Browsing by Author "Brewer, Timothy R."
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Item Open Access The application of data innovations to geomorphological impact analyses in coastal areas: An East Anglia, UK, case study(Elsevier, 2019-07-20) Rumson, Alexander G.; Hallett, Stephen H.; Brewer, Timothy R.Rapidly advancing surveying technologies, capable of generating high resolution bathymetric and topographic data, allow precise measurements of geomorphological change and deformation. This permits great accuracy in the characterisation of volumetric change, sediment and debris flows, accumulations and erosion rates. However, such data can be utilised inadequately by coastal practitioners in their assessments of coastal change, due to a lack of awareness of the appropriate analytical techniques and the potential benefits offered by such data-driven approaches. This was found to be the case for the region of East Anglia, UK, which was analysed in this study. This paper evaluates the application of innovative geomorphological change detection (GCD) techniques for analysis of coastal change. The first half of the paper contains an extensive review of GCD methods and data sources used in previous studies. This leads to the selection and recommendation of an appropriate methodology for calculation of volumetric GCD, which has been subsequently applied and evaluated for 14 case study sites in East Anglia. This has involved combining open source point cloud datasets for broad spatial scales, covering an extended temporal period. The results comprise quantitative estimates of volumetric change for selected locations. This allows estimation of the sediment budgets for each stretch of coastline focused upon, revealing fluctuations in their rates of change. These quantitative results were combined with qualitative outputs, such as visual representations of change and we reveal how combining such methods assists identification of patterns and impacts linked to specific events. The study demonstrates how high-resolution point cloud data, which is now readily available, can be used to better inform coastal management practices, revealing trends, impacts and vulnerability in dynamic coastal regions. The results also indicate heterogeneous impacts of events, such as the 2013 East Coast Storm Surge, across the study area of East Anglia.Item Open Access The application of land evaluation technique in the north-east of Libya(Cranfield University, 2006-08-10T13:45:00Z) Nwer, Bashir Ahmad Bashir; Hallett, Stephen; Brewer, Timothy R.Land evaluation is a prerequisite to achieving optimum utilisation of available land resources for agricultural production. The principal purpose of land evaluation is to predict the potential and the limitations of land for changing use. Food security is one of the most important issues of agriculture policy in Libya. The country aims to obtain self‐sufficiency for its in agricultural products which contribute largely to the diet of most of the population. Therefore, eighty per cent of water transferred from aquifer‐sourced in the south of the country to the north, is planned for agriculture development. Cereal crops such wheat, barley, maize and sorghum are given the highest priority. There is, therefore, a pressing need to develop an optimal land evaluation method to identify in which part of a region these selected crops could e grown favourably. The model should be developed in accordance with the priorities of the Libyan Government in developing a practical and applicable land evaluation system that can be used by the average computer user. The FAO Framework was selected to conduct the land suitability assessment. This selection was based upon extensive and critical review of land evaluation methodologies and an evaluation of the objectives for and of the data available for study area. The FAO framework is a set of guidelines rather than a classification system, and model used builds upon this.Item Open Access Application of remote sensing and GIS for modeling and assessment of land use/cover change in Amman/Jordan(2013-10-23T00:00:00Z) Al-Bakri, Jawad T.; Duqqah, Mohmmad; Brewer, Timothy R.Modeling and assessment of land use/cover and its impacts play a crucial role in land use planning and formulation of sustainable land use policies. In this study, remote sensing data were used within geographic information system (GIS) to map and predict land use/cover changes near Amman, where half of Jordan's population is living. Images of Landsat TM, ETM+ and OLI were processed and visually interpreted to derive land use/cover for the years 1983, 1989, 1994, 1998, 2003 and 2013. The output maps were analyzed using GIS and cross-tabulated to quantify land use/cover changes for the different periods. The main changes that altered the character of land use/cover in the area were the expansion of urban areas and the recession of forests, agricultural areas (after 1998) and rangelands. The Markov Chain was used to predict future land use/cover, based on the historical changes during 1983-2013. Results showed that prediction of land use/cover would depend on the time interval of the multi-temporal satellite imagery from which the probability of change was derived. The error of prediction was in the range of 2% - 5%, with more accurate prediction for urbanization and less accurate prediction for agricultural areas. The trends of land use/cover change showed that urban areas would expand at the expense of agricultural land and would form 33% of the study area (50km x 60km) by year 2043. The impact of these land use/cover changes would be the increased water demand and wastewater generation in the future.Item Open Access The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments(Cranfield University, 2010-12) Kampouraki, Maria; Wood, G. A.; Brewer, Timothy R.Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability.Item Open Access Assessment of Land Cover Change in North Eastern Nigeria(Cranfield University, 2008) Garba, Samuel Sule; Brewer, Timothy R.Land cover change provides a means of understanding and managing the problems of degradation and shortage of land and water resources and the conflicts therewith in the north eastern Nigeria. This research assessed how tree, shrub grass, bare ground changed from 1986 to 2005 using the NigeriaSat-1 and Landsat images calibrated with field survey data. Thirteen subclasses of the land cover were spectrally analysed and classified severally, however uncertainties in the classification made the merger into four classes necessary. Changes were analysed according to persistence, swapping, loss and gain analysis, multi-year transition of each land cover in succession, location of intensive change, and regional change density. Uncertainties were analysed by confusion and transition error matrices. The overall accuracies of the classifications were between 60% and 75%, and the transition and change accuracies were between 45% and 60%. Approximately 60% of the area of study remained unchanged during the period. Of the remainder, approximately 11% of the area interchanged between shrub grass and bare ground. Shrub grass was found to be the most unstable category and the source of most misclassification. The loss of tree was general but more intensive in the Fadama making it the most vulnerable. How local people perceived land cover change was sought through group interview and the results concurred generally with the assessment of the changes. NigeriaSat-1 imagery was tested for its quality and whether the addition of the middle infrared wavebands improved the classification. NigeriaSat-1 failed to classify the 13 classes and the middle infrared did not improve the classification, thus comparable to Landsat data, although the test was done with dry season images and the result may likely be different for wet season imagery. The 8 km AHVRR-NDVI was found to be useful in assessing the timing of image acquisition, but the data could not provide sufficient spatial resolution to warrant its usage for local scale studies.Item Open Access Assessment of land cover change in north eastern Nigeria 1986 to 2005(Canadian Center of Science and Education, 2013-11-22) Garba, Samuel; Brewer, Timothy R.Environmental disturbance such as drought, overgrazing, and increase in population in north eastern Nigeriaover the years has led to degradation, shortage of land and water resources and sometimes violent conflict amongcommunities. Land cover change provides a vital means of understanding and managing these problems. Thusthis research provided an assessment of how tree, shrub grass, bare ground and urban land cover changed from1986 to 2005. NigeriaSat-1 and Landsat images were used with data obtained from field survey for the landcover classifications. Change in the land covers were analysed according to persistence, swapping, net loss andgain. Uncertainties were analysed by confusion matrices. The overall accuracies of the classifications used forthe analysis are between 60% and 75%. The transition and change accuracies are between 45% and 60%.Approximately 60% of the area of study remained unchanged during the period. Of the remainder,approximately 11% of the area interchanged between shrub grass and bare ground. The most unstable categorywas shrub grass and was also the source of misclassification. The changes in general concurred with theperception of change in the area and gave some insight on the change that occurred.Item Open Access Characterising urban catchments for explaining storm runoff and application in UK flood estimation(2019-02) Miller, James; Brewer, Timothy R.; Hess, Tim M.The impacts of urbanisation on catchment hydrology have been the focus of investigation over the last few decades, but quantifying and predicting the impacts remains an ongoing area of active research. One such area has been improving characterisation of urban land cover to predict urbanisation impacts whereby lumped catchment characterisation of urban land cover limits the ability of attribution and modelling methods to consider the spatial role of land cover in runoff response. This thesis evaluates the potential for spatially explicit characterisations of urban land cover based on landscape metrics, commonly employed in landscape ecology, to explain storm runoff in urban catchments and their application in UK flood estimation methods. Rainfall and channel flow monitoring across two towns containing 18 variably urbanised sub-catchments were used to provide high-resolution time-series of rainfall and runoff and to identify storm events which were quantified using a range of hydrological metrics. Analysing storm runoff along a rural-urban gradient showed a lumped measure of urban extent can generally explain differences in the hydrological response between rural and urban catchments but not between more urbanised catchments in which soil moisture does not play a contributing role. Using high resolution geospatial data can improve the representation of the urban environment and landscape metrics can better represent the form and function of urban land cover, improving estimates of the index flood QMED over lumped catchment descriptors. Regression analysis of hydrological metrics showed the potential of landscape metrics for explaining inter-catchment differences in rainfall-runoff and point to the importance of considering the location and connectivity of urban surfaces. Landscape metrics provide a workable means of overcoming the limitations inherent in using lumped characterisation of complex urban land cover and their ability to express connectivity, size and location of urban land cover promises potential applications in hydrological applications such as UK design flood estimation methods.Item Open Access Coastal management and adaptation: an integrated data-driven approach(2019-03) Rumson, Alexander G.; Hallett, Stephen; Brewer, Timothy R.Coastal regions are some of the most exposed to environmental hazards, yet the coast is the preferred settlement site for a high percentage of the global population, and most major global cities are located on or near the coast. This research adopts a predominantly anthropocentric approach to the analysis of coastal risk and resilience. This centres on the pervasive hazards of coastal flooding and erosion. Coastal management decision-making practices are shown to be reliant on access to current and accurate information. However, constraints have been imposed on information flows between scientists, policy makers and practitioners, due to a lack of awareness and utilisation of available data sources. This research seeks to tackle this issue in evaluating how innovations in the use of data and analytics can be applied to further the application of science within decision-making processes related to coastal risk adaptation. In achieving this aim a range of research methodologies have been employed and the progression of topics covered mark a shift from themes of risk to resilience. The work focuses on a case study region of East Anglia, UK, benefiting from the input of a partner organisation, responsible for the region’s coasts: Coastal Partnership East. An initial review revealed how data can be utilised effectively within coastal decision-making practices, highlighting scope for application of advanced Big Data techniques to the analysis of coastal datasets. The process of risk evaluation has been examined in detail, and the range of possibilities afforded by open source coastal datasets were revealed. Subsequently, open source coastal terrain and bathymetric, point cloud datasets were identified for 14 sites within the case study area. These were then utilised within a practical application of a geomorphological change detection (GCD) method. This revealed how analysis of high spatial and temporal resolution point cloud data can accurately reveal and quantify physical coastal impacts. Additionally, the research reveals how data innovations can facilitate adaptation through insurance; more specifically how the use of empirical evidence in pricing of coastal flood insurance can result in both communication and distribution of risk. The various strands of knowledge generated throughout this study reveal how an extensive range of data types, sources, and advanced forms of analysis, can together allow coastal resilience assessments to be founded on empirical evidence. This research serves to demonstrate how the application of advanced data-driven analytical processes can reduce levels of uncertainty and subjectivity inherent within current coastal environmental management practices. Adoption of methods presented within this research could further the possibilities for sustainable and resilient management of the incredibly valuable environmental resource which is the coast.Item Open Access Coastal risk adaptation: the potential role of accessible geospatial Big Data(Elsevier, 2017-06-03) Rumson, Alexander G.; Hallett, Stephen H.; Brewer, Timothy R.Increasing numbers of people are living in and using coastal areas. Combined with the presence of pervasive coastal threats, such as flooding and erosion, this is having widespread impacts on coastal populations, infrastructure and ecosystems. For the right adaptive strategies to be adopted, and planning decisions to be made, rigorous evaluation of the available options is required. This evaluation hinges on the availability and use of suitable datasets. For knowledge to be derived from coastal datasets, such data needs to be combined and analysed in an effective manner. This paper reviews a wide range of literature relating to data-driven approaches to coastal risk evaluation, revealing how limitations have been imposed on many of these methods, due to restrictions in computing power and access to data. The rapidly emerging field of ‘Big Data’ can help overcome many of these hurdles. ‘Big Data’ involves powerful computer infrastructures, enabling storage, processing and real-time analysis of large volumes and varieties of data, in a fast and reliable manner. Through consideration of examples of how ‘Big Data’ technologies are being applied to fields related to coastal risk, it becomes apparent that geospatial Big Data solutions hold clear potential to improve the process of risk based decision making on the coast. ‘Big Data’ does not provide a stand-alone solution to the issues and gaps outlined in this paper, yet these technological methods hold the potential to optimise data-driven approaches, enabling robust risk profiles to be generated for coastal regions.Item Open Access Combined sensor of dielectric constant and visible and near infrared spectroscopy to measure soil compaction using artificial neural networks(Cranfield University, 2014-05) Al-Asadi, Raed; Mouazen, A. M.; Brewer, Timothy R.Soil compaction is a widely spread problem in agricultural soils that has negative agronomic and environmental impacts. The former may lead to poor crop growth and yield, whereas the latter may lead to poor hydraulic properties of soils, and high risk to flooding, soil erosion and degradation. Therefore, the elimination of soil compaction must be done on regular bases. One of the main parameters to quantify soil compaction is soil bulk density (BD). Mapping of within field variation in soil BD will be a main requirement for within field management of soil compaction. The aim of this research was to develop a new approach for the measurement of soil BD as an indicator of soil compaction. The research relies on the fusion of data from visible and near infrared spectroscopy (vis-NIRS), to measure soil gravimetric moisture content (ω), with frequency domain reflectometry (FDR) data to measure soil volumetric moisture content (θv). The values of the estimated ω and θv, for the same undisturbed soil samples were collected from selected locations, textures, soil moisture contents and land use systems to derive soil BD. A total of 1013 samples were collected from 32 sites in the England and Wales. Two calibration techniques for vis-NIRS were evaluated, namely, partial least squares regression (PLSR) and artificial neural networks (ANN). ThetaProbe calibration was performed using the general formula (GF), soil specific calibration (SSC), the output voltage (OV) and artificial neural networks (ANN). ANN analyses for both ω and θv properties were based either on a single input variable or multiple input variables (data fusion). Effects of texture, moisture content, and land use on the prediction accuracy on ω, θv and BD were evaluated to arrive at the best experimental conditions for the measurement of BD with the proposed new system. A prototype was developed and tested under laboratory conditions and implemented in-situ for mapping of ω, θv and BD. When using the entire dataset (general data set), results proved that high measurement accuracy can be obtained for ω and θv with PLSR and the best performing traditional calibration method of the ThetaProbe with R2 values of 0.91 and 0.97, and root mean square error of prediction (RMSEp) of 0.027 g g-1 and 0.019 cm3 cm-3, respectively. However, the ANN – data fusion method resulted in improved accuracy (R2 = 0.98 and RMSEp = 0.014 g g-1 and 0.015 cm3 cm-3, respectively). This data fusion approach gave the best accuracy for BD assessment when only vis-NIRS spectra and ThetaProbe V were used as an input data (R2 = 0.81 and RMSEp = 0.095 g cm-3). The moisture level (L) impact on BD prediction revealed that the accuracy improved with soil moisture increasing, with RMSEp values of 0.081, 0.068 and 0.061 g cm-3, for average ω of 0.11, 0.20 and 0.28 g g-1, respectively. The influence of soil texture was discussed in relation with the clay content in %. It was found that clay positively affected vis-NIRS accuracy for ω measurement and no obvious impact on the dielectric sensor readings was observed, hence, no clear influence of the soil textures on the accuracy of BD prediction. But, RMSEp values of BD assessment ranged from 0.046 to 0.115 g cm-3. The land use effect of BD prediction showed measurement of grassland soils are more accurate compared to arable land soils, with RMSEp values of 0.083 and 0.097 g cm-3, respectively. The prototype measuring system showed moderate accuracy during the laboratory test and encouraging precision of measuring soil BD in the field test, with RMSEp of 0.077 and 0.104 g cm-3 of measurement for arable land and grassland soils, respectively. Further development of the prototype measuring system expected to improve prediction accuracy of soil BD. It can be concluded that BD can be measured accurately by combining the vis-NIRS and FDR techniques based on an ANN-data fusion approach.Item Open Access A Critical Evaluation of Remote Sensing Based Land Cover Mapping Methodologies(Cranfield University, 2008-06-05) Farmer, Elizabeth A.; Sannier, C.; Brewer, Timothy R.A novel, disaggregated approach to land cover survey is developed on the basis of land cover attributes; the parameters typically used to delineate land cover classes. The recording of land cover attributes, via objective measurement techniques, is advocated as it eliminates the requirement for surveyors to delineate and classify land cover; a process proven to be subjective and error prone. Within the North York Moors National Park, a field methodology is developed to characterise five attributes: species composition, cover, height, structure and density. The utility of land cover attributes to act as land cover ‘building blocks’ is demonstrated via classification of the field data to the Monitoring Landscape Change in the National Parks (MLCNP), National Land Use Database (NLUD) and Phase 1 Habitat Mapping (P1) schemes. Integration of the classified field data and a SPOT5 satellite image is demonstrated within per-pixel and object-orientated classification environments. Per-pixel classification produced overall accuracies of 81%, 80% and 76% at the field samples for the MLCNP, NLUD and P1 schemes, respectively. However, independent validation produced significantly lower accuracies. These decreases are demonstrated to be a function of sample fraction. Object-orientated classification, exemplified for the MLCNP schema at 3 segmentation scales, achieved accuracies approaching 75%. The aggregation of attributes to classes underutilises the potential of the remotely sensed data to describe landscape variability. Consequently, classification and geostatistical techniques capable of land cover attribute parameterisation, across the study area, are reviewed and exemplified for a sub-pixel classification. Land cover attributes provide a flexible source of field data which has been proven to support multiple land cover classification schemes and classification scales (sub-pixel, pixel and object). This multi-scaled/schemed approach enables the differential treatment of regions, within the remote sensing image, as a function of landscape characteristics and the users’ requirements providing a flexible mapping solution.Item Open Access Erosion hazard assessment in the upper Ewaso Ng’iro basin of Kenya: application of GIS, USLE and EUROSEM(1999-09-15) Mati, Bancy Mbura; Morgan, R. P. C.; Gichuki, F. N.; Quinton, J. N.; Brewer, Timothy R.; Liniger, H. P.A methodology was developed for assessing soil erosion hazard in the Upper Ewaso Ng’iro basin of Kenya, using Geographic Information Systems (GIS), the Universal Soil Loss Equation (USLE) and the European Soil Erosion Model (EUROSEM). The USLE was used in a GIS environment by creating thematic maps of R, K, L, S, C and P and then calculating soil loss by raster-grid modelling with Arc/Info GRID. The rainfall erosivity factor (R) was derived from relationships between rainfall amount and erosivity using erosion plot data from within the catchment. The nature of the relationship was found to be a function of agro-climatic zones of the region. Mean annual erosivities ranged from 145 to 990 J m'2 hr'1. For a given amount of rainfall, erosivity was higher in zone IV than in the wetter zones II-III. The soil erodibility factor (K) was estimated using the USLE nomograph and data from laboratory analysis of field samples collected from representative major soil mapping units. The K-values were low to medium, ranging from 0.10 to 0.25 over 84 percent of the basin. The topographic factor (LS) was obtained by creating Digital Elevation Models (DEMs) of the basin with TOPOGRIDTOOL of Arc/Info. These were then used to determine the slope steepness and length factor values, calculated with raster-grid modelling. Although DEMs proved a useful tool, maximum values of both steepness and length had to be set in this reconnaissance study to achieve reasonable results. A finer resolution of input data and a smaller grid cell size are needed for accurate determination. The cover and management factors (C) were obtained by determining the land cover types within the basin using remotely sensed data (SPOT 1 colour composite prints) and ground truthing studies. The factor values were estimated from USLE guide tables and measurements of cover from plots and test sites. Some 70 percent of the basin is covered by rangelands. The conservation practice (P) factor values were estimated from USLE guide tables and then applied to areas where soil conservation had been introduced according to maps obtained from the Ministry of Agriculture. The USLE was validated using data from erosion plots. A value of R2 = 0.645 was obtained between predicted and measured values but the standard error was rather high (e = 5.745 t ha’1 yr'1). Using an annual soil loss of 9.0 t ha'1 yr'1 as tolerance level, some 36 percent of the basin was found to experience unacceptably high erosion rates. Most of this area was communal grazing land and cropland where soil conservation measures had not been applied. A critical land cover type within the grazing land is shrubland, where vegetation cover is less than 40 percent and high erosion risk was predicted and confirmed by field surveys. EUROSEM could not be integrated within a GIS in the time available for research. It was therefore simulated outside GIS environment, where it was applied to Embori and Mukogodo plot data using separate data sets for calibration and validation. Calibration was used to obtain input parameters for saturated hydraulic conductivity, cohesion and Manning’s roughness coefficients. Validation gave correlation coefficients of 0.907 and 0.840 for predictions of storm runoff and soil loss respectively at Embori; the corresponding values for bare soil plots at Mukogodo were 0.895 and 0.577. However, EUROSEM predicted runoff poorly (R2 = 0.570) and failed to predict soil loss at all the vegetated plots at Mukogodo. The model was applied to simulated vegetation covers of barley, maize, grass and forest for a 36.7 mm rainstorm at Embori. The simulated soil losses showed an exponential decrease with increasing cover. At a threshold cover of 70 percent, soil loss diminished to zero under grass and forest and decreased to a minimum value under barley and maize. These results support the USLE simulations, which showed that areas with more than 70 percent cover (such as forest) had a low erosion hazard, even with steep slopes and high rainfall erosivities. This research has demonstrated that GIS can be used with the USLE to assess and quantify erosion hazard, giving results that can be used for conservation planning. EUROSEM can be applied successfully to bare soil and cropland, but application to other land covers requires further investigation. Land cover and topography are the main factors controlling the spatial distribution of soil loss in the Upper Ewaso Ng’iro basin. Future conservation activities should be concentrated on the rangelands.Item Open Access Evaluating landscape metrics for characterising hydrological response to storm events in urbanised catchments(Taylor and Francis, 2020-05-12) Miller, James D.; Stewart, Elisabeth; Hess, Tim; Brewer, Timothy R.Hydrological response of an urban catchment to storm events is determined by a number of factors including the degree of urbanisation and distribution and connectivity of urbanised surfaces. Therefore, the ability of spatially averaged catchment descriptors to characterise storm response is limited. Landscape metrics, widely used in ecology to quantify landscape structure, are employed to quantify urban land-cover patterns across a rural-urban gradient of catchments and attribute hydrological response. Attribution of all response metrics, except peak flow, is improved by combining lumped catchment descriptors with spatially explicit landscape metrics. Those representing connectedness and shape of suburban and natural greenspace improve characterisation of percentage runoff and storm runoff. Connectivity and location of urban surfaces are more important than impervious area alone for attribution of timing, validating findings from distributed hydrological modelling studies. Findings suggest potential improvements in attribution of storm runoff in ungauged urban catchments using landscape metrics.Item Open Access Evaluating the circular economy for sanitation: findings from a multi-case approach(Elsevier, 2020-07-15) Mallory, Adrian; Akrofi, Daniel; Dizon, Jenica; Mohanty, Sourav; Parker, Alison; Rey Vicario, Dolores; Prasad, Sharada S.; Welvita, Indunee; Brewer, Timothy R.; Mekala, Sneha; Bundhoo, Dilshaad; Lynch, Kenny; Mishra, Prajna; Willcock, Simon; Hutchings, PaulAddressing the lack of sanitation globally is a major global challenge with 700 million people still practicing open defecation. Circular Economy (CE) in the context of sanitation focuses on the whole sanitation chain which includes the provision of toilets, the collection of waste, treatment and transformation into sanitation-derived products including fertiliser, fuel and clean water. After a qualitative study from five case studies across India, covering different treatment technologies, waste-derived products, markets and contexts; this research identifies the main barriers and enablers for circular sanitation business models to succeed. A framework assessing the technical and social system changes required to enable circular sanitation models was derived from the case studies. Some of these changes can be achieved with increased enforcement, policies and subsidies for fertilisers, and integration of sanitation with other waste streams to increase its viability. Major changes such as the cultural norms around re-use, demographic shifts and soil depletion would be outside the scope of a single project, policy or planning initiative. The move to CE sanitation may still be desirable from a policy perspective but we argue that shifting to CE models should not be seen as a panacea that can solve the global sanitation crisis. Delivering the public good of safe sanitation services for all, whether circular or not, will continue to be a difficult taskItem Open Access Evidence of similarities in ecosystem service flow across the rural–urban spectrum(MDPI, 2021-04-17) Welivita, Indunee; Willcock, Simon; Lewis, Amy; Bundhoo, Dilshaad; Brewer, Timothy R.; Cooper, Sarah; Lynch, Kenneth; Mekala, Sneha; Mishra, Prajna Paramita; Venkatesh, Kongala; Rey Vicario, Dolores; Hutchings, PaulIn 2006, the world’s population passed the threshold of being equally split between rural and urban areas. Since this point, urbanisation has continued, and the majority of the global population are now urban inhabitants. With this ongoing change, it is likely that the way people receive benefits from nature (ecosystem services; ES) has also evolved. Environmental theory suggests that rural residents depend directly on their local environment (conceptualised as green-loop systems), whereas urban residents have relatively indirect relationships with distant ecosystems (conceptualised as red-loop systems). Here, we evaluate this theory using survey data from >3000 households in and around Hyderabad, India. Controlling for other confounding socioeconomic variables, we investigate how flows of 10 ES vary across rural, peri-urban and urban areas. For most of the ES we investigated, we found no statistical differences in the levels of direct or indirect use of an ecosystem, the distance to the ecosystem, nor the quantities of ES used between rural and urban residents (p > 0.05). However, our results do show that urban people themselves often travel shorter distances than rural people to access most ES, likely because improved infrastructure in urban areas allows for the transport of ES from wider ecosystems to the locality of the beneficiaries’ place of residence. Thus, while we find some evidence to support red-loop–green-loop theory, we conclude that ES flows across the rural-urban spectrum may show more similarities than might be expected. As such, the impact of future urbanisation on ES flows may be limited, because many flows in both rural and urban areas have already undergone globalisationItem Open Access Flood modellling approaches for large lowland tropical catchments.(Cranfield University, 2019-10) Mazivanhanga, Charles; Garabowski, Robert C.; Brewer, Timothy R.Flooding is increasing in tropical regions, where millions of people are at risk, and challenges exist in providing reliable predictions and warnings. This research responds to this challenge by identifying and applying physics-based and data-based hydrological modelling approaches for large-scale flood modelling in lowland tropical regions. First, a distributed hydrological model was developed to accurately represent catchment conditions and processes in the model. Second, empirical data from nested catchments were analysed using statistical scaling relationships to complement the accuracy of peak discharge estimates. Finally, the effects of uncertainty propagation and interactions were quantified to increase the reliability of model results. The research was conducted in the Grijalva catchment area (57 958 km²) southeast of Mexico. A large-scale model with a 2 x 2 km grid cell resolution was developed using the SHETRAN hydrological model and run enforced with 3-hour input rainfall data. Geostatistical techniques were used to quantify and reduce errors in input data, and all diverted flows were accounted for to optimise simulations. For the first time, the application of the Scaling theory of floods was applied in the study area to improve the estimation of peak discharge. A Monte Carlo technique was used to propagate and quantify rainfall and parameter uncertainties through a coupled hydrologic and hydraulic model and into model results. Although the model under-predicted the magnitude of peak discharge, calibration results showed satisfactory model performance (NSCE = 0.72, CC = 0.74, Bias = –0.44% and RMSE 139.56 mm) and validation results were good (NSCE = 0.56, CC = 0.60, Bias = –6.3% and RMSE 62.59 mm). A statistical log-log relationship between intercepts (α) and peak discharge, from the smallest nested catchment, was used to complement the simulation of peak discharge magnitudes. It was observed that given rainfall uncertainties of ±71%, ranging from 63 to 73%; the model generates discharge with uncertainties of ± 46%, ranging from 45 to 49% and errors of ±46% ranging from 45 to 46%. The propagated uncertainties resulted in flood inundation extents of ±4.34 km² varying from 1.66 to 7.02 km² Thus, flood modelling in large tropical regions can be achieved by optimally integrating several datasets with the best combination of the model parameter, input and output datasets based on uncertainty and error quantification and removal approaches.Item Open Access GIS tool to assist the management, display and archiving of water catchment areas and their diffuse pollution pressures.(2006-08) Yahya, Madi-Kimba; Brewer, Timothy R.This project aims to develop an application to illustrate the extent at which relational database management can be used to improve catchment area management within the Environment Agency through the application of Geographic Information Systems (GIS). The goal is to advance fundamental understanding of the spatial dimensions of these databases, their interrelationships and how they can aid decision making. The Environment Agency (EA) has possession of a lot of GIS data that is not fully utilized and this project is a first step in the right direction to getting more users within the EA to make full use of the data available within their organization to aid them make informed strategic and tactical decisions. This was done by developing a Graphical User Interface (GUI) that is user friendly and helps access the database quickly and easily. A toolbar was also developed to host command buttons that have been customized to make the use of ArcMap easier. The user will be able to execute functions without the need of extensive background knowledge of ArcGIS and the intricacies associated with each individual function. Ordnance Survey MasterMap data is also used (to a limited extent due to time constraints) to illustrate the scenarios faced and the decision processes that will need to be followed. This example will show how the relationship between Hazard assessment and risk mitigation within catchments can be linked through a GIS system.Item Open Access Image segmentation for improved consistency in image-interpretation of opium poppy(Taylor and Francis, 2016-02-18) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.; Brewer, Timothy R.The image-interpretation of opium poppy crops from very high resolution satellite imagery forms part of the annual Afghanistan opium surveys conducted by the United Nations Office on Drugs and Crime and the United States Government. We tested the effect of generalization of field delineations on the final estimates of poppy cultivation using survey data from Helmand province in 2009 and an area frame sampling approach. The sample data was reinterpreted from pan-sharpened IKONOS scenes using two increasing levels of generalization consistent with observed practice. Samples were also generated from manual labelling of image segmentation and from a digital object classification. Generalization was found to bias the cultivation estimate between 6.6% and 13.9%, which is greater than the sample error for the highest level. Object classification of image-segmented samples increased the cultivation estimate by 30.2% because of systematic labelling error. Manual labelling of image-segmented samples gave a similar estimate to the original interpretation. The research demonstrates that small changes in poppy interpretation can result in systematic differences in final estimates that are not included within confidence intervals. Segmented parcels were similar to manually digitized fields and could provide increased consistency in field delineation at a reduced cost. The results are significant for Afghanistan’s opium monitoring programmes and other surveys where sample data are collected by remote sensing.Item Open Access Impact of rapid urban expansion on green space structure(Elsevier, 2017-06-12) Nor, Amal Najihah Muhamad; Corstanje, Ronald; Harris, Jim A.; Brewer, Timothy R.Rapid urban expansion has had a significant impact on green space structure. A wide variety of modelling approaches have been tested to simulate urban expansion; however, the effectiveness of simulations of the spatial structure of urban expansion remains unexplored. This study aims to model and predict urban expansion in three cities (Kuala Lumpur, Metro Manila and Jakarta), all experiencing rapid urban expansion, and to identify which are the main drivers, including spatial planning, in the resulting spatial patterns. Land Change Modeller (LCM)-Markov Chain models were used, parameterised on changes observed between 1988/1989 and 1999 and verified with the urban form observed for 2014. These models were then used to simulate urban expansion for the year 2030. The spatial structure of the simulated 2030 land use was then compared with the 2030 master plan for each city using spatial metrics. LCM-Markov Chain models proved to be a suitable method for simulating the development of future land use. There were also important differences in the projected spatial structure for 2030 when compared to the planned development in each city; substantive differences in the size, density, distance, shape and spatial pattern. Evidence suggests that these spatial patterns are influenced by the forms of rapid urban expansion experienced in these cities and respective master planning policies of the municipalities of the cities. The use of integrated simulation modelling and landscape ecology analytics supplies significant insights into the evolution of the spatial structure of urban expansion and identifies constraints and informs intervention for spatial planning and policies in cities.Item Open Access Integration of ecosystem services into a conceptual spatial planning framework based on a landscape ecology perspective(Springer, 2018-10-28) Almenar, Javier Babí; Rugani, Benedetto; Geneletti, Davide; Brewer, Timothy R.Context The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues. Objectives This paper presents a conceptual framework suitable for spatial planning in human dominated environments supported by landscape ecological thinking. It seeks to facilitate the integration of ecosystem services into current practice, including landscape metrics as suitable indicators. Methods A literature review supported the revision of existing open questions pertaining to ecosystem services as well as their integration into landscape ecology and spatial planning. A posterior reflection of the current state-of-the-art was then used as a basis for developing the spatial planning conceptual framework. Results and conclusion The framework is articulated around four phases (characterisation, assessment, design, and monitoring) and three concepts (character, service, and value). It advocates integration of public participation, consideration of “landscape services”, the inclusion of ecosystem disservices, and the use of landscape metrics for qualitative assessment of services. As a result, the framework looks to enhance spatial planning practice by providing: (i) a better consideration of landscape configuration in the supply of services (ii) the integration of anthropogenic services with ecosystem services; (iii) the consideration of costs derived from ecosystems (e.g. disservices); and (iv) an aid to the understanding of ecosystem services terminology for spatial planning professionals and decision makers.