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||Jennifer Rover*, United States Geological Survey, Qiang Zhou, ASRC Federal InuTeq, contractor to USGS EROS, Alisa Gallant, United States Geological Survey, Characterizing wetland dynamics using dense Landsat time series data||20||3:20 PM|
|Presenter||Robert Kennedy*, Oregon State University, Samuel Hooper, Oregon State University, Justin Braaten, Oregon State University, Joseph Hughes, Oregon State University, Peder Nelson , Oregon State University, Zhiqiang Yang, Oregon State University, Viewed from space and woven with the cloud: Landscape narratives told with Landsat time-series algorithms applied to Google Earth Engine||20||3:40 PM|
|Presenter||Chengbin Deng*, Binghamton University, SUNY, The impacts of input Landsat images for subpixel urban impervious surface mapping||20||4:00 PM|
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