scholarly journals Developing an Introductory UAV/Drone Mapping Training Program for Seagrass Monitoring and Research

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 70
Author(s):  
Bo Yang ◽  
Timothy L. Hawthorne ◽  
Margot Hessing-Lewis ◽  
Emmett J. Duffy ◽  
Luba Y. Reshitnyk ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Learning Curve ◽  
Public Participation ◽  
Training Program ◽  
Route Planning ◽  
Federal Aviation Administration ◽  
The United States ◽  
Image Correction ◽  
Novice Users ◽  
Public Participation Gis

Unoccupied Aerial Vehicles (UAVs), or drone technologies, with their high spatial resolution, temporal flexibility, and ability to repeat photogrammetry, afford a significant advancement in other remote sensing approaches for coastal mapping, habitat monitoring, and environmental management. However, geographical drone mapping and in situ fieldwork often come with a steep learning curve requiring a background in drone operations, Geographic Information Systems (GIS), remote sensing and related analytical techniques. Such a learning curve can be an obstacle for field implementation for researchers, community organizations and citizen scientists wishing to include introductory drone operations into their work. In this study, we develop a comprehensive drone training program for research partners and community members to use cost-effective, consumer-quality drones to engage in introductory drone mapping of coastal seagrass monitoring sites along the west coast of North America. As a first step toward a longer-term Public Participation GIS process in the study area, the training program includes lessons for beginner drone users related to flying drones, autonomous route planning and mapping, field safety, GIS analysis, image correction and processing, and Federal Aviation Administration (FAA) certification and regulations. Training our research partners and students, who are in most cases novice users, is the first step in a larger process to increase participation in a broader project for seagrass monitoring in our case study. While our training program originated in the United States, we discuss our experiences for research partners and communities around the globe to become more confident in introductory drone operations for basic science. In particular, our work targets novice users without a strong background in geographic research or remote sensing. Such training provides technical guidance on the implementation of a drone mapping program for coastal research, and synthesizes our approaches to provide broad guidance for using drones in support of a developing Public Participation GIS process.

scholarly journals Description and Derived Climatologies of Automated In Situ Eddy-Dissipation-Rate Reports of Atmospheric Turbulence

2014 ◽  
Vol 53 (6) ◽  
pp. 1416-1432 ◽  
Author(s):  
R. D. Sharman ◽  
L. B. Cornman ◽  
G. Meymaris ◽  
J. Pearson ◽  
T. Farrar
Keyword(s):  
Atmospheric Turbulence ◽  
Dissipation Rate ◽  
Route Planning ◽  
Federal Aviation Administration ◽  
The United States ◽  
Commercial Aircraft ◽  
Upper Troposphere ◽  
Obvious Benefit ◽  
Turbulence Data

AbstractThe statistical properties of turbulence at upper levels in the atmosphere [upper troposphere and lower stratosphere (UTLS)] are still not well known, partly because of the lack of adequate routine observations. This is despite the obvious benefit that such observations would have for alerting aircraft of potentially hazardous conditions, either in real time or for route planning. To address this deficiency, a research project sponsored by the Federal Aviation Administration has developed a software package that automatically estimates and reports atmospheric turbulence intensity levels (as EDR ≡ ε1/3, where ε is the energy or eddy dissipation rate). The package has been tested and evaluated on commercial aircraft. The amount of turbulence data gathered from these in situ reports is unprecedented. As of January 2014, there are ~200 aircraft outfitted with this system, contributing to over 137 million archived records of EDR values through 2013, most of which were taken at cruise levels of commercial aircraft, that is, in the UTLS. In this paper, techniques used for estimating EDR are outlined and comparisons with pilot reports from the same or nearby aircraft are presented. These reports allow calibration of EDR in terms of traditionally reported intensity categories (“light,” “moderate,” or “severe”). The results of some statistical analyses of EDR values are also presented. These analyses are restricted to the United States for now, but, as this program is expanded to international carriers, such data will begin to become available over other areas of the globe.

scholarly journals Learning Curve Analysis of Wind Power and Photovoltaics Technology in US: Cost Reduction and the Importance of Research, Development and Demonstration

2019 ◽  
Vol 11 (8) ◽  
pp. 2310 ◽  
Author(s):  
Yi Zhou ◽  
Alun Gu
Keyword(s):  
Renewable Energy ◽  
Learning Curve ◽  
Wind Power ◽  
Cost Reduction ◽  
Rapid Development ◽  
Carbon Price ◽  
The United States ◽  
Research Development ◽  
Power Utility ◽  
Total Social Cost

The strategic transition from fossil energy to renewable energy is an irreversible global trend, but the pace of renewable energy deployment and the path of cost reduction are uncertain. In this paper, a two-factor learning-curve model of wind power and photovoltaics (PV) was established based on the latest empirical data from the United States, and the paths of cost reduction and corresponding social impacts were explored through scenario analysis. The results demonstrate that both of the technologies are undergoing a period of rapid development, with the learning-by-searching ratio (LSR) being greatly improved in comparison with the previous literature. Research, development, and demonstration (RD&D) have contributed to investment cost reduction in the past decade, and the cost difference between high and low RD&D spending scenarios is predicted to be 5.5%, 8.9%, and 11.27% for wind power, utility-scale PV, and residential PV, respectively, in 2030. Although higher RD&D requires more capital, it can effectively promote cost reduction, reduce the total social cost of deploying renewable energy, and reduce the abatement carbon price that is needed to promote deployment. RD&D and the institutional support behind it are of great importance in allowing renewables to penetrate the commercial market and contribute to long-term social welfare.

U.S. Federal Aviation Administration Neighborhood Environmental Survey: Study Motivation and Results

2021 ◽  
Vol 263 (1) ◽  
pp. 5815-5827
Author(s):  
Sean Doyle ◽  
Donald Scata ◽  
James Hileman
Keyword(s):  
Noise Exposure ◽  
Survey Methodology ◽  
Research Effort ◽  
Research Priorities ◽  
Federal Aviation Administration ◽  
The United States ◽  
Aircraft Noise ◽  
Survey Study ◽  
Environmental Survey ◽  
Additional Information

As part of the agency's broader noise research program, the Federal Aviation Administration (FAA) has undertaken a multi-year research effort to quantify the impacts of aircraft noise exposure on communities around commercial service airports in the United States (U.S.). The overall goal of the study was to produce an updated and nationally representative civil aircraft dose-response curve; providing the relationship between annoyance and aircraft noise exposure around U.S. airport communities. To meet this goal, the FAA sponsored a research team to help design and conduct a national survey, known as the Neighborhood Environmental Survey (NES). By assessing the results of the NES through both internal review and input from public comment, the FAA seeks to better inform its noise research priorities and noise policies. This paper will outline the FAA's motivation to conduct the NES as well as how its findings will help inform ongoing work to address aircraft noise concerns. Additional information describing the noise methodology and survey methodology are provided in companion papers.

scholarly journals Estimating annual water storage variations in medium-scale (2000–10 000 km<sup>2</sup>) basins using microwave-based soil moisture retrievals

2017 ◽  
Vol 21 (3) ◽  
pp. 1849-1862 ◽  
Author(s):  
Wade T. Crow ◽  
Eunjin Han ◽  
Dongryeol Ryu ◽  
Christopher R. Hain ◽  
Martha C. Anderson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Budget ◽  
Surface Soil ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
The United States ◽  
Surface Soil Moisture ◽  
Medium Scale ◽  
Annual Water

Abstract. Due to their shallow vertical support, remotely sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (dS ∕ dt). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying dS ∕ dt via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2000–10 000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain statistically significant information concerning dS ∕ dt. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing products to enhance the spatial resolution of dS ∕ dt estimates acquired from gravity remote sensing.

scholarly journals Ensuring Compliance From 35,000 Feet: Accountability and Trade-Offs in Aviation Safety Regulatory Networks

2016 ◽  
Vol 50 (10) ◽  
pp. 1478-1507 ◽  
Author(s):  
Russell W. Mills ◽  
Christopher J. Koliba ◽  
Dorit Rubinstein Reiss
Keyword(s):  
Regulatory Networks ◽  
Federal Aviation Administration ◽  
The United States ◽  
Network Governance ◽  
Regulatory Regime ◽  
Democratic Accountability ◽  
Public Administrators ◽  
Trade Offs ◽  
Process Oriented ◽  
Air Carriers

A puzzle that faces public administrators within regulatory networks is how to balance the need for public or democratic accountability with increasing demands from interest groups and elected officials to utilize the expertise of the private sector in developing process-oriented programs that ensure compliance. This article builds upon the network governance accountability framework developed by Koliba, Mills, and Zia to explore the dominant accountability frames and the accountability trade-offs that shape the process-oriented regulatory regime used by the Federal Aviation Administration (FAA) to oversee and regulate air carriers in the United States.

scholarly journals Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

2018 ◽  
Vol 10 (12) ◽  
pp. 2027 ◽  
Author(s):  
Itiya Aneece ◽  
Prasad Thenkabail
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
The United States ◽  
Google Earth ◽  
Growth Stages ◽  
Agricultural Crops ◽  
Spectral Library ◽  
Crop Types ◽  
Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

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