Students will describe how the spread of COVID-19 is affected by population density and explain why patterns in the spread of COVID-19 are happening over time. 

Students analyze Landsat images of Atlanta, Georgia to explore the relationship between surface temperature and vegetation.

Students will identify and describe the relationship between land cover classification and surface temperature as they relate to the urban heat island effect. Students will also describe patterns between population density and the locations of urban heat islands. 

Students will analyze how surface (skin) temperatures vary across a community and determine what factors contribute to this variation. Students will describe how human activity can affect the local environment. 

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.

This USGS activity leads students to an understanding of what remote sensing means and how researchers use it to study changes to the Earth’s surface, such as deforestation. 

Carbon dioxide concentration in the atmosphere is affected by many processes including fires, deforestation, and plant respiration. Students will evaluate a Landsat image to determine the rate of carbon dioxide sequestration in a particular area.

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.

This StoryMap allows students to explore the urban heat island effect using land surface temperature and vegetation data in a 5 E-learning cycle. Students investigate the processes that create differences in surface temperatures, as well as how human activities have led to the creation of urban heat islands. 

The Earth System Satellite Images help students observe and analyze global Earth and environmental data, understand the relationship among different environmental variables, and explore how the data change seasonally and over longer timescales.