2024-03-29T02:09:11Z
http://mcfns.com/index.php/Journal/oai
oai:ojs.mcfns.com:article/7
2011-05-01T23:01:31Z
Journal:Sampling
“Total-Balancing†an inventory: A method for unbiased inventories using highly biased non-sample data at variable scales
Iles, Kim
Mensuration, forest inventory, forest biometrics, mathematical forestry, growth and yield
Inventory, Unbiased, Mean, Estimators, constrained estimator,
The described here method can provide unbiased estimates and sampling errors with increasingly precise polygon information from non-sample sources that are often free and readily available. It is extremely flexible, and it appears to be in line with the main trend of modern sampling you first estimate using any information available, and then you sample to adjust those estimates. At later dates, further readjustment can be done at will, as long as the total is maintained. The situation with forest inventory is very similar to mapmaking. For many years the only acceptable method of improving a map was to start over with a fresh sheet of paper and do the entire job again with great fidelity to current map accuracy standards. Those days are over. The same is true of forest inventory. A better concept is let s just change the parts that are not good enough . The other parts change so little as not to be noticed. Stratified cruises with standard descriptions for multiple stands and repeating the process every 20 years while ignoring the existing inventory are old and outdated processes. In an age where information pours down upon us from every direction, it is time we started to use it effectively. It is so easy to insure statistical unbiasedness, good polygon estimates, and valid sampling errors by the progress described here that it is hard to imagine why anyone would strike the old forest inventory off the records and independently do it again from a standing start. MCFNS-1:10-13.
Contemporary Journal Concept Press
Kim Iles & Associates, Nanaimo, B.C., Canada
2009-02-02
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
Total-Balancing, mathematical forestry
application/pdf
application/postscript
http://mcfns.com/index.php/Journal/article/view/MCFNS.1-10
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 1, No 1: MCFNS February 28, 2009; Pages: 10-13 (4)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS.1-10/18
http://mcfns.com/index.php/Journal/article/view/MCFNS.1-10/24
Worldwide, landscape,
Contemporary
height, diameter, volume, density, volume, basal area,
oai:ojs.mcfns.com:article/307
2023-10-15T04:45:37Z
Journal:Sampling
Nearest-tree and Variable Polygon Sampling
Iles, Kim
Mensuration; sampling;
n-tree sampling; nearest tree; Voronoi polygons; unbiased estimates.
Sampling a nearest neighbor is often presented as a Hansen-Hurwitz or Horvitz-Thompsonestimation process. This may not be the most informative viewpoint, and measuring the probability ofselection is not necessary. The measurement of the nearest object as a “depth” over the selection area canbe done by a sampling process, and the total estimated without knowing the polygon areas. The process isunbiased, quite general, and easy to understand. It can be extended to more than just the nearest objectto a sample point and to many different polygon shapes. This paper is an extension, simplification andgeneralization of an earlier paper in this journal (Iles, K. 2009. “Nearest-tree” estimations—A discussionof their geometry, MCFNS 1(2), pp. 47–51.), but does not require a random orientation or weighting forthe direction of measurement from the tree to the polygon border.
Contemporary Journal Concept Press
2023-04-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
Analytical
application/pdf
http://mcfns.com/index.php/Journal/article/view/15.1
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 15, No 1: MCFNS April 30, 2023; 1-7(7)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/15.1/2023.1
Global
All stages of forest succession.
Volumes; diameters; heights;
Copyright (c) 2023 Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)
https://creativecommons.org/licenses/by-nc-nd/4.0
oai:ojs.mcfns.com:article/188
2016-04-26T12:41:21Z
Journal:Sampling
Plot intensity and cycle-length effects on growth and removals estimates from forest inventories
Van Deusen, Paul
Roesch, Francis A.
Inventory
{Forest inventory; Remote Sensing; Remeasurement Cycle; FIA data
ontinuous forest inventories use permanent plots that are remeasured to provide information on growth, removals and mortality. Typically, all plots are remeasured within a narrow time span, but the USDA Forest Service has popularized a variant referred to as an annual forest inventory where a percentage of the permanent plots are remeasured every year. We discuss trade-offs between number of field plots and cycle length and provide some insight with example applications showing how these decisions impact growth and removals estimates. We also discuss a variant of the traditional growth over removals ratio estimator that limits degradation in estimate quality as cycle lengths increase.
Contemporary Journal Concept Press
NCASI and USDA Forest Service
2015-03-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
Sampling
application/pdf
http://mcfns.com/index.php/Journal/article/view/MCFNS7.1_4
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 7, No 1: MCFNS March 30, 2015; 33-38(6)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS7.1_4/MCFNS7_4
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS7.1_4/185
US with emphasis on South
oai:ojs.mcfns.com:article/157
2013-10-01T10:22:41Z
Journal:Sampling
Trends and projections from annual forest inventory plots and coarsened exact matching
Van Deusen, Paul C
Roesch, Francis A
Inventory
Forest inventory, Moving average, Inventory projection, FIA data
The coarsened exact matching (CEM) method is used to match annual forest inventory plots awaiting remeasurement with plots that have already been remeasured. This results in a model-free approach for short term inventory projections. CEM has many desirable properties relative to other matching methods and is easy to apply within a SQL database. The combination of short term projections with a 3 or 5 year moving window is suggested for providing trend estimates that include the current year and a few years into the future. The default projection represents business as usual. A method to bias the plot matching to generate desired scenarios is also developed. These ideas and methods are demonstrated with several applications to forest inventory data. Scenarios are generated where increasing future harvest levels are stochastically controlled to demonstrate this capability with operational data.
Contemporary Journal Concept Press
NCASI, USDA Forest Service
2013-09-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
hotdeck matching
application/pdf
text/x-tex
http://mcfns.com/index.php/Journal/article/view/MCFNS_157
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 5, No 2: MCFNS September 30, 2013; 126-134(8)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS_157/MCFNS-157
http://mcfns.com/index.php/Journal/article/view/MCFNS_157/Source-157
Lower 48 states
2000-2015
FIA data
oai:ojs.mcfns.com:article/18
2011-05-01T22:51:26Z
Journal:Sampling
“Nearest-tree†estimations - A discussion of their geometry
Iles, Kim
Mensuration
A discussion of the Nearest-tree estimations from the point of view of their geometry. MCFNS 1(2):47-51.
Contemporary Journal Concept Press
Kim Iles & Associates Ltd.
2009-08-28
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
“Nearest-tree†estimations
application/pdf
application/postscript
http://mcfns.com/index.php/Journal/article/view/MCFNS.1-47
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 1, No 2: MCFNS August 28, 2009; Pages: 47-51 (5)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS.1-47/MCFNS-1%3A47_PDF
http://mcfns.com/index.php/Journal/article/view/MCFNS.1-47/MCFNS-1%3A47_PS
http://mcfns.com/index.php/Journal/article/view/MCFNS.1-47/MCFNS-1%3A47_TEX
Age; Species; Height; Diameter; Basal Area
oai:ojs.mcfns.com:article/212
2023-10-19T13:43:11Z
Journal:Sampling
Effect of perturbing the geographic coordinates of forest inventory plots on hotspot cluster detection
Randolph, KaDonna
Inventory
FIA data; point pattern analysis; SaTScan; spatial scan statistic
The USDA Forest Service Forest Inventory and Analysis (FIA) program makes and keeps current an inventory of all forest land in the United States. Data from this ongoing inventory are available to the public, though FIA is restricted from releasing exact plot locations by the 2000 Interior and Related Agencies Appropriations Act (H.R. 3423). To comply with this policy while at the same time offering its data to the public, FIA makes approximate plot locations available through a process known as perturbing and swapping. This process has little to no effect on some research questions and a considerable effect on others. In this study, using the perturbed and swapped, i.e., the publicly available plot locations, was shown to affect the location, size, and composition of clusters of standing dead trees in the eastern United States as detected by the free spatial scanning software program SaTScanTM. When employing SaTScan with publicly available FIA plot coordinates as compared to using the confidential FIA plot coordinates, users risk identifying a cluster that does not exist (false positive) or failing to identify a cluster that does exist (false negative), or both.
Contemporary Journal Concept Press
USDA Forest Service
2017-03-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
spatial scan
application/pdf
http://mcfns.com/index.php/Journal/article/view/9.1
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 9, No 1: MCFNS March 30, 2017; 1-13 (13)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/9.1/9.1.1
http://mcfns.com/index.php/Journal/article/downloadSuppFile/9.1/261
eastern United States
Contemporary; all ages;
standing dead trees
Copyright (c) 2017 Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)
oai:ojs.mcfns.com:article/169
2013-10-01T10:22:41Z
Journal:Sampling
Quantitative assessment of predicted climate change pressure on North American tree species
Potter, Kevin M
Hargrove, William W
forest health; inventory; conservation
climate change; range shift pressure; risk assessment; multivariate clustering; human-assisted migration; niche occupancy; forest health monitoring; conservation
Changing climate may pose a threat to forest tree species, forcing three potential population-level responses: toleration/adaptation, movement to suitable environmental conditions, or local extirpation. Assessments that prioritize and classify tree species for management and conservation activities in the face of climate change will need to incorporate estimates of the risk posed by climate change to each species. To assist in such assessments, we developed a set of four quantitative metrics of potential climate change pressure on forest tree species: (1) percent change in suitable area, (2) range stability over time, (3) range shift pressure, and (4) current realized niche occupancy. All four metrics are derived from climate change environmental suitability maps generated using the Multivariate Spatio-Temporal Clustering (MSTC) technique, which combines aspects of traditional geographical information systems and statistical clustering techniques. As part of the Forecasts of Climate-Associated Shifts in Tree Species (ForeCASTS) project, we calculated the predicted climate change pressure statistics for North American tree species using occurrence data from the USDA Forest Service Forest Inventory and Analysis (FIA) program. Of 172 modeled tree species, all but two were projected to decline in suitable area in the future under the Hadley B1 Global Circulation Model/scenario combination. Eastern species under Hadley B1 were predicted to experience a greater decline in suitable area and less range stability than western species, although predicted range shift did not differ between the regions. Eastern species were more likely than western species, on average, to be habitat generalists. Along with the consideration of important species life-history traits and of threats other than climate change, the metrics described here should be valuable for efforts to determine which species to target for monitoring efforts and conservation actions.
Contemporary Journal Concept Press
United States Department of Agriculture, Forest Health Monitoring
United States Department of Agriculture, Eastern Forest Environmental Threat Assessment Center
2013-09-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
multivariate clustering
application/pdf
text/x-tex
http://mcfns.com/index.php/Journal/article/view/MCFNS_169
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 5, No 2: MCFNS September 30, 2013; 151-169(18)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS_169/MCFNS-169
http://mcfns.com/index.php/Journal/article/view/MCFNS_169/Source-169
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_169/161
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_169/162
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_169/163
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_169/164
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_169/165
continental United States
tree occurrence data
oai:ojs.mcfns.com:article/63
2013-03-21T22:40:36Z
Journal:Sampling
Nearest Neighbor Bias -- A simple example
Iles, Kim
Natural Resource Management, Forestry, Mensuration, Biometrics, GIS and Remote Sensing
nearest neighbor, bias
This is a short research note with an illustrative example describing the bias inherent in the nearest neighbor method. MCFNS 2(1):18-19.
Contemporary Journal Concept Press
2010-02-18
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
Mathematical logic and statistical demonstration
application/pdf
http://mcfns.com/index.php/Journal/article/view/MCFNS.2-18
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 2, No 1: MCFNS February 28, 2010; Pages: 18-19 (2)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS.2-18/MCFNS_2%3A18-19
Independent of space
Indendent of time.
Simulated conceptual sudo data
oai:ojs.mcfns.com:article/234
2019-11-04T19:19:07Z
Journal:Sampling
Optimisation of tetrazolium concentration and immersion time in the viability test of Swietenia macrophylla seeds by using Response Surface Methodology
GarcÃa Quintana, Yudel
Abreu Naranjo, Reinier
Arteaga Crespo, Yasiel
Reyes Morán, Héctor
forestry; seed.
embryo; response surface methodology; prediction; forest.
The aim of this study was to optimise tetrazolium concentration and immersion time in the viability test of S. macrophylla seeds by using Response Surface Methodology (RSM). For this, a RSM Central Composite Design (CCD), type 23 was applied. The quantification of viable and non-viable seed germs was performed using the interpretation of topological patterns. The viability of the seed expressed as a percentage was selected as a response variable whilst the tetrazolium concentration and immersion time were independent factors. The quadratic polynomial model of the four evaluated aspects was best adjusted with 0.99 and 0.93 for the coefficients R2 and Predicted-R2, respectively. Using ANOVA, it was demonstrated that only immersion had a significant effect. The optimisation study showed that it is possible to achieve values of viability above 90% at low tetrazolium concentrations (0.05%) using immersion times between 75 and 90 minutes.
Contemporary Journal Concept Press
Universidad Estatal Amazónica
2019-10-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
Optimisation; Response surface methodology
application/pdf
http://mcfns.com/index.php/Journal/article/view/11.2
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 11, No 2: MCFNS October 30, 2019; 257-263(7)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/11.2/2019.2
Pastaza
2017
seed viability
Copyright (c) 2019 Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)
https://creativecommons.org/licenses/by-nc-nd/4.0
oai:ojs.mcfns.com:article/163
2013-10-01T10:22:41Z
Journal:Sampling
Comparing spatial and non-spatial approaches for predicting forest soil organic carbon at unsampled locations
Clough, Brian J
Green, Edwin J
Spatial analysis; Forest Carbon
Forest soil carbon; Geostatistics; Regression; Co-kriging; New Jersey; Coastal Plain
Prediction of soil organic carbon (SOC) at unsampled locations is central to statistical modeling of regional SOC stocks. This is often accomplished by applying geostatistical techniques to plot inventory data. However, in many cases inventory data is sparsely sampled ( 0.1 plots/km^2) relative to the region of interest, and it is unknown if geostatistics provides any advantage. Our objective was to test whether modeling spatial autocorrelation, in multivariate and univariate predictive models, improved estimates of SOC at prediction locations based on sparsely-sampled inventory data. We conducted our study using a dataset sampled across all forested land in the Coastal Plain physiographic province of New Jersey, USA. We considered five models for predicting SOC, two linear regression models (intercept only and multiple regression with predictor variables), ordinary kriging (a univariate spatial approach), and two multivariate spatial methods (regression kriging and co-kriging). We conducted a simulation study in which we compared the predictive performance (in terms of root mean squared error) of all five models. Our results suggest that our sparsely-sampled SOC data exhibits no spatial structure (Moran’s I=0.05, p=0.39), though several of the covariates are spatially autocorrelated. Multiple linear regression had the best performance in the simulation study, while co-kriging performed the worst. Our results suggest that when inventory data is dispersed across the region of interest, modeling spatial autocorrelation does not provide significant advantage for predicting SOC at unsampled locations. However, it is unknown whether this autocorrelation does not exist at broad scales, or if sparse sampling strategies are unable to detect it. We conclude that in these situations, multiple regression provides a straightforward alternative to predicting SOC for mapping studies, but that more work on the spatial structure of soil carbon across multiple scales is needed.
Contemporary Journal Concept Press
2013-09-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
Multiple regression; Co-kriging
application/pdf
text/x-tex
http://mcfns.com/index.php/Journal/article/view/MCFNS_163
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 5, No 2: MCFNS September 30, 2013; 115-125(10)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS_163/MCFNS-163
http://mcfns.com/index.php/Journal/article/view/MCFNS_163/Source-163
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_163/Fig.%201
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_163/Fig.%202
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_163/preamble163
Coastal Plain; New Jersey
Year
forest soil samples; remote sensing data
oai:ojs.mcfns.com:article/116
2013-03-22T22:48:39Z
Journal:Sampling
Relative Efficiency of Point Sampling Change Estimators
Therien, Guillaume
Inventory;
variable-radius plot; fixed-area plot; change estimation
Concerns about the efficiency and the reliability of point sampling to estimate change in forest growth variables have been omnipresent ever since point sampling appeared in the literature some 60 years ago. Change estimators for point samples based on point-to-tree distance in variable-radius plots were introduced in 1981 but are rarely implemented despite easy access to point-to-tree distance. The statistical efficiency and bias of these estimators were compared to traditional fixed-area plot estimators using stem-mapped permanent sample plots. Methods using variable-radius plots and point-to-tree distance were more efficient to estimate volume and basal area while fixed-area plots were more efficient to estimate stems/ha. Compatible and time-additive estimators are proposed for estimating survivor, mortality, and ingrowth change using point samples. These estimators are unbiased under unrestrictive conditions.
Contemporary Journal Concept Press
2011-08-05
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
application/pdf
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-64
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 3, No 2: MCFNS August 28, 2011; Pages: 64-72 (9)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS.3-64/MCFNS-2%3A64
oai:ojs.mcfns.com:article/290
2023-10-19T16:11:22Z
Journal:Sampling
ALTERNATE RANKED SET SAMPLING FOR SKEWED AND MOUND SHAPED SYMMETRIC DISTRIBUTIONS: ACCOUNTING FOR FORESTRY AND ENVIRONMENTAL RESEARCH
Nautiyal, Raman
Tiwari, Neeraj
Chandra, Girish
Kershaw, Jr., John A.
Shaktan, Trishla
sampling methods
Above Ground Biomass; Ranked Set Sampling, Relative Precision, Distributions, Unbiasedness
Ranked Set Sampling (RSS) is a sampling strategy which is advantageous when measurement of sampling units is very difficult but when small sets of units can be ranked according to other methods that do not require actual measurements. The units corresponding to each rank are used in RSS and RSS performs better than simple random sampling (SRS) when estimating the population mean of forestry or environmental parameters (say, below ground biomass). A new RSS procedure based on alternate order statistics for estimating the population mean (ARSS) is suggested in this paper. ARSS measures only the first, third, fifth and so on units so that the information on remaining order statistics is captured from their respective neighboring order statistics. The bias correction term in the proposed estimator is included and calculated for some skewed and symmetric (both mound and U shaped) distributions. The estimators under ARSS are then compared to the estimators based on balanced RSS and Neyman’s optimal unbalanced RSS allocations. Based on the computed Relative Precisions, estimators based on ARSS are recommended for even set sizes of skewed distributions and odd set sizes of mound shaped symmetric distributions. RPs of these distributions are uniformly better than the other two methods (balanced and Neyman’s RSS). To demonstrate the performance of the different estimators, an example from forestry that estimates total biomass of three tree species is presented. The proposed method is efficient in forestry and environmental applications.
Contemporary Journal Concept Press
2021-11-01
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
Ranked Set Sampling
application/pdf
http://mcfns.com/index.php/Journal/article/view/13.7
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 13, No 2: MCFNS October 30, 2021; 14-26(12)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/13.7/2021.7
western Himalaya
NA
NA
Copyright (c) 2021 Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)
https://creativecommons.org/licenses/by-nc-nd/4.0
oai:ojs.mcfns.com:article/175
2013-10-01T10:22:41Z
Journal:Sampling
Trend analyses and projections using national forest inventory data
Liknes, Greg C
Morin, Randall S
Canham, Charles D
Inventory; Modeling
forest inventory; forest monitoring; trends; projections; time series; FIA
We present a collection of papers derived from the 2012 Forest Inventory and Analysis (FIA) Symposium held on December 4-6, 2012 in Baltimore, MD, USA. The symposium featured 128 oral presentations with nearly 200 attendees from the United States and other countries. A proceedings from the symposium included 75 papers as well as abstracts for all presentations and posters. The symposium theme, \textit{Moving from Status to Trends}, focused on the ability to perform trend or time series analysis using national forest inventory data. A wide range of topics were covered including forest products, social dimensions of forestry, landscape change, analytical tools, Landsat time series, forest carbon, and many others. Based on these presentations, we have assembled a selection of several papers that presented examples of trend analyses and projections using forest inventory data. This Special Issue contains four of these papers that passed the MCFNS double-blind peer-review by a minimum of three peers.
Contemporary Journal Concept Press
2013-09-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
application/pdf
text/x-tex
http://mcfns.com/index.php/Journal/article/view/MCFNS_175
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 5, No 2: MCFNS September 30, 2013; 112-114(3)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS_175/MCFNS-175
http://mcfns.com/index.php/Journal/article/view/MCFNS_175/Source-175
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_175/170
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_175/174
recent past, current, future
oai:ojs.mcfns.com:article/143
2012-09-30T23:59:04Z
Journal:Sampling
Some Current Subsampling Techniques in Forestry
Iles, Kim
Mensuration, Sampling, Inventory
Because different tree parameters are of differing importance, and have different variability, efficiency in sampling would suggest that some of the principle variables be subsampled. One convenient way to do this is to sample different numbers of items at the same sample locations. This paper is a review of some current techniques in subsampling for measured values, especially with Variable Plot sampling, but including Fixed Plot and 3P sampling as well.
Contemporary Journal Concept Press
2012-09-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
Angle Sampling
application/pdf
http://mcfns.com/index.php/Journal/article/view/143
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 4, No 2: MCFNS September 30, 2012; Pages: 77-80 (4)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/143/MCNFS-4.2_77
World wide
Contemporary
Diameters, Heights, Basal Area, Volume
oai:ojs.mcfns.com:article/64
2021-11-22T03:52:19Z
Journal:Sampling
NEAREST NEIGHBOR BIAS IN THE SUBSTITUTION OF MISSING VALUES
Cieszewski, Chris J.
Iles, Kim
Mensuration, Geostatistics, Forest and Natural-Resource Inventory, Mapping
Mapping; Nearest Neighbor Imputation; Nearest Neighbor Bias; Large-area Forest Inventories; Multi-source Data Fusion.
We present a simplified illustration of the bias inherent in the general case of the Nearest Neighbor (NN) method used to substitute missing values. This presentation doesn't make any assumptions about the geometry of the sampled subjects. The general examples illustrate that the bias exists mainly at the limits of the data range and not necessarily within the center part of the range. However, the latter is also possible around any significant data gaps. Since the NN data domain stretches across an arbitrary subject characteristic rather than across the physical space, it is possible to reduce the bias by assuring that the domain range of the considered attribute is well-represented within its entire range, especially at its upper and lower limits.
Contemporary Journal Concept Press
2021-11-01
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
kNN
application/pdf
http://mcfns.com/index.php/Journal/article/view/13.8
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 13, No 2: MCFNS October 30, 2021; 27-30(4)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/13.8/2021.8
http://mcfns.com/index.php/Journal/article/downloadSuppFile/13.8/41
Worldwide
Contemporary
Volume, Basal Area, Biomass, Species Groups, Cover Types
https://creativecommons.org/licenses/by-nc-nd/4.0
oai:ojs.mcfns.com:article/179
2016-04-26T12:43:04Z
Journal:Sampling
A Test of the Mean Distance Method for Forest Regeneration Assessment
Unger, Daniel
Stovall, Jeremy
Oswald, Brian
Kulhavy, David
Hung, I-Kuai
Inventory
sampling, plot, distance, accuracy, seedlings
A new distance-based estimator for forest regeneration assessment, the mean distance method, was developed by combining ideas and techniques from the wandering quarter method, T-square sampling and the random pairs method. The performance of the mean distance method was compared to conventional 4.05 square meter plot sampling through simulation analysis on 405 square meter blocks of a field surveyed clumped distribution and a computer generated random distribution at different levels of density of 100, 50 and 25%. The mean distance method accurately estimated density on the random populations but the mean distance method estimates were more variable than those of 4.05 square meter plot sampling. The mean distance method overestimated actual density and was less precise than plot sampling when both methods were tested on the clumped populations. The optimum sample sizes needed for the mean distance method to achieve the same precision as 4.05 square meter plot sampling at all three density levels, for both the random and clumped spatial distributions, were at least 10 times larger than the sample size used for 4.05 square meter plot sampling.
Contemporary Journal Concept Press
2014-09-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
simulation, testing a new method of forest regeneration via distance sampling
application/pdf
text/x-tex
http://mcfns.com/index.php/Journal/article/view/6_54
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 6, No 2: MCFNS September 30, 2014; 54-61 (8)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/6_54/179pdf
http://mcfns.com/index.php/Journal/article/view/6_54/179tex
anywhere
seedling stage
seedlings per acre/hectare
oai:ojs.mcfns.com:article/165
2013-10-01T10:22:41Z
Journal:Sampling
Changes in forest habitat classes under alternative climate and land-use change scenarios in the northeast and midwest, USA
Tavernia, Brian G
Nelson, Mark D
Goerndt, Michael E
Walters, Brian F
Toney, Chris
Landscape Ecology; Climate Change; Wildlife Ecology; Forest Ecology and Management
Wildlife Habitat; Bioenergy; Biomass Harvest; Climate Change; Young Forest; Early Successional Habitat; FIA; Forest Projections
Large-scale and long-term habitat management plans are needed to maintain the diversity of habitat classes required by wildlife species. Planning efforts would benefit from assessments of potential climate and land-use change effects on habitats. We assessed climate and land-use driven changes in areas of closed- and open-canopy forest across the Northeast and Midwest by 2060. Our assessments were made using projections based on A1B and A2 future scenarios developed by the Intergovernmental Panel on Climate Change. Presently, forest land covers 70.2 million ha and is evenly divided between closed- and open-canopy habitats. Projections indicated that total forest land would decrease by 3.8 or 4.5 million ha for A2 and A1B, respectively. Within persisting forest land, the balance between closed- and open-canopy habitats depended on assumed harvest rates of woody biomass. Standard harvest rates led to closed-canopy habitat attaining a slight majority of total forest land area. Intensive harvest rates resulted in the majority of forest land being in open-canopy habitat for A1B or maintained the even split between closed- and open-canopy habitats for A2. Ultimately, managers need to identify benchmark habitat conditions informed by historical conditions and wildlife population dynamics and plan to meet these benchmarks in dynamic forest landscapes.
Contemporary Journal Concept Press
Department of Forestry University of Missouri
Forest Inventory and Analysis Unit, USDA Forest Service, Northern Research Station
2013-09-30
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Experimental Design Works
application/pdf
text/x-tex
http://mcfns.com/index.php/Journal/article/view/MCFNS_165
Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Vol 5, No 2: MCFNS September 30, 2013; 135-150(15)
1946-7664
eng
http://mcfns.com/index.php/Journal/article/view/MCFNS_165/MCFNS-165
http://mcfns.com/index.php/Journal/article/view/MCFNS_165/Source-165
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_165/156
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_165/157
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_165/158
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_165/159
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_165/160
http://mcfns.com/index.php/Journal/article/downloadSuppFile/MCFNS_165/168
Northeast; Midwest