Fishing forecast by using satellite data

Global Nominee

Fishing forecast by using satellite data received a Global Nomination.


Develop a web tool, mobile device app or add-on for existing apps or websites that leverages NASA imagery and climate data to illustrate the impacts of our changing Earth in areas of interest to you. Some ideas to explore include:

  • Use NASA Earth observations data, social media, smart phones, and Short Message Service (SMS) text phones to collect Earth observations and connect public in local, regional, and national networks to communicate about our changing planet.
  • Examine current natural events curated by NASA’s EONET (Earth Observatory Natural Event Tracker) by browsing global historical and near real-time imagery from space.
  • Upload images or other data points demonstrating visible observations and how they compare to satellite data. For example, generating early-warning alerts or validating precipitation rates reported from NASA’s Global Precipitation Measurement Mission.
  • Integrate NASA imagery with a mobile assistant to allow for dynamic image generation based on a basic request structure.

Our purpose is to make satellite data more accessible to the general public. Scientific satellite data is usually offered in the complicated HD5 format. Instead, we parse and offer data on water resources (rainfall, snow depth, sea surface temperature, etc.) in JSON format, which is more accessible and easy to understand. We also provide a friendly way of visualizing this data using tile maps, and provide concrete examples of use employing visualizations on ocean currents and fish population.

With this data it is easy to create time series on fish population and water temperature, and to employ statistical analysis to better understand their relationship, allowing anyone to forecast the best time and places to fish, and to improve the management of water resources. In the future, we expect to collect data not only on ocean resource especially fish, but also on agricultural resources that can help to improve food production planning.

Resources Used
  • GCOM-W1(shizuku)
  • Postgres
  • GDAL
  • AnacondaPython
  • R
  • OpenStreetMap
  • Chart.JS
  • Bootstrap
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