This set of three videos illustrates how math is used in satellite data analysis. NASA climate scientist Claire Parkinson explains how the Arctic and Antarctic sea ice covers are measured from satellite data and how math is used to determine trends... (View More) in the data. In the first video, she leads viewers from satellite data collection through obtaining a time series of monthly Arctic and Antarctic average sea ice extents for November 1978-December 2012. In the second video, she begins with the time series from the first video, removes the seasonal cycle by calculating yearly averages, and proceeds to calculate the slopes of the lines to get trends in the data, revealing decreasing sea ice coverage in the Arctic and increasing sea ice coverage in the Antarctic. In the third video, she uses a more advanced technique to remove the seasonal cycle and shows that the trends are close to the same, whichever method is used. She emphasizes the power of math and that the techniques shown for satellite sea ice data can also be applied to a wide range of data sets. Note: See Related & Supplemental Resources for the maps and data files (1978-2015) that will allow you to do the calculations shown in the video. These also include data for different regions of the Arctic and Antarctic, enabling learners to do additional calculations beyond those shown in the videos. (View Less)

This chapter describes how to set a scale and measure distances and areas on satellite images. Using ImageJ, a freely available image analysis program that runs on most operating systems, users set the spatial calibration of an image, then select... (View More) and measure distances and areas on it. The measurement results are reported in real-world units. The technique is most useful and accurate for nadir view (straight down) images. In this chapter, users examine satellite images of the Aral Sea, which has shrunk dramatically since 1960 because the rivers that flow into it have been tapped for irrigation. Users access satellite images of the region, then set a scale and measure the width of the sea each year. On another set of images, they highlight areas that represent water and measure them to see how these areas of the sea changed. This chapter is part of the Earth Exploration Toolbook, which provides teachers and/or students with direct practice for using scientific tools to analyze Earth science data. Students should begin on the Case Study page. (View Less)

In this activity, users download and graph modeled climate data to explore variability in climate change. Most people know that climate changes are predicted over the next hundred years, but they may not be aware that these changes are likely to... (View More) vary from region to region. Using data from the University of New Hampshire's EOS-WEBSTER, a digital library of Earth Science data, users will obtain annual predictions for minimum temperature, maximum temperature, precipitation, and solar radiation for each of these 5 states: New York, Georgia, Colorado, Minnesota, and California. Data will span the years 2000 through 2100. Users will import the data into Excel and analyze it to see what, if any, regional variability exists. Finally, they will download data for their own state, compare these results with the results from the other 5 states and use their results to answer questions related to climate change. This chapter is part of the Earth Exploration Toolbook (EET). Each EET chapter provides teachers and/or students with direct practice for using scientific tools to analyze Earth science data. Students should begin on the Case Study page. (View Less)

In this chapter, students will explore relationships between air quality and population density using the image visualization tool, Google Earth. You will learn how to download NO2 data and analyze them to develop a conceptual understanding of how... (View More) population and topography can influence the air quality of a region. Once you have learned the techniques, you are encouraged to explore seasonal changes in nitrogen dioxide concentrations at other locations. This chapter is part of the Earth Exploration Toolbook (EET). Each EET chapter provides teachers and/or students with direct practice for using scientific tools to analyze Earth science data. Students should begin on the Case Study page. (View Less)

El Niño impacts sea surface height, sea surface temperature and wind vectors. In this lesson, data for each of those three characteristics from the 1997-98 El Niño event will be analyzed and compared. Students will work in teams of 3 throughout... (View More) the activity, initially doing a team analysis of a single characteristic, and then jig-sawing into other groups to share information. The lesson culminates in a series of questions, a written summary and a comparison to similar observations done by scientists. This lesson uses student- and citizen science-friendly microsets of authentic NASA Earth system science data from the MY NASA DATA project. It also includes related links, extensions, an online glossary, and a list of related AP Environmental Science topics. (View Less)

This self-paced, interactive tutorial enables learners to identify and measure iceberg size from remotely-sensed satellite images. Two techniques are explored: the geometric shape method, which provides a rapid rough estimate of area; and the pixel... (View More) count method, which employs special software to measure the size more accurately. This resource is part of the tutorial series, Satellite Observations in Science Education, and is the second of three modules in the tutorial, Hunting Icebergs. (Note: requires Java plug-in) (View Less)

This self-paced, interactive tutorial guides learners through the decision-making process in locating data that will enable the identification of tabular icebergs, including: selecting the appropriate satellite orbit, and identifying the optimal... (View More) solar and infrared wavelength values to discriminate between water and ice in remotely-sensed images. This resource is part of the tutorial series, Satellite Observations in Science Education, and is the first of three modules in the tutorial, Hunting Icebergs. (Note: requires Java plug-in) (View Less)

This self-paced, interactive tutorial teaches how to estimate the travel time, acceleration, and trajectory of iceberg movement from satellite images. Factors that impact the complex motion of icebergs, such as weather, ice processes and... (View More) oceanographic influences are also explored. This resource is part of the tutorial series, Satellite Observations in Science Education, and is the third of three modules in the tutorial, Hunting Icebergs. (Note: requires Java plug-in) (View Less)