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Activity Number: 192 - Contributed Poster Presentations: Section on Statistics and the Environment
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #330848
Title: Spatiotemporal Data Fusion of Remote Sensing Data Using Space-Time Dynamic Linear Models
Author(s): Maggie Johnson* and Brian Reich and Marschall Furman and Joshua Gray
Companies: SAMSI and North Carolina State University and North Carolina State University and North Carolina State University
Keywords: data fusion; spatiotemporal models; dynamic linear model; remote sensing

Characterizing growth cycle events in vegetation, such as the timing of spring green-up, from massive spatiotemporal remote sensing datasets is desirable for a wide area of applications. For example, the timings of plant life cycle events are very sensitive to weather conditions, and are often used to assess the impacts of changes in weather and climate. Likewise, quantifying and predicting changes in crop greenness can have a large impact on agricultural strategies. However, due to the limitations of imaging spectrometers, a sensor with high temporal frequency of measurements must have lower spatial resolution, and vice versa. We develop a space-time dynamic linear modeling framework to fuse high temporal frequency data (MODIS) with high spatial resolution data (Landsat) to create daily, 30 meter resolution data products of vegetation greenness. Our method is able to handle the spatial change-of-support problem, as well as the high percentage of missing values in the data. Additionally, we introduce an approximate, computationally efficient procedure for parameter estimation which is scalable to the massive size of the data.

Authors who are presenting talks have a * after their name.

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