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Tracking fog occurrence and drivers in a mountainous Costa Rican rainforest using phenological camera imagery
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| Type: | Resource | |
| Storage: | The size of this resource is 16.3 KB | |
| Created: | Jun 17, 2020 at 9:37 p.m. | |
| Last updated: | Aug 17, 2021 at 7:03 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Discoverable |
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| Views: | 797 |
| Downloads: | 1 |
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Abstract
Fog patterns were determined using web camera images collected at half-hour intervals and uploaded to the PhenoCam network. These images were analyzed to determine fog presence and intensity using the K-Means iterative algorithm, as implemented in Python. Atmospheric conditions were clustered into five different categories: clear, overcast, light fog, medium fog, and heavy fog. Ecohydrological variable data was gathered from sensors placed within the forest and at a nearby weather station. The quantified fog data was then compared with the ecohydrological variables; the diurnal patterns of fog and precipitation were determined over the entire dataset and during dry and wet months. In April, rain was present 2% of the time and fog was present in 68% of the images and in September rain was present 18% of the time and fog was present in 40% of the images. Occurrence of heavy fog conditions are consistently higher in January and December but daily appeared to be highest in the early mornings. A generalized linear model was used to relate fog occurrence with temperature, relative humidity, solar radiation, and wind speed.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Related Resources
| The content of this resource is derived from | https://ameriflux.lbl.gov/sites/siteinfo/Cr-SoC |
| The content of this resource is derived from | https://phenocam.sr.unh.edu/webcam/sites/soltis/ |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | Collaborative Research: Continental-Scale Monitoring, Modeling and Forecasting of Phenological Responses to Climate Change | EF-1065029 |
| U.S. Department of Energy, Office of Science, Biological and Environmental Research | IMPROVING LAND-SURFACE MODELING OF EVAPOTRANSPIRATION PROCESSES IN TROPICAL FORESTS | DE‐SC0010654 |
| National Science Foundation | REU Site: Ecohydrology of Tropical Montane Forests -- Diversity in Science, Interdisciplinary Breadth, and Global Awareness | EAR‐1659848 |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
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| Jaeyoung Song | Texas A&M University |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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