| Sean Kearney |
|
 |
Sean P. Kearney Research Ecologist CONTACT INFORMATION |
Degrees:
|
B.A. International Business (Minor: Economics) |
2008 | Western Washington University |
|
M.Sc. International Agricultural Development (Focus: Soil Science) |
2011 |
University of California, Davis |
| Ph.D. Soil Science | 2017 | University of British Columbia, Vancouver |
Research interests:
Dr. Kearney’s research is focused on combining field and remote sensing data to solve agricultural land management challenges. His work ranges from improving methods to monitor indicators of land health (e.g., erosion, carbon storage) to using spatial analysis tools to better understand the interactions between human activity and environmental change across landscape, regional and global scales. Dr. Kearney’s work links ground data with a variety of remotely sensed datasets, including satellite imagery, GPS collar data on livestock and wildlife, airborne laser scanning (LiDAR) and others.
Current research questions:
What drives changes in rangeland conditions across space and time, and how are these linked to multiple rangeland management objectives, including cattle productivity, wildlife habitat and biodiversity?
- How can we improve fine-scale mapping of rangeland conditions such as forage quality, forage biomass and habitat provisioning using near-real-time satellite time series?
- What is the relative influence of plant functional groups, topography, soils and seasonal precipitation on grassland phenology and productivity, and cattle weight gains?
- How do adaptive rangeland management practices such as pulse grazing and rotational herd management influence foraging behavior, cattle weight gain and rangeland conditions?
- What is driving differences in cattle foraging behavior across space, time and management strategies?
Publications:
Augustine, D.J., Derner, J.D., Porensky, L.M., Hoover, D.L., Ritten, J.P., Kearney, S.P., Ma, L., Peck, D., Wilmer, H., and the CARM Stakeholder Group. 2024. The LTAR Grazing Land Common Experiment at the Central Plains Experimental Range: Collaborative adaptive rangeland management. Journal of Environmental Quality: 1–9. https://doi.org/10.1002/jeq2.20599
Peirce, E.S., Kearney, S.P., Santamaria, N., Augustine, D.J. and L.M. Porensky. 2024. Predictions of aboveground herbaceous production from satellite-derived APAR are more sensitive to ecosite than grazing management strategy in shortgrass steppe. Remote Sensing 16(15): 2780. https://www.mdpi.com/2072-4292/16/15/2780
Kearney, S.P., Porensky, L.M., Augustine, D.J. and Pellatz, D.W. 2023. Toward broad-scale mapping and characterization of prairie dog colonies from airborne imagery using deep learning. Ecological Indicators 154: 110684. https://doi.org/10.1016/j.ecolind.2023.110684
Augustine, D.J., Kearney, S.P., Raynor, E.J., Porensky, L.M. and Derner, J.D. 2023. Adaptive, multi-paddock, rotational grazing management alters foraging behavior and spatial grazing distribution of free-ranging cattle. Agriculture, Ecosystems & Environment 352: 108521. https://doi.org/10.1016/j.agee.2023.108521
Hoover, D.L., Abendroth, L.J., Browning, D.M., Saha, A., Snyder, K., Wagle, P., Witthaus, L., Baffaut, C., Biederman, J.A., Bosch, D.D., Bracho, R., Clark, P., Ellsworth, P., Fay, P.A., Flerchinger, G., Kearney, S., Levers, L., Saliendra, N., Schmer, M., Schomberg, H., Scott, R.L. 2022. Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales. Science of The Total Environment 864, 160992. https://doi.org/10.1016/j.scitotenv.2022.160992
Augustine, D.J., Raynor, E.J., Kearney, S.P. and Derner, J.D. 2022. Can measurements of foraging behaviour predict variation in weight gains of free-ranging cattle? Animal Production Science. https://doi.org/10.1071/AN21560
Kearney, S.P., Porensky, L.M., Augustine, D.J., Derner, J.D., Gao, F. 2022. Predicting Spatial-Temporal Patterns of Diet Quality and Large Herbivore Performance Using Satellite Time Series. Ecological Applications 32(2): e2503. https://doi.org/10.1002/eap.2503.
Kearney, S.P., Porensky, L.M., Augustine, D.J., Gaffney, R., Derner, J.D. 2022. Monitoring standing herbaceous biomass and thresholds in semiarid rangelands from harmonized Landsat 8 and Sentinel-2 imagery to support within-season adaptive management. Remote Sensing of Environment 271: 1-15. https://doi.org/10.1016/j.rse.2022.112907.
Gaffney, R., D.J. Augustine, S.P. Kearney, and L.M. Porensky. 2021. Using hyperspectral imagery to characterize rangeland vegetation composition at process-relevant scales. Remote Sensing 13:4603. doi: 10.3390/rs13224603
Kearney, S.P., N.C . Coops, S .Sethi, G.B. Stenhouse. 2020. Maintaining accurate, current, rural road network data: An extraction and updating routine using RapidEye, participatory GIS and deep learning. International Journal of Applied Earth Observation and Geoinformation 87. https://doi.org/10.1016/j.jag.2019.102031
McClelland, Cameron JR., N. C. Coops, E.E. Berman, S.P. Kearney, S. E. Nielsen, A . C. Burton, G. B .Stenhouse. 2019. Detecting changes in understorey and canopy vegetation cycles in West Central Alberta using a fusion of Landsat and MODIS. Applied Vegetation Science. https://doi.org/10.1111/avsc.12466
Kearney, S. P., N. C. Coops, G. B. Stenhouse, S. E. Nielsen, T. Hermosilla, J. C. White, M. A. Wulder. 2019. Grizzly bear selection of recently harvested forests is dependent on forest recovery rate and landscape composition. Forest Ecology and Management. 449. https://doi.org/10.1016/j.foreco.2019.117459
Berman, E. E., N.C .Coops, S.P .Kearney, G.B. Stenhouse. 2019. Grizzly bear response to fine spatial and temporal scale spring snow cover in Western Alberta. PloS One 14 (4), e0215243. https://doi.org/10.1371/journal.pone.0215243
Kearney, S. P., Coops, N. C., Stenhouse, G. B., & Nelson, T. A. 2019. EcoAnthromes of Alberta : An example of disturbance-informed ecological regionalization using remote sensing. Journal of Environmental Management, 234, 297–310. https://doi.org/10.1016/j.jenvman.2018.12.076
Coogan, S. C. P., Coops, N. C., Janz, D. M., Cattet, M. R. L., Kearney, S. P., Stenhouse, G. B., & Nielsen, S. E. 2019. Towards grizzly bear population recovery in a modern landscape. Journal of Applied Ecology, 56, 93–99. https://doi.org/10.1111/1365-2664.13259
García, E., P. Siles, L. Eash, R. Van Der Hoek, S. P. Kearney, S. M. Smukler, S. J. Fonte. 2019. Participatory evaluation of improved grasses and forage legumes for smallholder livestock production in Central America. Experimental Agriculture. 55. 5. 776-792. https://doi.org/10.1017/S0014479718000364
Coops, N. C., Kearney, S. P., Bolton, D. K., & Radeloff, V. C. 2018. Remotely-sensed productivity clusters capture global biodiversity patterns. Scientific Reports, 8, 1–12. https://doi.org/10.1038/s41598-018-34162-8
Kearney, S.P., Fonte, S.J., García, E., Smukler, S.M., 2017. Improving the utility of erosion pins: Absolute value of pin height change as an indicator of relative erosion. Catena 163, 427–432. doi:10.1016/j.catena.2017.12.008
Kearney, S.P., Fonte, S.J., García, E.D., Siles, P., Chan, K.M.A., Smukler, S.M., 2017. Evaluating ecosystem service trade-offs and synergies from slash-and-mulch agroforestry systems in El Salvador. Ecological Indicators. doi:10.1016/j.ecolind.2017.08.032
Kearney, S.P., Coops, N.C., Chan, K.M.A., Fonte, S.J., Siles, P., Smukler, S.M., 2017. Predicting carbon benefits from climate-smart agriculture: High-resolution carbon mapping and uncertainty assessment in El Salvador. Journal Environmental Management. 202, 287–298. doi:10.1016/j.jenvman.2017.07.039
Kearney, S.P., Smukler, S.M., 2016. Determining Greenhouse Gas Emissions and Removals Associated with Land-Use and Land-Cover Change, in: Rosenstock, T.S., Rufino, M.C., Butterbach-Bahl, K., Wollenberg, E., Richards, M. (Eds.), Methods for Measuring Greenhouse Gas Balances and Evaluating Migration Options in Smallholder Agriculture. Springer. doi:10.1007/978-3-319-29794-1_3
Kearney, S.P., Fonte, S.J., Salomon, A., Six, J., Scow, K.M., 2012. Forty percent revenue increase by combining organic and mineral nutrient amendments in Ugandan smallholder market vegetable production. Agronomy for Sustainable Development 32, 831–839. doi:10.1007/s13593-012-0097-6
