Developing computer courseware for forest management

Computers are an important tool for managing forests, and they can be used to help teach forestry students about forest management. This paper describes a project to develop computer-based learning models that will be integrated in a comprehensive courseware package suitable for teaching an entire undergraduate forest management course. The forest management courseware will supplement a traditional textbook and take advantage of the things that can be done better with a computer-based approach. While the computer is not likely to rival the textbook in the areas of depth and portability, it can provide an interactive medium to relieve some of the more tedious aspects of traditional course materials. Come of the more promising features of computer-based instruction are the non-linear, multi-level possibilities of hypermedia, interactive tutorials, animated graphics, computer-administered problem sets, and simulation programs. These features can relieve the student and the instructor of some of the more tedious traditional teaching activities to allow more focus on concepts and more general issues

Life-Cycle impacts of Inland Northwest and Northeast/North central forest resources

Determining the life-cycle inventory (LCI) and impact of forest harvest, regeneration, and growth is necessary in conducting a life-cycle assessment of wood products. This publication provides quantitative assessments of the economic and environmental impacts of forest management activities covering portions of the Inland Northwest (INW), including Montana, Idaho, and eastern Washington, and of the Northeastern and North Central (NE/NC) forests from Minnesota to Maine and south as far as Missouri, West Virginia, and Pennsylvania. The management scenarios provide the inputs needed to develop an LCI on all the inputs and outputs for wood products as impacted by forest treatments and the harvesting of logs in the region. Productive timberlands were grouped according to forest type, productivity, management intensity, and ownership into three broad forest types in the west: cold, dry, and moist; and four in the east: spruce/fir, northern hardwoods, oak/hickory, and aspen/birch. Spruce/fir represented the feedstock to softwood lumber and a composite of northern hardwoods and oak/hickory the feedstock to hardwood lumber. Simulations used the US Forest Service Forest Vegetation Simulator to estimate standing and harvested biomass and log volumes passed on as resources to the manufacturing segments for lumber, plywood, or oriented strandboard. The combinations of ownership, management intensity, and forest type were stratified and averaged to produce a single estimate of yield and the corresponding harvesting impacts. Both historic harvest rates and increased management intensity scenarios were simulated for each region. In the INW, the shift to the higher intensity scenario increased the average production of merchantable volume at harvest to 249 - 399 m3/ha when averaged across the forested land in each ownership class. For the NE/NC region, the production of merchantable volume averaged 263 m3/ha for softwood and 328 m3/ha for hardwood forests with an insignificant volume response from shifting land into more intensive management. Average growth varied widely for INW forest categories from a low on federal land for the base case of 0.7 - 6.7 m3/haha·yr for moist state and private land under the intensive management alternative. Current condition estimates of softwood log and bark carbon exported for mill processing in the INW and NE/NC regions were 751 and 988 kg/ha·yr, respectively

What to Teach in Forest Management and How to Teach It

More than ever, forest management today encompasses a wide range of topics, from ecosystem management and public forest planning to the details of industrial timber management, including financial analysis. The discussion will provide a forum where individuals who teach forest management can discuss their ideas on what should be emphasized in forest management classes and effective methods for teaching these concepts. As preparation for the conference, I will obtain syllabi for forest management classes around the country and internationally. These will be used to develop a list of the range of subjects currently taught in these classes and methods used for teaching them. A synopsis of this review, including copies of all of the syllabi that are obtained, will be distributed to participants. The desired outcome of the discussion is that forest management instructors will revisit their current course content and teaching methods, possibly identifying needed changes in emphasis and/or new teaching approaches. The discussion will involve all those who show up

OUTCOMES OF THE 2009 SYMPOSIUM ON SYSTEMS ANALYSIS IN FOREST RESOURCES

Abstract.This is the Introduction to and an overview of the Special Section of papers from the 2009 Symposium on Systems Analysis in Forest Resources (SSAFR) held in Charleston, South Carolina on Ma

Statistical Models for categorical data: brief review for applications in Ecology

A brief review of statistical models for prediction of categorical data is presented, with emphasis on the binary type. Several methods have been adopted to build predictive models for binary and other types of categorical data and response variables. The focus here is on generalized linear models and generalized additive models, widely applied in problems in Ecology, when the goal is to fit a model to data of presence/absence type or any other categorical response. The estimation methods used for generalized linear models and generalized additive models as well its statistical properties are discussed. Some examples in ecology are addresse

Trends in financing Minnesota's public forest land management agencies 1987-1992.

This paper brings together financial data from the Minnesota Department of Natural Resources, Division of Forestry, thirteen county land departments in northern Minnesota and the USDA Forest Service into one document.Research primarily funded by a grant from the Charles K. Blandin Foundation, Grand Rapids, Minnesota under a project entitled: "Relating timber prices to timber values and public forest invetment in Minnesota." Also partially funded by the Minnesota Agricultural Experiment Station, Project no. MIN-42-089 and the College of Natural Resources. Minnesota Agricultural Experiment Station publication no. 21,612

Statistical models for categorical data: brief review for applications in ecology

A brief review of statistical models for prediction of categorical data is presented, with emphasis on the binary type. Several methods have been adopted to build predictive models for binary and other types of categorical data and response variables. The focus here is on generalized linear models and generalized additive models, widely applied in problems in Ecology, when the goal is to fit a model to data of presence/absence type or any other categorical response.The estimation methods used for generalized linear models and generalized additive models as well its statistical properties are discussed. Some examples in ecology are addressed.Research of M. Rosário Ramos was partially supported by FCT-Fundação para a Ciência e a Tecnologia, Portugal, through the project PEst OE/MAT/UI0209/2014. Research of Manuela M. Oliveira, Marc E. McDill, and José G. Borges has received funding from ForEAdapt project, funded by the European Union Seventh Framework Programme (FP7-PEOPLE-2010-IRSES) under grant agreement n° PIRSES-GA-2010-269257.info:eu-repo/semantics/publishedVersio

The Management of Industrial Forest PlantationsTheoretical Foundations and Applications /

XIII, 543 p. 118 illus., 57 illus. in color.onlin

A Voxel-Based Individual Tree Stem Detection Method Using Airborne LiDAR in Mature Northeastern U.S. Forests

This paper describes a new method for detecting individual tree stems that was designed to perform well in the challenging hardwood-dominated, mixed-species forests common to the northeastern U.S., where canopy height-based methods have proven unreliable. Most prior research in individual tree detection has been performed in homogenous coniferous or conifer-dominated forests with limited hardwood presence. The study area in central Pennsylvania, United States, includes 17+ tree species and contains over 90% hardwoods. Existing methods have shown reduced performance as the proportion of hardwood species increases, due in large part to the crown-focused approaches they have employed. Top-down approaches are not reliable in deciduous stands due to the inherent complexity of the canopy and tree crowns in such stands. This complexity makes it difficult to segment trees and accurately predict tree stem locations based on detected crown segments. The proposed voxel column-based approach has advantages over both traditional canopy height model-based methods and computationally demanding point-based solutions. The method was tested on 1125 reference trees, ≥10 cm diameter at breast height (DBH), and it detected 68% of all reference trees and 87% of medium and large (sawtimber-sized) trees ≥28 cm DBH. Significantly, the commission rate (false predictions) was negligible as most raw false positives were confirmed in follow-up field visits to be either small trees below the threshold for recording or trees that were otherwise missed during the initial ground survey. Minimizing false positives was a priority in tuning the method. Follow-up in-situ evaluation of individual omission and commission instances was facilitated by the high spatial accuracy of predicted tree locations generated by the method. The mean and maximum predicted-to-reference tree distances were 0.59 m and 2.99 m, respectively, with over 80% of matches within <1 m. A new tree-matching method utilizing linear integer programming is presented that enables rigorous, repeatable matching of predicted and reference trees and performance evaluation. Results indicate this new tree detection method has potential to be operationalized for both traditional forest management activities and in providing the more frequent and scalable inventories required by a growing forest carbon offsets industry

Evaluating Inter-Rater Reliability and Statistical Power of Vegetation Measures Assessing Deer Impact

Long-term vegetation monitoring projects are often used to evaluate how plant communities change through time in response to some external influence. Here, we evaluate the efficacy of vegetation monitoring to consistently detect changes in white-tailed deer browsing effects. Specifically, we compared inter-rater reliability (Cohen&#8217;s &#954; and Lin&#8217;s concordance correlation coefficient) between two identically trained field crews for several plant metrics used by Pennsylvania state agencies to monitor deer browsing impact. Additionally, we conducted a power analysis to determine the effect of sampling scale (1/2500th or 1/750th ha plots) on the ability to detect changes in tree seedling stem counts over time. Inter-rater reliability across sampling crews was substantial for most metrics based on direct measurements, while the observational based Deer Impact Index (DII) had only moderate inter-rater reliability. The smaller, 1/2500th ha sampling scale resulted in higher statistical power to detect changes in tree seedling stem counts due to reduced observer error. Overall, this study indicates that extensive training on plant identification, project protocols, and consistent data collection methods can result in reliable vegetation metrics useful for tracking understory responses to white-tailed deer browsing. Smaller sampling scales and objective plant measures (i.e., seedling counts, species richness) improve inter-rater reliability over subjective measures of deer impact (i.e., DII). However, considering objective plant measures when making a subjective assessment regarding deer browsing effects may also improve DII inter-rater reliability