Satellite Earth observation to support sustainable rural development

Traditional survey and census data are not sufficient for measuring poverty and progress towards achieving the Sustainable Development Goals (SDGs). Satellite Earth Observation (EO) is a novel data source that has considerable potential to augment data for sustainable rural development. To realise the full potential of EO data as a proxy for socioeconomic conditions, end-users – both expert and non-expert – must be able to make the right decisions about what data to use and how to use it. In this review, we present an outline of what needs to be done to operationalise, and increase confidence in, EO data for sustainable rural development and monitoring the socioeconomic targets of the SDGs. We find that most approaches developed so far operate at a single spatial scale, for a single point in time, and proxy only one socioeconomic metric. Moreover, research has been geographically focused across three main regions: West Africa, East Africa, and the Indian Subcontinent, which underscores a need to conduct research into the utility of EO for monitoring poverty across more regions, to identify transferable EO proxies and methods. A variety of data from different EO platforms have been integrated into such analyses, with Landsat and MODIS datasets proving to be the most utilised to-date. Meanwhile, there is an apparent underutilisation of fusion capabilities with disparate datasets, in terms of (i) other EO datasets such as RADAR data, and (ii) non-traditional datasets such as geospatial population layers. We identify five key areas requiring further development to encourage operational uptake of EO for proxying socioeconomic conditions and conclude by linking these with the technical and implementational challenges identified across the review to make explicit recommendations. This review contributes towards developing transparent data systems to assemble the high-quality data required to monitor socioeconomic conditions across rural spaces at fine temporal and spatial scales

Census-Independent Population Estimation using Representation Learning

Knowledge of population distribution is critical for building infrastructure, distributing resources, and monitoring the progress of sustainable development goals. Although censuses can provide this information, they are typically conducted every 10 years with some countries having forgone the process for several decades. Population can change in the intercensal period due to rapid migration, development, urbanisation, natural disasters, and conflicts. Census-independent population estimation approaches using alternative data sources, such as satellite imagery, have shown promise in providing frequent and reliable population estimates locally. Existing approaches, however, require significant human supervision, for example annotating buildings and accessing various public datasets, and therefore, are not easily reproducible. We explore recent representation learning approaches, and assess the transferability of representations to population estimation in Mozambique. Using representation learning reduces required human supervision, since features are extracted automatically, making the process of population estimation more sustainable and likely to be transferable to other regions or countries. We compare the resulting population estimates to existing population products from GRID3, Facebook (HRSL) and WorldPop. We observe that our approach matches the most accurate of these maps, and is interpretable in the sense that it recognises built-up areas to be an informative indicator of population

Agricultural shocks and drivers of livelihood precariousness across Indian rural communities

Spatial factors, such as environmental conditions, distance to natural resources and access to services can influence the impacts of climate change on rural household livelihood activities. But neither the determinants of precarious livelihoods nor their spatial context has been well understood. This paper investigates the drivers of livelihood precariousness using a place-based approach. We identify five community types in rural regions of the Mahanadi Delta, India; exurban, agro-industrial, rainfed agriculture, irrigated agriculture and resource periphery by clustering three types of community capitals (natural, social and physical). Based on this typology, we characterise the associations between precarious livelihood activities (unemployment or engagement in agricultural labour) with agricultural shocks and household capitals. Results demonstrate that, the type of community influences the impact of agricultural shocks on livelihoods as four of the five community types had increased likelihoods of precarious livelihoods being pursued when agricultural shocks increased. Our research demonstrates that the bundle of locally available community capitals influences households' coping strategies and livelihood opportunities. For example, higher levels of physical capital were associated with a lower likelihood of precarious livelihoods in agro-industrial communities but had no significant impact in the other four. Results also indicate that agricultural shocks drive livelihood precariousness (odds ratios between 1.03 and 1.07) for all but the best-connected communities, while access to household capitals tends to reduce it. Our results suggest that poverty alleviation programmes should include community typologies in their approach to provide place-specific interventions that would strengthen context-specific household capitals, thus reducing livelihood precariousness

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

Collective influence of household and community capitals on agricultural employment as a measure of rural poverty in the Mahanadi Delta, India

The main determinants of agricultural employment are related to households’ access to private assets and the influence of inherited social–economic stratification and power relationships. However, despite the recommendations of rural studies which have shown the importance of multilevel approaches to rural poverty, very few studies have explored quantitatively the effects of common-pool resources and household livelihood capitals on agricultural employment. Understanding the influence of access to both common-pool resources and private assets on rural livelihoods can enrich our understanding of the drivers of rural poverty in agrarian societies, which is central to achieving sustainable development pathways. Based on a participatory assessment conducted in rural communities in India, this paper differentiates two levels of livelihood capitals (household capitals and community capitals) and quantifies them using national census data and remotely sensed satellite sensor data. We characterise the effects of these two levels of livelihood capitals on precarious agricultural employment by using multilevel logistic regression. Our study brings a new perspective on livelihood studies and rural economics by demonstrating that common-pool resources and private assets do not have the same effect on agricultural livelihoods. It identifies that a lack of access to human, financial and social capitals at the household level increases the levels of precarious agricultural employment, such as daily-wage agricultural labour. Households located in communities with greater access to collective natural capital are less likely to be agricultural labourers. The statistical models also show that proximity to rural centres and access to financial infrastructures increase the likelihood of being a landless agricultural labourer. These findings suggest that investment in rural infrastructure might increase livelihood vulnerability, if not accompanied by an improvement in the provisioning of complementary rural services, such as access to rural finance, and by the implementation of agricultural tenancy laws to protect smallholders’ productive assets

Exploring the role of seasonal variation in livestock feed composition on diet quality and methane emissions in Kenyan livestock

Livestock contribute significantly to greenhouse gas (GHG) emissions, mainly through enteric fermentation and manure management. Feed composition plays a crucial role in both livestock diet quality and emissions. However, feed availability varies seasonally particularly in tropical environments such as Kenya with long dry periods. This study investigated the influence of seasonal feed variations and relative availability of feed types in Kenya on livestock diet quality and enteric methane (CH4) emissions. Geographical information systems and data sources including crop distribution data, land use information, and nutritive value data were employed to generate livestock diet composition maps. Data from field measurements were used to validate these maps. Livestock diets varied across the country, with coarse-stemmed cereals and legume crops dominant in mixed rainfed temperate systems, diverse feed resources including cultivated forages in mixed rainfed humid systems, and pasture in mixed rainfed arid and livestock-only systems. Livestock diet quality measured as dry matter digestibility (DMD), was approximately 581 g/kg DM. This value was greater than the default digestibility value of 550 g/kg DM set by the Intergovernmental Panel on Climate Change (IPCC) for livestock production systems in the region. Monte carlo simulation revealed a range of possible outcomes of DMD depending on the livestock production system and the composition of livestock diet. Estimated minimum and maximum DMD differed within and between livestock production systems (513.2 - 664.5 g/kg DM), thus exhibiting uncertainty and potential spatial and temporal variability. Methane emissions varied between and within livestock production systems (36.6 – 67.5 kg CH4/head/yr for adult female cows; p < 0.05). Variability in methane emissions can be attributed to factors such as changes in feed availability and management practices. These results provide evidence that livestock diet quality and methane emissions are influenced by inter- and intra-season variations in feed composition. Consequently, season and location-specific strategies and interventions are necessary to improve livestock feeding practices as part of efforts to reduce methane emissions from livestock