ABSTRACT:The paper deals with using a TIR camera on an UAV for acquiring multitemporal thermal images of a building block against the background of detecting, monitoring and analysing urban heat islands. It is motivated by a research project called EO2HEAVEN (Earth Observation and Environmental Modelling for the Mitigation of Health Risks) which analyses the influence of environmental effects to human health. Therefore, the aim is the generation of thermal orthophotos from UAV data which can be used for further thematic analysis. The paper describes the data acquisition on the one hand and the processing of the obtained data on the other hand. The data acquisition comprises three image flights at different times of day from which only the first two missions could be processed until now. The low image contrasts, the radiometric differences between images as well as the poor initial positioning and orientation values limit the suitability of available software for automatic tie point measurement so that this step was outsourced and implemented in C++. The following aerial triangulation and orthophoto generation was realised in TerraPhoto (Terrasolid). However, two orthophotos could be generated with a geometric resolution of 15 cm. Furthermore, the radiation temperatures from the thermal images were compared to ground measurements to check the correctness of the camera measurements.
Laser scanning is a fast and efficient 3-D measurement technique to capture surface points describing the geometry of a complex object in an accurate and reliable way. Besides airborne laser scanning, terrestrial laser scanning finds growing interest for forestry applications. These two different recording platforms show large differences in resolution, recording area and scan viewing direction. Using both datasets for a combined point cloud analysis may yield advantages because of their largely complementary information. In this paper, methods will be presented to automatically register airborne and terrestrial laser scanner point clouds of a forest stand. In a first step, tree detection is performed in both datasets in an automatic manner. In a second step, corresponding tree positions are determined using RANSAC. Finally, the geometric transformation is performed, divided in a coarse and fine registration. After a coarse registration, the fine registration is done in an iterative manner (ICP) using the point clouds itself. The methods are tested and validated with a dataset of a forest stand. The presented registration results provide accuracies which fulfill the forestry requirements.
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