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Abstract: Airborne Lidar (Light detection and ranging) is an efficient tool for the generation of Digital Terrain Models (DTMs). Although many studies have been conducted in generating DTMs using Lidar data, it is still a challenging research area. The difficulty in filtering large buildings as well as a diversity of urban features makes the design of urban DTM generating methods an ongoing priority. This research adopted an upward-fusion methodology to generate urban DTMs using airborne Lidar data. Firstly, several preliminary DTMs of different resolutions were obtained using a local minimum method. Next, upward fusion was conducted between these DTMs. This process began with the DTM of the largest grid size, which was treated as a trend surface. A finer DTM was compared with this large scale DTM. By setting appropriate thresholds, a new DTM was achieved by selecting qualified elevation values from the finer DTM and retaining the value of the trend surface when the value from the finer DTM was beyond the threshold. This process continued iteratively until all preliminary DTMs had been included in the upward fusion and a refined DTM of high resolution was achieved. To further reduce possible errors in the resulting DTM, an extended local minimum method was proposed for filtering outliers and generating preliminary DTMs. A case study was carried out in the city of Cambridge, which represents an urban landscape with a variety of building forms, street patterns and vegetation structures. The time efficiency, results of the accuracy assessments and comparison with leading software proved the success of the case study and indicated that upward-fusion was an effective method for the generation of urban DTMs with airborne Lidar data and could improve the accuracy of other DTM generating algorithms. This paper also proposed possible approaches for further improvements on this methodology. [Copyright &y& Elsevier]

Abstract: This paper investigates occupant behaviour of window-use in night-time naturally ventilated offices on the basis of a pilot field study, conducted during the summers of 2006 and 2007 in Cambridge, UK, and then demonstrates the effects of employing night-time ventilation on indoor thermal conditions using predictive models of occupant window-use. A longitudinal field study shows that occupants make good use of night-time natural ventilation strategies when provided with openings that allow secure ventilation, and that there is a noticeable time of day effect in window-use patterns (i.e. increased probability of action on arrival and departure). We develop logistic models of window-use for night-time naturally ventilated offices, which are subsequently applied to a behaviour algorithm, including Markov chains and Monte Carlo methods. The simulations using the behaviour algorithm demonstrate a good agreement with the observational data of window-use, and reveal how building design and occupant behaviour collectively affect the thermal performance of offices. They illustrate that the provision of secure ventilation leads to more frequent use of the window, and thus contributes significantly to the achievement of a comfortable indoor environment during the daytime occupied period. For example, the maximum temperature for a night-time ventilated office is found to be 3°C below the predicted value for a daytime-only ventilated office. [Copyright &y& Elsevier]