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Dec 09, 2024
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PLSCS 4290 - Remote Sensing and Modeling for Ecosystems Fall. 3 credits. Letter grades only.
Prerequisite: knowledge of the basics of remote sensing, calculus, physics, and programming skills, and some background in agro-ecosystems. Co-meets with PLSCS 5290 .
Y. Sun.
This course introduces advanced concepts of remote sensing and numerical modeling, with hands-on experience in data acquisition, processing, and interpretation. This course aims to explore key questions facing the agronomic and natural eco-systems using remote sensing techniques and ecological modeling at various scales. It provides hands-on experience in remote sensing techniques and using datasets/tools and model simulations to address research questions.
Outcome 1: Describe the basic principles in remote sensing.
Outcome 2: Describe the spectral signatures of land surface properties and appropriate application.
Outcome 3: Acquire satellite dataset from NASA, ESA, and Google Earth Engine.
Outcome 4: Process remote sensing data using ENVI, and R (or Python).
Outcome 5: Run mechanistic model simulations in the CLM framework.
Outcome 6: Apply remote sensing observations and model simulations to interpret agro-ecological phenomena.
Outcome 7: Conduct an independent applications-based project.
Outcome 8: Develop and present an oral and collaborative group project.
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