C289 Applied Remote Sensing

C289 will comprise four main modules designed to cater to various learning curves (represented by corresponding color blocks in the weekly schedule).

The first module will emphasize the fundamentals of the electromagnetic spectrum and basic image data processing techniques. You'll learn how to acquire and process remote sensing data and understand the interaction between electromagnetic radiation and various surfaces. Spectral signatures and vegetation indices will provide insights into how these tools are used to analyze and interpret vegetation and land cover changes. Finally, we will examine emerging technologies and their applications in environmental monitoring, highlighting the latest advancements and their practical implications.

The second module will focus on the practical applications of remote sensing, including image classification, mapping, and change analysis. Students will analyze urbanization expansion in the Bay Area over the past few decades using various techniques, including both unsupervised and supervised classification. The accuracy of these mapping techniques will be evaluated and compared. Additionally, time series analysis of urban sprawl will be conducted to quantitatively assess trends, and potential driving factors behind these changes will be explored.

The third module will introduce students to cutting-edge remote sensing techniques applicable across various spatial scales, from individual buildings or gardens to neighborhoods and broader landscape or regional levels. Students with diverse backgrounds and research interests will find opportunities to engage with scales relevant to their work. A field trip will involve using a handheld LiDAR (LiGrip O1 Lite), allowing each student to scan a landscape object of their choice and analyze it in the lab. Additionally, students will learn about aerial LiDAR and explore cloud computing platforms for remote sensing data processing and analysis.

The final module will involve an applied independent project focused on addressing a local built or natural environment concern. In this project, each student will act as a consultant, delivering a "user-friendly" yet analytically robust remote sensing-based solution to the client's problem(s). This solution will incorporate scientific data and analysis to tackle the client's environmental challenge, demonstrating its potential impact on planning and informing policy decisions. Students will be responsible for conceptualizing the project, collecting data, conducting analysis, and presenting their findings.