In recent decades, China's multiple cities have expanded rapidly, intensifying urban heat island (UHI) effects. The spatiotemporal patterns of UHI intensity and their driving factors have become a research focus. Some studies focus on machine learning or statistical methods regarding the spatiotemporal patterns of the UHI intensity in multiple cities and their driving factors, with few exploring UHI intensity variations using interpretable neural networks. We calculated the UHI of 31 provincial capital cities across seven physical geographic regions over two decades, analyzed cluster characteristics with Fourier fitting and K-means algorithms, and disclosed their driving factor using the explainable deep learning model (TabNET). Results show that (1) Between 2000 and 2020, the spatial expansion rate of first-tier cities was 176.92 %, while that of second-tier cities reached 197.12 %. (2) 94.62 % of the cities experienced increases in UHI during the summer months, with Urumqi showing the smallest change in UHI across the seasons and Shenyang showing the greatest change. (3) The four distinct UHI patterns observed across 31 Chinese cities can be categorized into six clusters that closely correspond to China's six natural geographical regions, indicating its dominant influence on UHI spatial variation. (4) The geographical location, population, GDP, elevation, month, and vegetation cover are significant drivers of UHI, with weights of 20.3 %, 14 %, 13.3 %, 13.2 %, 12.2 % and 10 %, respectively. Our results visually summarize the regional UHI patterns and disclose driving factors, providing intuitive guidance for mitigating UHI and urban thermal environment optimization.

Cellular Automata (CA) spatial models have become a de facto means by which to simulate future scenarios of land use activity, particularly urban development. Increasingly, such models are being employed within large scale climate impact, adaption and resilience studies to provide future scenarios of land use change and urban development, facilitating the calculation of the economic impact of future climate hazards and development land use adaption options. However, many spatial CA models only provide information on whether land has undergone a transition from one use/activity to a new one. In the case of urban development, they are unable to provide information on the spatial pattern of new development at an intra-cell level of spatial fidelity. However, in many cases such information is important as the spatial impacts of hazards and the adaption and resilience of new urban development will be dependent on the precise spatial configuration of buildings, roads and urban green space. In this paper an Urban Fabric Generator (UFG) is presented that takes the outputs of an Urban Development Model (UDM) and simulates plausible spatial configurations of buildings, roads and urban green space. The utility of the UFG is demonstrated via two flooding case studies, where generated spatial patterns of urban form are used to parameterise a hydrodynamic pluvial flood model that evaluates the impact of future climate driven rainfall on property damage and the utility of infrastructure investment with regards to improving surface water flood resilience.

The processes of urbanisation and climate change are necessitating the transformation of cities towards sustainable cities that are robustly adapted to natural hazards, while simultaneously reducing energy and resource usage to mitigate further climatic change. Frequently such objectives conflict with each other, negatively affecting sustainability as a whole. For example, urban intensification with the intention of lowering transport energy costs has been found to exacerbate urban heat islands, increase flood risk and lead to poor health outcomes. This paper presents the use of an evolutionary computing spatial optimisation framework as one method by which multiple positively and negatively correlated sustainability objectives can be evaluated in time and space to assist urban planning. A coupled genetic algorithm and pareto optimisation approach is used to evaluate spatial configurations of future development against sustainability objectives (e.g., reduced heat risk, minimal flood risk, brown field development, optimal mobility). The developed approach is evaluated in a Greater London Authority (GLA) case study that simulates future urban development patterns that satisfy projected population growth whilst being sensitive to climate induced hazards and current planning policies. The spatial optimization framework developed significantly improves upon the existing urban development plan with the Pareto-front found to be 35% better than the proposed spatial plan for London. However, trade-offs between objectives were found to exist, most notably it was not possible to achieve a pairwise optimization between heat and flood risk and urban sprawl and heat risk.

Urban heat island (UHI) not only reflects the environmental thermal comfort and energy consumption, but also affects the urban meso-scale climate. There are many researches related with UHI mainly focusing on urban and rural area, while neglecting dynamic rural -urban transition especially in a rapid urbanization in China. Beijing and Zhengzhou are studied by using city clustering algorithm (CCA) and boundary generation algorithm (BGA) to delineate the urban, peri-urban and rural boundaries from 2000 to 2023 within three stages. Fourier transform model was used to identify the UHI patterns. Results show: 1) Two cities have undergone obvious expansions in 20 years, with a consistent mean LST decrease from urban to peri-urban and rural areas in three stages. 2) The distribution of UHII was more consistent in Beijing, while it varied more in Zhengzhou across seasons. 3) The UHI patterns notably differ, with Zhengzhou experiencing variable patterns and Beijing consistently showing oblate patterns. 4) The profiles of UHII and NDVI in two cities varied seasonally and reflected urban expansions in terms of longitude and latitude. Understanding the long-term changes and patterns of urban heat islands in different cities will provide information for formulating adaptive policies for urban sustainability.

This paper traces the journey from theory to application in several projects undertaken in a regional area of Western Australia. This includes design and construction of coastal rock revetments, low profile geotextile sand container groynes, and beach nourishment projects. The importance of understanding the subtleties of coastal dynamics in a particular area, the application, and limitations of widely applied coastal engineering practices, and the importance of positive relationships with experienced local contractors in delivering coastal projects is outlined.

Extreme heat poses a major threat to population health. Heatwave frequency, magnitude and duration are expected to increase through climate change, requiring resilient planning to mitigate the effects of heat on vulnerable populations and to prepare the health system for the impact of intense heat exposure in the future. Existing heat vulnerability indicators do not include linked health data at a fine spatial level and do not consider urban morphology properties that are now known to be important in the assessment of population vulnerability to extreme heat. This study aims to produce a fine-level Heat Health Vulnerability Indicator (HHVI) that integrates linked population health data, demographic determinants, environment, and urban morphology parameters into weighted spatial layers of exposure, sensitivity and adaptive capacity. The resulting spatial indicator identifies areas that are vulnerable to extreme heat, along with the associated human health outcomes and the potential mitigating or amplifying effect of the built environment. A case study for the state of New South Wales, Australia, highlights the indicator's suitability to inform future planning decisions that lead to improved health and habitat interventions for climate-resilient cities.

This paper aims to improve understanding of the relationship between estimated land surface and air temperature measurements for greater London UK. A 23-year time series (1985-2008) of 1,141 Advanced Very High Resolution Radiometer (AVHRR) images and hourly UK Meteorological Office weather station measurements for Greater London is employed to critically evaluate the relationship between estimated surface temperature (EST) and screen-level air temperature. This analysis is used to produce an empirical model to predict spatially complete air temperatures for London. Empirical model results indicate that careful temporal and spatial pairing of AVHRR EST and air temperatures provides a sufficiently high-quality coupled time series to develop a robust spatial prediction of London air temperatures. A global model over multiple years produced an R-2=0.68 with RSME of 5.04 degrees C. However, for individual year summer-seasons and individual validation in-situ weather stations the RMSE was noticeably lower (2.51 - 3.11) showing that on a yearly basis empirically estimated air temperatures provide a means by which in-situ terrestrial measurements can be viably augmented.

Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.

Critical services depend on infrastructure networks for their operation and any disruption to these networks can have significant impacts on society, the economy, and quality of life. Such networks can be characterised as graphs which can be used to understand their structural properties, and the effect on their behaviour and robustness to hazards. Using a suite of graphs and critical infrastructure networks, this study aims to show that networks which exhibit a hierarchical structure are more likely to be less robust comparatively to non-hierarchical networks when exposed to failures, including those which supply critical services. This study investigates the properties of a hierarchical structure through identifying a set of key characteristics from an ensemble of graph models which are then used in a comparative analysis against a suite of spatial critical infrastructure networks. A failure model is implemented and applied to understand the implications of hierarchical structures in real world networks for their robustness to perturbations. The study concludes that a set of three graph metrics, cycle basis, maximum betweenness centrality and assortativity coefficient, can be used to identify the extent of a hierarchy in graphs, where a lack of robustness is linked to the hierarchical structure, a feature exhibited in both graph models and infrastructure networks.

Given the increased hazards faced by transport corridors such as climate induced extreme weather, it is essential that local spatial hotspots of potential landslide susceptibility can be recognised. In this research, an evidential reasoning multi-source geospatial integration approach for the broad-scale recognition and prediction of landslide susceptibility in transport corridors was developed. Airborne laser scanning and Ordnance Survey DTM data is used to derive slope stability parameters, while Compact Airborne Spectrographic Imager (CASI) imagery and existing national scale digital map datasets are used to characterise the spatial variability of land cover, land use and soil type. A novel approach to characterisation of soil moisture distribution within transport corridors was developed that incorporates the effects of the catchment contribution to local zones of moisture concentration in earthworks. The derived topographic and land use properties are integrated within the evidential reasoning approach to characterise numeric measures of belief, disbelief and uncertainty regarding slope instability spatially within the transport corridor. The model highlighted the importance of slope, concave curvature and permeable soils with variable intercalations accounting for over 80% of slope instability and an overall predictive capability of 77.75% based on independent validation dataset.

Utility networks comprise a fundamental part of our complex urban systems and the integration of digital representations of these networks across multiple spatial scales can be used to help address priority challenges. Deteriorating water utility infrastructure and low routing redundancy result in network fragility and thus supply outages when assets fail. Water distribution network configurations can be optimised for higher resilience but digital representations of the networks used for simulations and analyses are not integrated with the finer scale networks inside buildings. This integration is hindered by differences in conceptualisation and semantics employed by the relevant data standards. We suggest that the geospatial and geometric data contained in Building Information Modelling (BIM) and water distribution network (WDN) models can be used for their integration; and that this supports the use cases of optimising dynamic network partitioning, reducing the risk of underground utility strikes and planning for future network configurations with higher topological redundancy. In this study, we develop and demonstrate the application of a weight-based spatial algorithm for inferring water network connections between urban-scale WDNs and BIM models, showing that spatial data can be used in the absence of complete or consistent semantic representations. We suggest that the method has potential for transferability to infrastructure for other utility resources (such as waste water, electricity and gas) and make recommendations such as standardising the representation of connection points between disjoint utility network models and extending the normal practical spatial remit of BIM MEP modelling to encompass the space between buildings and WDNs.

Reliable transportation infrastructure is crucial to ensure the mobility, safety and economy of urban areas. Flooding in urban environments can disrupt the flow of people, goods, services and emergency responders as a result of disruption or damage to transport systems. Pervasive sensors for urban monitoring and traffic surveillance, coupled with big data analytics, provide new opportunities for managing the impacts of urban flooding through intelligent traffic management systems in real-time.A framework has been developed to assess the effect of urban surface water on road network traffic movements, accounting for real-time traffic conditions and changes in road capacity under flood conditions. Through this framework, inferred future traffic disruptions and short-term congestions, along with their spatiotemporal prorogation can be provided to assist flood risk warning and safety guidance. Within this framework, both flood modelling results from the HiPIMS 2D hydrodynamic model, and traffic prediction from machine leaning, are integrated to enable improved traffic forecasting that accounts for surface water conditions. Information from 130 traffic counters and 46 CCTV cameras distributed over Newcastle upon Tyne (UK) are employed which include information on location, historical traffic flow, and imagery.Figure 1 shows a flowchart of the traffic routing system. Congestion is evaluated on the basis of the level of service (LOS) value which is a function of both free flow speed and actual traffic density providing a quantitative measure for the quality of vehicle traffic service. Surface water results in decreased driving speeds which can in turn cause in a sudden increase of traffic density near the flooded road, and queuing in connected roads. A relationship among flood depth, free flow speed, flow rate and density has been constructed to examine the density curve variation in the whole process along with the surface flood dynamic. Based on the new speed-flow model and congestion degree an updated road network can be acquired using geometric calculation and network analysis. Finally, flooded traffic flows are rerouted by shortest path calculation associated with the origin-destination and changes in road capacity and vehicle speeds. A case study under a flood event similar to the one on June 28th 2012, which is a return period of 1 in 100 years, is demonstrated for Newcastle upon Tyne (UK).

Extreme rainfall events pose an ever increasing threat to cities due to the potential for surface water flooding resulting in damage to properties and major disruption of transport systems. Modern sensor networks offer enormous potential for the real-time monitoring of urban systems and potentially allow improved situational awareness of impeding hazards and their impacts such as flooding. However, monitoring in itself is not enough if we are to be able to adapt in in real-time to hazards. Systems are required that allow analytics and models, that feed of real-time observations, to make predictions of impacts and suggest adaption options ahead of the hazard event. The Flood-PREPARED project is developing a system for real-time adaption to surface water flooding. The system comprises of advanced spatiotemporal models of rainfall, surface water flooding and road traffic impacts. These models are linked and orchestrated within into a Big Data workflow that allows events to be simulated using emerging rainfall data recorded by a short range weather radar. This approach allows nowcasting to be undertaken where predictions of surface water inundation and impacts on the road network can be predicted ahead of the rainfall event reaching the city; thus providing the ability for an improved adaptive response to the actual event.

Modern early warning system (EWS) requires sophisticated knowledge of the natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and closely linked to a variety of other hazards. EWS for landslides prediction and detection relies on scientific methods and models which requires input from the time series data, such as the earth observation (EO) and urban environment data. Such data sets are produced by a variety of remote sensing satellites and Internet of things sensors which are deployed in the landslide prone areas. To this end, the automatic discovery of potential time series data sources has become a challenge due to the complexity and high variety of data sources. To solve this hard research problem, in this paper, we propose a novel ontology, namely Landslip Ontology, to provide the knowledge base that establishes relationship between landslide hazard and EO and urban data sources. The purpose of Landslip Ontology is to facilitate time series data source discovery for the verification and prediction of landslide hazards. The ontology is evaluated based on scenarios and competency questions to verify the coverage and consistency. Moreover, the ontology can also be used to realize the implementation of data sources discovery system which is an essential component in EWS that needs to manage (store, search, process) rich information from heterogeneous data sources.

Monoculture plantation woodlands are particularly vulnerable to disturbance events as species uniformity makes such stands highly susceptible to pests and diseases. Red band needle blight (caused by the fungus Dothistroma septosporum) is a disease which has a particularly significant economic impact on pine plantation forests worldwide, affecting diameter and height growth. However, monitoring its spread and intensity is complicated by the fact that the diseased trees are often only visible from aircraft in the advanced stages of the epidemic. Remote sensing could potentially aid in the detection of infected stands and in monitoring disease development and spread. Thermography is one of the techniques that can be used for monitoring changes in the physiological state of plants following infection. However, the use of thermography in forestry has so far been restricted by poor spatial resolution (satellite-based sensors) or high data acquirement costs (airborne sensors). This paper investigates the use of Unmanned Aerial Vehicle (UAV)-borne thermal systems for detecting disease-induced canopy temperature increase and explores the influence of the imaging time and weather conditions on the detected relationship. Furthermore, the potential of a number of airborne LiDAR-derived structural metrics for detection of changes in the canopy structure following the infection are investigated. The study was located in a diseased Scots pine (Arius sylvestris) stand in Queen Elizabeth II Forest Park (central Scotland, UK), where 60 sample trees were surveyed. The thermal imagery was acquired at six different times of a day from an altitude of 60 m. Statistically significant correlation between canopy temperature depression (CTD) and disease levels was found for most of the flights (R-2 between 0.27 and 0.41), which may be related to the needle damage symptoms caused by the disease, i.e. loss of cellular integrity, necrosis and eventual desiccation. Furthermore, the standard deviation of the crown temperature exhibited weak but statistically significant correlation (R-2 between 0.11 and 0.13). The combination of CTD and standard deviation of crown temperature in a partial least squares regression (PLSR) further improved the observed relationship with the estimated disease level. Inclusion of LiDAR structural metrics was also investigated but only provided a slight improvement. A change in environmental conditions altered the magnitude of differences between canopy temperatures; no significant correlation with disease level was found in the morning flight, whilst the strongest relationship was obtained at the time of highest solar radiation, which coincides with the time of maximum photosynthetic activity.

In order to assess the potential future impacts of climate change on urban areas, tools to assist decision-makers to understand future patterns of risk are required. This paper presents a modelling framework to allow the downscaling of national- and regional-scale population and employment projections to local scale land-use changes, providing scenarios of future socio-economic change. A coupled spatial interaction population model and cellular automata land development model produces future urbanisation maps based on planning policy scenarios. The framework is demonstrated on Greater London, UK, with a set of future population and land-use scenarios being tested against flood risk under climate change. The framework is developed in Python using open-source databases and is designed to be transferable to other cities worldwide.

Urban areas face a conundrum, they need to reduce their greenhouse gas emissions and consumption of resources, whilst also increasing their resilience to climate change and extreme weather, and improving wellbeing. However, it is widely recognized that well intended intervention to address one of these sustainability objectives in isolation can undermine other objectives. This paper presents a framework to efficiently identify spatial development strategies that provide the best outcomes against multiple objectives. The framework has been applied to London (UK) to identify strategies that can simultaneously: (i) minimize exposure to future heat wave events; (ii) minimize the risk from flood events; (iii) minimize transport emissions; (iv) minimize urban sprawl; (v) maximize brownfield development; and, (vi) prevent development of greenspace that is recognized as important to wellbeing. Prioritizing each objective in isolation leads to considerably different spatial planning structures, exposing conflicts between many objectives. These include tradeoffs between urban heat risk and transport emissions; and also previously undocumented conflicts between minimizing flood and heat risks. Allowing greater flexibility in development density is shown to provide benefits in terms of heat risk reduction, whilst not significantly affecting mitigation objectives. The framework is shown to significantly improve upon the London Spatial Development Strategy for the objectives analyzed. Further analysis identifies optimal spatial strategies to achieve a Low Carbon, Low Risk or Low Density city - however, these cannot be simultaneously maximized. This work shows there are difficult, and often irreconcilable, choices to be made in the spatial planning of sustainable cities. Spatial search and optimization tools strengthen the evidence-base for planning. Rapid identification of development strategies that satisfy, and minimize conflicts between, multiple objectives helps planners to develop strategies that simultaneously improve urban sustainability and reduce the risks from natural hazards.

The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. This article assesses the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data (5 cm spatial resolution, similar to 7 day temporal resolution) were acquired in tandem with an intensive ground campaign during the spring season of 2015 across a 15 ha mixed woodland. Phenophase transition dates were estimated at an individual tree-level using UAV time series of Normalized Difference Vegetation Index (NDVI) and Green Chromatic Coordinate (GCC) and validated against visual observations of tree phenology. UAV-derived start of season dates could be predicted with an accuracy of < 1 week. The analysis was scaled to a plot level, where ground (visual assessment and understorey development), UAV and Landsat metrics were compared, indicating UAV data is effective for tracking canopy phenology, as opposed to ecosystem dynamics detected by satellites. The UAV data were used to automatically map phonological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This, and a large temporal gap in the Landsat series, accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R-2 < 0.50) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, providing information which could improve characterization of vegetation phonology at multiple scales.

Current trends in research for detection of infections in forests almost exclusively involve the use of a single imaging sensor. However, combining information from a range of sensors could potentially enhance the ability to diagnose and quantify the infection. This study investigated the potential of combining hyperspectral and LiDAR data for red band needle blight detection. A comparative study was performed on the spectral signatures retrieved for two plots established in lodgepole pine stands and on a range of LiDAR metrics retrieved at individual tree-level. Leaf spectroscopy of green and partially chlorotic needles affected by red band needle blight highlighted the green, red and short near-infrared parts of the electromagnetic spectrum as the most promising. A good separation was found between the two pine stands using a number of spectral indices utilising those spectral regions. Similarly, a distinction was found when intra-canopy distribution of LiDAR returns was analysed. The percentage of ground returns within canopy extents and the height-normalised 50th percentile (height normalisation was performed to each tree's canopy extents) were identified as the most useful features among LiDAR metrics for separation of trees between the plots. Analysis based on those metrics yielded an accuracy of 80.9%, indicating a potential for using LiDAR metrics to detect disease-induced defoliation. Stepwise discriminant function analysis identified Enhanced Vegetation Index, Normalised Green Red Difference Index, percentage of ground returns, and the height-normalised 50th percentile to be the best predictors for detection of changes in the canopy resulting from red band needle blight. Using a combination of these variables led to a substantial decrease of unexplained variance within the data and an improvement in discrimination accuracy (96.7%). The results suggest combining information from different sensors can improve the ability to detect red band needle blight.

Strategic infrastructure plays a key role in the functioning of urban areas, especially when dealing with emergency response to natural disasters. Urban areas and their infrastructure are threatened by natural hazards, which is likely to be exacerbated by climate change and intense urbanization in the near future. The UK National Flood Resilience Review (2016) committed £2.3 billion to be invested to reduce flood risk, of which £12.5 million specifically for temporary defenses. At present, the state of the art does not provide a proven efficient methodology specifically designed to optimally invest these resources; in light of this, a consolidated urban planning spatial optimization methodology is originally used for allocating resource storing space and ultimately optimize flood emergency management. This study developed and applied a RAOGA (Resource Allocation Optimization Genetic Algorithm) to balance the particular trade-off between simultaneous minimization of response time and costs. The presented optimization framework balances several competing tensions that include: (1) the identification of, and the cost of using, possible sites (warehouses) to store flood temporary defenses; (2) the identification of strategic infrastructure location; (3) transport optimization for moving emergency response resources into place. The methodology is applied to a regional case study (Yorkshire, UK) as proof of concept. Such a framework has the potential to lead a new generation of mathematically-based emergency response planning, targeted to policy makers dealing with urban planning and emergency management.