Dagupan River Basin Exposure and Vulnerability Assessment of Buildings Extracted from LiDAR Derived Datasets

• 作者: Annie Melinda Paz-Alberto Jo Adrianne A. Espiritu Kathrina Mapanao
• 来源期刊: 美国气候变化期刊(英文)
• 学科: 地球科学
• 关键词: Geographic Information System FLOOD DATABASE Extraction HAZARD
• 年，卷（期）: 2020,（4）
• 页码范围: 454-479
• 页数: 26页
• 分类号: P73

摘要

The Philippines has a geographical and geological setting of a typhoon-prone country. Tropical cyclones have a high incidence of passing through the Philippine islands during the months of September to November with an average time overland at 11 hours north of 14.5<span style="white-space:nowrap;">&#176;</span> contrasted to 20 hours south of 14.5<span style="white-space:nowrap;">&#176;</span>. Due to the frequent occurrence of typhoons and tropical cyclones in this country, most of the provinces of the Philippines experience flood-related disasters that affect the people, their livelihood and many infrastructures. It is deemed necessary for the Philippines to come up with strategies to prevent further damage to its people and their properties. In this study, through the use of important parameters such as earth observations, Light Detection and Ranging (LiDAR) and Geographic Information System (GIS), assessment of buildings in Dagupan, the Philippines with the possibility of being affected by floods during different typhoon scenarios was done. GIS overlay analysis of the CLSU Phil-LiDAR 1 Project outputs, the 3D building GIS database, and flood hazard maps was done for the assessment. One (1) meter resolution LiDAR Digital Elevation Models (DEMs), geo-tagged video captured data and high-resolution images in Google Earth were used for processing and analysis to produce a 3D building GIS database. HEC HMS and HEC RAS were used to develop flood models that were used to produce flood hazard maps with different hazard levels. The results of this study were the series of flood exposure maps and vulnerability maps with statistics at different rainfall scenarios. Moreover, the buildings that will be affected by flood in the area were quantified and these were categorized according to their type. It was observed that as the rainfall return period increases, the number of buildings predicted to be exposed and vulnerable to flood also increases. The houses, business establishments, government offices, hospitals and other