Remote Sensing Time Series Analysis with Moderate Spatial Resolution Imagery I

Type: Paper
Theme:
Sponsor Groups: Remote Sensing Specialty Group
Poster #:
Day: 4/6/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Balcony B, Marriott, Mezzanine Level
Organizers: Chunyuan Diao, Xiaoyang Zhang, George Xian
Chairs: Xiaoyang Zhang

Call for Submissions

Recent advances in remote sensing have facilitated the use of large volume of satellite imagery for understanding the dynamics of 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 wide 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 the change of earth system 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 continuing Landsat missions in the global scale, time series of moderate spatial resolution imagery offers tremendous potentials to conduct not only historic change analysis but also near real time monitoring 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 change detection)
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 October 25, 2018 or the extended deadline.

Organizers:
Chunyuan Diao (University of Illinois at Urbana-Champaign, chunyuan@illinois.edu)
George Xian (USGS Earth Resources Observation and Science Center, xian@usgs.gov)
Xiaoyang Zhang (South Dakota State University, xiaoyang.zhang@sdstate.edu)


Description

Recent advances in remote sensing have facilitated the use of large volume of satellite imagery for understanding the dynamics of 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 wide 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 the change of earth system 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 continuing Landsat missions in the global scale, time series of moderate spatial resolution imagery offers tremendous potentials to conduct not only historic change analysis but also near real time monitoring 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 change detection)
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 October 25, 2018 or the extended deadline.

Organizers:
Chunyuan Diao (University of Illinois at Urbana-Champaign, chunyuan@illinois.edu)
George Xian (USGS Earth Resources Observation and Science Center, xian@usgs.gov)
Xiaoyang Zhang (South Dakota State University, xiaoyang.zhang@sdstate.edu)


Agenda

Type Details Minutes Start Time
Presenter Anthony Campbell*, University of Rhode Island, Yeqiao Wang, University of Rhode Island, Google Earth Engine Time Series for Salt Marsh Change Analysis of the mid-Atlantic Coast (1999-2018) 20 1:10 PM
Presenter Johanna Buchner*, University of Wisconsin-Madison, He Yin, University of Wisconsin-Madison, David Frantz, Humboldt University Berlin, Benjamin Bleyhl, Humboldt University Berlin, Tobias Kuemmerle, Humboldt University Berlin, Tamar Bakuradze, Geographic, GIS & RS Consulting Center , Anna Komarova, Greenpeace Russia, Afag Rizayeva, University of Wisconsin-Madison, Hovik Sayadyan, Yervan State University, Garik Tepanosyan, Armenian National Academy of Sciences, Volker C. Radeloff, University of Wisconsin-Madison, Large-area land-cover and land-use change mapping in the Caucasus Mountains using topographically corrected multi-temporal Landsat composites 20 1:30 PM
Presenter Xiaoyang Zhang*, , Jiamin Wang, South Dakota State University, Understanding the Complexity of Land Surface Phenology Derived from Multiple Spatial Resolution Remotely Sensed Data 20 1:50 PM
Presenter Miranda Rose*, University of Tennessee, Nicholas N. Nagle, University of Tennessee at Knoxville, Using Landsat Phenology Curves to Characterize Fire Impacted Forests in South Carolina, USA 20 2:10 PM
Presenter Patrick Danielson*, Stinger Ghaffarian Technologies (SGT), Limin Yang, Consultant, Stinger Ghaffarian Technologies (SGT), Suming Jin, ASRC Federal InuTeq, Collin Homer, U.S. Geological Survey (USGS) , Overview of the New National Land Cover Database (NLCD) 2016 Land Cover and Land Cover Change Products 20 2:30 PM

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