There is interest in mapping open water bodies using remote sensing data. Coverage and persistence of open water is currently a poorly measured variable due to its spatial and temporal variability across landscapes, especially in remote areas. The presence and persistence of open water is one of the primary indicators of conditions suitable for mosquito breeding habitats. Predicting the risk of mosquito caused disease outbreaks is a required step towards their control and eradication. Satellite observations can provide needed data to support agency decisions for deployment of preventative measures and control resources. This study, which will try to map open water bodies with satellite data, will be carried out using a decision tree based open source software algorithm called Random Forests to find correlations between the remote sensing data and open water bodies and their color properties. Software has been written in R to ingest data from the Landsat 7 satellite, convert it into an R data frame, input it into the Random Forest Software algorithm and output a classification of open water bodies and their color properties. Knowledge of the location and color properties of open water bodies and their dynamic nature can be used as early warning indicators for mosquito caused disease outbreaks.


Applied Statistics | Categorical Data Analysis | Numerical Analysis and Scientific Computing | Optics


DR. Erika Podest

Lab site

NASA Jet Propulsion Laboratory (JPL)

Funding Acknowledgement

This material is based upon work supported by the S.D. Bechtel Jr. Foundation and is made possible with contributions from the National Science Foundation under Grant No. 1340110, Howard Hughes Medical Institute, Chevron Corporation, National Marine Sanctuary Foundation, and from the host research center. Any opinions, findings, and conclusions or recommendations expressed in this material are solely those of the authors. The STAR Program is administered by the Cal Poly Center for Excellence in STEM Education on behalf of the California State University system.



URL: https://digitalcommons.calpoly.edu/star/338


To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.