With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies.

The creation and use of ontologies has become increasingly relevant for complex systems in recent years. This is because of the growing number of use of cases that rely on real-world integration of disparate systems, the need for semantic congruence across boundaries and the expectations of users for conceptual clarity within evolving domains or systems of interest. These needs are evident in most spheres of research involving complex systems, but they are particularly apparent in infrastructure and cities where traditionally siloed and sectoral approaches have dominated, undermining the potential for integration to solve societal challenges such as net zero, resilience to climate change, equity and affordability. This paper reports on findings of a literature review on infrastructure and city ontologies and puts forward some hypotheses inferred from the literature findings. The hypotheses are discussed with reference to the literature and provide avenues for further research on (a) belief systems that underpin non-top-level ontologies and the potential for interference from them, (b) the need for a small number of top-level ontologies and translation mechanisms between them and (c) clarity on the role of standards and information systems in the adaptability and quality of data sets using ontologies. A gap is also identified in the extent that ontologies can support more complex automated coupling and data transformation when dealing with different scales.

Flood-related image datasets from social media, smartphones, CCTV cameras, and unmanned aerial vehicles (UAVs) present valuable data for the management of flood risk, and particularly for the application of modern convolutional neural networks (CNNs) to specific flood-related problems such as flood extent detection and flood depth estimation. This review discusses the increasing role of CNNs in flood research with a growing number of published datasets, particularly since 2018. We note the lack of open and labelled flood image datasets and the growing need for an open, benchmark data library for image classification, object detection, and segmentation relevant to flood management. Such a library would provide benchmark datasets to advance CNN flood applications in general and serve as a resource, providing data scientists and the flood research community with the necessary data for model training and validation.

Timely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed.

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.

A growing literature highlights the presence of spatial differences in solar photovoltaic (PV) adoption patterns. Central to forward planning is an understanding of what affects PV growth, yet insights into the determinants of PV adoption in the literature are limited. What factors do drive the adoption at local level? Are the effects of these factors geographically uniform or are there nuances? What is the nature of these nuances? Existing studies so far use aggregate macro datasets with limited ability to capture the role of peer effects. This paper considers some established variables but also broadens the base of variables to try to identify new indicators relating to PV adoption. Specifically, it analyses domestic PV adoption in the UK at local level using data on the number of charities as a proxy to capture the opportunities to initiate social interactions and peer effects. A geographically weighted regression model that considers the spatially varying relationship between PV adoption and socio-economic explanatory variables reveals significantly more variability than the global regression. Our results show that charities and self-employment positively influence PV uptake while other socio-economic variables such as population density has bidirectional impacts.

Oil palm provides an important source of edible oils and fats, accounting for > 30% of total global production and > 55% of the international trade in these foodstuffs. The palms produce fresh fruit bunches, comprising several hundreds of small fruitlets, which are compressed and steamed to extract the oil. Soil nutrients in oil palm estates become depleted after decades of heavy harvesting and require fertilisers. Liquid palm oil mill effluent, solid empty fruit bunches and other residues can have deleterious environmental impacts and require careful management. The problems of residue disposal and soil nutrient impoverishment can be linked and managed by composting the oil palm mill residues and distributing the biofertiliser produced back to the plantation. Using case studies from West Malaysia we present an early stage practical tool for the planning of the distribution component of such a cycle. The computer-based tool uses multiple field-based and remote sensing data sources to integrate the effects of local soil conditions, transport distances, environmental protection and management priorities and then models customised distribution plans. The tool operates at plantation level and can be augmented with detailed local data, but the approach is extensible and potentially applicable to regional or national planning.

Powering refrigerated warehouses by renewable energy sources (RES) turns from an extravagancy to a routine. RES intermittency requires suitable energy storage for both off-grid and on-grid applications. Cryogenic energy storage, integrated synergistically with RES and large refrigerated warehouses, is a promising environmentally friendly technology (addressed by the EU CryoHub project). Hence, studies were carried out to identify where large energy-intensive refrigerated warehouses are situated across Europe and how much power they consume. By employing diverse instruments and data sources, some 1049 warehouses were established, while 503 energy intensive ones were mapped and further co-located with 3200 solar PV and 11700 onshore wind parks to discover the best areas for RES integration across EU28. As compared with similar international surveys, the CryoHub statistics covers simultaneously warehouse capacity, geographical location and energy data, which permit a comprehensive analysis and strategic planning in both food refrigeration and energy sectors.

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.

This paper describes work carried out within the EU-funded FOOTPRINT project to characterize the diversity of European agricultural and environmental conditions with respect to parameters which most influence the environmental fate of pesticides. Pan-European datasets for soils, climate, land cover and cropping were intersected, using GIS, to identify the full range of unique combinations of climate, soil and crop types which characterize European agriculture. The resulting FOOTPRINT European agro-environmental dataset constitutes a large number of polygons (approximately 1,700,000) with attribute data files for i) area fractions of annual crops related to each arable-type polygon (as an indicator of its probability of occurrence); and, ii) area fractions of each soil type in each polygon (as an indicator of its probability of occurrence). A total of 25,044 unique combinations of climate zones, agricultural land cover classes, administrative units and soil map units were identified. The same soil/crop combinations occur in many polygons which have the same climate while the fractions of the soils and arable crops are different. The number of unique combinations of climate, soil and agricultural land cover class is therefore only 7961. 26-year daily meteorological data, soil profile characteristics and crop management features were associated with each unique combination. The agro-environmental scenarios developed can be used to underpin the parameterization of environmental fate models for pesticides and should also have relevance for other agricultural pollutants. The implications for the improvement and further development of risk assessment procedures for pesticides are discussed.