Comparison of Model-Assisted Endogenous Poststratification Methods for Estimation of Above-Ground Biomass Change in Oregon, USA
Abstract
:1. Introduction
Objective
- A GREG estimator, GREG-EPS, based on the interactions of multiple categorical variables [13].
- An EPS estimator using the method proposed by Breidt and Opsomer [11] that uses a generalized linear model to form strata, GL-EPS.
- An EPS estimator using McConville and Toth’s [14] method based on recursive partitioning trees, TREE-EPS.
2. Material and Methods
2.1. Study Area
2.2. Auxiliary Information
2.3. Estimation Framework
2.3.1. Population
2.3.2. Target Parameter
2.3.3. PNW-FIA Sampling Design and Sample
2.4. Development of Models for EPS
2.4.1. General Model Selection Considerations
2.4.2. GREG-EPS
- First, arranged all combinations on a tree that was constructed using the variables , and . Branches in the first level of the tree were defined using , and branches for the second and third levels were based on and respectively. Each combination of ,,, , , , and , resulted in a leaf that was placed in its corresponding branch depending on , and .
- Leaves with four plots per year or more were set as fixed leaves. The remaining leaves were merged with other leaves (fixed or not) in the same branch. The merging process ran separately in each branch and started with the leaf with a smaller area in the branch. The selected leaf was merged with the closest leaf in the same branch. The Gower distance [21], computed with all categorical variables, was used to determine which leaf was the closest to the selected leaf. This distance was selected because it allows treating differently categorical variables with an implicit ordering (i.e., all categorical variables derived from a continuous one) and categorical variables without an implicit ordering (e.g., . The two leaves were merged into a single leaf, and the expected number of plots per year was recomputed based on the area of the group resulting from the merge. If the expected number of plots per year of the resulting leaf was four or more, or if the small leaf was merged to a fixed leaf, the result was tagged as fixed, and it was not considered as a target for further merging steps.
- Step 2 was repeated until all the resulting leaves in a branch had an expected number of four plots per year or all leaves in one branch were merged into a single leaf.
- When merging all leaves in one branch did not yield an area from which to expect four plots per year, the merging process continued but considered merging groups from branches of the previous level of the tree (i.e., the algorithm continued with branches defined by and first, and then with branches defined by only).
2.4.3. GL-EPS
- The maximum value of reported by [16] was 8.75 Mg ha−1 year−1. Based on this value, we defined the following five positive intervals and .
- For disturbances causing losses in forest AGB with magnitudes comparable to growth, we used the thresholds used for growth but with negative signs. The last interval accommodates large and negative values of occurring after stand-replacing disturbances such as clear cuts.
2.4.4. TREE-EPS
2.5. Estimators of and Variance Estimators
2.5.1. Approximation to Sampling Design Weights and Point and Variance Estimators
2.5.2. Point and Variance Estimators
2.5.3. Comparison to Current PNW-FIA Estimators and Horvitz-Thompson Estimators
3. Results
3.1. EPS and PS Assisting Models and Summaries
3.2. Estimates of Changes for the State for Specific 10-Year Periods
3.3. Estimators of Running Means
4. Discussion
4.1. Similarities between Model-Assisted Estimators
4.2. Model Selection
4.3. Differences between Model-Assisted Estimators
4.4. Other Considerations of Practical Importance
4.4.1. Estimation of Variance and Estimation of Change for Periods Not Matching the PNW-FIA Panel Frequency
4.4.2. Auxiliary Variables
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Type | Source | Variables, Acronym | Pre-Processing | Variable Type | Temporal |
---|---|---|---|---|---|
Proxies for potential forest AGB productivity | 1 Arc second Shuttle Radar Topography Mission, SRTM, Google earth engine | Elevation, | Resampling bilinear interpolation | Co | Static |
Elevation categories, | Division in 3 elevation categories of equal area | *Ca | |||
Slope, | Computed from | Co | |||
Heat load index, | Computed from | Co | |||
800 m resolution PRISM 30-year normals & Sun hours from SRTM | Paterson climate productivity index, | Resampling bilinear interpolation.Solar radiation ArcGIS tool | Co | ||
Categories Paterson climate productivity index, | Division in 3 categories of equal area | *Ca | |||
US Forest Service | Cleland’s level 3 ecoregions, | Rasterization | Ca | ||
Ownership | Bureau of land management, BLM | Ownership, | Rasterization & reclassification | Ca | |
Proxies for disturbance | Monitoring trends in burn severity, MTBS. | Fire severity, | Maximum fire severity for 10-year periods. Resampling nearest neighbors | Ca | Dynamic |
Landscape Change Monitoring System, LCMS. | Disturbances, | Computation of accumulated disturbances for 10-year periods | Co | ||
MTBS- LCMS | Disturbance-categories, | Reclassification of and thresholds for | *Ca | ||
MRLC National Land Cover Database, NLCD. | Land cover change, | Resampling nearest-neighbor. Reclassification and computation of change | Ca | ||
Change in multi-year CMS AGB map | Fekety and Hudak, (2019) & Hudak et al., (2020) [8,9] | Independent prediction of derived from Fekety and Hudak, (2019) [8] predictions of AGB for multiple years, | Resampling with bilinear interpolation. Computation of differences in predicted forest AGB between years. | Co | |
Categories of change derived from independent predictions of AGB for multiple years, | Reclassification of based on intervals defined from values reported by [16] | *Ca |
Period | Total Number of Plots | Number of Plots by EU | Excluded Plots | ||
---|---|---|---|---|---|
NF | OL | WL | |||
2001–2011 | 1310 | 675 | 592 | 28 | 15 |
2002–2012 | 1412 | 681 | 682 | 29 | 20 |
2003–2013 | 1402 | 687 | 656 | 29 | 30 |
2004–2014 | 1418 | 703 | 671 | 29 | 15 |
2005–2015 | 1420 | 704 | 662 | 33 | 21 |
2006–2016 | 1348 | 680 | 623 | 22 | 23 |
2007–2017 | 1331 | 650 | 645 | 18 | 18 |
2008–2018 | 1340 | 674 | 616 | 25 | 25 |
2001–2011 | 2002–2012 | 2003–2013 | 2004–2014 | 2005–2015 | 2006–2016 | 2007–2017 | 2008–2018 | All Periods | ||
---|---|---|---|---|---|---|---|---|---|---|
GREG-EPS | Total # of strata | 84 | ||||||||
Sampled strata | 84 | 84 | 83 | 84 | 83 | 84 | 84 | 84 | 84 | |
% area sampled | 100.00 | 100.00 | 99.89 | 100.00 | 99.80 | 100.00 | 100.00 | 100.00 | 100.00 | |
GREG-EPS-CMS | Total # of strata | 97 | ||||||||
Sampled strata | 95 | 97 | 97 | 97 | 97 | 97 | 94 | 95 | 97 | |
% area sampled | 99.47 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 99.07 | 99.51 | 100.00 | |
GL-EPS | Total # of strata | 30 | ||||||||
Sampled strata | 21 | 20 | 19 | 21 | 20 | 18 | 18 | 20 | 27 | |
% area sampled | 99.75 | 99.87 | 99.80 | 99.85 | 99.74 | 99.83 | 99.56 | 99.78 | 100.00 | |
GL-EPS-CMS | Total # of strata | 30 | ||||||||
Sampled strata | 27 | 23 | 24 | 24 | 23 | 21 | 23 | 25 | 30 | |
% area sampled | 99.65 | 99.30 | 99.60 | 99.76 | 99.60 | 99.77 | 99.53 | 99.71 | 100.00 | |
TREE-EPS | Total # of strata | 44 | ||||||||
Sampled strata | 44 | 44 | 44 | 44 | 44 | 44 | 44 | 44 | 44 | |
% area sampled | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | |
TREE-EPS-CMS | Total # of strata | 34 | ||||||||
Sampled strata | 34 | 34 | 34 | 34 | 34 | 34 | 34 | 34 | 34 | |
% area sampled | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | |
FIA-PS | Total # of strata | 191 | ||||||||
Sampled strata | 167 | 173 | 175 | 177 | 171 | 166 | 163 | 167 | 191 | |
% area sampled | 96.79 | 97.68 | 97.64 | 97.43 | 97.50 | 96.61 | 95.40 | 96.92 | 100.00 |
Method | for 10-Year Periods | for 10-Year Periods |
---|---|---|
GREG-EPS | 37.95% | 36.25% |
GREG-EPS-CMS | 40.13% | 35.91% |
GL-EPS | 48.02% | 38.36% |
GL-EPS-CMS | 47.99% | 42.27% |
TREE-EPS | 41.05% | 31.66% |
TREE-EPS-CMS | 40.27% | 30.16% |
FIA-PS | 28.24% | 20.68% |
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Mauro, F.; Monleon, V.J.; Gray, A.N.; Kuegler, O.; Temesgen, H.; Hudak, A.T.; Fekety, P.A.; Yang, Z. Comparison of Model-Assisted Endogenous Poststratification Methods for Estimation of Above-Ground Biomass Change in Oregon, USA. Remote Sens. 2022, 14, 6024. https://doi.org/10.3390/rs14236024
Mauro F, Monleon VJ, Gray AN, Kuegler O, Temesgen H, Hudak AT, Fekety PA, Yang Z. Comparison of Model-Assisted Endogenous Poststratification Methods for Estimation of Above-Ground Biomass Change in Oregon, USA. Remote Sensing. 2022; 14(23):6024. https://doi.org/10.3390/rs14236024
Chicago/Turabian StyleMauro, Francisco, Vicente J. Monleon, Andrew N. Gray, Olaf Kuegler, Hailemariam Temesgen, Andrew T. Hudak, Patrick A. Fekety, and Zhiqiang Yang. 2022. "Comparison of Model-Assisted Endogenous Poststratification Methods for Estimation of Above-Ground Biomass Change in Oregon, USA" Remote Sensing 14, no. 23: 6024. https://doi.org/10.3390/rs14236024