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Classification of river corridors: Issues to be addressed in developing an operational methodology
Author(s) -
Gurnell A. M.,
Angold P.,
Gregory K. J.
Publication year - 1994
Publication title -
aquatic conservation: marine and freshwater ecosystems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.95
H-Index - 77
eISSN - 1099-0755
pISSN - 1052-7613
DOI - 10.1002/aqc.3270040304
Subject(s) - computer science , variety (cybernetics) , raw data , thematic map , field (mathematics) , spatial analysis , data mining , generalization , geographic information system , classification scheme , multidisciplinary approach , data science , data type , operations research , geography , cartography , artificial intelligence , remote sensing , engineering , mathematics , mathematical analysis , social science , sociology , pure mathematics , programming language
The classification of river corridors may simply aim to describe what is there in a concise form or it may form a tool to support the assessment of conservation or enhancement potential and management decision‐making. An analysis of 140 international publications on river corridor classification illustrates temporal trends towards (i) multidisciplinary bases for classification related to (ii) increasingly small spatial units and spatially hierarchical structures, with (iii) the majority of the schemes being developed in North America and most of the remainder generated within Europe. An operational classification scheme that is robust and widely applicable will be based on a wide range of information types with a hierarchical structure incorporating different types and resolutions of information suitable to support classifications for different applications. In developing an operational scheme, spatial units for data handling must be defined which present a compromise between the natural boundaries representative of the character of the river corridor, and the environmentally arbitrary boundaries which often define the spatial units against which environmental data sets have been collected. A variety of data sources may support classification, including routine field surveys and laboratory determinations, thematic maps and remotely sensed information. Different sources present different problems of spatial resolution, generalization, age and changing data standards. Problems arise when data from different sources are integrated to derive a classification. GIS provides a flexible technology for such integration but the results may be misleading if GIS functions are not used intelligently. Data handling should (i) maintain separation between raw data and their derivatives, (ii) apply spatial aggregation or smoothing of data to a consistent spatial resolution prior to integration, and (iii) the spatial resolution selected should be appropriate for the hierarchical level to which it applies.