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A rapid rise of urban population is making cities denser. Consequently, the proportion of impervious surface cover has enlarged, increasing the amount and speed of run‐off reaching urban catchment areas, which may cause flash flooding. Trees play a key role to reduce run‐off in the city, as they intercept rainfall and store part of it on their leaves and branches, reducing the amount and speed of water running onto impervious surfaces. Storage capacity will depend on the rainfall event, the climate conditions and tree characteristics and canopy density. These canopy characteristics vary greatly among different species and their phenology. Furthermore, these canopy characteristics can vary greatly among individual trees of the same age, size, and species. This study tested how canopy density and leaf characteristics of three different tree species affect storage capacity under simulated rainfall conditions. Three species were selected (Ulmus procera, Platanus × acerifolia, and Corymbia maculata), each being of the same height and similar canopy dimensions. Storage capacity was measured using a mass balance approach during a 15‐min indoor, simulated rainfall event (2.5 mm/hr). Canopy metrics were estimated using a terrestrial laser scanner. Canopy surface area was measured through destructive harvest and leaf/twig/branch scanning. To investigate variations in the canopy leaf density, leaves were systematically removed to create four treatments: full, half, quarter, and woody. Canopy storage capacity was well correlated to plant surface area (m2), plant area index (m2/m2), and plant area density (m2/m3). All analyses indicated U. procera as the most efficient species for storing rainfall water within a canopy of equal volume or area index. Results reveal the complexity of evaluating interception of rainfall by tree canopies. This study contributes to the discipline and practice by distinguishing how variation in the leaf density is important to consider when selecting urban tree species to be planted.

ABSTRACT:Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.