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Landscape Dynamics in an Iconic Watershed of Northwestern Mexico: Vegetation Condition Insights Using Landsat and PlanetScope Data

Lara Cornejo-Denman,J. R. Romo-León,K. Hartfield,W. Leeuwen,G. Ponce-Campos,A. Castellanos-Villegas

Published 2020 in Remote Sensing

ABSTRACT

Natural vegetation in arid and semi-arid environments of Northwestern Mexico has been subject to transformation due to extensive and intensive human occupation related mostly to primary activities. Keystone habitats such as riparian ecosystems are extremely sensitive to land use changes that occur in their surrounding landscape. In this study, we developed remote sensing-based land cover classifications and post-classification fragmentation analysis, by using data from Landsat’s moderate resolution sensors Thematic Mapper and Operational Land Imager (TM and OLI) to assess land use changes and the shift in landscape configuration in a riparian corridor of a dynamic watershed in central Sonora during the last 30 years. In addition, we derived a high spatial resolution classification (using PlanetScope-PS2 imagery) to assess the “recent state” of the riparian corridor. According to our results, riparian vegetation has increased by 40%, although only 9% of this coverage corresponds to obligate riparian species. Scrub area shows a declining trend, with a loss of more than 17,000 ha due to the expansion of mesquite and buffelgrass-dominated areas. The use of moderate resolution Landsat data was essential to register changes in vegetation cover through time, however, higher resolution PlanetScope data were fundamental for the detection of limited aerial extent classes such as obligate riparian vegetation. The unregulated development of anthropogenic activities is suggested to be the main driver of land cover change processes for arid ecosystems in this region. These results highlight the urgent need for alternative management and restoration projects in an area where there is almost a total lack of protection regulations or conservation efforts.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Remote Sensing

  • Publication date

    2020-08-01

  • Fields of study

    Geography, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs12162519
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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