KEYWORDS: Agriculture, Ecosystems, Data modeling, Systems modeling, Climatology, Climate change, Environmental monitoring, Remote sensing, Process modeling, Biological research
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.
Knowledge of internal log defects, obtained by scanning, is critical to efficiency improvements for future hardwood sawmills. Nevertheless, before computed tomography (CT) scanning can be applied in industrial operations, we need to automatically interpret scan information so that it can provide the saw operator with the information necessary to make proper sawing decisions. Our current approach to automatically label features in CT images of hardwood logs classifies each pixel individually using a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this ANN was able to classify clearwood, bark, decay, knots, and voids in CT images of two species of oak with 95% pixel-wise accuracy. Recently we have investigated other ANN classifiers, comparing 2D versus 3D neighborhoods and species-dependent (single species) versus species- independent (multiple species) classifiers using oak, yellow poplar, and cherry CT images. When considered individually, the resulting species-dependent classifiers yield similar levels of accuracy (96 - 98%). 3D neighborhoods work better for multiple-species classifiers and 2D is better for single-species. Under certain conditions there is no statistical difference in accuracy between single- and multiple-species classifiers, suggesting that a multiple- species classifier can be applied broadly with high accuracy.
Millions of wooden pallets are discarded annually due to damage or because their low cost makes them readily disposable. Higher quality wooden pallets, however, can be built from high quality deckboards and stringers, and have a much longer life cycle and a lower cost per trip. The long- term goal of this project is to develop an automated pallet part inspection system to sort pallet parts according to grade. Ultrasonic time of flight (TOF) measurements in a pitch-catch arrangement are being used to distinguish types of defects, including knots, decay, cross grain, and voids, from clear wood. Rolling transducers of 3 different frequencies have been used to collect measurements on four oak deckboards of 1/2 inch thickness. Ultrasonic C-scans taken on a 1/2 inch by 1/2 inch grid indicate that TOF with 84 KHz transducers can be used to partially distinguish between several deckboard features and clear wood. Nevertheless, future application of these results to defect detection must not be limited to single, pixel value classification, but must include pixel neighborhoods with textural information.
Conference Committee Involvement (8)
Remote Sensing and Modeling of Ecosystems for Sustainability VII
3 August 2010 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability VI
5 August 2009 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability V
13 August 2008 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability IV
28 August 2007 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability III
14 August 2006 | San Diego, California, United States
Sensors for Harsh Environments II
23 October 2005 | Boston, MA, United States
Remote Sensing and Modeling of Ecosystems for Sustainability II
2 August 2005 | San Diego, California, United States
Sensors for Harsh Environments
27 October 2004 | Philadelphia, Pennsylvania, United States
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