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Author: Bianca N. I. Eskelson Publisher: ISBN: Category : Forest monitoring Languages : en Pages : 280
Book Description
The Forest Inventory and Analysis (FIA) program conducts an annual inventory throughout the United States. In the western United States, 10% of all plots (one panel) are measured annually, and a moving average is used for estimating current condition and change of forest attributes while alternative methods are sought in all regions of the United States. This dissertation explored alternatives to the moving average in the Pacific Northwest using Current Vegetation Survey data collected in Oregon and Washington. Several nearest neighbor imputation methods were examined for their suitability to update plot-level forest attributes (basal area/ha, stems/ha, volume/ha, biomass/ha) to the current point in time. The results were compared to estimates obtained using a moving average and a weighted moving average. In terms of bias and accuracy, the weighted moving average performed better than the moving average. When the most recent measurements of the variables of interest were used as ancillary data, randomForest imputation outperformed both the moving average and the weighted moving average. For estimating current basal area/ha, stems/ha, volume/ha, and biomass/ha, tree-level imputation outperformed plot-level imputation. The difference in bias and accuracy between tree- and plot-level imputation was more pronounced when the variables of interest were summarized by species groups. Nearest neighbor imputation methods were also investigated for estimating mean annual change in selected forest attributes. The imputed mean annual change was used to update unmeasured panels to the current point in time. In terms of bias and accuracy, the resulting estimates of current basal area/ha, stems/ha, volume/ha, and biomass/ha outperformed the results obtained using plot-level imputation. Information on hard to estimate forest attributes such as cavity tree and snag abundance are important for wildlife management plans. Using FIA data collected in Washington, Oregon, and California, nearest neighbor imputation approaches and negative binomial regression models were examined for their suitability in estimating cavity tree and snag abundance. The negative binomial models were preferred to the nearest neighbor imputation approaches.
Author: Bianca N. I. Eskelson Publisher: ISBN: Category : Forest monitoring Languages : en Pages : 280
Book Description
The Forest Inventory and Analysis (FIA) program conducts an annual inventory throughout the United States. In the western United States, 10% of all plots (one panel) are measured annually, and a moving average is used for estimating current condition and change of forest attributes while alternative methods are sought in all regions of the United States. This dissertation explored alternatives to the moving average in the Pacific Northwest using Current Vegetation Survey data collected in Oregon and Washington. Several nearest neighbor imputation methods were examined for their suitability to update plot-level forest attributes (basal area/ha, stems/ha, volume/ha, biomass/ha) to the current point in time. The results were compared to estimates obtained using a moving average and a weighted moving average. In terms of bias and accuracy, the weighted moving average performed better than the moving average. When the most recent measurements of the variables of interest were used as ancillary data, randomForest imputation outperformed both the moving average and the weighted moving average. For estimating current basal area/ha, stems/ha, volume/ha, and biomass/ha, tree-level imputation outperformed plot-level imputation. The difference in bias and accuracy between tree- and plot-level imputation was more pronounced when the variables of interest were summarized by species groups. Nearest neighbor imputation methods were also investigated for estimating mean annual change in selected forest attributes. The imputed mean annual change was used to update unmeasured panels to the current point in time. In terms of bias and accuracy, the resulting estimates of current basal area/ha, stems/ha, volume/ha, and biomass/ha outperformed the results obtained using plot-level imputation. Information on hard to estimate forest attributes such as cavity tree and snag abundance are important for wildlife management plans. Using FIA data collected in Washington, Oregon, and California, nearest neighbor imputation approaches and negative binomial regression models were examined for their suitability in estimating cavity tree and snag abundance. The negative binomial models were preferred to the nearest neighbor imputation approaches.
Author: Hans T. Schreuder Publisher: ISBN: Category : Forest surveys Languages : en Pages : 6
Book Description
A key product of both Forest Inventory and Analysis (FIA) of the USDA Forest Service and the Natural Resources Inventory (NRI) of the Natural Resources Conservation Service is a scientific data base that should be defensible in court. Multiple imputation procedures (MIPs) have been proposed both for missing value estimation and prediction of non-remeasured cells in annualized forest inventories such as the Southern Annual Forest Inventory System (SAFIS). MIPs generate clean-looking data bases that are easily used but hide a serious weakness: under different assumptions made by reasonable people, very different data bases and conclusions can be generated. A MIP is an interesting idea for prediction but should only be used for analyses by users, not for filling in data in a public data base. Simple illustrations are given to make our points. To maintain a defensible data base, FIA and NRI should only provide algorithms to facilitate user-generated data for prediction of non-remeasured cells. Users, not FIA and NRI, should be responsible for generating data bases that utilize these algorithms or other algorithms of their choosing, incorporating assumptions that they are willing to make. But they should be encouraged to work with FIA and NRI personnel in utilizing such algorithms.
Author: Ram K. Deo Publisher: ISBN: Category : Forest management Languages : en Pages : 246
Book Description
Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.
Author: Hunter Stanke Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 77
Book Description
Forest Inventory and Analysis (FIA) is a US Department of Agriculture Forest Service program that aims to monitor changes in forests across the US. FIA hosts one of the largest ecological datasets in the world, though its complexity limits access for many potential users. rFIA is an R package designed to simplify the estimation of forest attributes using data collected by the FIA Program. Specifically, rFIA improves access to the spatio-temporal estimation capacity of the FIA Database via space-time indexed summaries of forest variables within user-defined population boundaries. The package implements multiple design-based estimators, and has been validated against official estimates and sampling errors produced by the FIA Program. The package has been made open-source is freely available for download from the Comprehensive R Archive Network.In recent decades, forests of the western US have experienced unprecedented change in climate and forest disturbance regimes, and widespread shifts in forest composition, structure, and function are expected in response. However, large-scale, comprehensive assessments of tree population performance have yet to be conducted in the region. We develop an index of forest population performance based on repeated censuses of field plots, and apply this index to assess the status of the most abundant tree species in the western US. Our study provides empirical evidence to suggest tree species in the western US are exhibiting strong divergence in population performance, with over half (70%) of species experiencing range-wide population decline. We found spatial variation in population performance across the ranges of all species, indicating range shifts are already underway. Our results further indicate that species decline can seldom be attributed to a single forest disturbance agent, highlighting the importance of considering multiple risks factors in broad-scale forest management.
Author: Andrew P. Robinson Publisher: Springer Science & Business Media ISBN: 1441977627 Category : Medical Languages : en Pages : 342
Book Description
Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics.
Author: Okikiola Michael Alegbeleye Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
The Forest Inventory and Analysis (FIA) program of the United States Department of Agriculture Forest Service collects forest inventory data that provide estimates with reasonable accuracy at the national scale. However, for smaller domains, these estimates are often not as accurate due to the small sample size. Small area estimation improves the accuracy of the estimates at smaller domains by relying on auxiliary information. This study compared direct (FIA estimates), indirect (multiple linear regression), and composite estimators (Fay-Herriot) using auxiliary information derived from Landsat and Global Ecosystem Dynamics Investigation (GEDI) to obtain county-level estimates of forest attributes namely total and merchantable volume (m3 ha-1), aboveground biomass (Mg ha-1), basal area (m2 ha-1), and Lorey's mean height (m). Compared with FIA estimates, the composite estimator reduced error by 75-78% for all the variables of interest. This shows that a reasonable amount of precision can be achieved with auxiliary information from Landsat and GEDI, improving FIA estimates at the county level.
Author: Bianca N. I. Eskelson
Author: Bianca N. I. Eskelson
Author: Hans T. Schreuder
Author: Hans T. Schreuder
Author: Ram K. Deo
Author: Barry Wilson
Author: Pacific Northwest Research Station (Portland, Or.). Forest Inventory & Analysis
Author: Richard A. Birdsey
Author: Hunter Stanke
Author: Andrew P. Robinson
Author: Okikiola Michael Alegbeleye