Using remote sensing to assess the protective role of coastal woody vegetation against tsunami waves

Olwig, Mette Fog; Sørensen, Mads Sølvsten; Rasmussen, Michael Schultz; Danielsen, Finn; Selvam, V.; Hansen, L.; Nyborg, Lotte; Vestergaard, Kristian; Parish, Faizal; Karunagaran, V. M.

doi:10.1080/01431160701420597

Cited by 39 publications

(27 citation statements)

References 44 publications

(90 reference statements)

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“…However, Omira's approach considers a smaller number of building attributes, excluding in particular those related to building surroundings (e.g. natural barriers, the shielding effect provided by other buildings, the presence of large movable objects) which were found to be very influential on the final level of damage observed after past tsunamis (Dominey-Howes and Papathoma, 2007;Reese et al, 2007;Olwig et al, 2007;Matsutomi et al, 2006;Tanaka et al, 2006). Further, Omira et al (2009) divide the exposed buildings in different classes according to their main structural characteristics, and give the same score (the "classification factor") to all the buildings of the same class.…”

Section: The Ptva Model: Description Applications and Competing Methodsmentioning

confidence: 99%

Applying and validating the PTVA-3 Model at the Aeolian Islands, Italy: assessment of the vulnerability of buildings to tsunamis

Dall’Osso

Maramai

Graziani

et al. 2010

Nat. Hazards Earth Syst. Sci.

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Abstract. The volcanic archipelago of the Aeolian Islands (Sicily, Italy) is included on the UNESCO World Heritage list and is visited by more than 200 000 tourists per year. Due to its geological characteristics, the risk related to volcanic and seismic activity is particularly high. Since 1916 the archipelago has been hit by eight local tsunamis. The most recent and intense of these events happened on 30 December 2002. It was triggered by two successive landslides along the north-western side of the Stromboli volcano (Sciara del Fuoco), which poured approximately 2-3×10 7 m 3 of rocks and debris into the Tyrrhenian Sea. The waves impacted across the whole archipelago, but most of the damage to buildings and infrastructures occurred on the islands of Stromboli (maximum run-up 11 m) and Panarea.The aim of this study is to assess the vulnerability of buildings to damage from tsunamis located within the same area inundated by the 2002 event. The assessment is carried out by using the PTVA-3 Model (Papathoma Tsunami Vulnerability Assessment, version 3). The PTVA-3 Model calculates a Relative Vulnerability Index (RVI) for every building, based on a set of selected physical and structural attributes. Run-up values within the area inundated by the 2002 tsunami were measured and mapped by the Istituto Italiano di Geofisica e Vulcanologia (INGV) and the University of Bologna during field surveys in January 2003. Results of the assessment show that if the same tsunami were to occur today, 54 buildings would be affected in Stromboli, and 5 in Panarea. The overall vulnerability level obtained in this analysis for Stromboli and Panarea are "average"/"low" and "very low", respectively. Nonetheless, 14 buildings in StromCorrespondence to: F. Dall'Osso (filippodallosso@gmail.com) boli are classified as having a "high" or "average" vulnerability. For some buildings, we were able to validate the RVI scores calculated by the PTVA-3 Model through a qualitative comparison with photographs taken by INGV and the University of Bologna during the post-tsunami survey. With the exception of a single structure, which is partially covered by a coastal dune on the seaward side, we found a good degree of accuracy between the PTVA-3 Model forecast assessments and the actual degree of damage experienced by buildings. This validation of the model increases our confidence in its predictive capability. Given the high tsunami risk for the archipelago, our results provide a framework for prioritising investments in prevention measures and addressing the most relevant vulnerability issues of the built environment, particularly on the island of Stromboli.

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Section: The Ptva Model: Description Applications and Competing Methodsmentioning

confidence: 99%

Applying and validating the PTVA-3 Model at the Aeolian Islands, Italy: assessment of the vulnerability of buildings to tsunamis

Dall’Osso

Maramai

Graziani

et al. 2010

Nat. Hazards Earth Syst. Sci.

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“… Spatial distribution and current state [130];  Species discrimination [2,77,[131][132][133][134];  Biomass estimation [135];  Assessment of vegetation indices, LAI [136,137];  Change detection [125,130]; and  Assessment of the protective role of mangroves in coastal protection [138].…”

Section: Overview Of Mangrove-mapping Studies Based On High-resolutiomentioning

confidence: 99%

“…These investigations were carried out on mangrove sites in India [138], Taiwan [125], Sri Lanka [131], Malaysia [139], Kenya [132], Egypt [133], French Guiana [135], Panama [24,77,134], Belize [130], Mexico [136,137], and Texas [2].…”

Section: Overview Of Mangrove-mapping Studies Based On High-resolutiomentioning

confidence: 99%

Remote Sensing of Mangrove Ecosystems: A Review

et al. 2011

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Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high-and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR) data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the OPEN ACCESSRemote Sens. 2011, 3 879 services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove ecosystem mapping, as well as the numerous methods and techniques used for data analyses, and to further discuss their potential and limitations.

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“…Satellite images in a time series sequence can assess changes from the present to several decades earlier 17 . On a larger scale, and directly relevant to this study, Quick Bird and IKONOS satellite images have been used for pre-tsunami and post-tsunami assessment of woody vegetation and Casuarina forests, showing how they provided protection to landward areas 18 . Previous literature 2, 5-7 describes how natural disasters and human uses of the coastal zone have impacted beach forest function.…”

Section: Introductionmentioning

confidence: 99%

Beach forest changes (2003–2013) in the tsunami-affected area of Phang Nga, Thailand from multi-temporal satellite data

Kongapai¹,

Sompongchaiyakul²,

Jitpraphai³

et al. 2016

ScienceAsia

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Beach forests are important ecological zones in many coastal regions. Many are under increasing anthropogenic and natural stress. Beach forest changes and their causes were examined in three tsunami-impacted sites with different land-use/cover (LULC): Ban Nam Khem (BNK, fishing village), Khao Lak (KL, tourist destination), and Thai Mueang (TM, part of a national park). Vegetation surveys, GIS, and interpretation of time series IKONOS and THEOS imagery using supervised classification (ENVI 4.7) from 2003-2013 were performed. Six beach forest tree and shrub species were found in BNK and KL, dominated by Casuarina equisetifolia, while 24 tree/shrub species, dominated by Syzygium grande, were observed at TM. After the tsunami, beach forests were severely damaged in BNK (45%), KL (40%), and TM (23%). Recovery of beach forests in 8 years varied from BNK (58%), KL (39%) with low rates in KL and high rates in TM (62%). Substantial portions of the three sites were still characterized as beach forest in 2013, though the forests now included areas that had recovered from the tsunami damage and/or altered LULC (e.g., barren land to beach forests and vice versa). Anthropogenic factors represented 40% (BNK), 56% (KL), and 5% (TM) of the changes with urbanization being a leading cause in tourist areas (KL; 24%). The study highlights the need for improved understanding of beach vegetation, tsunami impacts, and LULC to provide sustainable management of beach forests in Thailand in three sites with different anthropogenic characteristics.

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Using remote sensing to assess the protective role of coastal woody vegetation against tsunami waves

Cited by 39 publications

References 44 publications

Applying and validating the PTVA-3 Model at the Aeolian Islands, Italy: assessment of the vulnerability of buildings to tsunamis

Applying and validating the PTVA-3 Model at the Aeolian Islands, Italy: assessment of the vulnerability of buildings to tsunamis

Remote Sensing of Mangrove Ecosystems: A Review

Beach forest changes (2003–2013) in the tsunami-affected area of Phang Nga, Thailand from multi-temporal satellite data

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