Time Series Remote Sensing in Characterizing Land Surface Dynamics

Type: Virtual Paper
Theme:
Sponsor Groups:
Poster #:
Day: 4/10/2021
Start / End Time: 3:05 PM / 4:20 PM (PDT)
Room: Virtual 17
Organizers: Chunyuan Diao, Zijun Yang
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 the organizers.

Organizers:
Chunyuan Diao (University of Illinois at Urbana-Champaign, chunyuan@illinois.edu)
Zijun Yang (University of Illinois at Urbana-Champaign, zijuny2@illinois.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. 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 the organizers.

Organizers:
Chunyuan Diao (University of Illinois at Urbana-Champaign, chunyuan@illinois.edu)
Zijun Yang (University of Illinois at Urbana-Champaign, zijuny2@illinois.edu)


Agenda

Type Details Minutes Start Time
Presenter Zijun Yang*, University of Illinois at Urbana-Champaign, Chunyuan Diao, University of Illinois at Urbana-Champaign, A novel deep learning-based phenology matching model for characterizing crop phenological stages with fused high spatio-temporal resolution imagery 15 3:05 PM
Presenter Francisco Laso*, University of North Carolina - Chapel Hill, Fátima Lorena Benítez, Universidad San Francisco de Quito, Gonzalo Rivas-Torres, Universidad San Francisco de Quito, Carolina Sampedro, Universidad San Francisco de Quito, Javier Arce-Nazario, University of North Carolina - Chapel Hill, Putting farmers on the map: land cover classification of Galapagos agroecosystems 15 3:20 PM
Presenter Chunyuan Diao*, University of Illinois at Urbana-Champaign, Zijun Yang, University of Illinois at Urbana-Champaign, Retrieval of crop growing progress with remote sensing and phenology-matching models 15 3:35 PM
Presenter Michael 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 3:50 PM
Presenter Kaitlin Walker*, University of Alabama in Huntsville, Robert Griffin, University of Alabama in Huntsville, Kelsey Herndon, NASA SERVIR, Africa Flores, NASA SERVIR, Matt Finer, Amazon Conservation, Monitoring of the Andean Amazon Project (MAAP), Land Management Practices and Fires in Amazonia: Using Remote Sensing to Assess Fire and Carbon Dynamics in Indigenous and Protected Areas in Mato Grosso, Brazil 15 4:05 PM

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