the impacts of three flight altitudes and three canopy cover on the estimation accuracy of understory terrain.The results showed that when canopy cover exceeded a specific value,UAV–LiDAR flight altitudes significantly affected understory terrain estimation.Given a forest canopy cover,the reduction in ground point coverage increased significantly as the flight altitude increased;given a flight altitude,the higher the canopy cover,the more significant the reduction in ground point coverage.In forests with a canopy cover≥0.9,there were substantial differences in the accuracies of understory digital elevation models(DEMs)generated using UAV–LiDAR at different flight altitudes.For forests with a canopy cover<0.9,the mean absolute error(MAE)of understory DEMs from UAV–LiDAR at different flight altitudes was≤0.17 m and the root mean square error(RMSE)was≤0.24 m.However,for forests with a canopy cover≥0.9,the UAV–LiDAR flight altitude significantly affected the accuracy of understory DEMs.At the same flight altitude,the MAE and RMSE of the estimated elevation for forests with a canopy cover≥0.9 were approximately twice those of the estimated elevation for forests with a canopy cover<0.9.In forests with low canopy cover,it is possible to improve data collection efficiency by selecting a higher flight altitude.However,UAV–LiDAR flight altitudes significantly affected understory terrain estimation in forests with high canopy cover,it is essential to adopt terrain-following flight modes,reduce flight altitudes,and maintain a consistent flight altitude during longterm monitoring in high canopy cover forests.展开更多
This paper investigates the power generation characteristics of solar cells mounted on unmanned aerial vehicles(UAVs)under the coupled influence of flight conditions and the sur-rounding environment.Firstly,the study ...This paper investigates the power generation characteristics of solar cells mounted on unmanned aerial vehicles(UAVs)under the coupled influence of flight conditions and the sur-rounding environment.Firstly,the study reveals that the voltage,current,and power output of the solar cells undergo consistent temporal variations throughout the day,primarily driven by voltage fluctuations,with a peak occurring around noon.Secondly,it is observed that the cells’performance is significantly more influenced by temporal variations in external light intensity than by temperature changes resulting from variations in flight speed.Finally,the study finds that the impact of flight altitude on the cells’performance is slightly more pronounced than the influence of temporal variations in external light intensity.展开更多
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 ...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.展开更多
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 da...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.展开更多
基金supported by the National Natural Science Foundation of China(No.32271876)the Research on Key Technologies of Intelligent Monitoring and Carbon Sink Metering of Forest Resources in Fujian Province(No.2022FKJ03)the Science and Technology Innovation Project of Fujian Agriculture and Forestry University(No.KFB23172A,KFB23173A).
文摘Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement and forest resource surveys.The UAV–LiDAR flight altitude and forest canopy cover significantly impact the accuracy of understory terrain estimation.However,since no research examined their combined effects,we aimed to investigate this relationship.This will help optimize UAV–LiDAR flight parameters for understory terrain estimation and forest surveys across various canopy cover.This study analyzed the impacts of three flight altitudes and three canopy cover on the estimation accuracy of understory terrain.The results showed that when canopy cover exceeded a specific value,UAV–LiDAR flight altitudes significantly affected understory terrain estimation.Given a forest canopy cover,the reduction in ground point coverage increased significantly as the flight altitude increased;given a flight altitude,the higher the canopy cover,the more significant the reduction in ground point coverage.In forests with a canopy cover≥0.9,there were substantial differences in the accuracies of understory digital elevation models(DEMs)generated using UAV–LiDAR at different flight altitudes.For forests with a canopy cover<0.9,the mean absolute error(MAE)of understory DEMs from UAV–LiDAR at different flight altitudes was≤0.17 m and the root mean square error(RMSE)was≤0.24 m.However,for forests with a canopy cover≥0.9,the UAV–LiDAR flight altitude significantly affected the accuracy of understory DEMs.At the same flight altitude,the MAE and RMSE of the estimated elevation for forests with a canopy cover≥0.9 were approximately twice those of the estimated elevation for forests with a canopy cover<0.9.In forests with low canopy cover,it is possible to improve data collection efficiency by selecting a higher flight altitude.However,UAV–LiDAR flight altitudes significantly affected understory terrain estimation in forests with high canopy cover,it is essential to adopt terrain-following flight modes,reduce flight altitudes,and maintain a consistent flight altitude during longterm monitoring in high canopy cover forests.
基金supported by the National Natural Science Foundation of China(Nos.12464010,52462035)2022 Jiangxi Province High-Level and High-Skilled Leading Talent Training Project Selected(No.63)+1 种基金Jiujiang“Xuncheng Talents”(No.JJXC2023032)Jiujiang Basic Research Program Project(2025).
文摘This paper investigates the power generation characteristics of solar cells mounted on unmanned aerial vehicles(UAVs)under the coupled influence of flight conditions and the sur-rounding environment.Firstly,the study reveals that the voltage,current,and power output of the solar cells undergo consistent temporal variations throughout the day,primarily driven by voltage fluctuations,with a peak occurring around noon.Secondly,it is observed that the cells’performance is significantly more influenced by temporal variations in external light intensity than by temperature changes resulting from variations in flight speed.Finally,the study finds that the impact of flight altitude on the cells’performance is slightly more pronounced than the influence of temporal variations in external light intensity.
基金supported by TERNA Rete Italia S.p.A.Parco Nazionale dell’Aspromontesupport provided by COST-European Cooperation in Science and Technology through the Action ES1305“European Network for the Radar Surveillance of Animal Movement”(ENRAM)
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.41801023 and 42071056).
文摘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.
