Advances in Remote Sensing

Volume 7, Issue 1 (March 2018)

ISSN Print: 2169-267X   ISSN Online: 2169-2688

Google-based Impact Factor: 1.48  Citations  

A Method for Determining Batillaria attramentaria Distribution Using Aerial Balloon Photography and a Vegetation Index Camera: Demonstration at the Yatsu Tidal Flat, Chiba Prefecture

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DOI: 10.4236/ars.2018.71002    527 Downloads   893 Views  Citations

ABSTRACT

Located in Chiba Prefecture, the Yatsu Tidal Flat is an important stopover for birds migrating between cold regions such as Siberia and warm regions such as Southeast Asia and Australia. Its importance led to its selection in 1993 as the first tidal flat in Japan to be registered under the Convention on Wetlands of International Importance especially as Waterfowl Habitat (the Ramsar Convention). However, the Yatsu Tidal Flat has in more recent years witnessed blooms of Ulva spp. (sea lettuce) and an increase in exotic species such as Batillaria attramentaria (Japanese mud snail) and Mercenaria mercenaria (hard clam), fueling concerns that the increasing spatial domination of the tidal flat by such species and competition with other species for food may drive a decline in the habitat’s self-cleaning capabilities. For this study, we focused on Batillaria attramentaria, which is now so widely distributed in the Yatsu Tidal Flat as to preclude reliable monitoring via aerial photographs or satellite imagery. Accordingly, we tested the utility of a simplified method for obtaining data on the distribution of Batillaria attramentaria by using aerial balloon photography and a vegetation index camera capable of generating NDVI data. Our results show that under certain conditions, this method can indeed be used to determine Batillaria attramentaria distribution.

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Shiraki, Y. , Kitazawa, T. , Lee, S. and Senga, Y. (2018) A Method for Determining Batillaria attramentaria Distribution Using Aerial Balloon Photography and a Vegetation Index Camera: Demonstration at the Yatsu Tidal Flat, Chiba Prefecture. Advances in Remote Sensing, 7, 15-24. doi: 10.4236/ars.2018.71002.

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