Recent advances in remote sensing have facilitated the use of large volume of satellite imagery for understanding the dynamic natural and human-induced processes. Time series of earth observation data from coarse resolution sensors (e.g., AVHRR, SPOT VGT, and MODIS) set the stage for operational monitoring of land surface dynamics over large geographic regions across time. Recently, a new generation of time series studies using moderate spatial resolution imagery (sub 100-m) opens up opportunities for studying dynamic earth system processes in unparalleled details. In particular, the global Landsat archive acquired over the past four decades has been increasingly explored to improve our scientific understanding of types, trends, causes, and consequences of various dynamic processes. Complemented by Sentinel-2 and other global Landsat-class missions, time series of moderate spatial resolution imagery offers tremendous potentials to conduct near real time monitoring and revolutionize our understanding of complex earth systems. The wealth of information provided by increased temporal frequency, improved spatial resolution, and sheer data volume calls for innovative data analysis algorithms and monitoring strategies.
This session invites papers focusing on both theoretical and methodology research and applications to advance remote sensing time series analysis with moderate spatial resolution imagery.
Potential session topics include, but not limited to:
1) Time series algorithm development (e.g., curve fitting, trend analysis, and prediction)
2) Multi-source image fusion or data integration
3) Time series remote sensing based domain applications (e.g., vegetation phenology, land cover and land use change, land surface biophysical characteristics)
4) Validation and assessment of remotely sensed time series analysis
To present a paper in the session, please (1) register and submit your abstract through AAG, and (2) send your personal identification number (PIN), paper title, and abstract to one of the co-organizers by November 8, 2017.
Chengbin Deng (State University of New York at Binghamton, firstname.lastname@example.org)
Chunyuan Diao (University of Illinois at Urbana-Champaign, email@example.com)
Zhe Zhu (Texas Tech University, firstname.lastname@example.org)
|Presenter||Tracy Whelen*, University of Massachusetts - Amherst, Josef Kellndorfer, Earth Big Data, LLC, Paul Siqueira, University of Massachusetts - Amherst, Identifying Forest Disturbance Using Synthetic Aperture Radar (SAR) Time Series||20||5:20 PM|
|Presenter||Jose I. Ochoa*, Geography Graduate Group. University of California, Davis, Robert J. Hijmans, Department of Environmental Sciences and Policy. University of California, Davis, Land cover classification in heterogeneous and cloudy areas: a comparison of data from different satellite sensors||20||5:40 PM|
|Presenter||Matthew Lucas*, University of Hawaii at Manoa Dept. of Natural Resources and Environmental Management , Clay Trauernicht , University of Hawaii at Manoa Dept. of Natural Resources and Environmental Management , Kimberly Carlson, University of Hawaii at Manoa Dept. of Natural Resources and Environmental Management , Spatially quantifying and attributing 17 years of vegetation and land cover transitions across Hawai`i||20||6:00 PM|
|Presenter||Wei Guan, The Climate Corporation, Xiaoyuan Yang*, The Climate Corporation, Using deep learning models to synthesize high resolution images from low resolution sensors for precision agriculture applications||20||6:20 PM|
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