Stratified forest inventory estimation with mapped plots

2005 ◽  
Vol 35 (10) ◽  
pp. 2382-2386 ◽  
Author(s):  
Paul C Van Deusen
Keyword(s):  
Forest Inventory ◽  
Forest Service ◽  
Forest Types ◽  
Inventory Data ◽  
Expansion Factor ◽  
Usda Forest Service ◽  
Weighted Estimation ◽  
Forest Conditions

Weighted estimation formulas are developed for producing stratified estimates of means and variances where data come from plots that can contain multiple forest conditions. Each plot is mapped to allow the analyst to focus on specific forest types or conditions. The weights required to accommodate mapped plots are somewhat more complicated than the weights for unmapped plots. In particular, these weights depend on the mapped condition of interest. The implication is that a single plot weight or expansion factor will not suffice for all analyses as it does for unmapped plots. The methods are demonstrated using USDA Forest Service inventory data.

scholarly journals A Method for Evaluating Fixed-Radius Plot Size When Sampling Western Juniper Seedlings and Saplings

2002 ◽  
Vol 17 (4) ◽  
pp. 207-208
Author(s):  
David L. Azuma ◽  
Larry Bednar
Keyword(s):  
Forest Inventory ◽  
Forest Service ◽  
Usda Forest Service ◽  
Forest Inventory And Analysis ◽  
Size Selection ◽  
Western Juniper ◽  
Plot Size ◽  
Fixed Radius ◽  
Selection For

Abstract This note outlines a method for evaluating plot size selection for an inventory of western juniper woodlands in eastern Oregon. The Forest Inventory and Analysis (FIA) program of the USDA Forest Service in Portland, Oregon, used this method to evaluate several plot sizes to measure seedlings and saplings in the 1998 inventory of eastern Oregon. By choosing a 5 m radius plot, the probability of tallying no seedlings or saplings on four subplots is less than 10% for the three sample densities (0.01, 0.02, and 0.03 trees/m2) used. West. J. Appl. For. 17(4):207–208.

scholarly journals Comparison of Low-Cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning across Multiple Forest Types in California, USA

2021 ◽  
Vol 13 (21) ◽  
pp. 4292
Author(s):  
James E. Lamping ◽  
Harold S. J. Zald ◽  
Buddhika D. Madurapperuma ◽  
Jim Graham
Keyword(s):  
Forest Inventory ◽  
Low Cost ◽  
Forest Types ◽  
Canopy Density ◽  
Positioning Systems ◽  
Aerial Photogrammetry ◽  
3D Data ◽  
Wide Range ◽  
Forest Attributes ◽  
Forest Conditions

Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurements, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this study was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74–0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17–0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53–0.85), and were comparable to predictions made using lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions.

Cubic and Board Foot Volume Models for the Central States

1991 ◽  
Vol 8 (2) ◽  
pp. 47-57 ◽  
Author(s):  
Jerold T. Hahn ◽  
Mark H. Hansen
Keyword(s):  
Forest Inventory ◽  
Site Index ◽  
Forest Service ◽  
Timber Species ◽  
Usda Forest Service ◽  
Data Set ◽  
Forest Inventory And Analysis ◽  
Regression Techniques ◽  
Species Groups ◽  
Volume Models

Abstract This paper presents tree volume models developed for major timber species in the Central States (Indiana, Illinois, Missouri, and Iowa). Models for estimating gross tree volume (either cubic foot or board foot International ¼-in. log rule) and percent cull were developed for 23 species or species groups. These models estimate volume based on observed dbh and tree site index. Nonlinear regression techniques were used to fit a Weibull-type function to estimate gross volume with a data set containing observations from more than 50,000 trees measured throughout the region. A simple linear model was used to estimate percent cull in a tree for each of several tree classes. These models are being used in the statewide inventories now underway in Missouri and Iowa and may be used by anyone desiring volume-per-tree estimates that are comparable to USDA Forest Service Forest Inventory and Analysis estimates in these areas. North. J. Appl. For. 8(2):47-57

Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service

2005 ◽  
Vol 35 (12) ◽  
pp. 2968-2980 ◽  
Author(s):  
Ronald E McRoberts ◽  
Geoffrey R Holden ◽  
Mark D Nelson ◽  
Greg C Liknes ◽  
Dale D Gormanson
Keyword(s):  
Satellite Imagery ◽  
Forest Inventory ◽  
Forest Service ◽  
Analysis Program ◽  
Ancillary Data ◽  
Usda Forest Service ◽  
Forest Inventory And Analysis ◽  
New Approach ◽  
Areas Of Interest ◽  
Effective Source

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.

scholarly journals Comparisons of Modeled Height Predictions to Ocular Height Estimates

1998 ◽  
Vol 22 (4) ◽  
pp. 216-221 ◽  
Author(s):  
W.A. Bechtold ◽  
S.J. Zarnoch ◽  
W.G. Burkman
Keyword(s):  
Forest Inventory ◽  
Forest Service ◽  
Usda Forest Service ◽  
Individual Tree ◽  
Forest Inventory And Analysis ◽  
Measurement Quality ◽  
Total Height ◽  
Hardwood Species ◽  
Quality Objective ◽  
Height Predictions

Abstract Equations used by USDA Forest Service Forest Inventory and Analysis projects to predict individual tree heights on the basis of species and dbh were improved by the addition of mean overstory height. However, ocular estimates of total height by field crews were more accurate than the statistically improved models, especially for hardwood species. Height predictions from the improved equations attained the desired measurement quality objective only 57% of the time, while ocular estimates achieved the desired accuracy 75% of the time. South. J. Appl. For. 22(4):216-221.

scholarly journals A Unique Academic–Government Collaboration Yields First Report of Detailed Habitat Description for Haemaphysalis longicornis (Ixodida: Ixodidae) in Madison County, KY

2021 ◽  
Author(s):  
J T Vogt ◽  
B D Allen ◽  
D Paulsen ◽  
R T Trout Fryxell
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Forest Service ◽  
Haemaphysalis Longicornis ◽  
Analysis Program ◽  
Ancillary Data ◽  
Usda Forest Service ◽  
Forest Inventory And Analysis ◽  
First Report ◽  
Madison County

Abstract Haemaphysalis longicornis Neumann, Asian longhorned tick, was collected in Madison County, Kentucky, United States as part of an ongoing collaborative-tick surveillance project. This is the first collection of this invasive tick that includes ancillary data on habitat and landscape features derived from the USDA Forest Service, Forest Inventory and Analysis program.

Assessing Timber Availability in Upland Hardwood Forests

1992 ◽  
Vol 16 (2) ◽  
pp. 82-88
Author(s):  
Dennis M. May ◽  
Chris B. LeDoux
Keyword(s):  
Prediction Model ◽  
Forest Inventory ◽  
Hardwood Forests ◽  
Forest Service ◽  
Hardwood Forest ◽  
Cost Prediction ◽  
Usda Forest Service ◽  
Forest Inventory And Analysis ◽  
Experiment Station ◽  
Forest Experiment Station

Abstract Reported forest inventory statistics gathered by the USDA Forest Service, Southern Forest Experiment Station, Forest Inventory and Analysis (SOFIA) have been criticized because not all of the inventory volume reported is truly available for harvest. In response to this criticism, a procedure has been developed for assessing timber availability from reported inventory statistics for upland hardwood forests. The procedure uses forest inventory and ownership statistics gathered by SOFIA, a stump-to-mill cost prediction model developed by the USDA Forest Service, Northeastern Forest Experiment Station, and published wood price reports. Under the specific assumptions and conditions set forth in a demonstration of the procedure, a quarter of Tennessee's reported upland hardwood forest, containing about 40% of the reported inventory volume, was estimated to be available for harvest. The usefulness of the procedure in assessing available timber supplies for individual mills was also demonstrated. South. J. Appl. For. 16(2):82-88.

scholarly journals Avoiding Spurious Conclusions from Forest Service Estimates of Timber Volume, Growth, Removal, and Mortality

2004 ◽  
Vol 21 (4) ◽  
pp. 194-199 ◽  
Author(s):  
William Luppold ◽  
William H. McWilliams
Keyword(s):  
Forest Inventory ◽  
Sampling Error ◽  
Volume Growth ◽  
Forest Health ◽  
Forest Service ◽  
Timber Species ◽  
Forest Change ◽  
Usda Forest Service ◽  
Specific Species ◽  
Sampling Procedures

Abstract Forest inventory statistics developed by the USDA Forest Service Forest Inventory and Analysis (FIA) units are useful in examining a variety of economic and biological issues, including forest-industry plant location, biological supplies of specific timber species, forest health, and long-term sustainability of timber resources. In general, these statistics accurately represent the resource, especially at the inventory-unit and state levels. However, several issues related to data collection must be understood to prevent spurious conclusions, especially when examining forest change characteristics such as removals, mortality, net growth, or the growth-removal ratio. Because FIA statistics are developed by sampling procedures, they are subject to sampling error. As the size of the forest area under study decreases, the number of observations used to develop FIA statistics decreases. The number of observations used to calculate FIA statistics can be particularly inadequate when examining net growth, removals, mortality, and growth-removal ratios of specific species for geographical areas smaller than inventory units. North. J. Appl. For. 21(4):194–199.

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