Coral bleaching is caused by prolonged exposure to warm water, and the temperature of the ocean is easily measured from satellites. NOAA Coral Reef Watch (CRW) data measure the temperature stress that leads to coral bleaching. In this lesson, you will learn how to use CRW data to predict bleaching. The formulas match our operational methodology, so users will gain in-depth knowledge of how our data are produced.
The lesson begins with our sea surface temperature (SST) data files, and leads you through the steps to calculate a long-term average temperature. This average, or climatology, is subtracted from a temperature image to produce an SST anomaly. You will then calculate a specialized anomaly, called a HotSpot, that shows areas that are above the average temperature for the warmest month. Finally, you will look for regions under HotSpot stress for a prolonged period, calculating a metric called the Degree Heating Week.
To show how these data are used in the real world, the lesson ends with an activity based on a 2005 bleaching event in the Caribbean. You will predict coral bleaching from satellite data, then compare your predictions with real-life bleaching data.
Bilko is a complete system for learning and teaching remote sensing image analysis. Current lessons teach the application of remote sensing to oceanography and coastal management, but Bilko routines may be applied to the analysis of any image in an appropriate format, and include a wide range of standard image processing functions. Supported by UNESCO, Bilko is available to registered users absolutely free! All you need to download software or lessons is your e-mail address.
Development of this lesson was supported by the Remote Sensing Working Group of the Coral Reef Targeted Research (CRTR) Program, a partnership between the Global Environment Facility, the World Bank, The University of Queensland, NOAA, and approximately 40 research institutes and other third parties around the world.