Unmanned Aerial Systems (UAS) offer users the ability to capture large amounts of imagery at unprecedented spatial resolutions due their flexible designs, low costs, automated workflows, and minimal technical knowledge barriers. Their rapid extension into new disciplines promotes necessity question understand implications data processing parameter decisions on respective output completeness. This research provides a culmination quantitative insight using an eBee Plus, fixed-wing UAS for...

Abstract Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important assigning executing countermeasures. Intentions often intangible unobservable, variety of tangible classes inferred as proxy. However, inference drone using observational data alone inherently unreliable due learning bias....

Forest disturbances—driven by pests, pathogens, and discrete events—have led to billions of dollars in lost ecosystem services management costs. To understand the patterns severity these stressors across complex landscapes, there must be an increase reliable data at scales compatible with actions. Unmanned aerial systems (UAS or UAV) offer a capable platform for collecting local scale (e.g., individual tree) forestry data. In this study, we evaluate capability UAS multispectral imagery...

Unpiloted aerial systems (UAS) and light detection ranging (lidar) sensors provide users with an increasingly accessible mechanism for precision forestry. As these technologies are further adopted, questions arise as to how select processing methods influencing subsequent high-resolution modelling analysis. This study addresses specific individual tree (ITD) impact the successful of trees varying sizes within complex forests. First, while many studies have compared ITD over several sites,...

With the increasing occurrence of cyanobacteria blooms, it is crucial to improve our ability monitor impacted lakes accurately, efficiently, and safely. Cyanobacteria are naturally occurring in many waters globally. Some species can release neurotoxins which cause skin irritations, gastrointestinal illness, pet/livestock fatalities, possibly additional complications after long-term exposure. Using a DJI M300 RTK Unmanned Aerial Vehicle equipped with MicaSense 10-band dual camera system, six...

Effective management of bark beetle infestations requires prompt detection attacked trees. Early attack is also called green attack, since tree foliage does not yet show any visible signs decline. In several systems, including mountain pine and European spruce beetle, unpiloted aerial vehicle (UAV)-based remote sensing has successfully detected early attack. We explore the utility for southern (SPB; Dendroctonus frontalis Zimm.), paired with detailed ground surveys to link decline symptoms...

Thematic mapping provides today’s analysts with an essential geospatial science tool for conveying spatial information. The advancement of remote sensing and computer technologies has provided classification methods at both pixel-based object-based analysis, increasingly complex environments. These thematic maps then serve as vital resources a variety research management needs. However, to properly use the resulting map decision-making support tool, assessment accuracy must be performed....

The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools methodologies in remote sensing, computer vision, data science offered innovative pathways enhancing the effectiveness comprehension these sampling designs. Now with aid Unmanned Aerial Systems (UAS) advanced image processing techniques, we never closer to mapping forests at field-based inventory scales. Our research,...

Remotely sensed imagery has been used to support forest ecology and management for decades. In modern times, the propagation of high-spatial-resolution image analysis techniques automated workflows have further strengthened this synergy, leading inquiry into more complex, local-scale, ecosystem characteristics. To appropriately inform decisions in forestry management, most reliable efficient methods should be adopted. For reason, our research compares visual interpretation digital...

Abstract Fully autonomous aerial platforms or drones are increasingly used across diverse application areas. Uncooperative do not announce their identity nor file flight plans and can pose a potential risk to variety of critical infrastructures. Understanding an uncooperative drone's intention is important assigning executing countermeasures. Drones have rapidly changing design, flexible capabilities, underpinning algorithms. This makes distinguishing malicious from naive intentions...

The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart Southeast Asia. In addition to serving as foundation for highly productive fisheries system, this home numerous globally threatened species. Despite decades recognition by several government international agencies fact that nine protected areas have been established within region, land cover such grasslands experienced considerable decline since turn century. This project used local...

Spongy moth (Lymantria dispar dispar) has caused considerable damage to oak trees across eastern deciduous forests. Forest management, post-outbreak, is resource intensive and typically focused on ecosystem restoration or loss mitigation. Some local forest managers government partners are exploring developing technologies such as Unpiloted Aerial Systems (UASs, UAVs, drones) enhance their ability gather reliable fine-scale information. However, with limited resources the complexity of...

Russian MK-4 multispectral satellite photography has been investigated for potential in land cover classification. Thematic maps were generated using maximum likelihood, neural network and context classifiers. Classifications of the raw spectral data, transforms, combined spectral/textural data evaluated. Low point-based class accuracies resulted types exhibiting high spatial variability at given pixel spacing 7.5m, while more spatially homogeneous well classified. Several issues arose which...

This paper explores multi-person pose estimation for reducing the risk of airborne pathogens. The recent COVID-19 pandemic highlights these risks in a globally connected world. We developed several techniques which analyse CCTV inputs crowd analysis. framework utilised automated homography from feature positions to determine interpersonal distance. It also incorporates mask detection by using features an image classification pipeline. A further model predicts behaviour each person their...