1,288 research outputs found
Exploring the spatial associations between census based socioeconomic conditions and remotely sensed environmental metrics in Assam northeast India
No full textThis 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
A community-level assessment of factors affecting livelihoods in nawalparasi district, Nepal
No full textThis field research investigated the livelihoods of rural communities in Nawalparasi District, Nepal. An adapted form of the Delphi technique was used to assess community perceptions regarding factors which affected their livelihoods. The six most significant factors were selected by participants and ranked in order of importance. Research findings indicated that important factors across communities included those related to water resources, education, health and roads. Climate and environmental change were found to be impacting on livelihoods, and results indicated that education and environmental awareness were two key factors affecting a community’s ability to adapt to change
Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals
No full textThe 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
A combined spectral and object-based approach to transparent cloud removal in an operational setting for Landsat ETM+
No full textThe automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375 ?m, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classification
Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation
No full textThe 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
Predicting socioeconomic conditions from satellite sensor data in rural developing countries: a case study using female literacy in Assam, India
No full textSocial data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GARerr) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GARerr model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.<br/
Exploring the links between census and environment using remotely sensed satellite sensor imagery
No full textRelationships are often found between socio-economic variables and environmental factors for relatively small study regions. This research forms an exploratory data analysis using logistic regression to explore the (non-causal) relationships between socio-economic variables from a national census (female literacy and involvement in economic alternatives to agricultural work) and environmental metrics extracted from Earth observation (EO) data. The relationships observed often supported those found in the literature and field observations. The research highlighted the limited but potentially valuable use of EO data for monitoring socio-economic conditions which may be used to target development assistance in the future.<br/
Community-level climate and environmental change adaptation initiatives in Nawalparasi, Nepal
No full textUnderstanding the evidence base for poverty-environment relationships using remotely sensed satellite data: An example from Assam, India
No full textThis article presents results from an investigation of the relationships between welfare and geographic metrics from over 14,000 villages in Assam, India. Geographic metrics accounted for 61% of the variation in the lowest welfare quintile and 57% in the highest welfare quintile. Travel time to market towns, percentage of a village covered with woodland, and percentage of a village covered with winter crop were significantly related to welfare. These results support findings in the literature across a range of different developing countries. Model accuracy is unprecedented considering that the majority of geographic metrics were derived from remotely sensed data.</p
Earth observation and geospatial data can predict the relative distribution of village level poverty in the Sundarban Biosphere Reserve, India
Get PDFThere 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 (<∼30%), large areas of agricultural land (>∼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
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