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

Type: Paper
Theme:
Sponsor Groups: Remote Sensing Specialty Group, Geographic Information Science and Systems Specialty Group
Poster #:
Day: 4/9/2020
Start / End Time: 9:35 AM / 10:50 AM
Room: Director's Row I, Sheraton, Plaza Building, Lobby Level
Organizers: Chunyuan Diao, Xiaoyang Zhang, Xiaolin Zhu
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. 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)


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 Ryan R Reker*, KBR, contractor at US Geological Survey - Earth Resources Observation & Science Center, Christopher P Barber, US Geological Survey - Earth Resources Observation & Science Center, Jesslyn Brown, US Geological Survey - Earth Resources Observation & Science Center, George Xian, US Geological Survey - Earth Resources Observation & Science Center, Roger Auch, US Geological Survey - Earth Resources Observation & Science Center, Continuous monitoring of the United States using all available Landsat data, the release of the U.S. Geological Survey’s next generation land change products: LCMAP 15 9:35 AM
Presenter ATSUSHI TOMITA*, Land IQ, Spatio-temporal relationships between oil palm plantation development, construction of palm oil processing mills and local factors derived from Landsat time series. 15 9:50 AM
Presenter Chunyuan Diao*, University of Illinois at Urbana-Champaign, Large-scale operational framework to characterize crop phenological stages with satellite time series 15 10:05 AM
Presenter Jian Wang*, The Ohio State University, The response of autumn leaf senescence date to precipitation amount and distribution 15 10:20 AM
Presenter Geyang Li*, University of Illinois at Urbana Champaign, Chunyuan Diao, University of Illinois at Urbana Champaign, Fine-Scale Crop Phenological Monitoring with Near-Surface Remote Sensing and High-Resolution Satellite Time Series 15 10:35 AM

To access contact information login