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Abstract. Airborne LiDAR data has become an important tool for both the scientific and industry based investigation of forest structure. The uses of discrete return observations have now reached a maturity level such that the operational use of this data is becoming increasingly common. However, due to the cost of data collection, temporal studies into forest change are often not feasible or completed at infrequent and at uneven intervals. To achieve high resolution temporal LiDAR surveys, this study has developed a micro-Unmanned Aerial Vehicle (UAV) equipped with a discrete return 4-layer LiDAR device and miniaturised positioning sensors. This UAV has been designed to be low-cost and to achieve maximum flying time. In order to achieve these objectives and overcome the accuracy restrictions presented by miniaturised sensors a novel processing strategy based on a Kalman smoother algorithm has been developed. This strategy includes the use of the structure from motion algorithm in estimating camera orientation, which is then used to restrain IMU drift. The feasibility of such a platform for monitoring forest change is shown by demonstrating that the pointing accuracy of this UAV LiDAR device is within the accuracy requirements set out by the Australian Intergovernmental Committee on Surveying and Mapping (ICSM) standards.
Wallace et al. (Wed,) studied this question.