Sudden impact disasters often result in the displacement of large numbers of people. These movements can occur prior to events, due to early warning messages, or take place post-event due to damages to shelters and livelihoods as well as a result of long-term reconstruction efforts. Displaced populations are especially vulnerable and often in need of support. However, timely and accurate data on the numbers and destinations of displaced populations are extremely challenging to collect across temporal and spatial scales, especially in the aftermath of disasters. Mobile phone call detail records were shown to be a valid data source for estimates of population movements after the 2010 Haiti earthquake, but their potential to provide near real-time ongoing measurements of population displacements immediately after a natural disaster has not been demonstrated. A computational architecture and analytical capacity were rapidly deployed within nine days of the Nepal earthquake of 25th April 2015, to provide spatiotemporally detailed estimates of population displacements from call detail records based on movements of 12 million de-identified mobile phones users. Analysis shows the evolution of population mobility patterns after the earthquake and the patterns of return to affected areas, at a high level of detail. Particularly notable is the movement of an estimated 390,000 people above normal from the Kathmandu valley after the earthquake, with most people moving to surrounding areas and the highly-populated areas in the central southern area of Nepal. This analysis provides an unprecedented level of information about human movement after a natural disaster, provided within a very short timeframe after the earthquake occurred. The patterns revealed using this method are almost impossible to find through other methods, and are of great interest to humanitarian agencies.

Much of the geomorphic work of rivers occurs underwater. As a result, high resolution quantification of geomorphic change in these submerged areas is important. Currently, to quantify this change, multiple methods are required to get high resolution data for both the exposed and submerged areas. Remote sensing methods are often limited to the exposed areas due to the challenges imposed by the water, and those remote sensing methods for below the water surface require the collection of extensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimes prohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifying above- and below-water geomorphic change using Structure-from-Motion photogrammetry and investigate the implications of water surface elevations, refraction correction measures, and the spatial variability of topographic errors. We use two epochs of imagery from a site on the River Teme, Herefordshire, UK, collected using a remotely piloted aircraft system (RPAS) and processed using Structure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification of submerged geomorphic change to levels of accuracy commensurate with exposed areas is possible without the need for calibration data or a dierent method from exposed areas; (2) there is minimal dierence in results produced by dierent refraction correction procedures using predominantly nadir imagery (small angle vs. multi-view), allowing users a choice of software packages/processing complexity; (3) improvements to our estimations of water surface elevations are critical for accurate topographic estimation in submerged areas and can reduce mean elevation error by up to 73%; and (4) we can use machine learning, in the form of multiple linear regressions, and a Gaussian Naïve Bayes classifier, based on the relationship between error and 11 independent variables, to generate a high resolution, spatially continuous model of geomorphic change in submerged areas, constrained by spatially variable error estimates. Our multiple regression model is capable of explaining up to 54% of magnitude and direction of topographic error, with accuracies of less than 0.04 m. With on-going testing and improvements, this machine learning approach has potential for routine application in spatially variable error estimation within the RPAS–SfM workflow.

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women’s activity spaces were smaller and more circular than men’s. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health.

Aerosol optical thickness (AOT) is an important parameter in radiative transfer models (RTMs) used for atmospheric correction of remotely-sensed data. It is often estimated from horizontal visibility measurements by use of the Koschmieder formula or other related methods built into RTMs. This article assesses the accuracy of this estimation, in the context of atmospheric correction, by comparing AERONET AOT data with AOT estimates from UK Met Office visibility data at a site in Hampshire, UK. Root mean square errors are calculated for a number of visibility categories (0–10, 10–20, 20–30, and 30–40 km) and are found to be high for all visibilities (ranging from half to more than double the mean AOT for each category). For all visibilities km, these errors are significantly higher than those from other AOT estimation methods. Simulations performed with the 6S RTM show that the effect of these errors on satellite-level radiances are large (up to 36 ), and the change in vegetation indices (normalized difference vegetation index and atmospherically resistant vegetation index) is smaller, but still significant. It is recommended that estimations of AOT based upon visibility measurements are only used if no alternatives are available, and that great caution is used when estimation is performed for visibilities km.

Earth observation data acquired in the optical region require atmospheric correction before they can be used quantitatively. Most operational methods of atmospheric correction assume that the atmospheric properties are uniform across the image, but this assumption is unlikely to be valid for large images. This study aims to characterize the spatial variation in atmospheric properties over a typical mid-latitude area (southern England), and to assess the errors that would result from applying a scene-based atmospheric correction to data collected under this variable atmosphere. Two key atmospheric properties – aerosol optical thickness (AOT) and precipitable water content (PWC) – are assessed over two clear days in June 2006, and results show an AOT range of approximately 0.1–0.5 and a PWC range of 1.5–3.0 cm. Radiative transfer modelling shows that errors in reflectance of up to 1.7 percentage points, and up to a 5% change in normalized difference vegetation index, can be caused by AOT variability, but that PWC variability has minimal effects. Sensitivity analyses also show that the high uncertainty of many data sources used to provide AOT values for atmospheric correction may also lead to significant errors in the resulting products. The spatial variability of the atmosphere cannot be ignored, and we are in need of operational, generic methods to perform a spatially variable atmospheric correction.