Spatio-Temporal Change Monitoring of Outside Manure Piles Using Unmanned Aerial Vehicle Images

Park, Geonung; Park, Kyunghun; Song, Bong-Geun

doi:10.3390/drones5010001

Cited by 3 publications

(4 citation statements)

References 54 publications

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“…If the area and boundary of OCPs can be accurately analyzed, it will be possible to predict the amount of OCPs. As UAV can analyze digital surface models (DSM) through point clouds, it can also analyze volume [11]. In order to utilize the results Therefore, the most effective method for OCP detection among the various machine learning techniques assessed herein was RF.…”

Section: Discussionmentioning

confidence: 99%

“…Given that compost contains large amounts of nitrogen and phosphorus, it is used to increase the growth of crops by supplying nutrients to the soil. However, excessive compost use is a major cause of eutrophication, as these nutrient-rich materials often reach nearby rivers due to surface runoff [11,12]. Compost is mainly applied outdoors in agricultural areas in South Korea, thus raising concerns about water pollution [13].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Outdoor Compost Pile Detection Using Unmanned Aerial Vehicle Images and Various Machine Learning Techniques

Song

Park

2021

Drones

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Since outdoor compost piles (OCPs) contain large amounts of nitrogen and phosphorus, they act as a major pollutant that deteriorates water quality, such as eutrophication and green algae, when the OCPs enter the river during rainfall. In South Korea, OCPs are frequently used, but there is a limitation that a lot of manpower and budget are consumed to investigate the current situation, so it is necessary to efficiently investigate the OCPs. This study compared the accuracy of various machine learning techniques for the efficient detection and management of outdoor compost piles (OCPs), a non-point pollution source in agricultural areas in South Korea, using unmanned aerial vehicle (UAV) images. RGB, multispectral, and thermal infrared UAV images were taken in August and October 2019. Additionally, vegetation indices (NDVI, NDRE, ENDVI, and GNDVI) and surface temperature were also considered. Four machine learning techniques, including support vector machine (SVM), decision tree (DT), random forest (RF), and k-NN, were implemented, and the machine learning technique with the highest accuracy was identified by adjusting several variables. The accuracy of all machine learning techniques was very high, reaching values of up to 0.96. Particularly, the accuracy of the RF method with the number of estimators set to 10 was highest, reaching 0.989 in August and 0.987 in October. The proposed method allows for the prediction of OCP location and area over large regions, thereby foregoing the need for OCP field measurements. Therefore, our findings provide highly useful data for the improvement of OCP management strategies and water quality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Outdoor Compost Pile Detection Using Unmanned Aerial Vehicle Images and Various Machine Learning Techniques

Song

Park

2021

Drones

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“…With the recent advancement of unmanned aerial vehicles (UAVs) and relevant sensor technologies, spectroscopic and position accuracy have increased and are readily employed in remote sensing analyses with aerial surveys [10][11][12][13][14][15]. In addition, the development of 4-band multi-spectral sensors, combined with very high resolution (VHR) RGB or thermal infrared sensors, have made it possible to obtain a diverse array of spectral imagery in a single flight.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Impact of Types of UAV-Derived Images on the Object-Based Classification of Land Cover in an Urban Area

Park

Song

et al. 2022

Drones

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The development of UAV sensors has made it possible to obtain a diverse array of spectral images in a single flight. In this study, high-resolution UAV-derived images of urban areas were employed to create land cover maps, including car-road, sidewalk, and street vegetation. A total of nine orthoimages were produced, and the variables effective in producing UAV-based land cover maps were identified. Based on analyses of the object-based images, 126 variables were derived by computing 14 statistical values for each image. The random forest (RF) classifier was used to evaluate the priority of the 126 variables. This was followed by optimizing the RF through variable reduction and by comparing the initial and optimized RF, the utility of the high-priority variable was evaluated. Computing variable importance, the most influential variables were evaluated in the order of normalized digital surface model (nDSM), normalized difference vegetation index (NDVI), land surface temperature (LST), soil adjusted vegetation index (SAVI), blue, green, red, rededge. Finally, no significant changes between initial and optimized RF in the classification were observed from a series of analyses even though the reduced variables number was applied for the classification.

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“…Unmanned aerial vehicles (UAVs) perform a wide range of civilian and military-related applications [1][2][3][4]. UAVs fly in a highly congested wireless spectrum environment due to their recent use as mobile base stations [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Compatibility Study of Quadcopter Uavs: Characteristic Mode Analysis of the Frame's Material and Shape Effect

Hamdalla¹,

Roacho-Valles²,

Caruso³

et al. 2022

PIER M

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The variation in flight attitude, line-of-sight, and speed of unmanned aerial vehicles (UAVs) affect their polarization-dependent coupling cross-section and resultant compatibility to pulsed electromagnetic energy. Here, we present the out-of-band electromagnetic compatibility (EMC) effects of UAV frame material and shape on the UAV subcomponents. Characteristic mode analysis (CMA) is employed to study the fundamental modes supported by UAVs which facilitate the interpretation of its electromagnetic response and the prediction of its effect on the nearby components. Using CMA, we develop a framework that optimizes the placement of wires and traces of printed circuit boards (PCBs) on the frame mitigating interference from undesired electromagnetic sources. A 3-D scanner is used to provide four versions of a quadrotor UAV to study the frame shape effect on the coupling. Materials of differing permittivity are assigned to these frame versions to assist in understanding the material effect on the EM coupling to the UAV.

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Spatio-Temporal Change Monitoring of Outside Manure Piles Using Unmanned Aerial Vehicle Images

Cited by 3 publications

References 54 publications

Comparison of Outdoor Compost Pile Detection Using Unmanned Aerial Vehicle Images and Various Machine Learning Techniques

Comparison of Outdoor Compost Pile Detection Using Unmanned Aerial Vehicle Images and Various Machine Learning Techniques

Analyzing Impact of Types of UAV-Derived Images on the Object-Based Classification of Land Cover in an Urban Area

Electromagnetic Compatibility Study of Quadcopter Uavs: Characteristic Mode Analysis of the Frame's Material and Shape Effect

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