**1. Introduction**

142 Remote Sensing of Planet Earth

Yuan, F.; Sawaya, K. E.; Loeffelholz, B. C & Bauer, M. E. (2005). Land Cover Classification

2-3, pp. 317-328, ISSN 0034-4257

and Change Analysis of the Twin Cities (Minnesota) Metropolitan Area by Multitemporal Landsat Remote Sensing. *Remote Sensing of Environment,* Vol.98, No.

> This chapter aims to introduce an application of remote sensing to recent tsunami disasters. In the past, acquiring tsunami damage information was limited to only field surveys and/or using aerial photographs. In the last decade, remote sensing was applied in many tsunami researches, such as tsunami damage detection. Satellite remote sensing can help us survey tsunami damage in many ways. In general, the application of remote sensing for tsunami disasters can be classified into three stages depending on time and disaster-related information. In the first stage, general damage information, such as tsunami inundation limits, can be obtained promptly using an analysis combined with ground truth information in GIS. The tsunami inundation area is one of the most important types of information in the immediate aftermath of a tsunami because it helps estimate the scale of the tsunami's impact. Travel to a tsunami-affected area for field surveys takes a lot of time, given the presence of damaged roads and bridges, with much debris as obstacles. In the second stage, detailed damage interpretation can be analysed; i.e., classification of the building damage level. Recently, the quality of commercial satellite images has improved. These images help us clarify, i.e., whether a house was washed away or survived; they can even classify more damage levels. The third stage combines the damage and hazard information obtained from a numerical simulation, such as the tsunami inundation depth. The damage data are compiled with the tsunami hazard data via GIS. Finally, a tsunami vulnerability function can be developed. This function is a necessary tool for assessing future tsunami risk.

The contents of this chapter are arranged in three sections:


Application of Remote Sensing for Tsunami Disaster 145

Fig. 2. (a) Vegetation change found from pre- and (b) post-event imageries and estimated

Because mitigation and protection against the 2004 Indian Ocean Tsunami was one of the important services that mangrove ecosystems provided in the affected areas, a six-year program to conserve and rehabilitate mangrove forests in the tsunami-impacted areas was implemented by the Thai Government after the tsunami. However, information on mangrove restoration and reforestation is limited to field surveys. Monitoring proposals were applied for a damaged mangrove area. Kamthonkiat et al. (2011) used ASTER images acquired in 2003, 2005 (two months after the 2004 Indian Ocean tsunami), 2006 and 2010 and the analysis using NDVI to monitor the mangrove recovery in tsunami-impacted areas in the southern part of Thailand. Figure 3 depicts the area of mangroves in 2003 in red and the area impacted by the tsunami in 2005 in dark blue and white for the same location. After the mangrove trees were uniformly or homogeneously replanted in the same location in the last quarter of 2005 in Takuapa District, the areas marked in red increased in 2006 and increased still further in 2010, as shown in Fig. 3 (*Note*: red represents vegetation or mangroves, white represents bare soil/sand, and blue/dark blue represent water). The recovery process can be detected, as some parts in light blue became red in 2006, and most became red in 2010 meaning the mangroves recovered to nearly the normal condition before the tsunami attack. These results show the abilities of geoinformatic technologies, especially regarding the time

Fig. 3. Damage and recovery process due to the 2004 tsunami in Takuapa, Thailand

**2003 2005 2006 2010** 

extent of tsunami inundation zone by the supervised classification of NDVI.

*Damage and recovery monitoring of mangrove* 

series analysis.
