CERES :: Browsing by Author "Corstanje, Ronald"

Browsing by Author "Corstanje, Ronald"

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Open Access
3D advance mapping of soil properties
(Cranfield University, 2012-07) Veronesi, Fabio; Mayr, T.; Corstanje, Ronald
Soil is extremely important for providing food, biomass and raw materials, water and nutrient storage; supporting biodiversity and providing foundations for man-made structures. However, its health is threatened by human activities, which can greatly affect the potential of soils to fulfil their functions and, consequently, result in environmental, economic and social damage. These issues require the characterisation of the impact and spatial extent of the problems. This can be achieved through the creation of detailed and comprehensive soil maps that describe both the spatial and vertical variability of key soil properties. Detailed three-dimensional (3D) digital soil maps can be readily used and embedded into environmental models. Three-dimensional soil mapping is not a new concept. However, only with the recent development of more powerful computers has it become feasible to undertake such data processing. Common techniques to estimate soil properties in the three-dimensional space include geostatistical interpolation, or a combination of depth functions and geostatistics. However, these two methods are both partially flawed. Geostatistical interpolation and kriging in particular, estimate soil properties in unsampled locations using a weighted average of the nearby observations. In order to produce the best possible estimate, this form of interpolation minimises the variance of each weighted average, thus decreasing the standard deviation of the estimates, compared to the soil observations. This appears as a smoothing effect on the data and, as a consequence, kriging interpolation is not reliable when the dataset is not sampled with a sampling designs optimised for geostatistics. Depth function approaches, as they are generally applied in literature, implement a spline regression of the soil profile data that aims to better describe the changes of the soil properties with depth. Subsequently, the spline is resampled at determined depths and, for each of these depths, a bi-dimensional (2D) geostatistical interpolation is performed. Consequently, the 3D soil model is a combination of a series of bi-dimensional slices. This approach can effectively decrease or eliminate any smoothing issues, but the way in which the model is created, by combining several 2D horizontal slices, can potentially lead to erroneous estimations. The fact that the geostatistical interpolation is performed in 2D implies that an unsampled location is estimated only by considering values at the same depth, thus excluding the vertical variability from the mapping, and potentially undermining the accuracy of the method. For these reasons, the literature review identified a clear need for developing, a new method for accurately estimating soil properties in 3D – the target of this research, The method studied in this thesis explores the concept of soil specific depth functions, which are simple mathematical equations, chosen for their ability to describe the general profile pattern of a soil dataset. This way, fitting the depth function to a particular sample becomes a diagnostic tool. If the pattern shown in a particular soil profile is dissimilar to the average pattern described by the depth function, it means that in that region there are localised changes in the soil profiles, and these can be identified from the goodness of fit of the function. This way, areas where soil properties have a homogeneous profile pattern can be easily identified and the depth function can be changed accordingly. The application of this new mapping technique is based on the geostatistical interpolation of the depth function coefficients across the study area. Subsequently, the equation is solved for each interpolated location to create a 3D lattice of soil properties estimations. For this way of mapping, this new methodology was denoted as top-down mapping method. The methodology was assessed through three case studies, where the top-down mapping method was developed, tested, and validated. Three datasets of diverse soil properties and at different spatial extents were selected. The results were validated primarily using cross-validation and, when possible, by comparing the estimates with independently sampled datasets (independent validation). In addition, the results were compared with estimates obtained using established literature methods, such as 3D kriging interpolation and the spline approach, in order to define some basic rule of application. The results indicate that the top-down mapping method can be used in circumstances where the soil profiles present a pattern that can be described by a function with maximum three coefficients. If this condition is met, as it was with key soil properties during the research, the top-down mapping method can be used for obtaining reliable estimates at different spatial extents.
Open Access
A Method to Assess the Performance of SAR-derived Surface Soil Moisture Products
(Cranfield University, 2021-04-13 09:13) Beale, John; Waine, Toby; Corstanje, Ronald; Evans, Jonathan
A Method to Assess the Performance of SAR-derived Surface Soil Moisture Products John Beale, Toby Waine, Jonathan Evans, Ronald Corstanje This study brought together existing research data obtained from a number of different sources, some of which were upon request and subject to licence restrictions. Full details of how these data may be obtained are in this document
Open Access
Agricultural decision-making under uncertainty: a loss function on the kriging variance from soil properties predicted by infrared and X-ray fluorescence spectroscopy
(EGU: European Geophysical Union, 2021-04-30) Breure, Timo Samuel; Haefele, Stephan M.; Webster, Richard; Hannam, Jacqueline A.; Corstanje, Ronald; Milne, Alice E.
Open Access
The application of digital soil mapping to improve the resolution of national soil properties across Great Britain.
(Cranfield University, 2018-10) Campbell, Grant; Corstanje, Ronald; Hannam, Jacqueline A.; Black, H. I. J.; Lilly, A.
Many countries have created soil maps to illustrate the variety of soil properties and support how soils can be used. Traditional soil mapping by field survey and interpretation has been the most recognised form of soil mapping for many years and an effective way to capture a variable soil landscape. Such maps have enabled scientists and stakeholders to improve their understanding of relationships between soils and other landscape factors such as geology and land cover. However, with the amount of soil information growing and technology improving, Digital Soil Mapping (DSM) has been developed as an alternative approach to generate soil property predictions and to produce finer resolution soils data. Currently, DSM produces maps based on training of models with observed soils data and environmental covariates and then releases these to stakeholders to evaluate their utility. This PhD has taken a different approach by addressing stakeholder needs at the beginning of the process. The overall aim of this PhD was to improve the spatial resolution of soil properties across Great Britain (GB), as informed by stakeholders. Three main aims were identified. The first assessed what current soils data and information stakeholders currently use, and what improvements they want to see from future soil-related products. The second aim, using information from the questionnaire survey and a comparison of laboratory and analytical methods, is to develop DSM which could be applied across the whole of GB. This was done by comparing two modelling approaches: Boosted Regression Trees (BRTs) and Multivariate Adaptive Regression Splines (MARS) for mapping soil properties (loss-on-ignition, texture and pH) across two pilot areas. The characteristics of MARS and BRT models at both training and deployment stages are examined. The third outcome investigated how well the soil properties mapped across GB, building on the development of DSM in the pilot areas and whether they reflect expert pedological knowledge. This section also focusses on how suitable an independent validation dataset is at evaluating soil property predictions. This PhD has shown that stakeholders are aware of what soils data and information they are using and could clearly express what is needed to improve current maps. Wider use of soil information by non-soil experts would be improved by increasing data accessibility and user- friendly supporting materials. Fundamentally, most stakeholders require finer resolution than what is currently available which identifies an opportunity for DSM to fill some of this gap. To address these gaps and develop DSM across GB, this PhD focussed on mapping soil properties that were directly comparable across Scotland and England & Wales and also key to many stakeholder information needs. After investigation of laboratory and analytical methods from the two national soil surveys of Scotland and England & Wales, soil loss on ignition, soil texture and soil pH were chosen for developing DSM for GB. From the development of DSM, results showed that MARS models produced better statistical performances than BRTs for predicting soil properties within a training environment. However, when MARS models are deployed to larger areas, they extrapolate beyond their means and BRTs performed better. This is because MARS models perform more consistently when many variables are required. Furthermore, MARS models struggle with overfitting and missing data which subsequently leads to incorrect and unfeasible pedological relationships between soil properties. BRT models, despite not performing as well statistically, produce more consistent relationships between pedology and mapped soil properties. This is because BRT models introduce randomness in the boosting which reduces overfitting and improves the predictive performance. BRTs have shown to be more consistent in the mapping outputs than MARS because regressing to the mean is more favourable when most data matches up with one another. However, this does not necessarily mean that the full range of soils in these areas were being captured by the BRT model. This led to scaling up from the pilot areas to modelling soil properties across GB using a single regional BRT model and evaluating its performance. BRT modelling results for GB at 2D and 3D predict well for pH and LOI but less so for texture. Going forward, more data are required to produce more consistent modelling outputs especially for areas across GB where soil properties are not currently being predicted well. The GB modelling results also highlighted a poor performance of the model against an independent validation dataset. This is because the original data for both GB training and validation datasets were analysed and collected for different purposes. These datasets were taken at different time periods under a different sampling design. Furthermore, the data for both training and validation GB datasets were collected at different scales. At present, these first versions of soil property DSM maps for GB have produced variable results. However, this exercise has shown that the development of reliable DSM maps would benefit from interaction between pedologists, modellers and stakeholders to ensure that mapped outputs are of sufficient quality at adequate finer resolution and can be usable. Such DSM maps, alongside management recommendations, will help to address many global challenges associated to soils. However, DSM is not the panacea for all mapping needs. Until such time that DSM fully develops into DSA and adequately incorporates the breadth of information available in traditional soil maps, mapping from field survey and observation will continue to be necessary for stakeholders.
Open Access
Are existing soils data meeting the needs of stakeholders in Europe? An analysis of practical use from policy to field
(Elsevier, 2017-09-21) Campbell, Grant Alistair; Lilly, Allan; Corstanje, Ronald; Mayr, Thomas R.; Black, H.
Soils form a major component of the natural system and their functions underpin many key ecosystem goods and services. The fundamental importance of soils in the environment means that many different organisations and stakeholders make extensive use of soils data and information in their everyday working practices. For many reasons, stakeholders are not always aware that they are reliant upon soil data and information to support their activities. Various reviews of stakeholder needs and how soil information could be improved have been carried out in recent years. However, to date, there has been little consideration of user needs from a non-expert perspective. The aim of this study was to explore the use of explicit and hidden soil information in different organisations across Europe and gain a better understanding of improvements needed in soil data and information to assist in practical use by non-expert stakeholders. An on-line questionnaire was used to investigate different uses of soils data and information with 310 responses obtained from 77 organisations across Europe. Results illustrate the widespread use of soil data and information across diverse organisations within Europe, particularly spatial products and soil functional assessments and tools. A wide range of improvements were expressed with a prevalence for finer scale resolution, trends over time, future scenarios, improved accuracy, non-technical supporting information and better capacity to use GIS. An underlying message is that existing legacy soils data need to be supplemented by new up-to-date data to meet stakeholder needs and information gaps.
Open Access
Assessment of heat mitigation capacity of urban greenspaces with the use of InVEST urban cooling model, verified with day-time land surface temperature data
(Elsevier, 2021-06-04) Zawadzka, J. E.; Harris, Jim A.; Corstanje, Ronald
Accurate quantification of the heat mitigation capacity of urban greenspaces is essential in planning decisions due to increased thermal pressures on existing and new urban environments associated with climate change. However, this often requires data analytical skillsets that may not be available to the planning community. The recently developed InVEST 3.8.7 Urban Cooling model addresses this limitation by using several easily accessible parameters, assigned to a land cover map, to produce a heat mitigation index (HMI) intended to estimate the cooling capacity of vegetation in a spatial context. In this study, we validated the HMI derived for three towns with differing morphologies by comparison to land surface temperature (LST) data using linear regression analysis. We found that the HMI can be used to explain a variable proportion of the variation in LST, with R2 ranging from 0.48 to 0.64 depending on the town, with stronger associations obtained for towns with a higher range of LST values. Higher resemblance to LST data was achieved after resampling of the 2 m resolution model outputs to 30 m resolution, inclusion of water bodies as cooling features, and using cooling distance away from large greenspaces of 100 m. On average, a change in the HMI of 0.1 was associated with 0.76 °C change in LST. We conclude that the model is suitable for assessment of heat mitigation interventions through incorporation of vegetation and water bodies into city plans at scales relevant to masterplans rather than fine-tuning of urban design.
Open Access
A bird's eye view: using circuit theory to study urban landscape connectivity for birds
(Springer, 2017-06-28) Grafius, Darren Ronald; Corstanje, Ronald; Siriwardena, Gavin M.; Plummer, Kate E.; Harris, Jim A.
Context Connectivity is fundamental to understanding how landscape form influences ecological function. However, uncertainties persist due to the difficulty and expense of gathering empirical data to drive or to validate connectivity models, especially in urban areas, where relationships are multifaceted and the habitat matrix cannot be considered to be binary. Objectives This research used circuit theory to model urban bird flows (i.e. ‘current’), and compared results to observed abundance. The aims were to explore the ability of this approach to predict wildlife flows and to test relationships between modelled connectivity and variation in abundance. Methods Circuitscape was used to model functional connectivity in Bedford, Luton/Dunstable, and Milton Keynes, UK, for great tits (Parus major) and blue tits (Cyanistes caeruleus), drawing parameters from published studies of woodland bird flows in urban environments. Model performance was then tested against observed abundance data. Results Modelled current showed a weak yet positive agreement with combined abundance for P. major and C. caeruleus. Weaker correlations were found for other woodland species, suggesting the approach may be expandable if re-parameterised. Conclusions Trees provide suitable habitat for urban woodland bird species, but their location in large, contiguous patches and corridors along barriers also facilitates connectivity networks throughout the urban matrix. Urban connectivity studies are well-served by the advantages of circuit theory approaches, and benefit from the empirical study of wildlife flows in these landscapes to parameterise this type of modelling more explicitly. Such results can prove informative and beneficial in designing urban green space and new developments.
Open Access
Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment
(Wiley, 2015-02-06) Albanito, Fabrizio; Beringer, Tim; Corstanje, Ronald; Poulter, Benjamin; Stephenson, Anna; Zawadzka, Joanna; Smith, Pete
The potential for climate change mitigation by bioenergy crops and terrestrial carbon sinks has been the object of intensive research in the past decade. There has been much debate about whether energy crops used to offset fossil fuel use, or carbon sequestration in forests, would provide the best climate mitigation benefit. Most current food cropland is unlikely to be used for bioenergy, but in many regions of the world, a proportion of cropland is being abandoned, particularly marginal croplands, and some of this land is now being used for bioenergy. In this study, we assess the consequences of land-use change on cropland. We first identify areas where cropland is so productive that it may never be converted and assess the potential of the remaining cropland to mitigate climate change by identifying which alternative land use provides the best climate benefit: C4 grass bioenergy crops, coppiced woody energy crops or allowing forest regrowth to create a carbon sink. We do not present this as a scenario of land-use change – we simply assess the best option in any given global location should a land-use change occur. To do this, we use global biomass potential studies based on food crop productivity, forest inventory data and dynamic global vegetation models to provide, for the first time, a global comparison of the climate change implications of either deploying bioenergy crops or allowing forest regeneration on current crop land, over a period of 20 years starting in the nominal year of 2000 ad. Globally, the extent of cropland on which conversion to energy crops or forest would result in a net carbon loss, and therefore likely always to remain as cropland, was estimated to be about 420.1 Mha, or 35.6% of the total cropland in Africa, 40.3% in Asia and Russia Federation, 30.8% in Europe-25, 48.4% in North America, 13.7% in South America and 58.5% in Oceania. Fast growing C4 grasses such as Miscanthus and switch-grass cultivars are the bioenergy feedstock with the highest climate mitigation potential. Fast growing C4 grasses such as Miscanthus and switch-grass cultivars provide the best climate mitigation option on ≈485 Mha of cropland worldwide with ~42% of this land characterized by a terrain slope equal or above 20%. If that land-use change did occur, it would displace ≈58.1 Pg fossil fuel C equivalent (Ceq oil). Woody energy crops such as poplar, willow and Eucalyptus species would be the best option on only 2.4% (≈26.3 Mha) of current cropland, and if this land-use change occurred, it would displace ≈0.9 Pg Ceq oil. Allowing cropland to revert to forest would be the best climate mitigation option on ≈17% of current cropland (≈184.5 Mha), and if this land-use change occurred, it would sequester ≈5.8 Pg C in biomass in the 20-year-old forest and ≈2.7 Pg C in soil. This study is spatially explicit, so also serves to identify the regional differences in the efficacy of different climate mitigation options, informing policymakers developing regionally or nationally appropriate mitigation actions.
Open Access
Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near- and mid-infrared spectra at the field-scale
(Elsevier, 2021-10-07) Breure, Timo Samuel; Prout, Jonah M.; Haefele, Stephan M.; Milne, Alice E.; Hannam, Jacqueline A.; Moreno-Rojas, S.; Corstanje, Ronald
The prediction accuracy of soil properties by proximal soil sensing has made their application more practical. However, in order to gain sufficient accuracy, samples are typically air-dried and milled before spectral measurements are made. Calibration of the spectra is usually achieved by making wet chemistry measurements on a subset of the field samples and local regression models fitted to aid subsequent prediction. Both sample handling and wet chemistry can be labour and resource intensive. This study aims to quantify the uncertainty associated with soil property estimates from different methods to reduce effort of field-scale calibrations of soil spectra. We consider two approaches to reduce these expenses for predictions made from visible-near-infrared ((V)NIR), mid-infrared (MIR) spectra and their combination. First, we considered reducing the level of processing of the samples by comparing the effect of different sample conditions (in-situ, unprocessed, air-dried and milled). Second, we explored the use of existing spectral libraries to inform calibrations (based on milled samples from the UK National Soil Inventory) with and without ‘spiking’ the spectral libraries with a small subset of samples from the study fields. Prediction accuracy of soil organic carbon, pH, clay, available P and K for each of these approaches was evaluated on samples from agricultural fields in the UK. Available P and K could only be moderately predicted with the field-scale dataset where samples were milled. Therefore this study found no evidence to suggest that there is scope to reduce costs associated with sample processing or field-scale calibration for available P and K. However, the results showed that there is potential to reduce time and cost implications of using (V)NIR and MIR spectra to predict soil organic carbon, clay and pH. Compared to field-scale calibrations from milled samples, we found that reduced sample processing lowered the ratio of performance to inter-quartile range (RPIQ) between 0% and 76%. The use of spectral libraries reduced the RPIQ of predictions relative to field-scale calibrations from milled samples between 54% and 82% and the RPIQ was reduced between 29% and 70% for predictions when spectral libraries were spiked. The increase in uncertainty was specific to the combination of soil property and sensor analysed. We conclude that there is always a trade-off between prediction accuracy and the costs associated with soil sampling, sample processing and wet chemical analysis. Therefore the relative merits of each approach will depend on the specific case in question.
Open Access
A conceptual model for climatic teleconnection signal control on groundwater variability in the UK and Europe
(Elsevier, 2017-07-22) Rust, William; Holman, Ian P.; Corstanje, Ronald; Bloomfield, John; Cuthbert, Mark
The ability to predict future variability of groundwater resources in time and space is of critical importance to drought management. Periodic control on groundwater levels from oscillatory climatic systems (such as the North Atlantic Oscillation) offers a potentially valuable source of longer term forecasting capability. While some studies have found evidence of the influence of such climatic oscillations within groundwater records, there is little information on how periodic signals propagate between a climatic system and a groundwater resource. This paper develops a conceptual model of this relationship for groundwater resources in the UK and Europe, based on a review of current research. The studies reviewed here reveal key spatial and temporal signal modulations between climatic oscillations, precipitation, groundwater recharge and groundwater discharge. Generally positive correlations are found between the NAO (as a dominant influence) and precipitation in northern Europe indicating a strong control on water available for groundwater recharge. These periodic signals in precipitation are transformed by the unsaturated and saturated zones, such that signals are damped and lagged. This modulation has been identified to varying degrees, and is dependent on the shape, storage and transmissivity of an aquifer system. This goes part way towards explaining the differences in periodic signal strength found across many groundwater systems in current research. So that an understanding of these relationships can be used by water managers in building resilience to drought, several research gaps have been identified. Among these are improved quantification of spatial groundwater sensitivity to periodic control, and better identification of the hydrogeological controls on signal lagging and damping. Principally, research needs to move towards developing improved predictive capability for the use of periodic climate oscillations as indicators of longer term groundwater variability.
Open Access
Data supporting 'Non-stationary control of the NAO on European rainfall and its implications for water resource management'
(Cranfield University, 2023-02-10 17:32) Rust, Will; Holman, Ian; Corstanje, Ronald; Cuthbert, Mark; P. Bloomfield, John
10-year window rolling correlation between NAOI and GPCC gridded rainfall data for Western Europe. Grid cells between -13-20° Longitude and 35-70° Latitude were used to represent Western Europe.
Open Access
Data underpinning the paper "Ecological Connectivity Networks in Rapidly Expanding Cities"
(Cranfield University, 2018-03-12 11:53) najihah Muhamad nor, Amal; Corstanje, Ronald; Harris, Jim; Grafius, Darren
Data used in the accompanying paper. Data is presented in MS Word summaries for easy previews. Additionally, the zip folder contains original data in the folders:- Birds*;- Cumulative cost**;- Eco network**;- Jakarta (Birds*, Boundary*, Clipbound*, Conefor*, Focal node*);- Kuala Lumpur (Boundary*, Focalnode*, Image2014*, Resistance*, Shortregion*);- Metro Manila (Boundary*, Focalnode*, image2014*, Resistance**, Linkage mapper* including xlsx link table);- Resistance**;* ArcGIS shapefiles (dbf, prj, sbn, sbx, shp, shx, xml)** jpg files
Open Access
Data underpinning the paper "Impact of rapid urban expansion on green space structure"
(Cranfield University, 2017-05-26 09:08) najihah Muhamad nor, Amal; Corstanje, Ronald; Harris, Jim; Brewer, Tim
These data could be opened in Microsoft Excel and ArcGIS software.
Open Access
A datamining approach to identifying spatial patterns of phosphorus forms in the Stormwater Treatment Areas in the Everglades
(Elsevier, 2016-10-18) Corstanje, Ronald; Grafius, Darren R.; Zawadzka, Joanna; Moreira Barradas, Joao; Vince, G.; Ivanoff, D.; Pietro, K.
The Everglades ecosystem in Florida, USA, is naturally phosphorus (P) limited, and faces threats of ecosystem change and associated losses to habitat, biodiversity, and ecosystem function if subjected to high inflows of P and other nutrients. In addition to changes in historic hydropattern, upstream agriculture (sugar cane, vegetable, citrus) and urbanization has placed the Everglades at risk due to nutrient-rich runoff. In response to this threat, the Stormwater Treatment Areas (STAs) were constructed along the northern boundary of the Everglades as engineered ecological systems designed to retain P from water flowing into the Everglades. This research investigated data collected over a period from 2002 to 2014 from the interior of the STAs using data mining and analysis techniques including (a) exploratory methods such as Principal Component Analysis to test for patterns and groupings in the data, and (b) modelling approaches to test for predictive relationships between environmental variables. The purpose of this research was to reveal and compare spatial trends and relationships between environmental variables across the various treatment cells, flow-ways, and STAs. Common spatial patterns and their drivers indicated that the flow-ways do not function along simple linear gradients; instead forming zonal patterns of P distribution that may increasingly align with the predominant flow path over time. Findings also indicate that the primary drivers of the spatial distribution of P in many of these systems relate to soil characteristics. The results suggest that coupled cycles may be a key component of these systems; i.e. the movement and transformation of P is coupled to that of nitrogen (N).
Open Access
Defining and quantifying the resilience of responses to disturbance: a conceptual and modelling approach from soil science
(Nature Publishing Group, 2016-06-22) Todman, Lindsay; Fraser, Fiona; Corstanje, Ronald; Deeks, Lynda K.; Harris, Jim A.; Pawlett, Mark; Ritz, Karl; Whitmore, A. P.
There are several conceptual definitions of resilience pertaining to environmental systems and, even if resilience is clearly defined in a particular context, it is challenging to quantify. We identify four characteristics of the response of a system function to disturbance that relate to “resilience”: (1) degree of return of the function to a reference level; (2) time taken to reach a new quasi-stable state; (3) rate (i.e. gradient) at which the function reaches the new state; (4) cumulative magnitude of the function (i.e. area under the curve) before a new state is reached. We develop metrics to quantify these characteristics based on an analogy with a mechanical spring and damper system. Using the example of the response of a soil function (respiration) to disturbance, we demonstrate that these metrics effectively discriminate key features of the dynamic response. Although any one of these characteristics could define resilience, each may lead to different insights and conclusions. The salient properties of a resilient response must thus be identified for different contexts. Because the temporal resolution of data affects the accurate determination of these metrics, we recommend that at least twelve measurements are made over the temporal range for which the response is expected.
Open Access
Developing above and below ground carbon stock models and tools for farm and landscape managment.
(Cranfield University, 2022-11) Beka, Styliani; Burgess, Paul J.; Corstanje, Ronald
Agriculture and land use are responsible for about 11% of the UK’s territorial greenhouse gas emissions. Therefore, a policy measure to mitigate climate change is to incentivise additional soil organic carbon and biomass carbon storage on farms. However, physical measurements of soil organic carbon and biomass carbon can be difficult due to the high cost and labour requirements. Hence, this PhD aimed to review, develop, apply and evaluate scalable and robust methods for creating soil organic carbon and biomass carbon maps and models, to enable more informed farm and landscape management. Current methods for developing farm-scale carbon inventories are reviewed and it is demonstrated that few models provide spatial estimates with a level of uncertainty. Additionally, three spatial soil organic carbon models with different scales of input and output data, for the top 10 cm of the soil for nine grassland sites are developed and evaluated. Across the evaluation dataset, the fine-scale models were able to better predict the soil organic carbon (0-10 cm) variability found in the measured values. This difference has important implications if soil organic carbon values derived from models are used to provide a baseline from which carbon payments are derived. An integrated spatial approach using LiDAR data and two Bayesian Belief Network models is developed to quantify the total biomass carbon stock of different land covers and landscape features across five case studies. The two Bayesian Belief Network models successfully allocated the total biomass carbon values to one of four classes with an error rate of 6.7% and 4.3% for the land cover and landscape features respectively. An advantage of the approach is that the predicted values can be determined remotely using historic land cover and LiDAR height data. A novel tool is then established that combines the empirical SOC model with the probabilistic biomass carbon model for baseline farm carbon stock estimation. The derived results include itemised values and related uncertainty for each land cover parcel and landscape feature. Lastly, an investigation of the opportunities and obstacles for spatial farm level C accountancy is conducted.
Open Access
Digital soil assessment for quantifying soil constraints to crop production: a case study for rice in Punjab, India
(Wiley, 2018-09-24) Okonkwo, Ezekiel Iloabuchi; Corstanje, Ronald; Kirk, Guy J. D.
Assessments of land capability for particular functions such as food production need to allow for uncertainties both in the criteria used to specify the function and in information on relevant soil properties. In this paper, we evaluate the use of digital soil assessment (DSA) for dynamic assessment of soil capability allowing for both uncertainties and spatial variability in soil properties and flexibility in the values of assessment criteria. We do this for soil constraints to rice production in the state of Punjab, India, where soil salinity and alkalinity are potentially important constraints to cropping. In DSA, spatial predictions of soil properties and associated uncertainties made with digital soil mapping (DSM) are used to assess soil functions. We use a combination of DSM and Monte Carlo simulation methods to estimate the spatial variation in soil electrical conductivity (ECe) and pH to 20 cm depth in soils across Punjab. We then use the estimates and associated uncertainties to assess the likelihood that soil salinity or alkalinity or both could constrain rice production. Results show that allowing for prediction uncertainties of soil attributes results in far smaller areas affected by salinity (1.2 vs. 2.0 Mha) and alkalinity (3.0 vs. 3.2 Mha). Results also show the importance of correctly setting threshold values for constraint criteria and the flexibility of the DSA approach for setting thresholds.
Open Access
Distinct respiratory responses of soils to complex organic substrate are governed predominantly by soil architecture and its microbial community
(Elsevier, 2016-10-13) Fraser, Fiona; Todman, L. C.; Corstanje, Ronald; Deeks, Lynda K.; Harris, Jim A.; Pawlett, Mark; Whitmore, A. P.; Ritz, Karl
Factors governing the turnover of organic matter (OM) added to soils, including substrate quality, climate, environment and biology, are well known, but their relative importance has been difficult to ascertain due to the interconnected nature of the soil system. This has made their inclusion in mechanistic models of OM turnover or nutrient cycling difficult despite the potential power of these models to unravel complex interactions. Using high temporal-resolution respirometery (6 min measurement intervals), we monitored the respiratory response of 67 soils sampled from across England and Wales over a 5 day period following the addition of a complex organic substrate (green barley powder). Four respiratory response archetypes were observed, characterised by different rates of respiration as well as different time-dependent patterns. We also found that it was possible to predict, with 95% accuracy, which type of respiratory behaviour a soil would exhibit based on certain physical and chemical soil properties combined with the size and phenotypic structure of the microbial community. Bulk density, microbial biomass carbon, water holding capacity and microbial community phenotype were identified as the four most important factors in predicting the soils’ respiratory responses using a Bayesian belief network. These results show that the size and constitution of the microbial community are as important as physico-chemical properties of a soil in governing the respiratory response to OM addition. Such a combination suggests that the 'architecture' of the soil, i.e. the integration of the spatial organisation of the environment and the interactions between the communities living and functioning within the pore networks, is fundamentally important in regulating such processes.
Open Access
Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data
(Taylor and Francis, 2019-03-25) Zawadzka, Joanna; Corstanje, Ronald; Harris, Jim A.; Truckell, Ian
We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m) resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83 K prior to and 0.76–1.21 K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.
Open Access
Ecological connectivity networks in rapidly expanding cities
(Elsevier, 2017-06-23) Nor, Amal Najihah Muhamad; Corstanje, Ronald; Harris, Jim A.; Grafius, Darren R.; Siriwardena, Gavin M.
Urban expansion increases fragmentation of the landscape. In effect, fragmentation decreases connectivity, causes green space loss and impacts upon the ecology and function of green space. Restoration of the functionality of green space often requires restoring the ecological connectivity of this green space within the city matrix. However, identifying ecological corridors that integrate different structural and functional connectivity of green space remains vague. Assessing connectivity for developing an ecological network by using efficient models is essential to improve these networks under rapid urban expansion. This paper presents a novel methodological approach to assess and model connectivity for the Eurasian tree sparrow (Passer montanus) and Yellow-vented bulbul (Pycnonotus goiavier) in three cities (Kuala Lumpur, Malaysia; Jakarta, Indonesia and Metro Manila, Philippines). The approach identifies potential priority corridors for ecological connectivity networks. The study combined circuit models, connectivity analysis and least-cost models to identify potential corridors by integrating structure and function of green space patches to provide reliable ecological connectivity network models in the cities. Relevant parameters such as landscape resistance and green space structure (vegetation density, patch size and patch distance) were derived from an expert and literature-based approach based on the preference of bird behaviour. The integrated models allowed the assessment of connectivity for both species using different measures of green space structure revealing the potential corridors and least-cost pathways for both bird species at the patch sites. The implementation of improvements to the identified corridors could increase the connectivity of green space. This study provides examples of how combining models can contribute to the improvement of ecological networks in rapidly expanding cities and demonstrates the usefulness of such models for biodiversity conservation and urban planning.

Browsing by Author "Corstanje, Ronald"

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