Browsing by Subject "forest inventory"

Now showing 1 - 13 of 13
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    Application of James-Stein and empirical Bayes procedures to simultaneous estimation problems in forest inventory
    (University of Minnesota, 1980-03-01) Burk, Thomas E.; Ek, Alan R.
    Traditional estimation procedures may ignore available auxilary information or use it only for regression, stratification, or in survey design. Such information, however, can be incorporated directly into estimation procedures. One case that has received much recent attention is where there exist K > 4 groups of simultaneous interest which are homogeneous (similar) with respect to their means. This paper describes two approaches (estimators) which incorporate auxilary information and thereby improve estimation efficiency as compared to commonly applied normal theory maximum likelihood estimators. Computer programs for implementation of the estimators are also described. Forest inventory problems provide an ideal application for such estimators. Simulation tests were conducted on four real forest populations covering volume and area estimation. Analysis of results showed consistent reductions in estimator total mean squared error. Confidence interval analyses are also presented.
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    Cloquet Forestry Center permanent plot records 1959-1976 : status report and plans for remeasurement.
    (University of Minnesota, 1982-07-01) Reber, Carol A.; Ek, Alan R.
    The Cloquet Forestry Center continuous forest inventory is one of the most extensive data sets of its kind in the Lake States. Numerous studies ranging from reproduction sampling analyses to the testing of growth projection models have been based on this data. This report describes the sampling design, observed variables and past analyses of the survey plus data management problems that affect its utility. Summary statistics are also presented for various cover type aggregates for past measurement dates. A discussion of forest development over time is followed with plans for the 1982 remeasurement.
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    Cost-effective Forest Inventory Designs: Field Data Collection
    (University of Minnesota Department of Forest Resources, 2018-02) Ek, Alan R.; Wilson, David C.; Edgar, Christopher B.; Zobel, John M.
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    Description and Implementation of the Great Lakes Forest Projection System (GLFPS)
    (University of Minnesota Department of Forest Resources, 2020-09) Wilson, David C.; Ek, Alan R.
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    Determination of forest type and stand size class across FIA inventory years
    (University of Minnesota Department of Forest Resources, 2019-11) Zobel, John M.; Ek, Alan R.; Edgar, Christopher B.
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    Evaluating Old Age for Forests in Minnesota from Forest Inventory Records
    (University of Minnesota Department of Forest Resources, 2017-06) Wilson, David C.; Ek, Alan R.
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    Evidence of Rapid Forest Change in Minnesota
    (University of Minnesota Department of Forest Resources, 2018-06) Wilson, David C.; Ek, Alan R.
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    Forest Cover Type and Productivity as Related to Physiography
    (University of Minnesota Department of Forest Resources, 2018-11) Wilson, David C.; Zobel, John M.; Ek, Alan R.
    An analysis of statewide forest inventory data indicates the occurrence of various forest cover types on the landscape is closely associated with physiographic conditions on those sites. Tables quantifying these relationships are provided as an aid to interpretation of forest inventory data in Minnesota and associated silvicultural opportunities.
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    Hardwood stand modeling using the Forest Vegetation Simulator (FVS)
    (University of Minnesota Department of Forest Resources, 2019-01) Wilson, David C.; Ek, Alan R.
    This note reports on the adaptation of the USDA Forest Service’s Forest Vegetation Simulator (FVS) to model individual stands and potential management scenarios for the hardwood forests of Southeastern Minnesota
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    Imputation of ecological detail using associated forest inventory, plant community and physiographic data
    (2016-11) Wilson, David
    The desire to consider additional ecological information in management planning has become a pressing concern in the field of forest ecology and management. While intensively managed forest stands provide ecological benefits, these can be different from the services and values supported by native ecosystems and plant communities. To better understand the implications of management for biological diversity, ecosystem services, timber production and other interests, an ecological classification methodology matched with existing forest inventory and management operations is proposed and developed. This methodology makes use of nearly 17,000 native plant community (NPC) observations provided by the Minnesota Department of Natural Resources (MNDNR) and others. These observations cover the period from 1964 – 2015, and coincide with stands monitored by the MNDNR Division of Forestry. The proposed imputation model (Chapter 1) represents an improvement over randomForest based methods in terms of accuracy, coverage, and the ability to consider complex categorical variables with essentially unlimited levels of detail. Extension of the methodology to include United States Department of Agriculture (USDA) Forest Inventory and Analysis (FIA) plot observations and additional predictive characteristics further improves classification results (Chapter 2). The net predictive capability is sufficient to produce estimates of the areal extent of major forested NPCs occurring in Minnesota. These estimates are derived from a process utilizing the spatial overlay of FIA plots with MNDNR stands having NPC observations. These “observed” FIA plots serve as training data to classify the full set of FIA plots observed in Minnesota. Finally, FIA data augmented with imputed NPC classifications are used to assess relationships between NPC classifications and growth and yield characteristics of the forests in each community (Chapter 3). Results indicate that NPC classification often corresponds to meaningful distinctions between different growth patterns and eventual yield of forested stands. Imputation can provide us with timely and accurate knowledge of NPC distribution, abundance, successional state, demographic, and economic relationships. This enhanced understanding of landscape-scale ecological conditions can, in turn, lead to better informed management decisions based on the extrapolation of observed ecological conditions and growth parameters to very similar, nearby management units.
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    Stand Volume, Biomass and Carbon Equations for the Upper Great Lakes Region
    (University of Minnesota Department of Forest Resources, 2019-07) Wilson, David C.; Ek, Alan R.
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    TARCV : a microcomputer program for timber appraisal report compilation for variable radius plot sampling.
    (University of Minnesota, 1986-12) Droessler, Terry; Ek, Alan R.
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    Using Single-Photon Lidar and Multispectral Imagery for Enhancing Forest Inventories
    (2019-10) Allen, Benjamin
    Modern forest management requires balancing multiple uses and management objectives, including timber production, wildlife habitat, and carbon sequestration. Forest inventories provide essential information for forest management decisions at a variety of spatial scales, including data about wood volume and the prevalence of various species. Traditional forest inventory systems rely primarily upon field data and design-based statistical estimators. These methods can provide unbiased estimates of inventory variables, albeit at a significant financial cost which limits the accuracy of the resulting data. Remote sensing technologies such as lidar and aerial photography have been used along with alternative statistical estimators to improve inventory accuracy and allow for spatially explicit maps of inventory data to be created. This research explored potential efficiency gains from the use of single-photon lidar and fall color aerial photography in a study area in northern Minnesota, USA. Remote sensing and field data combined in a model-assisted inferential framework were found to deliver relative efficiencies of approximately three for wood volume, with slightly lower values for basal area. Greater efficiency gains were found in coniferous-dominated forests than deciduous forests. The potential of these technologies to identify individual tree species and forest types was also examined. Classification between deciduous and coniferous-dominated forests provided overall classification accuracies of nearly 90% regardless of the classification algorithm used. By contrast, predictions of dominant species produced poor accuracy. Further research is needed to determine the economically optimal combination of remote sensing technologies for operational forest inventories.