Remote Sensing Time Series Analysis with Moderate Spatial Resolution Imagery III

Type: Paper
Theme:
Sponsor Groups: Remote Sensing Specialty Group
Poster #:
Day: 4/6/2019
Start / End Time: 5:00 PM / 6:40 PM (Eastern Standard Time)
Room: Balcony B, Marriott, Mezzanine Level
Organizers: Chunyuan Diao, Xiaoyang Zhang, George Xian
Chairs: Chunyuan Diao

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 Ayodeji Adesuyi*, University of Cape Town. South Africa, Zahn M┼▒nch, Stellenbosch University. Stellenbosch. South Africa, Automating land cover classification using time series NDVI: A scripting case study 20 5:00 PM
Presenter George Xian*, USGS EROS Data Center, Characterization of Urban Development and associated regional effect from Time Series Analysis 20 5:20 PM
Presenter Jennifer N. Hird*, Applied Geospatial Research Group, Dept of Geography, University of Calgary, Jahan Kariyeva, Alberta Biodiversity Monitoring Institute, Greg J. McDermid, Applied Geospatial Research Group, Dept of Geography, University of Calgary, Leveraging open-access Earth observation data streams and web-based cloud computing for the characterization of forest harvest area regeneration in Alberta, Canada. 20 5:40 PM
Presenter Shi Qiu*, Department of Natural Resources and the Environment, University of Connecticut, Zhe Zhu, Department of Natural Resources and the Environment, University of Connecticut, Binbin He, School of Resources and Environment, University of Electronic Science and Technology of China, Cirrus cloud detection using multi-temporal Landsat 8 images: When and how to detect cirrus cloud? 20 6:00 PM
Presenter Yanlin Yue*, Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shunbao Liao, College of Ecology and Environment, Institute of Disaster Prevention, Guangxing Gi, Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Jincai Zhao, Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Zhizhu Lai, Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Improving the Accuracy of Land Cover Classification Based on NDVI Time Series Library 20 6:20 PM

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