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

Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data

Freshwater ecosystems are declining faster than their terrestrial and marine counterparts because of physical pressures on habitats. European legislation requires member states to achieve ecological targets through the effective management of freshwater habitats. Maps of habitats across river networks would help diagnose environmental problems and plan for the delivery of improvement work. Existing habitat mapping methods are generally time consuming, require experts and are expensive to implement. Surveys based on sampling are cheaper but provide patchy representations of habitat distribution. In this study, we present a method for mapping habitat indices across networks using semi-quantitative data and a geostatistical technique called regression kriging. The method consists of the derivation of habitat indices using multivariate statistical techniques that are regressed on map-based covariates such as altitude, slope and geology. Regression kriging combines the Generalised Least Squares (GLS) regression technique with a spatial analysis of model residuals. Predictions from the GLS model are ‘corrected’ using weighted averages of model residuals following an analysis of spatial correlation. The method was applied to channel substrate data from the River Habitat Survey in Great Britain. A Channel Substrate Index (CSI) was derived using Correspondence Analysis and predicted using regression kriging. The model explained 74% of the main sample variability and 64% in a test sample. The model was applied to the English and Welsh river network and a map of CSI was produced. The proposed approach demonstrates how existing national monitoring data and geostatistical techniques can be used to produce continuous maps of habitat indices at the national scale

Small Water Bodies in Great Britain and Ireland: Ecosystem function, human-generated degradation, and options for restorative action

© 2018 Small, 1st and 2nd-order, headwater streams and ponds play essential roles in providing natural flood control, trapping sediments and contaminants, retaining nutrients, and maintaining biological diversity, which extend into downstream reaches, lakes and estuaries. However, the large geographic extent and high connectivity of these small water bodies with the surrounding terrestrial ecosystem makes them particularly vulnerable to growing land-use pressures and environmental change. The greatest pressure on the physical processes in these waters has been their extension and modification for agricultural and forestry drainage, resulting in highly modified discharge and temperature regimes that have implications for flood and drought control further downstream. The extensive length of the small stream network exposes rivers to a wide range of inputs, including nutrients, pesticides, heavy metals, sediment and emerging contaminants. Small water bodies have also been affected by invasions of non-native species, which along with the physical and chemical pressures, have affected most groups of organisms with consequent implications for the wider biodiversity within the catchment. Reducing the impacts and restoring the natural ecosystem function of these water bodies requires a three-tiered approach based on: restoration of channel hydromorphological dynamics; restoration and management of the riparian zone; and management of activities in the wider catchment that have both point-source and diffuse impacts. Such activities are expensive and so emphasis must be placed on integrated programmes that provide multiple benefits. Practical options need to be promoted through legislative regulation, financial incentives, markets for resource services and voluntary codes and actions

Calibration of Traffic Simulation Models using SPSA

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική

Robotic neurorehabilitation: a computational motor learning perspective

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Drainage network analysis and structuring of topologically noisy vector stream data

Drainage network analysis includes several operations that quantify the topological organization of stream networks. Network analysis operations are frequently performed on streams that are derived from digital elevation models (DEMs). While these methods are suited to application with fine-resolution DEM data, this is not the case for coarse DEMs or low-relief landscapes. In these cases, network analysis that is based on mapped vector streams is an alternative. This study presents a novel vector drainage network analysis technique for performing stream ordering, basin tagging, the identification of main stems and tributaries, and the calculation of total upstream channel length and distance to outlet. The algorithm uses a method for automatically identifying outlet nodes and for determining the upstream-downstream connections among links within vector stream networks while using the priority-flood method. The new algorithm was applied to test stream datasets in two Canadian study areas. The tests demonstrated that the new algorithm could efficiently process large hydrographic layers containing a variety of topological errors. The approach handled topological errors in the hydrography data that have challenged previous methods, including disjoint links, conjoined channels, and heterogeneity in the digitized direction of links. The method can provide a suitable alternative to DEM-based approaches to drainage network analysis, particularly in applications where stream burning would otherwise be necessary.</p

The relative contribution of sewage and diffuse phosphorus sources in the River Avon catchment, southern England: Implications for nutrient management

In order to effectively manage nutrient river load reductions and target remediation strategies, it is important to determine the relative contributions of diffuse and point sources across the river catchment. This study used a geographical information system (GIS) to apply phosphorus (P) export coefficients (obtained from the literature) to 58 water quality monitoring sites across a large, urbanised, mixed land use catchment, typical of southern lowland England (the River Avon, Warwickshire, UK). These coefficients were used to estimate the annual P load at each monitoring site, and also the relative contribution of point source (from sewage treatment works (STW)) and diffuse input (from both livestock and agricultural land use). The estimated annual P loads showed very close agreement (r2=0.98) with the measured total phosphorus (TP) loads. Sites with the highest proportion of P derived from STW had the highest TP concentrations and loads, and also had greater variations between seasons, with elevated P concentrations occurring during the summer months. The GIS model was re-run to determine the effect of an 80% reduction in P output from STW serving over 10 000 people, thereby assessing the effect of implementing the European Union's Urban Waste Water Treatment Directive (UWWTD). The exported TP load was reduced by 52%, but the sites with the highest TP concentrations were still those with the highest proportion of P derived from STW. The GIS model was re-run to estimate the impact of 80% P reductions at a further 11 STW of varying sizes. This reduced the total TP load by only 29 tonnes year−1, but greatly reduced the P concentrations in many highly nutrient contaminated tributaries. The number of sites with P concentrations greater than 1 mg l−1 was cut from 15 (before UWWTD implementation) to 2. These findings suggest that after UWWTD implementation, resources should focus on introducing tertiary sewage treatment at the remaining large STW, before targeting diffuse inputs. This conclusion is also likely to apply to other lowland river catchments in southern England, most of which have similar population densities to the River Avon

What are the implications of sea-level rise for a 1.5°C, 2°C and 3°C rise in global mean temperatures in the Ganges-Brahmaputra-Meghna and other vulnerable deltas?

Even if climate change mitigation is successful, sea levels will keep rising. With subsidence, relative sea-level rise represents a long-term threat to low-lying deltas. A large part of coastal Bangladesh was analysed using the Delta Dynamic Integrated Emulator Model to determine changes in flood depth, area and population affected given sea-level rise equivalent to global mean temperature rises of 1.5°C, 2.0°C and 3.0°C with respect to pre-industrial for three ensemble members of a modified A1B scenario. Annual climate variability today (with approximately 1.0°C of warming) is potentially more important, in terms of coastal impacts, than an additional 0.5°C warming. In coastal Bangladesh, the average depth of flooding in protected areas is projected to double to between 0.07m to 0.09m when temperatures are projected at 3.0°C compared with 1.5°C. In unprotected areas, depth of flooding is projected to increase by approximately 50% to 0.21-0.27m, whilst the average area inundated increases 2.5 times (from 5% to 13% of the region) in the same temperature frame. The greatest area of land flooded is projected in the central and north-east regions. In contrast, lower flood depths, less land area flooded and fewer people are projected in the poldered west of the region. Over multi-centennial timescales, climate change mitigation and controlled sedimentation to maintain relative delta height are key to a delta’s survival. With slow rates of sea-level rise, adaptation remains possible, but further support is required. Monitoring of sea-level rise and subsidence in deltas is recommended, together with improved data sets of elevation. <br/