Abstract
Abstract. The December 2004 tsunami strongly impacted coastal ecosystems along the Andaman Sea coast of Thailand. In this paper tsunami-induced damage of five different coastal forest ecosystems at the Phang-Nga province coast is analysed with a remote sensing driven approach based on multi-date IKONOS imagery. Two change detection algorithms, change vector analysis (CVA) and direct multi-date classification (DMC), are applied and compared regarding their applicability to assess tsunami impacts. The analysis shows that DMC outperforms CVA in terms of accuracy (Kappa values for DMC ranging between 0.947 and 0.950 and between 0.610–0.730 for CVA respectively) and the degree of detail of the created change classes. Results from DMC show that mangroves were the worst damaged among the five forests, with a 55% of directly damaged forest in the study area, followed by casuarina forest and coconut plantation. Additionally this study points out the uncertainties in both methods which are mainly due to a lack of ground truth information for the time between the two acquisition dates of satellite images. The created damage maps help to better understand the way the tsunami impacted coastal forests and give basic information for estimating tsunami sensitivity of coastal forests.
Highlights
The Indian Ocean Tsunami in 2004 was one of the most devastating natural disasters ever
Most of them are based on field work, e.g. measurements conducted by the Department of Marine and Coastal Resources (e.g. DMCR, 2005, 2006; DMCR and Thammasat University, 2005) or by UNEP (UNEP, 2005) or the Office of Natural Resources and Environmental Policy and Planning (ONEP) (ONEP, 2005)
In order to distinguish between different types of coastal forest, a land use map was created from the pre-tsunami IKONOS image by object oriented image analysis
Summary
On a larger spatial scale, remote sensing based studies were conducted in Thailand in order to provide a quick damage assessment: Vu et al (2007) used a dual-scale approach based on ASTER and IKONOS imagery, while Kouchi and Yamazaki (2007) used a digital elevation model and spectral indices (NDVI, NDWI, NDSI) developed from ASTER pre- and post-tsunami data. Other remote sensing studies refer to specific ecosystems like mangroves: Bahugana et al (2008) analyse tsunami impacts on coral reefs and mangroves at the Andaman and Nicobar Islands using an unsupervised post-classification approach based on multi-temporal RESOURCESAT and AWiFS data. Two different change detection techniques (change vector analysis, CVA, and direct multi-date classification, DMC) are compared. The results of this study help to better understand the way coastal forests have been impacted by the tsunami and provide a further step for in-depth-assessments of ecological and socio-ecological tsunami sensitivity of the coastal area
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