Transport accessibility is an important driver of urban growth and key to the sustainable development of cities. This paper presents a simple GIS-based tool developed to allow the rapid analysis of accessibility by different transport modes. Designed to be flexible and use publicly-available data, this tool (built in ArcGIS) uses generalized cost to measure transport costs across networks including monetary and distance components. The utility of the tool is demonstrated on London, UK, showing the differing patterns of accessibility across the city by different modes. It is shown that these patterns can be examined spatially, by accessibility to particular destinations (e.g., employment locations), or as a global measure across a whole city system. A number of future infrastructure scenarios are tested, examining the potential for increasing the use of low-carbon forms of transport. It is shown that private car journeys are still the least cost mode choice in London, but that infrastructure investments can play a part in reducing the cost of more sustainable transport options.

Critical national infrastructures, including energy, transport, digital communications, and water, are prone to flood damage. Their geographical extent is a determinant of, and is determined by, patterns of human development, which is often concentrated in floodplains. It is important to understand how infrastructure systems react to large‐scale flooding. In this paper, we present an integrated framework for critical infrastructure flood impact assessment. Within this integrated framework, we represent interdependent infrastructure assets through spatial network models. We quantify infrastructure flood impacts in terms of disrupted customers linked directly to flood assets and customers disrupted indirectly due to network effects. The analysis shows how spatial network models inform flood risk management practitioners to identify and compare critical infrastructures risks on flooded and non‐flooded land, for prioritising flood protection investments and improve resilience of cities. A case study of the Thames catchment in England is presented, which contains key infrastructure assets and highest population concentrations in United Kingdom.

We have measured the surface flow rate of the large east Greenland glacier, Sortebræ, through both the initiation and termination of a major surge using synthetic aperture radar (SAR) feature tracking, optimized to minimize error. The Sortebræ surge began between November and January 1992–1993, after at least 6 weeks of subfreezing temperatures over the whole glacier, and propagated rapidly up‐glacier from a central nucleus. Sortebræ reached sustained fast flow rates of up to 24 m d−1, and the active phase lasted for 28–32 months before terminating in June 1995. Termination was abrupt, coinciding with the arrival of the spring thaw and the apparent release of a large volume of stored water from a single outlet at the front. The surge mechanism is interpreted as a switch from channelized to distributed drainage, which at present is best explained by Kamb's linked cavity sliding model.

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Critical infrastructure networks, including transport, are crucial to the social and economic function of urban areas but are at increasing risk from natural hazards. Minimizing disruption to these networks should form part of a strategy to increase urban resilience. A framework for assessing the disruption from flood events to transport systems is presented that couples a high-resolution urban flood model with transport modelling and network analytics to assess the impacts of extreme rainfall events, and to quantify the resilience value of different adaptation options. A case study in Newcastle upon Tyne in the UK shows that both green roof infrastructure and traditional engineering interventions such as culverts or flood walls can reduce transport disruption from flooding. The magnitude of these benefits depends on the flood event and adaptation strategy, but for the scenarios considered here 3–22% improvements in city-wide travel times are achieved. The network metric of betweenness centrality, weighted by travel time, is shown to provide a rapid approach to identify and prioritize the most critical locations for flood risk management intervention. Protecting just the top ranked critical location from flooding provides an 11% reduction in person delays. A city-wide deployment of green roofs achieves a 26% reduction, and although key routes still flood, the benefits of this strategy are more evenly distributed across the transport network as flood depths are reduced across the model domain. Both options should form part of an urban flood risk management strategy, but this method can be used to optimize investment and target limited resources at critical locations, enabling green infrastructure strategies to be gradually implemented over the longer term to provide city-wide benefits. This framework provides a means of prioritizing limited financial resources to improve resilience. This is particularly important as flood management investments must typically exceed a far higher benefit–cost threshold than transport infrastructure investments. By capturing the value to the transport network from flood management interventions, it is possible to create new business models that provide benefits to, and enhance the resilience of, both transport and flood risk management infrastructures. Further work will develop the framework to consider other hazards and infrastructure networks.

This paper demonstrates the ability to generate quantitative remote sensing products by means of an Unmanned Aerial Vehicle (UAV) equipped with one unaltered and one near infrared-modified Commercial Off-The-Shelf (COTS) camera. Radiometrically calibrated orthomosaics were generated for 17 dates, from which digital numbers (DNs) were corrected to surface reflectance and to Normalized Difference Vegetation Index (NDVI). Validation against ground measurements showed that 84-90% of the variation in the ground reflectance and 95-96% of the variation in the ground NDVI could be explained by the UAV-retrieved reflectance and NDVI respectively. Comparisons against Landsat 8 data showed relationships of 0.73≤R 2 ≥0.84 for reflectance and 0.86≤R 2 ≥0.89 for NDVI. It was not possible to generate a fully consistent time series of reflectance, due to variable illumination conditions during acquisition on some dates. However, the calculation of NDVI resulted in a more stable UAV time series, which was consistent with a Landsat series of NDVI extracted over a deciduous and evergreen woodland. The results confirm that COTS cameras, following calibration, can yield accurate reflectance estimates (under stable within-flight illumination conditions), and that consistent NDVI time series can be acquired in very variable illumination conditions. Such methods have significant potential in providing flexible, low-cost approaches to vegetation monitoring at fine spatial resolution and for usercontrolled revisit periods.

Commission VI, WG VI/4KEY WORDS: Stress detection, unmanned aerial vehicle, unmanned aerial system, UAV, UAS, camera calibration. ABSTRACT:Climate change has a major influence on forest health and growth, by indirectly affecting the distribution and abundance of forest pathogens, as well as the severity of tree diseases. Temperature rise and changes in precipitation may also allow the ranges of some species to expand, resulting in the introduction of non-native invasive species, which pose a significant risk to forests worldwide. The detection and robust monitoring of affected forest stands is therefore crucial for allowing management interventions to reduce the spread of infections. This paper investigates the use of a low-cost fixed-wing UAV-borne thermal system for monitoring disease-induced canopy temperature rise. Initially, camera calibration was performed revealing a significant overestimation (by over 1 K) of the temperature readings and a non-uniformity (exceeding 1 K) across the imagery. These effects have been minimised with a two-point calibration technique ensuring the offsets of mean image temperature readings from blackbody temperature did not exceed ± 0.23 K, whilst 95.4% of all the image pixels fell within ± 0.14 K (average) of mean temperature reading.The derived calibration parameters were applied to a test data set of UAV-borne imagery acquired over a Scots pine stand, representing a range of Red Band Needle Blight infection levels. At canopy level, the comparison of tree crown temperature recorded by a UAV-borne infrared camera suggests a small temperature increase related to disease progression (R = 0.527, p = 0.001); indicating that UAV-borne cameras might be able to detect sub-degree temperature differences induced by disease onset.