Automated Author ProfileBaldissara, Megan
University of Nebraska–Lincoln0009-0009-2097-7154
Baldissara, Megan
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.7 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Documenting wildlife–habitat relationships at multiple scales is essential for conservation. Remote sensing datasets and their derivatives (e.g., landcover data) enable efficient multi-scale assessment of ring-necked pheasant (Phasianus colchicus) habitat, albeit with trade-offs among their thematic, spatial, temporal, and/or spectral grains and extents. For example, the National Agriculture Imagery Program provides fine spatial but coarse spectral grain imagery, both important for identifying pheasant habitats. Spatial technologies and datasets relevant to pheasant research are advancing, yet the information on the data sources utilized in research to date is limited. Remote sensing and landcover datasets surveys in pheasant research could help fill information gaps in pheasant–habitat relationships. In this systematic review we filtered 1,110 peer-reviewed pheasant habitat studies to 65 from the Central U.S.A. Temporal trends were tested in the broad use of remote sensing and the selection of remote sensing platforms and data types. Of the selected studies, 26 used remote sensing or landcover data, which were classified by the thematic, spatial, temporal and spectral grains and extents. Remote sensing and landcover data products increased over time, particularly satellite-based landcover products with relatively coarse thematic resolutions (e.g., crops and grassland), moderate spatial grains (e.g., 30-meter), and spatial extents (e.g., smaller than the average U.S.A. county). Remote sensing photography/imagery with multispectral sensors and coarse spectral resolution (e.g., 3 bands with 100 nm width) was also prominent but remained constant over time. We found no evidence of research with remote sensing or landcover data at multiple temporal grains and extents. Several studies lacked scale reporting, potentially limiting our inference. Scale transparency is important due to species selecting their habitat at multiple scales, making findings scale-dependent. Effective conservation requires scale-appropriate strategies. As remote sensing advances, opportunities for ring-necked pheasant habitat multi-scale assessment that fill remaining pheasant–habitat relationships knowledge gaps and support management decisions will increase.
Authors
- Baldissara, Megan ;
- Barg, Allison ;
- Little, Andrew ;
- Tang, Zhenghong ;
- Wardlow, Brian ;
- Uden, Daniel