Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance Article Swipe
Elizabeth A. Freeman
,
Gretchen G. Moisen
,
John W. Coulston
,
Barry T. Wilson
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1139/cjfr-2014-0562
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1139/cjfr-2014-0562
As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy cover layer, a pilot project was launched to test the use of high-resolution photography coupled with extensive ancillary data to map the distribution of tree canopy cover over four study regions in the conterminous US. Two stochastic modeling techniques, random forests (RF) and stochastic gradient boosting (SGB), are compared. The objectives of this study were first to explore the sensitivity of RF and SGB to choices in tuning parameters and, second, to compare the performance of the two final models by assessing the importance of, and interaction between, predictor variables, the global accuracy metrics derived from an independent test set, as well as the visual quality of the resultant maps of tree canopy cover. The predictive accuracy of RF and SGB was remarkably similar on all four of our pilot regions. In all four study regions, the independent test set mean squared error (MSE) was identical to three decimal places, with the largest difference in Kansas where RF gave an MSE of 0.0113 and SGB gave an MSE of 0.0117. With correlated predictor variables, SGB had a tendency to concentrate variable importance in fewer variables, whereas RF tended to spread importance among more variables. RF is simpler to implement than SGB, as RF has fewer parameters needing tuning and also was less sensitive to these parameters. As stochastic techniques, both RF and SGB introduce a new component of uncertainty: repeated model runs will potentially result in different final predictions. We demonstrate how RF allows the production of a spatially explicit map of this stochastic uncertainty of the final model.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1139/cjfr-2014-0562
- https://www.nrcresearchpress.com/doi/pdf/10.1139/cjfr-2014-0562
- OA Status
- hybrid
- Cited By
- 200
- References
- 44
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2174882701Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1139/cjfr-2014-0562Digital Object Identifier
- Title
-
Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performanceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-08-04Full publication date if available
- Authors
-
Elizabeth A. Freeman, Gretchen G. Moisen, John W. Coulston, Barry T. WilsonList of authors in order
- Landing page
-
https://doi.org/10.1139/cjfr-2014-0562Publisher landing page
- PDF URL
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https://www.nrcresearchpress.com/doi/pdf/10.1139/cjfr-2014-0562Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
- OA URL
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https://www.nrcresearchpress.com/doi/pdf/10.1139/cjfr-2014-0562Direct OA link when available
- Concepts
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Random forest, Gradient boosting, Canopy, Statistics, Tree canopy, Tree (set theory), Mean squared error, Mathematics, Computer science, Machine learning, Geography, Mathematical analysis, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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200Total citation count in OpenAlex
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2025: 39, 2024: 33, 2023: 22, 2022: 17, 2021: 25Per-year citation counts (last 5 years)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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