Rapid socioeconomic development in earthquake-prone areas can cause rapid changes in seismic loss risks. These changes make it difficult to ensure that risk reduction strategies are realistic, practical and effective over time. To overcome this difficulty, ongoing changes in risk should be captured timely, definitively, and accurately and then specific and well-timed adjustments of the relevant strategies should be made. However, methods for rapidly characterizing such seismic disaster risks over a large area have not been sufficiently developed. By focusing on building loss risks, this paper presents the development of an integrated method that combines remote sensing data and local knowledge to resolve this problem. This method includes two key interdependent steps. (1) To extract the heights and footprint areas of a large number of buildings accurately and quickly from single high-resolution optical remote sensing images; (2) To estimate the floor areas, identify structural types, develop damage probability matrixes, and determine economic parameters for calculating monetary losses due to seismic damage to the buildings by reviewing building-relevant local knowledge based on these two parameters (i.e., the building heights and footprint areas). This method is demonstrated in the Tangshan area of China. Based on the integrated method, the total floor area of the residential and public office buildings in central Tangshan in 2009 was 3.99% lower than the corresponding area number obtained by a conventional earthquake loss estimation project. Our field-based verification indicated that the mean relative error of the method for estimating the floor areas of the assessed buildings was 2.99%. A simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake using this method indicated that the total damaged floor area of the residential and public office buildings and the associated direct monetary loses in the study area could have been 8.00 and 28.73 times greater, respectively, than in 1976 if this earthquake had recurred in 2009, which is a strong warning to the local people regarding the increasing challenges they may face.

Rural communities are generally more vulnerable to natural hazards when compared to urban communities. Moreover, rural communities are diverse and unique in their place, population, agricultural production and culture, which make it challenging for different rural settings to prepare for disasters. There is a little comparison made about the individual disaster preparedness among rural communities with different geographic landforms. In this study, we examined the individual disaster preparedness of rural residents in three drought-and-flood-prone villages with different landforms (plains, loess plateau and mountains) via a cross-sectional self-report structured questionnaire survey conducted in Northwest China. We also adopted an ecological framework to examine the determinants of villagers’ individual disaster preparedness across different dimensions: place, individual sociodemographic factors, family socioeconomic status, hazard adaptations, community and neighbourhood influences. We found that place was a significant factor for disaster preparedness when controlling individual sociodemographic and family socioeconomic factors. The level of preparedness in the plains was higher than both mountains and plateau. Moreover, the villagers who had out-migrated to work reported a higher level of disaster preparedness than did local villagers. In addition, the community and neighbourhood played an important role in determining individual disaster preparedness. This research highlights the needs for tailored community-based disaster risk reduction programs to improve villagers’ knowledge and skills of disaster preparedness.

This paper presents the development of an “Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth. This approach is a combination of the following interdependent components: (1) extraction of footprint areas of a large number of buildings from high-resolution Google Earth images; (2) estimation of floor numbers of these buildings with an integrated use of high-resolution Google Earth images, Tencent/Baidu Street Views, crowdsourcing data, and associated building-relevant local knowledge; and (3) identification of structural types of these buildings by a combined use of crowdsourcing data and associated building-relevant local knowledge. The efficacy of this “Internet+” approach was demonstrated through an application in Tangshan, China. Field-based verification indicated that the overall mean absolute percentage error of the proposed “Internet+” approach in assessing the total floor area of the addressed buildings was 4.64 %. The verification also showed that the overall consistency between the estimated structural types using the proposed approach and the actual structural types of the buildings with structural type uncertainties could reach 97.54 %, with a kappa coefficient of 0.94. Because of its good accuracy, noteworthy speed, substantial labor savings, negligible cost and distinctive capability in covering large areas in near real time, this “Internet+” approach might have promising prospects in actual seismic loss risk reduction challenges.

Earthquakes happen suddenly and are immensely destructive. They not only destroy entire societal production and infrastructure systems but also seriously interfere with daily life and reduce opportunities to earn income in earthquake-affected areas. In this paper, using the Ning’er Ms 6.4 earthquake in 2007 as an example, we analyzed the livelihood vulnerability of rural households in Ning’er County, Yunnan, based on data from questionnaires and on-site interviews. The results showed that on the whole, local rural household livelihoods are relatively vulnerable in the earthquake-affected area of Ning’er. The main reason for the high level of vulnerability of rural households is the lack of single or multiple incomes. Due to the shortage of household income, the capacity of rural households to manage the aftermath of an earthquake is low. Improving the income allocation and transformation level and expanding methods of earning income is an effective way for rural households to decrease livelihood vulnerability in earthquake-prone areas. Some suggestions are given for local rural households to enhance their livelihood income levels in the event of earthquakes.

Natural hazards not only cause direct losses of household income and assets but also affect the choice and outcome of livelihood strategies. Based on the questionnaire survey from 2007 on Mw 6.1 Ning’er earthquake-hit areas in Southwest China, we analyzed the relationship between livelihood assets (or capitals) and livelihood strategies of local rural households and identified the main factors influencing the choice of livelihood strategy. The results indicate that statistically significant differences exist in livelihood assets among livelihood strategies. The choice of livelihood strategies is affected significantly by the status of livelihood assets. High financial capitals enable households to engage in higher-returns or capital-intensive livelihood activities. Improving vocational education and skills training for rural residents, especially for the youth, is also important in developing new livelihood strategies beyond their traditional lifestyle. Higher social capitals provide households the opportunity to select a higher return and income livelihood strategy. In addition, as part of efforts to enhance physical capitals, housing construction planning and technical guidance are of critical importance to improve the anti-seismic performance of rural buildings and decrease their livelihood risk in earthquake-prone areas. It is important for farmers to improve and diversify their livelihood strategies according to regional geographical environment and the comparative advantages of their own livelihood assets.