Detection of seagrass beds in Khung Kraben Bay, Thailand, using ALOS AVNIR2 image

摘要

Coastal habitats having high productivity provide numerous ecological services such as foods, protection from strong waves through buffering effect, fixation of CO2 through photosynthesis, fostering biodiversity etc. However, increasing human impacts and climate change decrease or degrade coastal habitats. ASEAN region is developing most rapidly in the world. In the developing region, it is necessary to grasp present spatial distributions of habitats as a baseline data with standardized mapping methods. Remote sensing is one of the most effective methods for mapping. Japan Aerospace Exploration Agency (JAXA) provides non-commercial satellite images with ultra-high spatial resolution optical sensors (10 m), AVNIR2, similar to LANDS AT TM. Using ALOS AVNIR2 images it may be possible to make habitat map in the region. In Thailand, shrimp ponds cause degradation of coastal ecosystem through cutting mangroves and eutrophicated discharge from ponds. We examined capability of remote sesing with ALOS AVNIR2 to map seagrass beds in Khung Kraben Bay, Chanthaburi Province, Thailand, surrounded by shrimp ponds. We analyzed ALOS AVNIR2 taken on 25 January 2008. Ground truth survey was conducted in October 2010 using side scan sonar and scuba diving. The survey revealed that there were broad seagrass beds consisting of Enhalus acroides. We used a decision tree to detect seagrass beds in the bay with quite turbid seawater coupled with Depth-Invariant Index proposed by Lyzenga (1985) and bottom reflectances. We could succeed to detect seagrass beds. Thus it is concluded that ALOS AVNIR2 is practical to map seagrass beds in this region.