Elizabeth Frankenberg

As extreme events intensify in force and frequency across the globe, relating the damage and subsequent reconstruction to population health and well-being remains a critical frontier. Here we build a convolutional neural network to classify landcover from satellite images of Indonesia before and after the December 2004 Indian Ocean Tsunami and link those measures to population well-being to demonstrate methods that advance analyses of short-term impacts of extreme events and impacts 5 years later. Population data are from the Study of the Tsunami Aftermath and Recovery (STAR) and 2005 and 2010 censuses. We develop manually labelled training data for eight landcover classes and demonstrate the model performs well using standard metrics. Moreover, measures of change over time in landcover correlate strongly with multiple dimensions of well-being from our household survey data and with aggregate population statistics, both immediately after the event and in the subsequent five years.

Despite significant research on the effects of stress on the hypothalamic–pituitary–adrenal (HPA) axis, questions remain regarding long-term impacts of large-scale stressors. Leveraging data on exposure to an unanticipated major natural disaster, the 2004 Indian Ocean tsunami, we provide causal evidence of its imprint on hair cortisol levels fourteen years later. Data are drawn from the Study of the Tsunami Aftermath and Recovery, a population-representative longitudinal study of tsunami survivors who were living along the coast of Aceh, Indonesia, when the tsunami hit. Annual rounds of data, collected before, the year after and 2 y after the disaster provide detailed information about tsunami exposures and self-reported symptoms of post-traumatic stress. Hair samples collected 14 y after the tsunami from a sample of adult participants provide measures of cortisol levels, integrated over several months. Hair cortisol concentrations are substantially and significantly lower among females who were living, at the time of the tsunami, in communities directly damaged by the tsunami, in comparison with similar females living in other, nearby communities. Differences among males are small and not significant. Cortisol concentrations are lowest among those females living in damaged communities who reported elevated post-traumatic stress symptoms persistently for two years after the tsunami, indicating that the negative effects of exposure were largest for them. Low cortisol is also associated with contemporaneous reports of poor self-rated general and psychosocial health. Taken together, the evidence points to dysregulation in the HPA axis and “burnout” among these females fourteen years after exposure to the disaster.

Little is known about whether the provision of aid in the aftermath of a large-scale natural disaster affects psychological well-being. We investigate the effects of housing assistance, a key element of the reconstruction program implemented after the 2004 Indian Ocean tsunami. Population-representative individual-level longitudinal data collected in Aceh, Indonesia, during the decade after the tsunami as part of the Study of the Tsunami Aftermath and Recovery (STAR) are used. Housing aid was targeted to people whose homes were destroyed and, to a lesser extent, damaged by the tsunami and to those who lived, at the time of the tsunami, in communities that sustained the greatest damage. The effects of receipt of aid on post-traumatic stress reactivity (PTSR) are examined using panel data models that take into account observed and unobserved individual-specific fixed characteristics that affect both PTSR and aid receipt, drawing comparisons in each survey wave between individuals who had been living in the same kecamatan when the tsunami hit. Those who received aid have better psychological health; the effects increase with time since aid receipt and are the greatest at two years or longer after the receipt. The effects are concentrated among those whose homes were destroyed in the tsunami.

The majority of the aboveground biomass on the Earth’s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information about species composition for the forests in the study area, LiDAR data for canopy height, and the image texture and image texture ratio at two spatial resolutions for tree crown size, which is related to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All the data layers were fed to a Random Forest (RF) regression model. The study was carried out in eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train and test the model performance. The best model achieved an R² of 0.625 with a root mean squared error (RMSE) of 18.8 Mg/ha (47.6%) with the “out-of-bag” samples at 30 × 30 m spatial resolution. The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from very high-resolution imagery were selected. But the importance of the height metrics dwarfed all other variables. More tests of the conceptual model in places with a broader range of biomass and more diverse species composition are needed to evaluate the importance of other input variables.