scholarly journals rFIA: An R package for estimation of forest attributes with the US Forest Inventory and Analysis database

2020 ◽  
Vol 127 ◽  
pp. 104664 ◽  
Author(s):  
Hunter Stanke ◽  
Andrew O. Finley ◽  
Aaron S. Weed ◽  
Brian F. Walters ◽  
Grant M. Domke
2009 ◽  
Vol 33 (1) ◽  
pp. 29-34 ◽  
Author(s):  
David Chojnacky ◽  
Michael Amacher ◽  
Michael Gavazzi

Abstract Mass and carbon load estimates, such as those from forest soil organic matter (duff and litter), inform forestry decisions. The US Forest Inventory and Analysis (FIA) Program systematically collects data nationwide: a down woody material protocol specifies discrete duff and litter depth measurements, and a soils protocol specifies mass and carbon of duff and litter combined. Sampling duff and litter separately via the soils protocol would increase accuracy of subsequent bulk density calculations and mass and carbon estimates that use them. At 57 locations in North Carolina, Virginia, and West Virginia, we measured depth, mass, and carbon of duff and litter separately. Duff depth divided by total depth varied from 20% to 56%, duff was 1–4 times denser than litter, and the calculated median carbon-to-mass ratio for hardwood duff (0.37) was less than that for litter (0.45). Using FIA depth measurements, we calculated mass from (1) our mean density values, (2) a mass versus depth regression model we developed, and (3) published density values. Model mass calculations were lower than those using our mean densities, possibly because the latter ignore density differences with layer thickness. Our model could provide valuable mass and carbon estimates if fully developed with future FIA data (duff and litter separated).


2011 ◽  
Vol 184 (3) ◽  
pp. 1423-1433 ◽  
Author(s):  
Paul L. Patterson ◽  
John W. Coulston ◽  
Francis A. Roesch ◽  
James A. Westfall ◽  
Andrew D. Hill

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1045 ◽  
Author(s):  
Nicholas N. Nagle ◽  
Todd A. Schroeder ◽  
Brooke Rose

In this paper, we propose a new estimator for creating expansion factors for survey plots in the US Forest Service (USFS) Forest Inventory and Analysis program. This estimator was previously used in the GIS literature, where it was called Penalized Maximum Entropy Dasymetric Modeling. We show here that the method is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data, in that it produces a single set of expansion factors that can have a general purpose which can be used to produce small-area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques, such as GREG estimation, in that it is guaranteed to produce positive expansion factors. Here, we extend the previous method to include cross-validation, and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration.


2008 ◽  
Vol 25 (2) ◽  
pp. 93-98 ◽  
Author(s):  
David B. Kittredge ◽  
Anthony W. D'Amato ◽  
Paul Catanzaro ◽  
Jennifer Fish ◽  
Brett Butler

Abstract Woodland ownership for three regions of Massachusetts is estimated using property tax assessor data. These data are nonspatially explicit and are based on commercial, industrial, residential, or other activity rather than actual land cover. A heuristic was used to aggregate similar parcels to provide an estimate of actual landownership. The estimated average statewide ownership is 17.9 ac, and when properties less than 10 ac are excluded, the average rises to 42.5 ac. The median ownership varies from east to west in the state across the spectrum of suburban development radiating from the metropolitan Boston area, with the median being 4.8, 7.8, and 8.6 ac in the eastern, central, and western part of the state, respectively. These results are compared with ownership estimates generated by the US Forest Service Forest Inventory and Analysis.


2010 ◽  
Vol 34 (3) ◽  
pp. 131-137 ◽  
Author(s):  
Francis A. Roesch ◽  
Paul C. Van Deusen

Abstract Annual forest inventories present special challenges and opportunities for those analyzing the data arising from them. Here, we address one question currently being asked by analysts of the US Forest Service's Forest Inventory and Analysis Program's quickly accumulating annual inventory data. The question is simple but profound: When combining the next year's data for a particular variable with data from previous years, how does one know whether the same model as used in the past for this purpose continues to be applicable? Of the myriad approaches that have been developed for changepoint detection and anomaly detection, this report focuses on a simple quality-control approach known as a control chart that will allow analysts of annual forest inventory data to determine when a departure from a past trend is likely to have occurred.


2005 ◽  
Author(s):  
Ronald E. McRoberts ◽  
Gregory A Reams ◽  
Paul C. Van Deusen ◽  
William H. McWilliams ◽  
Chris J. Cieszewski ◽  
...  

Author(s):  
Barbara M. O'Connell ◽  
Barbara L. Conkling ◽  
Andrea M. Wilson ◽  
Elizabeth A. Burrill ◽  
Jeffrey A. Turner ◽  
...  

2019 ◽  
Author(s):  
Michelle K. Lazaro ◽  
Olaf Kuegler ◽  
Sharon M. Stanton ◽  
Ashley D. Lehman ◽  
Mary L. Taufete’e ◽  
...  

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