e-space
Manchester Metropolitan University's Research Repository

    Representing forested regions at small scales: automatic derivation from very large scale data

    Mackaness, William A., Perikleous, S. and Chaudhry, Omair Z. (2008) Representing forested regions at small scales: automatic derivation from very large scale data. The cartographic journal, 45 (1). pp. 6-17. ISSN 0008-7041

    File not available for download.

    Abstract

    As with any class of feature, it is important to be able to view woodland or forest at multiple levels of detail. At the detailed level, a map can show clusters of trees, tree types, tracks and paths; at the small scale, say 1:250 000, we can discern broad patterns of forests and other land use, which can inform planners and act as input to land resource models. Rather than store such information in separate databases (requiring multiple points of maintenance), the vision is that the information has a single point of storage and maintenance, and that from this detailed level, various, more generalised forms can be automatically derived. This paper presents a methodology and algorithm for automatically deriving forest patches suitable for representation at 1:250 000 scale directly from a detailed dataset. In addition to evaluation of the output, the paper demonstrates how such algorithms can be shared and utilised via 'generalisation web services', arguing that the sharing of such algorithms can help accelerate developments in map generalisation, and increase the uptake of research solutions within commercial systems.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    0Downloads
    6 month trend
    314Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record