Inventory Design and the Ten-Plots-per-Stand Syndrome

1984 ◽  
Vol 1 (4) ◽  
pp. 76-79
Author(s):  
Alan R. Ek ◽  
Dietmar W. Rose ◽  
Hans M. Gregersen
Keyword(s):  
Decision Making ◽  
Forest Inventory ◽  
Continuous Forest ◽  
Lake States ◽  
Planning Problems ◽  
Continuous Forest Inventory ◽  
Current Practices

Abstract Common Lake States practices of stand description and inventory, and continuous forest inventory (CFI) are criticized. The origin of current practices is discussed and suggestions are made for refinement of practices in light of emerging forest sampling and managment decision-making techniques. Emphasis is placed on developing data of varying precision levels that meet the requirements for specific forest managment and planning problems. North. J. Appl. For. 1:76-79, Dec. 1984.

On the error of estimates from successive forest inventories with SPR

1987 ◽  
Vol 17 (5) ◽  
pp. 442-447
Author(s):  
Tiberius Cunia
Keyword(s):  
Forest Inventory ◽  
Random Sampling ◽  
Regression Coefficients ◽  
Partial Replacement ◽  
Forest Inventories ◽  
Continuous Forest ◽  
Regression Functions ◽  
The Given ◽  
Linear Regressions ◽  
Continuous Forest Inventory

The approach used by Cunia to combine the error from sample plots with the error from volume or biomass tables when Continuous Forest Inventory (CFI) estimates of current values and growth are calculated is extended to the CFI systems using Sampling with Partial Replacement (SPR). The formulae are derived for the case of SPR on two measurement occasions when (i) volume or biomass tables are constructed from linear regressions for which an estimate of the covariance matrix of the regression coefficients is known, and (ii) the sample plots or points are selected by random sampling independently of the given volume or biomass regression functions.

Comparing Continuous Forest Inventory Programs Across the Northeast

2020 ◽  
Author(s):  
Matthias Nevins ◽  
James Duncan ◽  
Alexandra Kosiba
Keyword(s):  
Forest Inventory ◽  
Continuous Forest ◽  
Continuous Forest Inventory

On the error of continuous forest inventory estimates

1987 ◽  
Vol 17 (5) ◽  
pp. 436-441 ◽  
Author(s):  
Tiberius Cunia
Keyword(s):  
Forest Inventory ◽  
Total Error ◽  
Inventory Systems ◽  
Average Volume ◽  
Common Procedure ◽  
Error Components ◽  
Continuous Forest ◽  
The Common ◽  
Volume Estimates ◽  
Continuous Forest Inventory

Although not explicitly recognized, the error of the volume estimates in Continuous Forest Inventory Systems has two main error components, one due to sample plots and another due to volume tables. The common procedure by which the volume estimates are calculated takes into account only the first component; the second component is simply ignored. An approach is shown that introduces the error of the volume tables into the total error of the CFI estimates of average volume per tree, average volume per acre, and growth components such as net change from one to the next occasion, mortality or ingrowth volume.

scholarly journals Integration of statistical forest reflectance model and Sentinel-2 MSI images into a continuous forest inventory system

2020 ◽  
Vol 26 (2) ◽  
Author(s):  
Andres Kuusk ◽  
Mait Lang
Keyword(s):  
Forest Inventory ◽  
Total Error ◽  
Inventory Data ◽  
Spectral Signatures ◽  
Forest Inventory Data ◽  
Spectral Bands ◽  
Continuous Forest ◽  
Study Sites ◽  
Continuous Forest Inventory ◽  
Sentinel 2

Spectral signatures of forest stands in Sentinel-2 MSI spectral bands are simulated with the statistical forest reflectance model SFRM, and compared to the spectral signatures measured in spectral images at ten study sites in Estonia. As an overall measure of the agreement between simulated and measured spectral signatures is used the total error which is calculated as the sum of relative errors over spectral bands B2 to B11 of Sentinel-2. The distribution of the total error has strongly positive skewness at all study sites and all types of forests (broadleaf, pine and spruce forests). The right tile of the distribution is low. The stands of high value of the total error far right in the tail of the distribution may have some errors in their inventory data, or the inventory data are outdated. Pertinent stands should have priority in their in situ checking process. The model SFRM is a simple and reliable tool for the validity checking of forest inventory data, using routinely collected forest inventory data and operational satellite information of moderate spatial resolution. The model is simple and computationally efficient. Preparing input data for the model is a simple query in the forest inventory database. The suggested procedure can be incorporated into automated systems of continuous forest inventory.

scholarly journals Forest Vegetation Simulator Ingrowth Tool: Incorporating Ingrowth Tree Lists into Forest Vegetation Simulator Growth Projections

2008 ◽  
Vol 25 (3) ◽  
pp. 158-160 ◽  
Author(s):  
Justin E. Arseneault ◽  
John A. Kershaw ◽  
James B. McCarter ◽  
David A. MacLean
Keyword(s):  
Forest Inventory ◽  
Forest Vegetation ◽  
Forest Vegetation Simulator ◽  
Time Step ◽  
Time Intervals ◽  
Permanent Sample Plots ◽  
Python Language ◽  
Continuous Forest ◽  
Project Data ◽  
Continuous Forest Inventory

Abstract This article describes the Forest Vegetation Simulator Ingrowth Tool (FVS_IT), software developed in the Python language 5 and tested using the Northeast variant of FVS (FVS-NE). This tool incorporates specified ingrowth tree lists, stored in secondary tree list files, into FVS projections. It functions by retrieving information from an FVS keyword file, which is then modified to project data in a stepwise manner using user-defined time intervals. Between each time step in a simulation, FVS_IT incorporates ingrowth into projections by appending ingrowth tree records to projected tree lists and compiles a new tree list for the next time step. Outputs include both appended tree lists and stand summaries from FVS so that users can conduct further analyses. The FVS_IT application is useful when assessing and calibrating FVS using continuous forest inventory or permanent sample plots where periodic remeasurements include ingrowth trees.

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