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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Latent trajectory models for spatio-temporal dynamics in Alaskan ecosystems.

Xinyi Lu1, Mevin B Hooten2, Ann M Raiho3,4

  • 1Department of Statistics, Colorado State University, Fort Collins, Colorado, USA.

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|January 30, 2023
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Summary

Climate change is driving significant Arctic greening, with shrubs and trees expanding across Alaska. Our new model quantifies these ecosystem shifts using satellite data and predicts future land cover changes under various climate scenarios.

Keywords:
Bayesianclimate changedata augmentationecological successionstate-space models

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Area of Science:

  • Ecology
  • Remote Sensing
  • Climate Science

Background:

  • The Alaskan Arctic is experiencing rapid environmental changes, including shrub and tree expansion.
  • Understanding these ecosystem transformations is crucial for predicting future landscape dynamics.

Purpose of the Study:

  • To quantify the impact of climate change on Arctic ecosystem structural transformation.
  • To develop a predictive model for land cover transitions using remotely sensed data.

Main Methods:

  • Developed a Bayesian hierarchical model with latent trajectory processes to analyze dynamic ecosystem states.
  • Incorporated spatio-temporally heterogeneous climate drivers and accounted for temporal irregularity in survey data.
  • Utilized a Pólya-Gamma sampling strategy for computational efficiency.

Main Results:

  • The model successfully inferred rates of land cover transitions driven by climate change.
  • Characterized multi-scale spatial correlations within the study system.
  • Provided a framework for understanding ecosystem responses to climate shifts.

Conclusions:

  • The developed model effectively quantifies climate change impacts on Arctic ecosystems.
  • The model can predict future land cover changes under different climate scenarios.
  • This research offers valuable insights into Arctic ecosystem dynamics and resilience.