Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data De-Rived from sUAS Imagery

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of red-cockaded woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area’s vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

A Multifractal Analysis and Machine Learning Based Intrusion Detection System with an Application in a UAS/RADAR System

The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unmanned aerial systems (UAS), intrusion detection systems (IDS) based on multifractal (MF) spectral analysis have been used to provide accurate MF spectrum estimations of network traffic. Such an estimation is then used to detect and characterize flooding anomalies that can be observed in an unmanned aerial vehicle (UAV) network. However, the previous contributions have lacked the consideration of other types of network intrusions commonly observed in UAS networks, such as the man in the middle attack (MITM). In this work, this promising methodology has been accommodated to detect a spoofing attack within a UAS. This methodology highlights a robust approach in terms of false positive performance in detecting intrusions in a UAS location reporting system.

Combating the Current Pandemic and Preparing for the Next: Lessons Learned From the COVID-19 Pandemic From the Perspective of Deployed Special Operations Forces

ABSTRACT The coronavirus 2019 (COVID-19) pandemic continues to be a threat to global health, including the health of deployed armed forces. Servicemembers had to adjust to the “new normal” while maintaining the interests of the nation’s security as well as that of our host nation partners. This commentary examines how Special Operations Forces operating within four different regions worldwide leveraged the challenges presented by the onset of this pandemic in maintaining stability, sustaining a ready force, and operating forward deployed. Deployed forces face constant difficulties with logistical support, varied medical resources access and a medical system predominantly focused on trauma care. At the onset of the COVID-19 pandemic there was little guidance specific to these circumstances which required an improvised adaptation of the recommendations set by national and Department of Defense medical authorities. Plans were constantly revised to match the ever changing medical and operational environment. Strategies such as the “Bubble Philosophy” and tiered force protection measures helped our units to maintain a rigorous training cycle. New methods of communication and training with our host nation partners such as the use of Unmanned Aerial Systems (UAS) platforms to survey host nation training became standard. Through these measures all of our forces were able to maintain operational capacity, protect the force, and maintain rapport with the host nations. We hope these experiences will provide a rough framework for future forces faced with a similar struggle. We also want to stress that challenges vary depending on the area of operations and the pathogen responsible for the pandemic. Any feedback and collaboration that may come from this work is appreciated and encouraged.

Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat

Barley yellow dwarf (BYD) is one of the major viral diseases of cereals. Phenotyping BYD in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to, i) evaluate the use of high-throughput phenotyping (HTP) from unmanned aerial systems to improve BYD assessment and selection, ii) identify genomic regions associated with BYD resistance, and iii) evaluate genomic prediction models ability to predict BYD resistance. Up to 107 wheat lines were phenotyped during each of five field seasons under both insecticide treated and untreated plots. Across all seasons, BYD severity was lower with the insecticide treatment and plant height (PTHTM) and grain yield (GY) showed increased values relative to untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying resistance gene Bdv2 on chromosome 7DL. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a potentially novel genomic region for resistance on chromosome 5AS. Given the variable heritability of the trait (0.211 0.806), we obtained relatively good predictive ability for BYD severity ranging between 0.06 0.26. Including Bdv2 on the predictive model had a large effect for predicting BYD but almost no effect for PTHTM and GY. This study was the first attempt to characterize BYD using field-HTP and apply GS to predict the disease severity. These methods have the potential to improve BYD characterization and identifying new sources of resistance will be crucial for delivering BYD resistant germplasm.