Differentiating carbon sinks versus sources on a university campus using synergistic UAV NIR and visible signatures

Park, Seong-Il; Um, Jung‐Sup

doi:10.1007/s10661-018-7003-x

Cited by 9 publications

(7 citation statements)

References 42 publications

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“…Lightness of color is quantified as a percentage from darkness to maximum lightness (diffuse white) to describe its relative brightness The UAV imagery was processed using Agisoft Photoscan to produce an ortho-image utilizing the structure from the motion (SfM) algorithm. SfM is a photogrammetric technique for restoring a three-dimensional structure using corresponding points in two or more overlapping images from a two-dimensional image captured with a certain degree of overlap percentage [31,[34][35][36]. The position and posture of the camera required to produce ortho-imagery were estimated utilizing information (focal length, flight altitude, image size, etc.)…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Operational Potential of LRV Signatures Derived from UAV Imagery in Performance Evaluation of Cool Roofs

et al. 2019

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It is quite difficult to find studies regarding area-wide data from UAV (Unmanned Aerial Vehicle) remote sensing in evaluating the energy saving performance of a cool roof. Acknowledging these constraints, we investigated whether LRV (Light Reflectance Value) signatures derived from UAV imagery could be used effectively as an indicator of area-wide heating and cooling load that distinctively appears according to rooftop color. The case study provides some quantitative tangible evidence for two distinct colors: A whitish color roof appears near the edge of the highest LRV (91.36) and with a low temperature (rooftop surface temperature: (38.03 °C), while a blackish color roof shows the lowest LRV (18.14) with a very high temperature (65.03 °C) where solar radiation is extensively absorbed. A strong negative association (Pearson correlation coefficient, r = −0.76) was observed between the LRV and surface temperature, implying that a higher LRV (e.g., a white color) plays a decisive role in lowering the surface temperature. This research can be used as a valuable reference introducing LRV in evaluating the thermal performance of rooftop color as rooftops satisfying the requirement of a cool roof (reflecting 75% or more of incoming solar energy) are identified based on area-wide objective evidence from UAV imagery.

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Section: Methodsmentioning

confidence: 99%

Evaluating the Operational Potential of LRV Signatures Derived from UAV Imagery in Performance Evaluation of Cool Roofs

et al. 2019

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“…As previously mentioned, relevant researchers have explored the application of drones in smart city communication [4], road or environmental monitoring [5]- [6], disaster management [11], and distribution [12]. It should be pointed out that when using drones to detect roads and environments, the main objective of mathematical models is to collect or organize information [13].…”

Section: A the Application Of Uav In Urban Patrolmentioning

confidence: 99%

“…In addition to using drones [1] to provide reliable communication services for smart city scenarios in [4], drones can be used to comprehensively monitor urban facilities, transportation, and the environment. For example, in [5], drones are used to monitor roads in cities, while in [6], drones are used to monitor the environment of cities. Another potential application of drones in smart city scenarios is patrol missions [7]- [8].…”

Section: Introductionmentioning

confidence: 99%

A Patrol Platform Based on Unmanned Aerial Vehicle for Urban Safety and Intelligent Social Governance

Yang,

Ma,

Zhou

2024

IJACSA

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Urban patrols can detect emergencies in a timely manner and collect information, which helps to improve the quality of services in the city and enhance the comfort of residents. This study proposes the use of IoT-based drones for urban patrol tasks, aiming to explore the potential applications of drones in smart city governance. The main technical challenge in the process of urban patrols by drones is how to plan a flight path for them. Therefore, this article first designs a smart patrol system based on drones and Internet of Things (IoT). Meanwhile, as information collection is an important aspect of urban patrol tasks, a mathematical model with the goal of maximizing information collection has been established to provide costeffective patrol services. On this basis, in order to improve the accuracy of crow search algorithm (CSA), differential crow search strategy and variable flight step size are designed. In addition, the Levy flight strategy is introduced into the traditional CSA algorithm, and an improved crow search algorithm (ICSA) is proposed. Finally, a corresponding simulation environment was established based on the actual urban scene and compared with other algorithms. The numerical results indicate that compared with the other three swarm intelligence algorithms, the algorithm designed in this paper has more superiority.

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“…University campuses are well known as the living lab of the urban area since they exhibit spatial variation similar to city-specific landscapes containing a variety of buildings, such as those for vehicle traffic areas, road, green space, libraries, parking lots, lectures, dormitories, and so on [24][25][26]. Furthermore, the city center's typical land-use patterns and characteristics are shown at the KNU area, such as commercial and business centers, parks, high-and low-rise buildings, dormitories, road networks, playgrounds, green spaces, and other mixed uses [27]. Therefore, it is expected that the solar panels installed in KNU can serve as an ideal testbed for setting allowable errors or parameters for evaluating the tracking capability of solar panel malfunctioning caused by soil debris between the video stream and photo-mosaic at a city-wide scale.…”

Section: Data Collectionmentioning

confidence: 99%

Comparative Evaluation for Tracking the Capability of Solar Cell Malfunction Caused by Soil Debris between UAV Video versus Photo-Mosaic

Hwang

Schlüter

Park³

et al. 2022

Remote Sensing

Self Cite

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Monitoring the malfunction of the solar cells (for instance, 156 mm by 156 mm) caused by the soil debris requires a very low flight altitude when taking aerial photos, utilizing the autopilot function of unmanned aerial vehicle (UAV). The autopilot flight can only operate at a certain level of altitude that can guarantee collision avoidance for flight obstacles (for instance, power lines, trees, buildings) adjacent to the place where the solar panel is installed. For this reason, aerial photos taken by autopilot flight capture unnecessary objects (surrounding buildings and roads) around the solar panel at a tremendous level. Therefore, the autopilot-based thermal imaging causes severe data redundancy with very few matched key-points around the malfunctioned solar cells. This study aims to explore the tracking capability on soil debris defects in solar cell scale between UAV video versus photo-mosaic. This study experimentally validated that the video-based thermal imaging can track the thermal deficiency caused by the malfunction of the solar cell at the level of the photo-mosaic in terms of correlation of thermal signatures (0.98–0.99), detection on spatial patterns (81–100%), and distributional property (90–95%) with 2.5–3.4 times more matched key-points on solar cells. The results of this study could serve as a valuable reference for employing video stream in the process of investigating soil debris defects in solar cell scale.

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Differentiating carbon sinks versus sources on a university campus using synergistic UAV NIR and visible signatures

Cited by 9 publications

References 42 publications

Evaluating the Operational Potential of LRV Signatures Derived from UAV Imagery in Performance Evaluation of Cool Roofs

Evaluating the Operational Potential of LRV Signatures Derived from UAV Imagery in Performance Evaluation of Cool Roofs

A Patrol Platform Based on Unmanned Aerial Vehicle for Urban Safety and Intelligent Social Governance

Comparative Evaluation for Tracking the Capability of Solar Cell Malfunction Caused by Soil Debris between UAV Video versus Photo-Mosaic

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