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.
Educational Resources - Search Tool
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 graphic organizer may be used to help students analyze the processes and components of Earth System phenomena.
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.
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.
Students observe monthly images of changing vegetation patterns, looking for seasonal changes occurring throughout 2017. These data can be used by students to develop their own models of change.
Students collect and analyze temperature data to explore what governs how much energy is reflected.
Students are introduced to the Earthrise phenomenon by seeing the Earth as the Apollo 8 astronauts viewed our home planet for the first time from the Moon. They will analyze a time series of mapped plots of Earth science variables that NASA monitors to better understand the Earth
The Earth System Satellite Images, help the learner visualize how different Earth system variables change over time, establish cause and effect relationships for a specific variable, identify patterns, and determine relationships among variables over one year.