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Tracking fog occurrence and drivers in a mountainous Costa Rican rainforest using phenological camera imagery

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Created: Jun 17, 2020 at 9:37 p.m.
Last updated: Aug 17, 2021 at 7:03 p.m.
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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.

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Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Texas A&M University Soltis Center for Research and Educatino


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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


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
Jaeyoung Song Texas A&M University

How to Cite

Miller, G. R., E. Schweizerhof, A. Duffy (2021). Tracking fog occurrence and drivers in a mountainous Costa Rican rainforest using phenological camera imagery, HydroShare,

This resource is shared under the Creative Commons Attribution CC BY.


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