Distinguished Lecturer's Talk on Vegetation Characterization with Multi-Source Remote Sensing Data
Vegetation Characterization Using Earth Observation Remote Sensing
Earth observation remote sensing is a powerful tool for characterizing vegetation, providing insights into ecosystem health, agricultural productivity, and global biosphere patterns. Various sensor systems and platforms offer unique data capture capabilities, each with its own challenges and drawbacks.
Beyond data collection, numerous algorithms and approaches exist for measuring and mapping vegetation attributes. These applications often benefit from combining sensor data across temporal, spatial, and spectral scales. Cutting-edge methods fuse data from multiple platforms or incorporate non-remote sensing data.
This lecture will discuss:
- Sensor Selection and Tradeoffs: Factors to consider when choosing sensor systems for vegetation characterization, including spatial and temporal resolution, spectral range, and data availability.
- Common Vegetation Characterization Methods: Overview of traditional and advanced techniques, such as vegetation indices, spectral mixture analysis, and machine learning/deep learning approaches.
- Sensor and Data Fusion: Strategies for combining data from multiple sensors and platforms to leverage their strengths and improve vegetation characterization accuracy.
Examples and use cases will be presented, covering unsupervised and supervised problems, machine learning/deep learning applications, and best practices for different fusion approaches.
Date and Time
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- Date: 08 Nov 2024
- Time: 03:30 PM to 05:00 PM
- All times are (UTC+05:30) Chennai
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- Hyderabad, Andhra Pradesh
- India
- Building: ECE Department
- Room Number: Golden Jubilee Seminar Hall
Speakers
Dr. Keely Roth
Distinguished Lecturer's Talk on Vegetation Characterization with Multi-Source Remote Sensing Data
Dr. Keely Roth is a Senior Remote Sensing Scientist and Science Lead for Horticulture on the Geospatial Sciences team of The Climate Corporation. She is based in San Francisco, CA, and has 10+ years experience in remote sensing research and geospatial analysis. In her role at The Climate Corporation, she designs and leads research projects aimed at improving our ability to measure and map crop health during the growing season using field data and remotely sensed imagery from UAVs, planes, and satellites. Prior to joining The Climate Corporation, Keely was a postdoctoral research scientist in the Center for Spatial Technologies and Remote Sensing Lab at UC Davis. Her research was part of the NASA HyspIRI campaign to evaluate the capabilities of a spaceborne imaging spectrometer mission for characterizing plant functional traits across ecosystems. In her graduate research, she worked on projects related to measuring forest biomass, mapping ecosystem species composition and phenology, and tracking postfire vegetation recovery.
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