Time Series Remote Sensing in Characterizing Long-term Land Surface Dynamics I

Type: Paper
Theme:
Sponsor Groups: Remote Sensing Specialty Group, Geographic Information Science and Systems Specialty Group
Poster #:
Day: 4/9/2020
Start / End Time: 8:00 AM / 9:15 AM
Room: Director's Row I, Sheraton, Plaza Building, Lobby Level
Organizers: Chunyuan Diao, Jin Chen, Xiaoyang Zhang
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. The rich archive and continuing acquisition of remote sensing imagery across a range of spatial, temporal and spectral resolutions provide unprecedented opportunities to monitor the evolving land surface dynamics and their responses to climatic and environmental changes. The long-term time series of earth observation data, along with technical advancements, has largely improved our scientific understanding of types, trends, causes, and consequences of various dynamic processes (e.g., land surface phenology and land use land cover changes). It offers tremendous potentials to conduct not only historic change analysis but also near real-time monitoring of complex earth systems. The wealth of information thus facilitates the timely adaptive decision making in varying surface dynamic processes to increase the land resilience to human and climate impacts.

This session invites papers focusing on both theoretical and methodology research and applications to advance remote sensing time series analysis in charactering land surface dynamics.

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, data integration, missing data interpolation, and any technologies for generating high-quality time series data
3) Time series remote sensing based domain applications (e.g., vegetation phenology, land cover and land use change, land surface biophysical characteristics, ecosystem evolution and conservation, environmental monitoring, etc.)
4) Long-term, large-scale land surface dynamic analysis catalyzed by cloud computing (e.g., google earth engine) and high performance computing
5) 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 Oct. 30 or the extended deadline.

Organizers:
Chunyuan Diao (University of Illinois at Urbana-Champaign, chunyuan@illinois.edu)
Jin Chen (Beijing Normal University, chenjin@bnu.edu.cn)
Xiaoyang Zhang (South Dakota State University, xiaoyang.zhang@sdstate.edu)
Xiaolin Zhu (The Hong Kong Polytechnic University, xlzhu@polyu.edu.hk)


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. The rich archive and continuing acquisition of remote sensing imagery across a range of spatial, temporal and spectral resolutions provide unprecedented opportunities to monitor the evolving land surface dynamics and their responses to climatic and environmental changes. The long-term time series of earth observation data, along with technical advancements, has largely improved our scientific understanding of types, trends, causes, and consequences of various dynamic processes (e.g., land surface phenology and land use land cover changes). It offers tremendous potentials to conduct not only historic change analysis but also near real-time monitoring of complex earth systems. The wealth of information thus facilitates the timely adaptive decision making in varying surface dynamic processes to increase the land resilience to human and climate impacts.

This session invites papers focusing on both theoretical and methodology research and applications to advance remote sensing time series analysis in characterizing land surface dynamics.

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, data integration, missing data interpolation, and any technologies for generating high-quality time series data
3) Time series remote sensing based domain applications (e.g., vegetation phenology, land cover and land use change, land surface biophysical characteristics, ecosystem evolution and conservation, environmental monitoring, etc.)
4) Long-term, large-scale land surface dynamic analysis catalyzed by cloud computing (e.g., google earth engine) and high performance computing
5) 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 Oct. 30 or the extended deadline.

Organizers:
Chunyuan Diao (University of Illinois at Urbana-Champaign, chunyuan@illinois.edu)
Jin Chen (Beijing Normal University, chenjin@bnu.edu.cn)
Xiaoyang Zhang (South Dakota State University, xiaoyang.zhang@sdstate.edu)
Xiaolin Zhu (The Hong Kong Polytechnic University, xlzhu@polyu.edu.hk)


Agenda

Type Details Minutes Start Time
Presenter Dar Roberts*, University Of California, Santa Barbara, Christopher Kibler, University of California, Santa Barbara, Conor McMahon, University of California, Santa Barbara, A General Framework for Identifying Change using Standard Spectral Mixture Analysis 15 8:00 AM
Presenter Hilda U. Onuoha*, Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, Kansas, USA, Shawn J.M. Hutchinson, Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, Kansas, USA, Time Series Analysis of Phenometrics for Long-Term Grassland Trends across the Great Plains Ecoregion 15 8:15 AM
Presenter Xiaoyang Zhang*, , Yongchang Ye, South Dakota State University, Comparison between Time Series Polar-Orbiting and Geostationary Satellite Observations in Land Surface Phenology Detections 15 8:30 AM
Presenter Yang Chen*, Faculty of Geographical Science, Beijing Normal University, Qiang Li, Faculty of Geographical Science, Beijing Normal University, Ruyin Cao, School of Resources and Environment, University of Electronic Science and Technology of China, How to automatically determine the optimal input image pairs for NDVI spatiotemporal data fusion 15 8:45 AM
Presenter Shuai Wang*, Beijing Normal University, Jin Chen, Beijing Normal University, Response of Winter Wheat to Spring Frost in North China: Damage Estimation and Influential Factors 15 9:00 AM

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