Detecting Vietnam War bomb craters in declassified historical KH-9 satellite imagery

Thousands of people are injured every year from explosive remnants of war which include unexploded ordnance (UXO) and abandoned ordnance. UXO has negative long-term impacts on livelihoods and ecosystems in contaminated areas. Exact locations of remaining UXO are often unknown as survey and clearance activities can be dangerous, expensive and time-consuming. In Vietnam, Lao PDR and Cambodia, about 20% of the land remains contaminated by UXO from the Vietnam War. Recently declassified historical KH-9 satellite imagery, taken during and immediately after the Vietnam War, now provides an opportunity to map this remaining contamination. KH-9 imagery was acquired and orthorectified for two study areas in Southeast Asia. Bomb craters were manually labeled in a subset of the imagery to train convolutional neural networks (CNNs) for automated crater detection. The CNNs achieved a F1-Score of 0.61 and identified more than 500,000 bomb craters across the two study areas. The detected craters provided more precise information on the impact locations of bombs than target locations available from declassified U.S. bombing records. This could allow for a more precise localization of suspected hazardous areas during non-technical surveys as well as a more fine-grained determination of residual risk of UXO. The method is directly transferable to other areas in Southeast Asia and is cost-effective due to the low cost of the KH-9 imagery and the use of open-source software. The results also show the potential of integrating crater detection into data-driven decision making in mine action across more recent conflicts

Using open-source data to construct 20 metre resolution maps of children’s travel time to the nearest health facility

Physical access to health facilities is an important factor in determining treatment seeking behaviour and has implications for targets within the Sustainable Development Goals, including the right to health. The increased availability of high-resolution land cover and road data from satellite imagery offers opportunities for fine-grained estimations of physical access which can support delivery planning through the provision of more realistic estimates of travel times. The data presented here is of travel time to health facilities in Uganda, Zimbabwe, Tanzania, and Mozambique. Travel times have been calculated for different facility types in each country such as Dispensaries, Health Centres, Clinics and Hospitals. Cost allocation surfaces and travel times are provided for child walking speeds but can be altered easily to account for adult walking speeds and motorised transport. With a focus on Uganda, we describe the data and method and provide the travel maps, software and intermediate datasets for Uganda, Tanzania, Zimbabwe and Mozambique

Earth observation and geospatial data can predict the relative distribution of village level poverty in the Sundarban Biosphere Reserve, India

There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and map aspects of socioeconomic conditions to support survey and census activities. This is particularly relevant for the frequent monitoring required to assess progress towards the UNs' Sustainable Development Goals (SDGs). The Sundarban Biosphere Reserve (SBR) is a region of international ecological importance, containing the Indian portion of the world's largest mangrove forest. The region is densely populated and home to over 4.4 million people, many living in chronic poverty with a strong dependence on nature-based rural livelihoods. Such livelihoods are vulnerable to frequent natural hazards including cyclone landfall and storm surges. In this study we examine associations between environmental variables derived from EO and geospatial data with a village level multidimensional poverty metric using random forest machine learning, to provide evidence in support of policy formulation in the field of poverty reduction. We find that environmental variables can predict up to 78% of the relative distribution of the poorest villages within the SBR. Exposure to cyclone hazard was the most important variable for prediction of poverty. The poorest villages were associated with relatively small areas of rural settlement (&lt;∼30%), large areas of agricultural land (&gt;∼50%) and moderate to high cyclone hazard. The poorest villages were also associated with less productive agricultural land than the wealthiest. Analysis suggests villages with access to more diverse livelihood options, and a smaller dependence on agriculture may be more resilient to cyclone hazard. This study contributes to the understanding of poverty-environment dynamics within Low-and middle-income countries and the associations found can inform policy linked to socio-environmental scenarios within the SBR and potentially support monitoring of work towards SDG1 (No Poverty) across the region.</p

Socioecologically informed use of remote sensing data to predict rural household poverty

Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.</p

Assets and domestic units: methodological challenges for longitudinal studies of poverty dynamics

Tracking change in assets access and ownership in longitudinal research is difficult. Assets are rarely assigned to individuals. Their benefit and management are spread across domestic units which morph over time. We review the challenges of using assets to understand poverty dynamics, and tracking the domestic units that own and manage assets. Using case studies from longitudinal research we demonstrate that assets can afford useful insights into important change

Exploring the spatial associations between census based socioeconomic conditions and remotely sensed environmental metrics in Assam northeast India

This thesis explores and quantifies the associations between socioeconomic variables and environmental metrics. Remotely sensed satellite data is often used to monitor environmental conditions. However, it is less frequently used for socioeconomic purposes. Several studies have attempted to use remotely sensed data to monitor socioeconomic conditions in urban areas. Non-causal associations between poverty and development and environmental conditions are frequently found in the scientific literature for rural areas of developing countries. This research uses environmental metrics derived from remotely sensed imagery from an Earth observation satellite to explore if associations, similar to those in the literature, can be found for extensive spatial areas. If non-causal associations can be found between census-based socioeconomic variables and remotely sensed environmental metrics it may be possible to use remotely sensed imagery as a limited, but valuable source of information regarding socioeconomic conditions of rural communities. Socioeconomic data is collected in national census datasets at the household level. However, this fine spatial resolution means that it is an expensive process and is typically only conducted once every 10 years. This coarse temporal resolution limits the relevance of census data for planning resource allocation by governments and targeting development assistance, especially in rapidly changing economies. Therefore, the increased temporal resolution that remotely sensed imagery offers over the traditional ground survey methods may provide a way of increasing the understanding of information available to policy makers for monitoring socioeconomic conditions.An extensive area of Assam in northeast India was used as a case study to explore the associations between socioeconomic variables derived from the Indian national census and remotely sensed environmental metrics derived from Landsat Enhanced Thematic Mapper Plus (ETM+) data. Field work first identified; (i) two socioeconomic variables that appeared to be associated with poverty which were female literacy and participation in economic alternatives to agricultural work, and; (ii) a series of land cover types that appeared to be associated with broad level socioeconomic conditions. Cloud and transparent cloud cover were removed from satellite data prior to an object-based land cover classification which defined nine land cover types identified as having potential associations with poverty in the literature and a field work study. Socioeconomic and environmental data were integrated at the village level prior to statistical analysis. No village boundary information was available and therefore, research aimed to identify the most appropriate method of approximating the village boundary using Thiessen polygons and several radial buffer zones. Statistical analyses were conducted to explore; (i) the associations between female literacy and economic alternatives to agricultural work and several environmental metrics, and; (ii) which village boundary approximation provided the lowest AIC model fit statistic. Logistic regression and generalised autoregressive error models explored the associations between socioeconomic conditions and environmental metrics on a global level. Geographically weighted logistic regression was also used to explore the spatial variation in the associations. Findings indicated that significant associations exist between female literacy and economic alternatives to agricultural work and remotely sensed environmental metrics. Many of the associations identified could be interpreted meaningfully in relation to both the understanding gained from field observations and in relation to generally accepted associations in the literature. Thus, the quantitative findings of the research were in keeping with expectations and research hypotheses, lending credibility to the associations observed by other researchers. The methods used here could be developed further and the increased temporal resolution that remotely sensed imagery offers over the traditional ground survey methods may, in the future, increase the relevance and understanding of information available to policy makers for monitoring socioeconomic conditions

Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation

The red edge position (REP) in the vegetation spectral reflectance is a surrogate measure of vegetation chlorophyll content, and hence can be used to monitor the health and function of vegetation. The Multi-Spectral Instrument (MSI) aboard the future ESA Sentinel-2 (S-2) satellite will provide the opportunity for estimation of the REP at much higher spatial resolution (20 m) than has been previously possible with spaceborne sensors such as Medium Resolution Imaging Spectrometer (MERIS) aboard ENVISAT. This study aims to evaluate the potential of S-2 MSI sensor for estimation of canopy chlorophyll content, leaf area index (LAI) and leaf chlorophyll concentration (LCC) using data from multiple field campaigns. Included in the assessed field campaigns are results from SEN3Exp in Barrax, Spain composed of 35 elementary sampling units (ESUs) of LCC and LAI which have been assessed for correlation with simulated MSI data using a CASI airborne imaging spectrometer. Analysis also presents results from SicilyS2EVAL, a campaign consisting of 25 ESUs in Sicily, Italy supported by a simultaneous Specim Aisa-Eagle data acquisition. In addition, these results were compared to outputs from the PROSAIL model for similar values of biophysical variables in the ESUs. The paper in turn assessed the scope of S-2 for retrieval of biophysical variables using these combined datasets through investigating the performance of the relevant Vegetation Indices (VIs) as well as presenting the novel Inverted Red-Edge Chlorophyll Index (IRECI) and Sentinel-2 Red-Edge Position (S2REP). Results indicated significant relationships between both canopy chlorophyll content and LAI for simulated MSI data using IRECI or the Normalised Difference Vegetation Index (NDVI) while S2REP and the MERIS Terrestrial Chlorophyll Index (MTCI) were found to have the strongest correlation for retrieval of LCC

Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals

The UN has called for a ‘data revolution’ to help overcome the low quality and lack of regularly updated statistical data available in developing countries. But how do we achieve this with limited financial resources and insufficient capacity in national statistical offices around the world? Recent studies have demonstrated how information captured by satellite imagery can be combined with social datasets to increase our understanding of socioeconomic systems. Thus, in the future, satellite data may offer a cost-effective way to reliably measure and monitor progress towards development goals. We examine how satellite data can be linked with household and census datasets to provide information on socioeconomic conditions. We suggest that the Sustainable Livelihoods Approach provides an appropriate framework for which to develop remotely sensed earth observation (EO) data proxies for key socioeconomic conditions because it will allow the linking of data in a way that reflects more the way in which populations interact with landscapes. The aim of using EO data for mapping and predicting socioeconomic conditions is not to replace survey data but to provide more frequent information on likely socioeconomic conditions between census and survey enumeration. Timely recalibration of models predicting poverty from EO data would be necessary to reflect often rapid social, economic and political changes. However, if we are to acheive the SDGs more frequent data at finer spatial scales will be required and EO data provides a cos effective solution