scholarly journals MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 203-208
Author(s):  
O. Vlachopoulos ◽  
B. Leblon ◽  
J. Wang ◽  
A. Haddadi ◽  
A. LaRocque ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Goodness Of Fit ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Classification Model ◽  
Crop Damage ◽  
Validation Dataset ◽  
Positional Error ◽  
Sensing Platforms ◽  
Damage Monitoring

Abstract. Unmanned Aircraft Systems (UAS) are demonstrated cost- and time-effective remote sensing platforms for precision agriculture applications and crop damage monitoring. In this study, lodging damage on barley crops has been mapped from UAS imagery that was acquired over multiple barley fields with extensive lodging damages in two aerial surveys. A Random Forests classification model was trained and tested for the discrimination of lodged barley with an overall accuracy of 99.7% on the validation dataset. The crop areas with lodging were automatically delineated by vector analysis and compared to manually delineated areas using two spatial accuracy metrics, the Area Goodness of Fit (AGoF) and the Boundary Mean Positional Error (BMPE). The average AGoF was 97.95% and the average BMPE was 0.235 m.

scholarly journals Delineation of Crop Field Areas and Boundaries from UAS Imagery Using PBIA and GEOBIA with Random Forest Classification

2020 ◽  
Vol 12 (16) ◽  
pp. 2640
Author(s):  
Odysseas Vlachopoulos ◽  
Brigitte Leblon ◽  
Jinfei Wang ◽  
Ataollah Haddadi ◽  
Armand LaRocque ◽  
...  
Keyword(s):  
Random Forest ◽  
Precision Agriculture ◽  
Prince Edward Island ◽  
Goodness Of Fit ◽  
Vegetation Index ◽  
Crop Insurance ◽  
Accurate Determination ◽  
Unmanned Aircraft ◽  
Positional Error ◽  
Random Forest Classification

Unmanned aircraft systems (UAS) have been proven cost- and time-effective remote-sensing platforms for precision agriculture applications. This study presents a method for automatic delineation of field areas and boundaries that uses UAS multispectral orthomosaics acquired over 7 vegetated fields having a variety of crops in Prince Edward Island (PEI). This information is needed by crop insurance agencies and growers for an accurate determination of crop insurance premiums. The field areas and boundaries were delineated by applying both a pixel-based and an object-based supervised random forest (RF) classifier applied to reflectance and vegetation index images, followed by a vectorization pipeline. Both methodologies performed exceptionally well, resulting in a mean area goodness of fit (AGoF) for the field areas greater than 98% and a mean boundary mean positional error (BMPE) lower than 0.8 m for the seven surveyed fields.

scholarly journals Performance of Hyperspectral Imaging with Drone Swarms

2018 ◽  
Vol 2672 (45) ◽  
pp. 36-44
Author(s):  
Raj Bridgelall ◽  
James B. Rafert ◽  
Denver D. Tolliver
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Low Cost ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Aircraft Systems ◽  
Sensing Platforms ◽  
Spatial Coverage ◽  
Ground Coverage

The ongoing proliferation and diversification of remote sensing platforms offer greater flexibility to select from a range of hyperspectral imagers as payloads. The emergence of low-cost unmanned aircraft systems (drones) and their launch flexibility present an opportunity to maximize spectral resolution while scaling both daily spatial coverage and spatial resolution simultaneously by operating synchronized swarms. This article presents a model to compare the performance of hyperspectral-imaging platforms in their spatial coverage and spatial resolution envelope. The authors develop a data acquisition framework and use the model to compare the achievable performance among existing airborne and spaceborne hyperspectral imaging vehicles and drone swarms. The results show that, subject to cost and operational limitations, a platform implemented with drone swarms has the potential to provide greater spatial resolution for the same daily ground coverage compared with existing airborne platforms.

Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges

2014 ◽  
Vol 02 (03) ◽  
pp. 69-85 ◽  
Author(s):  
Ken Whitehead ◽  
Chris H. Hugenholtz
Keyword(s):  
Remote Sensing ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Environmental Applications ◽  
Research Experience ◽  
New Developments ◽  
Spatial And Temporal Scales ◽  
Environmental Processes ◽  
Aircraft Systems ◽  
Sensing Platforms

The recent development and proliferation of unmanned aircraft systems (UASs) has made it possible to examine environmental processes and changes occurring at spatial and temporal scales that would be difficult or impossible to detect using conventional remote sensing platforms. This review article highlights new developments in UAS-based remote sensing, focusing mainly on small UASs (<25 kg). Because this class is generally less expensive and more versatile than larger systems the use of small UASs for civil, commercial, and scientific applications is expected to expand considerably in the future. To highlight different environmental applications, we provide an overview of recent progress in remote sensing with small UASs, including photogrammetry, multispectral and hyperspectral imaging, thermal, and synthetic aperture radar and LiDAR. We also draw on the literature and our own research experience to identify some key research challenges, including limitations of the current generation of platforms and sensors, and the development of optimal methodologies for processing and analysis. While much of the potential of small UASs for remote sensing remains to be realised, it is likely that the next few years will see such systems being used to provide data for an ever-increasing range of environmental applications.

scholarly journals Perspective: Agricultural Aerial Application with Unmanned Aircraft Systems: Current Regulatory Framework and Analysis of Operators in the United States

2021 ◽  
Vol 64 (5) ◽  
pp. 1475-1481
Author(s):  
Roberto Rodriguez
Keyword(s):  
Precision Agriculture ◽  
Federal Aviation Administration ◽  
The United States ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
List Type ◽  
Aerial Application ◽  
Aircraft Systems ◽  
Agricultural Operations ◽  
The U.S

HighlightsThe FAA has used two exemptions (17261 and 18009) as precedents for approval of numerous agricultural operations for unmanned aircraft systems (UAS).While many operators have received exemptions, a significant portion have not received an agricultural aircraft operator certificate (AAOC), despite the need for both to operate UAS in agricultural operations.Operators who have both an exemption and an AAOC tend to be clustered in geographic areas, with many states without a single such operator.Abstract. Unmanned aircraft systems (UAS) have seen rapid growth in many industries in the U.S. since the introduction of small UAS regulations (14 CFR § 107). However, adoption of UAS for agricultural aerial application has been limited. Two landmark regulatory exemptions by the Federal Aviation Administration (FAA) have laid the foundation for commercial agricultural aerial application with UAS. Since the initial introduction of these exemptions, the pace of new exemptions for agricultural aerial application with UAS has remained steady. By the end of 2019, 64 operators had received exemptions in which the FAA cited one of the two landmark exemptions as a precedent. This study analyzed these exemptions to determine geographic distribution, aircraft manufacturer, number of employees, and time to operator certification. Results indicate that less than half of operators who received an exemption from the FAA became certified for aerial application. Additionally, certified operators were not evenly distributed throughout the U.S. despite the broader distribution of exemption holders. Two UAS manufacturers dominated the market, with over 80% of exemptions requesting UAS from one or both manufacturers. While regulatory hurdles for agricultural aerial application with UAS have been substantially reduced through the introduction of standardized exemptions, this has not resulted in the anticipated influx of certified operators. There are additional impediments preventing operator certification, including technological limitations of currently available UAS, which need to be addressed to improve the rate of UAS integration into agricultural aerial application. Keywords: Chemical applications, Drone, Precision agriculture, UAS, UAV, Unmanned aerial vehicle, Unmanned aircraft systems.

FLIGHT SAFETY OF UNMANNED AIRCRAFT SYSTEMS IN NON-SEGREGATED AIRSPACE

2011 ◽  
Vol 42 (6) ◽  
pp. 801-815 ◽  
Author(s):  
Boris Sergeevich Alyoshin ◽  
Valeriy Leonidovich Sukhanov ◽  
Vladimir Mikhaylovich Shibaev
Keyword(s):  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Flight Safety ◽  
Aircraft Systems
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