Daniel L. Schmoldt

Lumber value differences from reduced CT spatial resolution and simulated log sawing

Abstract In the past few years, computed tomography (CT) scanning technology has been applied to the detection of internal defects in hardwood logs for the purpose of obtaining a priori information that can be used to arrive at better log sawing decisions. Because sawyers currently cannot even see the inside of a log until the log faces are revealed by sawing, there is little perceived need to obtain scan images as detailed as those obtained in medical CT imaging. Yet, CT scanner speed and the usefulness of CT data for decision-making are dependent on the spatial resolution of scans. Spatial resolution is a function of three factors: physical pixel size, scan thickness, and scan frequency (pitch). A 3×2 3 factorial experiment was designed with two levels for each of these three factors, to test their effect on lumber values. Three hypothetical logs corresponding to three hardwood log grades were simulation-scanned, then simulation-sawed by a human operator using a modified Malcolm opening face heuristic. Log grade affected lumber value recovery as expected, although reduced spatial resolution (by doubling the pitch, thickness, and pixel size) exhibited no discernible pattern in our statistical tests for effects. Volume recovery for below grade boards was predicted very accurately by size, thickness, and pitch-size. The greatest opportunity for lumber value recovery improvement using information-augmented sawing appears to be in grade #2 logs.

Computer-aided decision making

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Determining climate effects on US total agricultural productivity

Significance Projections of the economic consequences of climate change are valuable for policy making but generally rely on integrated assessments that cannot account for highly localized climate effects. Most agricultural climate impact studies focus on local effects or partial productivity measures insufficient to capture national economic outcomes. Here, we directly link climate variables in specific US regions to total factor productivity (TFP). We quantify the national economic consequences of past climate variations, identify critical agricultural regions with national significance, and project future changes in TFP under different climate scenarios. We provide a physical understanding of these climate−economic links, show that the agricultural economy is becoming increasingly sensitive to climate, and lay a more concrete foundation for informed decision-making. The sensitivity of agricultural productivity to climate has not been sufficiently quantified. The total factor productivity (TFP) of the US agricultural economy has grown continuously for over half a century, with most of the growth typically attributed to technical change. Many studies have examined the effects of local climate on partial productivity measures such as crop yields and economic returns, but these measures cannot account for national-level impacts. Quantifying the relationships between TFP and climate is critical to understanding whether current US agricultural productivity growth will continue into the future. We analyze correlations between regional climate variations and national TFP changes, identify key climate indices, and build a multivariate regression model predicting the growth of agricultural TFP based on a physical understanding of its historical relationship with climate. We show that temperature and precipitation in distinct agricultural regions and seasons explain ∼70% of variations in TFP growth during 1981–2010. To date, the aggregate effects of these regional climate trends on TFP have been outweighed by improvements in technology. Should these relationships continue, however, the projected climate changes could cause TFP to drop by an average 2.84 to 4.34% per year under medium to high emissions scenarios. As a result, TFP could fall to pre-1980 levels by 2050 even when accounting for present rates of innovation. Our analysis provides an empirical foundation for integrated assessment by linking regional climate effects to national economic outcomes, offering a more objective resource for policy making.

Surface shape analysis of rough lumber for wane detection

The initial breakdown of hardwood logs into lumber produces boards with rough surfaces. These boards contain wane (missing wood that emanates from the log exterior, often containing residual bark) that is removed by edge and trim cuts prior to sale. Because hardwood lumber value is determined based on board size and quality, knowledge of wane position and defects is essential for selecting cuts that maximize profit. We have developed a system that uses structured light to obtain profile (thickness) images of unplaned boards, in addition to gray-scale images for defect detection. The focus of this paper is to describe a new approach for detecting wane boundaries through the analysis of these profile images. The problem is difficult because bark and other debris adversely affect the laser-based imaging process, and because variations in surface reflectance also cause inaccuracies in the measured thickness values. The problem is compounded by the need to perform wane detection rapidly in a manufacturing environment. The method that we have developed relies on a combination of column-wise image statistics, selective smoothing, and the analysis of surface shape. Initial wane edge estimates that are obtained using the smoothed image are then refined by analysis of the original image data. The paper provides a quantitative evaluation that indicates a dramatic improvement over traditional thresholding techniques.

Knowledge management: An application to wildfire prevention planning

Residential encroachment into wildland areas places an additional burden on fire management activities. Prevention programs, fuel management efforts, and suppression strategies, previously employed in wildland areas, require modification for protection of increased values at risk in this interface area. Knowledge-based computer systems are being investigated as knowledge management tools for the organization, synthesis, and application of information pertinent to fire science utilization. Many such systems contain expertise which has been captured from human experts and symbolically encoded for automatic manipulation by computer. Two systems, fire characteristics prediction and initial-attack force dispatch, have been developed elsewhere using this approach. This paper describes a third project, which is currently being developed for wildfire prevention planning. Initial efforts in elicitation of knowledge from experts have produced several benefits prior to system implementation. Results to date in fire management are encouraging, and provide support for the future potential of these methods for the management of knowledge gained from fire research.

An Ultraviolet Radiation Monitoring and Research Program for Agriculture

The United States Department of Agriculture (USDA) Ultraviolet-B Monitoring and Research Program (UVMRP) was initiated in 1992 through a grant to Colorado State University (Fort Collins, CO, USA), authorized by Congress under the USDA Cooperative State Research Education and Extension Service (CSREES) Special Research Grant authority, to provide the agricultural science research community with the information necessary to determine if changing levels of UV-B radiation would threaten food and fiber production in the United States. The UVMRP consists of three major components: (1) monitoring solar radiation with an emphasis on UV-B radiation; (2) research to determine the effects of UV radiation on specific plants and crops; and (3) crop growth and production assessment modeling to assess the impact of climate change scenarios on crop production. The monitoring network, consisting of UV and visible solar radiation measurement instrumentation installed at 37 climatological sites, is described in this chapter, along with the basic algorithms used to process the data and the calibration methods designed to provide accurate long term data records. Procedures developed to provide aerosol optical depth, columnar ozone, and enhanced products, such as integrated irradiances weighted with biological spectral weighting functions and summed over selected time periods, are also described. An updated, flexible web page interface that allows users to access various data products is documented. The UVMRP’s role in UV-B agricultural effects studies is summarized, including contributions by scientists at several collaborating universities. The UVMRP’s component that models agricultural sustainability by coupling a state of the science climate forecasting model to crop growth models in order to obtain the impact of climate change scenarios on crop yield is introduced. Future directions of UVMRP are also presented.

Wavelet-based hierarchical surface approximation from height fields

This paper presents a novel hierarchical approach to triangular mesh generation from height fields. A wavelet-based multiresolution analysis technique is used to estimate local shape information at different levels of resolution. Using predefined templates at the coarsest level, the method constructs an initial triangulation in which underlying object shapes are well preserved. Wavelet detail coefficients directly control the selection of appropriate templates, and are then used for subdividing and refining the initial mesh.

Turning small snapshots into a bigger picture for sustainability

The broader scientific community is slowly coming to grips with the concept of sustainability. An inherent difficultly with this concept has been that, unlike traditional scientific investigations that seek to explain how things currently function or how previous events led to current phenomena, sustainability research is forward-looking with the goal to understand how both current, and indeterminate future, societal needs can be met. This goal is further constrained by an imperative for maintaining ecological and environmental integrity. This conference addresses several important themes pertinent to the challenge of sustainable ecosystems: data collection and monitoring of natural systems and their components, analysis of those data in the context of biophysical ecosystem models, and application of model outputs to environmental and economic issues for management and policy making. Ecological scale and systems science are two important, but often underappreciated, concepts that are critical for advancing our understanding of sustainability. New sustainability sciences, appearing at the interface of traditional disciplines, are better poised to integrate these concepts. The most important objective of our data collection and modeling efforts will be to anticipate sustainability problems and recommendation alternative courses of action, while aiding social learning by the non-scientific community.