scholarly journals Measuring sustainability using the US Forest Inventory and Analysis Program.

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

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).


2005 ◽  
Vol 35 (12) ◽  
pp. 2968-2980 ◽  
Author(s):  
Ronald E McRoberts ◽  
Geoffrey R Holden ◽  
Mark D Nelson ◽  
Greg C Liknes ◽  
Dale D Gormanson

Forest inventory programs report estimates of forest variables for areas of interest ranging in size from municipalities, to counties, to states or provinces. Because of numerous factors, sample sizes are often insufficient to estimate attributes as precisely as is desired, unless the estimation process is enhanced using ancillary data. Classified satellite imagery has been shown to be an effective source of ancillary data that, when used with stratified estimation techniques, contributes to increased precision with little corresponding increase in cost. Stratification investigations conducted by the Forest Inventory and Analysis program of the USDA Forest Service are reviewed, and a new approach to stratification using satellite imagery is proposed. The results indicate that precision may be substantially increased for estimates of both forest area and volume per unit area.


2013 ◽  
Vol 111 (2) ◽  
pp. 132-138 ◽  
Author(s):  
David C. Chojnacky ◽  
Christine E. Blinn ◽  
Stephen P. Prisley

2004 ◽  
Vol 18 (1) ◽  
pp. 23-46 ◽  
Author(s):  
Ronald E. McRoberts ◽  
William H. McWilliams ◽  
Gregory A. Reams ◽  
Thomas L. Schmidt ◽  
Jennifer C. Jenkins ◽  
...  

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.


2018 ◽  
Author(s):  
Dale D. Gormanson ◽  
Scott A. Pugh ◽  
Charles J. Barnett ◽  
Patrick D. Miles ◽  
Randall S. Morin ◽  
...  

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