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Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities†

RIDD, M. K.·1995·International Journal of Remote Sensing

Abstract

Growing interest in urban systems as ecological entities calls for some standards in parameterizing biophysical composition of urban environments. A vegetation-impervious surface-soil ( V-I-S) model is presented as a possible basis for standardization. The V-I-S model may serve as a foundation for characterizing urban/near-urban environments universally, and for comparison of urban morphology within and between cities. Inasmuch as the model may be driven by satellite digital data, it may serve as a global model of urban ecosystem analysis and comparison world-wide. The V-I-S model may prove useful for urban change detection and growth modelling, for environmental impact analysis from urbanization, for energy- and water-related investigations, and for certain dimensions of human ecosystem analysis of the city as well.

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Metadata

Authors
RIDD, M. K.
Venue
International Journal of Remote Sensing
Published
1995-08
Volume
16
Issue
12
Pages
2165-2185
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
OpenAlex
W2049827513
Cited by
1,025
References
0
FWCI
2.12
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities†. https://app.cochat.ai/papers/10.1080/01431169508954549
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RIDD, M. K. (1995). Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities†. International Journal of Remote Sensing. https://doi.org/10.1080/01431169508954549

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.