Daytime onshore lake breezes are a critical factor controlling ozone abundance at coastal sites around Lake Michigan. Coastal counties along the western shore of Lake Michigan have historically observed high ozone episodes dating to the 1970s. We classified ozone episode days based on the extent or absence of the lake breeze (i.e., “inland”, “near-shore” or “no” lake breeze) to establish a climatology of these events. This work demonstrated variable gradients in ozone abundances based on these different types of meteorology, with the sharpest ozone concentration gradients on days with a near-shore lake breeze. On 76-82% of days in which ozone reached 70 ppb for at least 1 hour, a lake breeze was present. Evidence of ozone gradients from multiple observation platforms during the 2017 Lake Michigan Ozone Study (LMOS 2017) are shown for two days with different depths of lake breezes.

Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( $3840\times 2160$ ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from $0.29$ to $0.67$ , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

Unmanned aerial systems (UAS) are increasingly being used in search and rescue (SAR) operations to assist in the discovery of missing persons. UAS are useful to first responders in SAR operations due to rapid deployment, high data volume, and high spatial resolution data collection capabilities. Relying on traditional manual interpretation methods to find a missing person in imagery data sets containing several hundred images is both challenging and time consuming. To better find small signs of missing persons in large UAS datasets, computer assisted interpretation methods have been developed. This article presents the results of an initial evaluation of a computer assisted interpretation method tested against manual methods in a simulated SAR operation. The evaluation performed focused on using resources available to first responders performing SAR operations, specifically: RGB data, volunteers, and a commercially available software program. Results from this field test were mixed, as the traditional group discovered more objects but required more time, in man hours, to discover the objects. Further field experiments, based on the capabilities of current first responder groups, should be conducted to determine to what extent computer assisted methods are useful in SAR operations.

Recent advancements in unmanned aerial systems and GPS technology, allowing for centimeter precision without ground-based surveys, have been groundbreaking for applications in the field of forestry. As this technology becomes integrated into forest management approaches, it is important to consider the implementation of proper safety and data collection strategies. The creation of such documentation is beneficial, because it allows for those aspiring to create a UAS program to learn from others’ experiences, without bearing the consequences of past blunders associated with the development of these practices. When establishing a UAS program, it is pertinent to deeply research the necessary equipment, create documentation that establishes operational norms, and develop standards for in-field operations. Regarding multispectral vs. RGB sensor payloads, the sensor selection should be based upon what type of information is desired from the imagery acquired. It is also important to consider the methods for obtaining the most precise geolocation linked to the aerial imagery collected by the sensor. While selecting the proper UAS platform and sensor are key to establishing a UAS operation, other logistical strategies, such as flight crew training and operational planning, are equally important. Following the acquisition of proper equipment, further preparations must be made in order to ensure safe and efficient operations. The creation of crew resource management and safety management system documentation is an integral part of any successful UAS program. Standard operating procedure documents for individual tasks and undertakings are also a necessity. Standardized practices for the scheduling, communication, and management of the UAS fleet must also be formulated. Once field operations are set in motion, the continuous improvement of the documentation and best practices is paramount.

Photogrammetry is experiencing an era of democratization mostly due to the popularity and availability of many commercial off-the-shelf devices, such as drones and smartphones. They are used as the most convenient and effective tools for high-resolution image acquisition for a wide range of applications in science, engineering, management, and cultural heritage. However, the quality, particularly the geometric accuracy, of the outcomes from such consumer sensors is still unclear. Furthermore, the expected quality under different control schemes has yet to be thoroughly investigated. This paper intends to answer those questions with a comprehensive comparative evaluation. Photogrammetry, in particular, structure from motion, has been used to reconstruct a 3D building model from smartphone and consumer drone images, as well as from professional drone images, all under various ground control schemes. Results from this study show that the positioning accuracy of smartphone images under direct geo-referencing is 165.4 cm, however, this could be improved to 43.3 cm and 14.5 cm when introducing aerial lidar data and total station surveys as ground control, respectively. Similar results are found for consumer drone images as well. For comparison, this study shows the use of the professional drone is able to achieve a positioning accuracy of 3.7 cm. Furthermore, we demonstrate that through the combined use of drone and smartphone images we are able to obtain full coverage of the entire target with a 2.3 cm positioning accuracy. Our study concludes that smartphone images can achieve an accuracy equivalent to consumer drone images and can be used as the primary data source for building facade data collection.

Unmanned Aerial Systems (UAS) serve as an excellent remote-sensing platform to fulfill an aerial imagery data collection niche previously unattainable in forestry by satellites and manned aircraft. However, for UAS-derived data to be spatially representative, a precise network of ground control points (GCP) is often required, which can be tedious and limit the logistical benefits of UAS rapid deployment capabilities, especially in densely forested areas. Therefore, methods for efficient data collection without GCPs are highly desired in UAS remote sensing. Here, we demonstrate the use of postprocessing kinematic (PPK) technology to obtain subcentimeter precision in datasets of forested areas without the need for placing GCPs. We evaluated two key measures, positional variability and time efficiency, of the PPK technology by comparing them to traditional GCP methods. Results show that PPK displays consistently higher positional precision than traditional GCP approaches. Moreover, PPK surveys and processing take less time to complete than traditional GCP methods and require fewer logistical steps, especially in image acquisition. The time and resource savings with PPK as compared to GCP processing are undeniable. We conclude that PPK technology provides a practical means to produce precise aerial forest surveys.