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Updated: Jun 14, 2026
12:26
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
Published on: October 11, 2016
HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS.
Andrew O Finley1, Sudipto Banerjee, Ronald E McRoberts
1Departments of Forestry and Geography, Michigan State University, East Lansing, Michigan, USA,
The Annals of Applied Statistics
|March 31, 2010
Summary
This study enhances forest type group mapping by integrating georeferenced National Forest Inventory (NFI) data with environmental predictors using advanced regression models. This approach improves prediction accuracy for large-scale forest assessments.
Area of Science:
- Forestry
- Ecology
- Geospatial Analysis
Background:
- Spatially explicit forest type data are crucial for assessing forest sustainability, biodiversity, biomass, carbon sequestration, and health.
- Accurate forest type mapping is essential for effective natural resource management and ecological monitoring.
Purpose of the Study:
- To explore the effectiveness of coupling georeferenced National Forest Inventory (NFI) data with environmental predictor variables.
- To develop and apply spatially-varying multinomial logistic regression models for predicting forest type groups across large forested landscapes.
- To address computational challenges and discuss dimension-reducing spatial processes.
Main Methods:
- Utilized georeferenced National Forest Inventory (NFI) plot data.
- Integrated spatially complete environmental predictor variables.
- Employed spatially-varying multinomial logistic regression models.
- Discussed dimension-reducing spatial processes to manage model complexity.
Main Results:
- Demonstrated improved accuracy in predicting forest type groups across large areas.
- Illustrated the utility of the models using NFI data from Michigan, USA.
- Provided associated measures of uncertainty for the predictions.
Conclusions:
- Coupling NFI data with environmental predictors via spatially-varying regression models significantly enhances forest type group prediction accuracy.
- The methodology offers a robust approach for large-scale forest assessments and monitoring.
- Addressing computational demands through spatial processes is key to applying these rich models effectively.
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