Much previous research has problematized the use of a binary urban–rural distinction to describe human settlement patterns in and around cities. Peri-urban zones, on the edge of urban settlements, are important both in the sheer magnitude of human population and in terms of being home to vulnerable populations with high rates of poverty. This Perspective presents a framework that conceptualizes rural–urban transition through the prism of shifts in natural, engineered and institutional infrastructure to explain the processes of rapid change and the dip in service provision often found in peri-urban areas in the Global South. We draw on examples related to the provision of water and sanitation to illustrate the theory and discuss its implications for future research on the peri-urban. A research agenda is set out that emphasizes the importance of studying early warning signs of service dips using systems theory concepts such as flickering and critical slowing down. Through such approaches, research can better predict and explain what we call peri-urban turbulence and inform the development of mitigation strategies to reduce the vulnerabilities that peri-urban residents too often face during periods of rural–urban transition. The simplicity of the urban–rural divide for research hides and blurs many complexities of human settlements and natural ecosystems. This perspective examines the peri-urban landscape and provisions of service that take place across this frontier.

Context Ecological networks are systems of interconnected components that support biodiversity, ecological processes and ecosystem services. Such structures play a crucial role for nature conservation and people well-being in anthropogenic landscapes. Assessing connectivity by using efficient models and metrics is a sine qua non condition to preserve and improve appropriately these ecological networks. Objectives This study aims to present a novel methodological approach to assess and model connectivity for species conservation ( Bufo calamita ; the natterjack toad) and human recreation in the city. Methods The study used a combination least cost and circuit models to identify priority corridors in the City of Liège, Belgium. Green areas, habitats and relevant movement parameters were derived based on existing studies around (i) the occurrence, ecology and biology of the natterjack toad and (ii) human behavioural studies on urban pedestrians. Combining the two models allowed the assessment of connectivity for both species via two different metrics visualised using priority corridors on maps. Results The connectivity assessments identified lack of connectivity as the potential route to extinction of natterjack toads at one of the source sites. Conclusions This study provides examples of how combining least cost and circuit models can contribute to the improvement of urban ecological networks and demonstrates the usefulness of such models for nature conservation and urban planning.

Much previous research shows that safe disposal of human waste has a positive impact on human wellbeing, while preventing the degradation of ecosystems. However, to date, the role that ecosystems themselves play in treating human waste has been largely neglected. We conceptualize the role nature plays in treating human waste—acting as a pipeline and/or treatment plant. We estimate that nature is treating ~41.7 million tons of human waste per year worldwide, a service worth at least 4.4 ± 3.0 billion USD year−1. We demonstrate the opportunities and challenges of quantifying these “sanitation ecosystem services,” using 48 cities across the globe as a worked example. In highlighting this, we are not marginalizing the vital role of engineered infrastructure, but instead are promoting better understanding of how engineered and natural infrastructure interact within a circular economy. This is a promising route for further research and may allow adaptive design and management, reducing costs, and improving effectiveness and sustainability.

In 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 globalisation.

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.

Providing safe sanitation in the developing world is still a major hurdle to achieving Sustainable Development Goal number six, with 61% of the global population lacking safely managed sanitation services. Circular economy 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. As well as potentially reducing the cost of toilet provision, a circular economy approach also has the potential to enable positive environmental and health impacts, unlike other systems where waste may be discharged untreated into the environment. The implementation of a system level transformation is not simple, considering operator capacity, lack of funding, slowly growing acceptance by local communities, and a policy landscape which can be inconsistent in its support for the circular economy. As India invests in long-term infrastructure to improve citizens’ quality of life (e.g., Swachh Bharat Mission), it could incorporate circular economy principles into the design of infrastructure, creating effective urban nutrient and material cycles, enhancing economic development and welfare. This represents a significant opportunity for government and businesses in India to develop circular sanitation infrastructure to recover and valorise biological nutrients. After collecting information 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. Whilst there were many different institutional and technological arrangements, common issues of managing and enforcing incoming waste and competing with chemical fertilisers were found.

Addressing 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 task.

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.

Flood estimation methods in ungauged basins rely upon generalized relationships between flows and catchment properties. Generally such catchment properties are based on low-resolution national datasets from low density urbanized basins and do not consider location, connectivity and patch size. Such factors are more routinely represented in landscape metrics employed in ecology, and could be particularly useful for representing the diversity of urban land-use. Here, hydrologically relevant landscape metrics are brought together with refined land-use classes and catchment descriptors routinely applied in UK flood estimation methods to estimate the median annual flood (QMED) in order to evaluate the potential role of such metrics. The results show that using higher resolution geospatial data can improve the representation of the urban environment, having particular effects on the delineation of urban water features and catchment area, but not urban extent. Refinement of landscape metrics based on correlations resulted in 12 metrics and 5 catchment descriptors being tested against observed QMED at 18 sites using a weighted least squares regression. The revised equation showed that certain landscape metrics can better represent the hydrological complexity of an urban catchment in a single distributed numerical form, leading to improved estimates of QMED over non-distributed descriptors, for the selected case study sites. The ability of landscape metrics to express connectivity and relative size and location of urban development promises significant potential for application in urban flood estimation and catchment-scale hydrological modelling.

Explores two methods; rainfall thresholding and landslide susceptibility assessment that may be uses in the management of slope instability in landslide prone area

Natural and human factors exert a profound impact on the degradation of rangelands, human effects being the most significant factor in increasing the severity of deterioration. This occurs through agricultural expansion at the expense of rangelands, and with the number of domestic and wildlife animals exceeding the natural carrying capacity. This raises concerns about the ongoing sustainability of these land resources, as well as the sustainability of traditional pastoral land practices. Rangelands require effective management, which is dependent upon accurate and timely monitoring data to support the assessment of rangeland deterioration. Natural rangelands provide one of the significant pillars of support for the Libyan national economy. Despite the important role of rangeland in Libya from both economic and environmental perspectives, the vegetation cover of Libyan rangeland has changed adversely qualitatively and quantitatively over the past four decades. Ground-based observation methods are widely used to assess rangeland degradation in Libya. However, multi-temporal observations are often not integrated nor repeatable, making it difficult for rangeland managers to detect degradation consistently. Field study costs are also significantly high in comparison with their accuracy and reliability, both in terms of the time and resources required. Remote-sensing approaches offer the advantage of spanning large geographical areas with multiple spatial, spectral and temporal resolutions. These data can play a significant role in rangeland monitoring, permitting observation, monitoring and prediction of vegetation changes, productivity assessment, fire extent, vegetation and soil moisture measurement and quantifying the proliferation of invasive plant species. This paper reviews the factors causing rangeland degradation in Libya, identifying appropriate remote-sensing methods that can be used to implement appropriate monitoring procedures.

ContextThe 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.ObjectivesThis 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.MethodsA 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 conclusionThe 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.

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.

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.

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.

Soil erosion on arable land in lowland Britain has been the subject of field‐based surveys, which have assessed the volumes or masses of soil transported in channels across farmers’ fields. These surveys provide a unique database on the extent, frequency and rates of soil loss by water. This study synthesizes the key findings from those surveys and underscores the implications for soil erosion modelling. Rill erosion occurs in a small number of fields (consistently <10%). Over ~5 yr, a considerable proportion of the farmed landscape will suffer from rill erosion, but mostly in fields that erode only once. Mean erosion rates for lowland arable landscapes are much less than mean erosion rates for individual eroded fields within that landscape. These observations pose important challenges for modelling. Rainfall and cropping vary from year to year so that risk of wash or rill erosion in the same field also varies. Due to the infrequent occurrence of rill erosion, loss rates of eroding fields cannot be spatially extrapolated across the landscape, except in the case of wash erosion which takes place a number of times in almost all fields every year. A consistent pattern of increasing wash erosion, in terms of spatial extent, is emerging in lowland Britain. Resulting losses of fine silt and clay‐sized particles are small in amount and possibly insignificant in terms of loss of soil as a resource, but have significant implications for contaminant concentrations and pollution of water courses.

In 1871, the Prince of Wales fell ill with typhoid fever while staying at Londesborough Lodge, near Scarborough, UK. Over the next 2 months his illness, and the sanitary arrangements at Londesborough and Sandringham, became the subject of intense public scrutiny. According to The Lancet, Londesborough was most likely at fault, being in effect “placed at the summit of a great length of sewer”, with drains so inadequate that a “faint and deadly influence might have pervaded the whole house, or burst out of the closet” used by the Prince himself.

Methods for the spatial estimation of risk of harm to soil by erosion by water and wind and by soil organic matter decline are explored. Rates of harm are estimated for combinations of soil type and land cover (as a proxy for hazard frequency) and used to estimate risk of soil erosion and loss of soil organic carbon (SOC) for 1 km(2)pixels. Scenarios are proposed for defining the acceptability of risk of harm to soil: the most precautionary one corresponds to no net harm after natural regeneration of soil (i.e. a 1 in 20 chance of exceeding an erosion rate of <1 tha(-1)y(-1) and SOC content decline of 0 kg t(-1)y(-1) for mineral soils and a carbon stock decline of 0 tha(-1)y(-1) for organic soils). Areas at higher and lower than possible acceptable risk are mapped. The veracity of boundaries is compromised if areas of unacceptable risk are mapped to administrative boundaries. Errors in monitoring change in risk of harm to soil and inadequate information on risk reduction measures' efficacy, at landscape scales, make it impossible to use or monitor quantitative targets for risk reduction adequately. The consequences for priority area definition of expressing varying acceptable risk of harm to soil as a varying probability of exceeding a fixed level of harm, or, a varying level of harm being exceeded with a fixed probability, are discussed. Soil data and predictive models for rates of harm to soil would need considerable development and validation to implement a priority area approach robustly.

Remote sensing and landscape ecology concepts can provide a useful framework for state-and-transition models (STM) in order to quantify thresholds at different scales, and provide useful information for scientists, land managers, and conservationists in relation to resilience management. The overall aim of this research was to develop a spatially explicit STM to quantify thresholds based on the scale of disturbance processes impacting a grazing system. Specific objectives were to develop a conceptual STM framework for upland grazing ecosystems, to quantify spatial dynamics of stable and degraded pastures, and to assess threshold occurrence. Color aerial photography from Armboth Fell in the English Lake District National Park (United Kingdom) was classified into bare rock, dwarf shrub heath (DSH), and grassland/degraded wet heath (GDWH) in four pastures with different degrees of grazing pressure. Vegetation communities from these pastures were combined with soils, climate, and landform data to create a conceptual STM framework. Each pasture was sampled with 2-ha plots to quantify DSH and GDWH spatial structure. The proposed STM consisted of two reference and three alternative states. Low–grazing-pressure areas showed significantly higher percentage of DSH cover with larger contiguous patches and lower patch density than high–grazing-pressure areas. Breakpoints, considered to be thresholds, in mean patch area were identified in our data when DSH percentage cover was < 63% and GDWH, > 77%. The present study has shown the value of a robust, reliable, and repeatable approach to identify landscape dynamics and integrate it with field data to inform a conceptual STM framework for upland grazing ecosystems. It also demonstrates the importance of selecting scales relevant to the predominant disturbance process to test for threshold occurrence, and how this approach can be integrated with current assessment methods for resilience management.