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From Land Cover to Land Use: A Methodology to Assess Land Use from Remote Sensing Data

S. Martínez,D. Mollicone

Published 2012 in Remote Sensing

ABSTRACT

The “land use” concept has evolved during recent decades and it is now considered as the socioeconomic function of land. Land use representation and land use change assessment through remote sensing still remains one of the major challenges for the remote sensing scientific community. In this paper we present a methodological approach based on remote sensing techniques to assess land use in accordance with the requirements of the United Nations Framework Climate Change Convention, UNFCCC (1995). The methodology is based mainly on the recognition of the land key elements and their function and on the adoption of the “predominant land use” criteria in the classification scheme settled by rules. The concept that underpins these rules is that the land use function of land can be expressed through hierarchical relationships among key land elements, and that these functional relationships are based on thresholds reflecting the relevance and predominance of key land elements in the observed area. When analyses are supported by high (10–30 m) or very high ( < 10 m) spatial resolution remote sensing data, the methodology provides a systematic approach for the representation of land use that is consistent with the concepts and methodologies developed by the International Panel on Climate Change(IPCC) to fulfill UNFCCC commitments. In particular, data with high and very high spatial resolution provide good results, with overall accuracies above 87% in the identification of key land elements that characterize land use classes. The methodology could be used to assess land use in any context (e.g., for any land use category or in any country and region) as it is based on the definition of user/project rules that should be tailored on the land use function of any territory.

PUBLICATION RECORD

  • Publication year

    2012

  • Venue

    Remote Sensing

  • Publication date

    2012-04-17

  • Fields of study

    Geology, Geography, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs4041024
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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