Unmanned aircraft vehicles (UAVs) have shown their advantages in flexibility and resulting accuracy compared to remote sensing and aerial photogrammetry technology. In this study, a UAV equipped with optical and infrared thermal image sensors was used to document leaf temperatures of crops throughout their life cycle. Considering the spatial resolution of infrared thermal imaging is still inferior to optical images, this study proposes a scheme for generating the orthophoto of infrared thermal images and focuses on the use of super-resolution reconstruction techniques to improve the spatial resolution of infrared thermal imagery, promoting the effectiveness of subsequent image processing. The proposed method would facilitate comprehensive evaluation and analysis of thermal data of a wide range of agricultural land. The validations show that the super-resolution reconstruction technique is more effective than the general interpolation method in that it can not only increase the image resolution but also maintain the image content information, and verify the effectiveness of the orthographic image production as well.

In order to improve the reconnaissance, rescue processes and rehabilitation, the characteristics of unmanned aerial vehicles (UAV), including quickly recording, mobility, and high-spatial-resolution images, can provide maps on assisting the agricultural post-disaster investigation. In this study, geographic information system, image unsupervised classification, image segmentation, and digital surface model (DSM) are applied with UAV high-spatial-resolution images. The objectives of this study are to develop image interpretation techniques on banana plantation damages by typhoon disasters and to evaluate the abilities of agricultural damage interpretations among different techniques. As results, the damage region and the situation of banana plantations can be delineated by UAV orthophotos. However, DSM cannot discriminate the degrees of wind-thrown in plantations. In addition, comparing the interpretation damage rates of pixel-based and of object-oriented image unsupervised classifications, with ground-based official approves (according to 30 samples of cadastral applications with the baseline of 20% of damage rate), the damage rate of pixel-based image interpretation is 86.7% and the damage rate of object-oriented image interpretation is 96.7%. Both image interpretations can discriminate the banana plantation damages effectively. Although the latter performs better than the former, it demands higher on techniques, labor forces, and time consuming. Therefore, we suggest adopting the pixel-based unsupervised image classification to provide the maps on assisting in post-disaster operation and investigation for wide-region banana plantations.

NPlanetary boundary layer (PBL), the skin layer of atmosphere, has strong interactions with earth surface, human activities, and vegetation. The PBL structure and development has effects on many weather phenomena as well as air quality. In this study, we aim to observe the PBL structure and the evolution of air pollution vertically using an unmanned aerial system (UAS). The experiment was carried out in the New Taipei city nearby the Taipei sounding station in August of 2017. We had conducted 16 flights in total and 7 of them on 29th August. In addition, data from three sounding balloons and lidar observations enhance the data set for studying the PBL development for a typical summertime day on 29th. Our results show that the UAS provides the low-level (0-3 km) atmospheric profile with parameters (temperature, RH, and Pressure) in good agreement with the data observed by meteorological soundings, especially for the PBL height detection. The uncertainty analysis suggests that the radiation heating effect is the main issue causing the overestimation of temperature and RH. This potential error can be considered in the next generation sensor design. Furthermore, we compare the UAS observations with lidar depolarization ratio profiles. Normally, aerosols distributed within the PBL top height and lidar can easily detect the discontinue layer between aerosol and clean air (above PBL). The discontinue layer is associated to an inversion layer which can be observed by sounding data. Our lidar observation and UAS sounding has good agreement on the evolution of PBL top height during the daytime. All the observational results show the accuracy and reliable of the UAS for PBL application. In the future, this UAS will continue to promote the deployment of more sensors to obtain more comprehensive observation data. The understanding of PBL thermodynamic mechanism and numerical simulation can be improved with the new UAS technology applied.

Unmanned Aerial Vehicle (UAV) acquires high spatial resolution and highly overlapped images at low flying altitude. As the light-weight UAV is susceptible by strong winds and turbulence, the UAV instability will usually cause the blurred image and degrade the image quality. The image degradation function can be evaluated by point-spread function (PSF) and it is usually derived from the signalized target. This study presented an automatic LSF (line spread function)-derived feature to detect blurred image. It is based on the linear feature from the image itself. The linear features are detected by line segment detector (LSD) and the LSF is estimated by differentiating edge spread function (ESF). Only the step edges are preserved to calculate LSF-derived features such as size and azimuth of the ellipsoid. The test data includes UAV images and handheld images. We use the proposed LSF-derived features to separate the blur and non-blur images automatically. The overall accuracies reached 88.9% and 90% for UAV and handheld images, respectively. The experiments indicated that the proposed method is capable of detecting the blurred images automatically. Moreover, the blurred image can be excluded in photogrammetric processing to archive better accuracy.

In recent years, oblique photography has rapidly developed as an advanced technique which can easily acquire 3D panoramic data. It is a supplement to traditional aerial photography to provide a new source of information to the field of remote sensing. In this study, the GR6 Multi-Rotor UAS is used for oblique photogrammetry and is equipped with a gimbal, which was developed by our team, designed to carry cameras. The cameras are tested to carry out the procedures of oblique photography to generate their 3D urban landscape reconstruction and compare the results different oblique photos with traditional orthogonal photos. This is done to compare the discrepancies caused by the effect of the different directions of the angles in both the oblique photos and the vertical angle of the orthogonal photos from a single flight. At last, recommendations should be made.