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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes.

Andrew O Finley¹, Abhirup Datta², Bruce C Cook³

¹Michigan State University.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

|September 24, 2019

Summary

This summary is machine-generated.

This study introduces improved Nearest Neighbor Gaussian Process (NNGP) models for faster, more reliable forest canopy mapping. The enhanced models provide the first statistically robust forest canopy map for Alaska's Tanana Inventory Unit.

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Area of Science:

Environmental science
Computer science
Statistical modeling

Background:

Nearest Neighbor Gaussian Process (NNGP) models offer a framework for spatial data analysis.
Existing NNGP models face challenges in computational efficiency and reproducibility.
Hierarchical NNGP models have been proposed to address these limitations.

Purpose of the Study:

To develop and evaluate alternate formulations of hierarchical Nearest Neighbor Gaussian Process (NNGP) models.
To improve computational performance, including convergence speed and CPU memory management.
To enhance the robustness and reproducibility of Bayesian inference for spatial modeling.

Main Methods:

Development of novel algorithms for NNGP models.
Integration of high-performance numerical linear algebra libraries.
Assessment of computational and inferential benefits using simulated and real-world remote sensing data (LiDAR).

Main Results:

Demonstrated improvements in CPU memory management and computational speed.
Validated the effectiveness of alternate NNGP specifications through simulations.
Generated the first statistically robust forest canopy map for the Tanana Inventory Unit (TIU) using LiDAR data.

Conclusions:

Alternate NNGP formulations offer significant computational and inferential advantages.
The developed methods enable more efficient and reliable spatial data analysis.
The resulting forest canopy map provides valuable ecological insights for the TIU.