Effective assessment of biodiversity in cities requires detailed vegetation maps. To date, most remote sensing of urban vegetation has focused on thematically coarse land cover products. Detailed habitat maps are created by manual interpretation of aerial photographs, but this is time consuming and costly at large scale. To address this issue, we tested the effectiveness of object-based classifications that use automated image segmentation to extract meaningful ground features from imagery. We applied these techniques to very high resolution multispectral Ikonos images to produce vegetation community maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and a multi-scale segmentation algorithm used to produce a hierarchical network of image objects. The upper level included four coarse strata: industrial/commercial (commercial buildings), residential (houses and backyard private gardens), vegetation (vegetation patches larger than 0.8/1ha), and water. We focused on the vegetation stratum that was segmented at more detailed level to extract and classify fifteen classes of vegetation communities. The first classification yielded a moderate overall classification accuracy (64%, κ = 0.52), which led us to consider a simplified classification with ten vegetation classes. The overall classification accuracy from the simplified classification was 77% with a κ value close to the excellent range (κ = 0.74). These results compared favourably with similar studies in other environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spectral contrast.

Photogrammetry is a non‐contact, high‐accuracy, practical and cost‐effective technique for a large number of medical applications. Lately, three‐dimensional (3D) laser scanning and digital imaging technology have raised the importance of digital photogrammetry technology to a new height in craniofacial mapping. Under the support of the Eighth Malaysian Development Plan, the Ministry of Science, Technology and the Environment (MOSTE) Malaysia allocated a grant to establish procedures for the development of a national craniofacial spatial database to assist the medical profession to provide better health services to the public. To populate the database with normal and abnormal (malformation, diseased and trauma and burn victims) craniofacial information, it is necessary to evaluate the technology needed to capture the essential data of craniofacial features. The paper provides a discussion on the basic features of the spatial data and the data capture techniques. Both are needed for the establishment of a national spatial craniofacial database. The discussion includes a brief review of the current status of two selected high‐accuracy craniofacial spatial data capture techniques, namely, digital photogrammetry and 3D laser scanning. The paper highlights a system which has been developed for a Malaysian craniofacial mapping project. Laboratory tests with mannequins showed that the photogrammetric and 3D laser scanning system could achieve an accuracy exceeding the design specification of ±0·7 mm (one standard deviation) for all the measured craniofacial distances. However, tests with two living subjects showed that the accuracy was in the order of ±1·2 mm because of facial movement during data capture.

No abstract

No abstract

Dolphins are agile animals and are very difficult to measure at sea. However, for the conservation of threatened or endangered species, measurement may be vital because it allows a demographic analysis of the population. Hector's dolphin (Cephalorhynchus hectori) is a rare species living only in coastal waters around New Zealand where it is studied from small boats. This paper describes a stereophotogrammetric technique developed to measure body length accurately at sea without having to capture the individual. Constant calibration with the use of a control frame allowed accurate body length measurements of dolphins to be made with this low cost system, with a measurement error of 4 per cent to 6 per cent of actual length.

No abstract

Unmanned Aerial Vehicles (UAVs) can be used to acquire highly accurate data in deformation survey, whereby low-cost digital cameras are commonly used in the UAV mapping. Thus, camera calibration is considered important in obtaining high-accuracy UAV mapping using low-cost digital cameras. The main focus of this study was to calibrate the UAV camera at different camera distances and check the measurement accuracy. The scope of this study included camera calibration in the laboratory and on the field, and the UAV image mapping accuracy assessment used calibration parameters of different camera distances. The camera distances used for the image calibration acquisition and mapping accuracy assessment were 1.5 metres in the laboratory, and 15 and 25 metres on the field using a Sony NEX6 digital camera. A large calibration field and a portable calibration frame were used as the tools for the camera calibration and for checking the accuracy of the measurement at different camera distances. Bundle adjustment concept was applied in Australis software to perform the camera calibration and accuracy assessment. The results showed that the camera distance at 25 metres is the optimum object distance as this is the best accuracy obtained from the laboratory as well as outdoor mapping. In conclusion, the camera calibration at several camera distances should be applied to acquire better accuracy in mapping and the best camera parameter for the UAV image mapping should be selected for highly accurate mapping measurement.

No abstract