New PDF release: Computer Applications in Sustainable Forest Management

By Guofan Shao, Keith M. Reynolds

ISBN-10: 1402043058

ISBN-13: 9781402043055

ISBN-10: 1402043872

ISBN-13: 9781402043871

This publication is the main entire and up to date remedy of machine functions in forestry. it's the first textual content on software program for woodland administration to stress integration of desktop functions. It additionally deals vital new insights on how you can proceed advancing computational applied sciences in wooded area management.

The authors are internationally-recognized specialists within the matters presented.

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Extra resources for Computer Applications in Sustainable Forest Management Including Perspectives on Collaboration and I

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2002), and consensus building (Vincke 1992). Although some authors have tended to cast these new approaches as alternatives to rational models, it is perhaps more constructive to think of them as enhancements to the rational model (Ekbia and Reynolds 2006). These new approaches are not addressed further in this volume, but we encourage readers to explore the concepts, and consider how they might be Shao and Reynolds 10 incorporated into the application of decision-making tools discussed in Chapter 8.

The second tradition exploited in high-spatial-resolution forestry remote sensing is that of digital image processing, including both satellite image analysis and machine vision applications. It is precisely because highspatial-resolution data allows a focus on the object of interest, a single tree, rather than the arbitrary pixel, that the analysis of such data incorporates elements of machine vision, and not just traditional remote sensing techniques. Indeed, object-based analysis is one of the themes of this chapter.

Erikson (2003) used a region growing approach to segment single tree crowns in aerial photographs from a mixed-species boreal forest in central Sweden. Erikson’s (2003) region growing starts at single pixels, which represents seeds, and grows out to include adjacent pixels that fall within spatial and spectral thresholds. The single best segment obtained for each position is used to represent the tree crown. In an evaluation of this approach, approximately 73% of the segments agreed with the manual delineation, and the estimate of the total number of stems was within about 93% of the manual count obtained from interpretation of the aerial imagery.

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Computer Applications in Sustainable Forest Management Including Perspectives on Collaboration and I by Guofan Shao, Keith M. Reynolds

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