Students are divided into three different groups and are assigned a category of drivers of change in regional trends of freshwater storage (Climate Change, Human Activity, and Natural Variability).

Students identify and classify kinds of land cover (such as vegetation, urban areas, water, and bare soil) in Landsat satellite images of Phoenix, Arizona taken in 1984 and 2018.

This story map 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. 

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. 

This unit plan is published by the NASA Climate Change Research Initiative's (CCRI) Applied Research STEM Curriculum Portfolio.  The CCRI Unit Plan, called “Urban Surface Temperatures and the Urban Heat Island Effects,“ has the purpose to educate students how climate is changi

Students learn how to estimate the "energy efficiency" of photosynthesis, or the amount of energy that plants absorb for any given location on Earth. This is the ratio of the amount of energy stored to the amount of light energy absorbed and is used to evaluate and model photosynthesis efficiency.

This lesson walks students through the use of Landsat false-color imagery and identification of different land cover features using these as models.

By matching pie charts with dates between 2002 and 2020, students will predict how air quality has changed over the past two decades. They will then use color-coded Air Quality Index signatures to assess the accuracy of their predictions.