scholarly journals Anomaly Detection for Analysis of Annual Inventory Data: A Quality Control Approach

2010 ◽  
Vol 34 (3) ◽  
pp. 131-137 ◽  
Author(s):  
Francis A. Roesch ◽  
Paul C. Van Deusen
Keyword(s):  
Quality Control ◽  
Anomaly Detection ◽  
Forest Inventory ◽  
Inventory Data ◽  
Forest Inventory And Analysis ◽  
Control Approach ◽  
Forest Inventories ◽  
The Us ◽  
Challenges And Opportunities ◽  
Past Trend

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.

scholarly journals Separating Duff and Litter for Improved Mass and Carbon Estimates

2009 ◽  
Vol 33 (1) ◽  
pp. 29-34 ◽  
Author(s):  
David Chojnacky ◽  
Michael Amacher ◽  
Michael Gavazzi
Keyword(s):  
North Carolina ◽  
Organic Matter ◽  
Forest Inventory ◽  
Mass Ratio ◽  
Forest Inventory And Analysis ◽  
The Us ◽  
Calculated Mass ◽  
Density Values ◽  
Carbon Load ◽  
Forest Soil Organic Matter

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).

scholarly journals An Automated Approach for Extracting Forest Inventory Data from Individual Trees Using a Handheld Mobile Laser Scanner

2021 ◽  
Vol 42 (3) ◽  
Author(s):  
Mustafa Zeybek ◽  
Can Vatandaşlar
Keyword(s):  
Data Processing ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Natural Forest ◽  
Estimation Accuracy ◽  
Future Research ◽  
Inventory Data ◽  
Pilot Studies ◽  
Cloud Data ◽  
Forest Inventories

Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near)natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83–0.99, p<0.05). The average RMSE for tree DBHs was 1.8 cm at the forest landscape level. As for tree detection, 92.5% of 292 trunks were correctly classified on point cloud data. In general, estimation accuracy was sufficient for operational forest inventory needs. However, they could markedly decrease in »hard plots« located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in »easy plots«. By improving the algorithmic performances, the accuracy levels can be further increased by future research.

scholarly journals A Tutorial on Model-Assisted Estimation with Application to Forest Inventory

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 244 ◽  
Author(s):  
Kelly S. McConville ◽  
Gretchen G. Moisen ◽  
Tracey S. Frescino
Keyword(s):  
Forest Inventory ◽  
R Package ◽  
Remotely Sensed ◽  
Variance Estimator ◽  
Inventory Data ◽  
Remotely Sensed Data ◽  
Forest Inventories ◽  
Regression Estimators ◽  
National Forest Inventories ◽  
The Many

National forest inventories in many countries combine expensive ground plot data with remotely-sensed information to improve precision in estimators of forest parameters. A simple post-stratified estimator is often the tool of choice because it has known statistical properties, is easy to implement, and is intuitive to the many users of inventory data. Because of the increased availability of remotely-sensed data with improved spatial, temporal, and thematic resolutions, there is a need to equip the inventory community with a more diverse array of statistical estimators. Focusing on generalized regression estimators, we step the reader through seven estimators including: Horvitz Thompson, ratio, post-stratification, regression, lasso, ridge, and elastic net. Using forest inventory data from Daggett county in Utah, USA as an example, we illustrate how to construct, as well as compare the relative performance of, these estimators. Augmented by simulations, we also show how the standard variance estimator suffers from greater negative bias than the bootstrap variance estimator, especially as the size of the assisting model grows. Each estimator is made readily accessible through the new R package, mase. We conclude with guidelines in the form of a decision tree on when to use which an estimator in forest inventory applications.

A primer for nonresponse in the US forest inventory and analysis program

2011 ◽  
Vol 184 (3) ◽  
pp. 1423-1433 ◽  
Author(s):  
Paul L. Patterson ◽  
John W. Coulston ◽  
Francis A. Roesch ◽  
James A. Westfall ◽  
Andrew D. Hill
Keyword(s):  
Forest Inventory ◽  
Analysis Program ◽  
Forest Inventory And Analysis ◽  
The Us

scholarly journals A Regularized Raking Estimator for Small-Area Mapping from Forest Inventory Surveys

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1045 ◽  
Author(s):  
Nicholas N. Nagle ◽  
Todd A. Schroeder ◽  
Brooke Rose
Keyword(s):  
Forest Inventory ◽  
Small Area ◽  
General Purpose ◽  
Previous Method ◽  
Ancillary Data ◽  
Forest Inventory And Analysis ◽  
Survey Techniques ◽  
The Us ◽  
Us Forest Service ◽  
Single Set

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.

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
Keyword(s):  
Forest Inventory ◽  
R Package ◽  
Forest Inventory And Analysis ◽  
The Us ◽  
Forest Attributes

scholarly journals Estimating Ownerships and Parcels of Nonindustrial Private Forestland in Massachusetts

2008 ◽  
Vol 25 (2) ◽  
pp. 93-98 ◽  
Author(s):  
David B. Kittredge ◽  
Anthony W. D'Amato ◽  
Paul Catanzaro ◽  
Jennifer Fish ◽  
Brett Butler
Keyword(s):  
Land Cover ◽  
Forest Inventory ◽  
Property Tax ◽  
Forest Service ◽  
The State ◽  
Forest Inventory And Analysis ◽  
The Us ◽  
Us Forest Service ◽  
Suburban Development ◽  
Private Forestland

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.

Basal Area Growth Estimators for Survivor Component: A Quality Control Application

1990 ◽  
Vol 14 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Charles E. Thomas ◽  
Francis A. Roesch
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Forest Inventory ◽  
Data Use ◽  
Basal Area ◽  
Forest Inventory And Analysis ◽  
Basal Area Growth ◽  
Area Growth ◽  
Forest Experiment Station ◽  
Control Application

Abstract Several possible estimators are available for basal area growth of survivor trees, when horizontal prism (or point plots (HPP) are remeasured. This study's comparison of three estimators not only provides a check for the estimate of basal area growth but suggests that they can provide a quality control indicator for yield procedures. An example is derived from remeasurements in Alabama for the Southern Forest Experiment Station by Forest Inventory and Analysis. Remeasurements are for 1962-72 and 1972-82. It is suggested that computation of two or perhaps all three of the estimators be routinely incorporated in analysis of remeasured HPP data. Use of the two elemental estimators can provide a quality assurance check on field procedures. South. J. Appl. For. 14(1):12-18.

scholarly journals Remote Sensing of Geodiversity as a Link to Biodiversity

2020 ◽  
pp. 225-253
Author(s):  
Sydne Record ◽  
Kyla M. Dahlin ◽  
Phoebe L. Zarnetske ◽  
Quentin D. Read ◽  
Sparkle L. Malone ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Shuttle Radar Topography Mission ◽  
Biodiversity Data ◽  
Geographic Ranges ◽  
Nature Conservancy ◽  
The Us ◽  
Challenges And Opportunities ◽  
The Relationship

AbstractTwo common approaches to conserving biodiversity are conserving the actors (species) and conserving the stage (habitat). Many management efforts focus on conserving the actors, but a major challenge to this strategy is uncertainty surrounding how species’ geographic ranges might shift in response to global change, including climate and land use change. The Nature Conservancy has moved to conserving the stage, with the aim of maintaining the processes that generate and support biodiversity. This strategy requires knowing how biodiversity responds to geodiversity—the abiotic features and processes that define the stage. Here we explore how remote sensing illuminates the relationship between biodiversity and geodiversity. We introduce a variety of geodiversity measures and discuss how they can be combined with biodiversity data. We then explore the relationship between biodiversity and geodiversity with tree biodiversity data from the US Forest Inventory and Analysis Program and geodiversity data from the Shuttle Radar Topography Mission as a case study and proof of concept. We find that whereas beta diversity was not well explained by geodiversity, both alpha and gamma diversities were positively related to geodiversity. We also outline the challenges and opportunities of using remote sensing to understand the relationship between biodiversity and geodiversity.

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