Multi-hazard risk assessment using GIS in urban areas: a case study for the city of Turrialba, Costa Rica

In the framework of the UNESCO sponsored project on “Capacity Building for Natural Disaster Reduction” a case study was carried out on multi-hazard risk assessment of the city of Turrialba, located in the central part of Costa Rica. The city with a population of 33,000 people is located in an area, which is regularly affected by flooding, landslides and earthquakes. In order to assist the local emergency commission and the municipality, a pilot study was carried out in the development of a GIS –based system for risk assessment and management. The work was made using an orthophoto as basis, on which all buildings, land parcels and roads, within the city and its direct surroundings were digitized, resulting in a digital parcel map, for which a number of hazard and vulnerability attributes were collected in the field. Based on historical information a GIS database was generated, which was used to generate flood depth maps for different return periods. For determining the seismic hazard a modified version of the Radius approach was used and the landslide hazard was determined based on the historical landslide inventory and a number of factor maps, using a statistical approach. The cadastral database of the city was used, in combination with the various hazard maps for different return periods to generate vulnerability maps for the city. In order to determine cost of the elements at risk, differentiation was made between the costs of the constructions and the costs of the contents of the buildings. The cost maps were combined with the vulnerability maps and the hazard maps per hazard type for the different return periods, in order to obtain graphs of probability versus potential damage. The resulting database can be a tool for local authorities to determine the effect of certain mitigation measures, for which a cost-benefit analysis can be carried out. The database also serves as an important tool in the disaster preparedness phase of disaster management at the municipal level

Rapid Inventory of Earthquake Damage (RIED)

The 25 January 1999 Quindío earthquake in Colombia was a major disaster for the coffee-growing region in Colombia. Most of the damage occurred in the city of Armenia and surrounding villages. Damage due to earthquakes is strongly related to topographic and subsurface geotechnical conditions underneath structures and houses. The RIED project used aerial photographs to obtain a rapid inventory of the earthquake damage right after the seismic event. This inventory was subsequently used to identify any existing relation with subsurface- and topographic conditions. Hazard zonation maps were made on the basis of seismic response analysis of a three-dimensional model of the subsurface that has been created in the GIS. Also indicative zonation maps were created outlining potential areas where topographic amplification may occur. These seismic zonation maps delineate those areas that are most likely affected by subsurface and topographic resonance effects during a future and similar earthquake. The maps have been presented to the city planning authorities of Armenia so that reconstruction of the damaged areas can be carried out in such a way that high risk areas will be avoided or that structures and houses will be built according to the standards for high seismic risk areas

Analisis dan Estimasi Dampak Longsorlahan terhadap Jaringan Jalan di Kecamatan Samigaluh, Kabupaten Kulonprogo

In this study, direct risk assessment was developed for various scenarios on the basis of hazard (e.g. spatial probability, temporal probability and magnitude class), vulnerability and estimating cost of road damage. Indirect risk assessment was derived from traffic interruption. The impact of landslide both direct and indirect impact were analyzed in the road segment 174. The research results show the highest direct impact of debris slide type of magnitude I located in the 20th mapping unit. The lowest direct impact of debris slide type of magnitude I can be founded in the 18th mapping unit. The direct impact of rock fall type of magnitude I which is located in the 6th mapping unit. Meanwhile, indirect impact which was caused by road blockage is Rp. 4,593,607.20 and Rp. 4,692,794.40 by using network analysis and community perception methods respectively. After class classification, road segment 174 is dominated by very low hazard, very low vulnerability and very low direct impact

Designing a spatial planning support system for rapid building damage survey after an earthquake: The case of Bogota D.C., Colombia

Damage assessment determines the safe condition of houses and buildings that were affected by a disaster. These elements must be inspected to determine if they can be occupied by people. The objective of the present research is to design a model for the planning of a rapid building damage survey after an earthquake and manage the spatial information collected. The model is built on three sub-models aiming to estimate the number of trained people required, their spatial allocation and the right information flow. The combination of cadastral data and organizational issues will be the input, to estimate the number of trained people required. To allocate the trained people, five methods were applied: average number of parcels or blocks, euclidean allocation, multiple-ring buffer, network analysis (service area), and route allocation. All the data required to respond in an emergency must be collected, updated and shared in order to have informed decisions. The results show wide ranges of values that can be utilized in the preparedness or in the response phase; the allocation methods can be used according to the data that every city has, but the highest level of accuracy comes from the route allocation method. The data must be available, updated and accessible to all the entities involved in the emergency response task, due to these reasons the research recommends the implementation of a Spatial Data Infrastructure (SDI) to manage the information and to predefine the meeting points to compile the collected information by using methods as mean center

Low-cost UAV surveys of hurricane damage in Dominica: automated processing with co-registration of pre-hurricane imagery for change analysis

In 2017, hurricane Maria caused unprecedented damage and fatalities on the Caribbean island of Dominica. In order to ‘build back better’ and to learn from the processes causing the damage, it is important to quickly document, evaluate and map changes, both in Dominica and in other high-risk countries. This paper presents an innovative and relatively low-cost and rapid workflow for accurately quantifying geomorphological changes in the aftermath of a natural disaster. We used unmanned aerial vehicle (UAV) surveys to collect aerial imagery from 44 hurricane-affected key sites on Dominica. We processed the imagery using structure from motion (SfM) as well as a purpose-built Python script for automated processing, enabling rapid data turnaround. We also compared the data to an earlier UAV survey undertaken shortly before hurricane Maria and established ways to co-register the imagery, in order to provide accurate change detection data sets. Consequently, our approach has had to differ considerably from the previous studies that have assessed the accuracy of UAV-derived data in relatively undisturbed settings. This study therefore provides an original contribution to UAV-based research, outlining a robust aerial methodology that is potentially of great value to post-disaster damage surveys and geomorphological change analysis. Our findings can be used (1) to utilise UAV in post-disaster change assessments; (2) to establish ground control points that enable before-and-after change analysis; and (3) to provide baseline data reference points in areas that might undergo future change. We recommend that countries which are at high risk from natural disasters develop capacity for low-cost UAV surveys, building teams that can create pre-disaster baseline surveys, respond within a few hours of a local disaster event and provide aerial photography of use for the damage assessments carried out by local and incoming disaster response teams

Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis

Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at line uri \u3ehttps://doi.org/10.17026/dans-x6c-y7x2\u3e (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.

Rapid Mapping of Landslides in the Western Ghats (India) Triggered by 2018 Extreme Monsoon Rainfall Using a Deep Learning Approach

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in theWestern Ghats of India.We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions