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LIDAR Remote Sensing Technologists uses remote sensing strategies to analyze data to solve problems in areas across the globe. They use LIDAR - Light Detection and Ranging - as a method of remote sensing to examine the surface of the Earth. 


Mathematical modelers use mathematics to create models that demonstrate complex processes or solve problems. Many mathematical modelers use their skills to create and animate 3D representations of their processes with the assistance of software technology. 


A model analyst develops models to help visualize, observe, and predict complicated data. Model analysis is the process of taking large amounts of data and separate it into a structure that makes it intelligible to the binary process of computers. An analyst also manages the flow of information between different user groups through the use of relational databases.



Remote sensing scientists use sensors to analyze data and solve regional, national and global concerns. For instance, natural resource management, urban planning, and climate and weather prediction are applications of remote sensing. Many scientists develop new sensor systems, analytical techniques, or new applications for existing systems. 


Grade Level: 6-8, 9-12

This lesson walks students through the use of Landsat false-color imagery and identification of different land cover features using these as models. Building from an original GLOBE lesson, this resource features Google Slide and Jamboard to assist in both face-to-face and virtual learning.


This video is a resource that can be used alongside any activity that involves creating and developing questions. While the video focuses on questions about trees, the basic principles are necessary for asking scientific questions. 


Joshua Stevens,Lead for Data Visualization for NASA Earth Observatory at NASA Goddard Space Flight Center. Learn how he translates data from NASA missions and instruments into intuitive maps, charts and graphics which meet high quality standards and are consistent with current research.


At the core of scientific visualization is the representation of data graphically - through images, animations, and videos - to improve understanding and develop insight. Data visualizers develop data-driven images, maps, and visualizations from information collected by Earth-observing satellites, airborne missions, and ground measurements. Visualizations allow us to explore data, phenomena and behavior; they are particularly effective for showing large scales of time and space, and "invisible" processes (e.g. flows of energy and matter) as integral parts of the models.


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