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共检索到9条 ,权限内显示50条;
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Data from: Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems
- 负责人:
- 关键词:
- 2017;Zea mays L.
- DOI:
- doi:10.5061/dryad.3295k54
- 摘要:
- Weekly unmanned aerial system (UAS) imagery was collected over the College Station, TX, 2017 Genomes to Fields (G2F) hybrid trial, across thr
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Evaluating behavioral responses of nesting lesser snow geese to unmanned aircraft surveys
- 负责人:
- 关键词:
- Anser caerulescens
- DOI:
- doi:10.5061/dryad.r21pc
- 摘要:
- Unmanned aircraft systems (UAS) are relatively new technologies gaining popularity among wildlife biologists. As with any new tool in wildlife
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Terrestrial mammalian wildlife responses to Unmanned Aerial Systems approaches
- 负责人:
- Bennitt, Emily
- 关键词:
- UAS UAV Drone Disturbance
- DOI:
- doi:10.5061/dryad.83m50j8
- 摘要:
- Unmanned Aerial Systems (UAS) are increasingly being used recreationally, commercially and for wildlife research, but very few studies hav
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Unmanned aerial systems measure structural habitat features for wildlife across multiple scales
- 负责人:
- Olsoy, Peter
- DOI:
- doi:10.5061/dryad.631q1
- 摘要:
- -scale resolutions across broad-scale extents is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging the gap between relative
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: A convolutional neural network for detecting sea turtles in drone imagery
- 负责人:
- Gray, Patrick C.
- 关键词:
- convolutional neural networks deep learning for ecology marine megafauna marine population monitoring object detection sea turtles unoccupied aircraft systems
- DOI:
- doi:10.5061/dryad.5h06vv2
- 摘要:
- h, management programs seeking to conserve these species are often hampered by limited information about population levels. 2.Unoccupied aircraft systems (UAS, aka
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: UAV wildlife radio telemetry: system and methods of localization
- 负责人:
- DOI:
- doi:10.5061/dryad.qq086mk
- 摘要:
- . 2. The integration of a radio telemetry system with an unmanned aerial vehicle (UAV) could significantly improve the capacity for data collection from VHF tags. We pr
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Drones count wildlife more accurately and precisely than humans
- 负责人:
- DOI:
- doi:10.5061/dryad.rd736
- 摘要:
- Knowing how many individuals are in a wildlife population allows informed management decisions to be made. Ecologists are increasingly usi
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Environmental variation partitioned into separate heritable components
- 负责人:
- 关键词:
- Genetic Variation;Phenotypic Plasticity;variation;quantitative genetics;drosophila melanogaster
- DOI:
- doi:10.5061/dryad.d8ts9
- 摘要:
- candidate genes controlling the four components and showed limited overlap in genes affecting them. Using the binary UAS-GAL4 system, we functionally validated
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Habitat structure modifies microclimate: an approach for mapping fine-scale thermal refuge
- 负责人:
- Milling, Charlotte R.
- 关键词:
- UAS Climate change FRAGSTATS operative temperature predictive mapping thermal map thermal refuge unmanned aerial systems
- DOI:
- doi:10.5061/dryad.6kv1198
- 摘要:
- . Habitat structure contributes to thermal heterogeneity at a variety of spatial scales, but quantifying microclimates at organism‐relevant resolutions remains
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