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Common approaches to mapping green infrastructure in urbanised landscapes invariably focus on measures of land use or land cover and associated functional or physical traits. However, such onedimensional perspectives do not accurately capture the character and complexity of the landscapes in which urban inhabitants live. The new approach presented in this paper demonstrates how open-source, high spatial and temporal resolution data with global coverage can be used to measure and represent the landscape qualities of urban environments. Through going beyond simple metrics of quantity, such as percentage green and blue cover, it is now possible to explore the extent to which landscape quality helps to unpick the mixed evidence presented in the literature on the benefits of urban nature to human well-being. Here we present a landscape approach, employing remote sensing, GIS and data reduction techniques to map urban green infrastructure elements in a large U.K. city region. Comparison with existing urban datasets demonstrates considerable improvement in terms of coverage and thematic detail. The characterisation of landscapes, using census tracts as spatial units, and subsequent exploration of associations with social-ecological attributes highlights the further detail that can be uncovered by the approach. For example, eight urban landscape types identified for the case study city exhibited associations with distinct socioeconomic conditions accountable not only to quantities but also qualities of green and blue space. The identification of individual landscape features through simultaneous measures of land use and land cover demonstrated unique and significant associations between the former and indicators of human health and ecological condition. The approach may therefore provide a promising basis for developing further insight into processes and characteristics that affect human health and well-being in urban areas, both in the United Kingdom and beyond.

We investigated the impact of climate change on the number of wildfires in the Peak District uplands of northern England. Wildfires in peat can result in severe carbon loss and damage to water supplies, and fighting such fires is difficult and costly in such a remote location. The Peak District is expected to experience warmer, wetter winters and hotter, drier summers. Local weather simulations from a weather generator were used to predict the future incidence and timing of fires. Wildfire predictions were based on past fire occurrence and weather over 27.5 yr. A Probit model of wildfire incidence was applied to simulated weather data, which were generated by a Markov process and validated against actual baseline weather data using statistical criteria and success in replicating past fire patterns. The impact of climate change on the phenology and ecology of moorland and on visitor numbers was considered. Simulations suggest an overall increase in occurrence of summer wildfires. The likelihood of spring wildfires is not reduced by wetter winter conditions; however, the chance of wildfires rises as rainfall decreases. Temperature rise has a non-linear impact, with the risk of wildfire occurrence rising disproportionately with temperature. Recreation use is a major source of ignition. Little change in wildfire incidence is projected in the near future, but as climate change intensifies, the danger of summer wildfires is projected to increase from 2070; therefore, fire risk management will be necessary in future. In addition, moorlands may have to be managed to reduce the chance of summer wildfires becoming catastrophic, with consequent damage to ecosystem services such as water supplies and peat carbon storage. Management measures may include controlled burning, grazing or mowing to remove fuel.

Warmer, drier summers brought by climate change increase the potential risk of wildfires on the moorland of the Peak District of northern England. Fires are costly to fight, damage the ecosystem, harm water catchments, cause erosion scars and disrupt transport. Fires release carbon dioxide to the atmosphere. Accurate forecasts of the timing of fires help deployment of fire fighting resources. A probit model is used to assess the chance of fires at different times of the year, days of the week and under various weather conditions. Current and past rainfall damp fire risk. The likelihood of fire increases with maximum temperature. Dry spells or recent fire activity also signal extra fire hazard. Certain days are fire prone due to visitors and some months of the year are more risky reflecting the changing flammability of moorland vegetation. The model back-predicts earlier fires during a hot dry summer. The impact of climate change on fire incidence is not straightforward. Risks may be reduced if wetter winters and earlier onset of spring add to plant moisture content. Yet a warm spring increases biomass and potential fuel load in summer. Climate change may cause the timing of moorland wildfires to shift from a damper and more verdant spring to drought-stressed summer.