The ocean's surface is not level, and sea levels change in response to changes in chemistry and temperature. Sophisticated satellite measurements are required for scientists to document current sea level rise.

The purpose of this activity is to have students use an Earth Systems perspective to identify the various causes associated with changes to Earth's forests as they review Landsat imagery of site locations from around the world.

Helping students build their understanding of Earth's spheres and how they are connected is difficult.  Review the graphics to help identify the parts of the Earth System and the processes that connect them at the local, regional, and global scales.

Check out this interactive data visualization and simulation tool.  It explores the impact of collapsing polar ice sheets (Greenland and Antarctica) and their impact on global mean sea level rise, along with shrinkage in the livable area around the world.

This graphic organizer may be used to help students analyze the processes and components of Earth System phenomena.

NASA Worldview is a free online visualization tool that is a great launchpad for learners who are new (or veteran) users of satellite data.

This mini lesson focuses on Landsat satellite data and how it is used to detect changes in land use. Students will answer questions based off of a NASA Video that features how Landsat data are interpreted in the forests of the Pacific Northwest, and gives examples of the effects insects and logging have with land management.

This investigation introduces students to the significant environmental changes occurring around the world. The investigation uses NASA satellite images of Brazil to illustrate deforestation as one type of environmental change.

My NASA Data has recently released several new resources, StoryMaps, for use in educational settings.

The Earth System Satellite Images, along with the Data Literacy Cubes, help the learner visualize how different Earth system variables change over time, identify patterns, and determine relationships among two variables in three months.