The Study of Utilization and Precision Based on the Comparison and Analysis of Drone-Based Coastal Hazard Data and Its Application in the Ocean Environment

Jun, Kye-Won; Jun, Byong-Hee; Lee, Ho-Jin; Kim, Sungduk; Tak, W.J.

doi:10.2112/si85-163.1

Cited by 6 publications

(6 citation statements)

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“…Drone-based aerial imagery was used in Brazil, South Korea and the USA (Albuquerque et al, 2018;Jun et al, 2018). Aerial, terrestrial, and mobile LiDAR surveys were used along the US, Australian and Polish coasts.…”

Section: Coastline Extraction and Mapping From Remote Sensing Datamentioning

confidence: 99%

Monitoring Coastline Dynamics Using Satellite Remote Sensing and Geographic Information Systems: A Review of Global Trends

Darwish

2024

Catrina: The International Journal of Environmental Sciences

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Section: Coastline Extraction and Mapping From Remote Sensing Datamentioning

confidence: 99%

Monitoring Coastline Dynamics Using Satellite Remote Sensing and Geographic Information Systems: A Review of Global Trends

Darwish

2024

Catrina: The International Journal of Environmental Sciences

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“…For the areas with weak-connection, we list four typical regions that are construction sites in urban, disaster regions in urban, blind coverage spots in the city, and the transportation road. In these areas, some recent studies use UAVs to offer an extended network coverage and perform some specified applications such Areas with weak-connection Urban construction sites Construction project management [119]- [121] Indoor construction monitoring [122], [123] Disaster regions Disaster surveillance [80], [124], [125] Emergency networks construction [126]- [129] Urban coverage blind spots Enhanced coverage in urban area [29], [80], [130]- [133] Patrolling and surveillance [134]- [139] Transportation systems Intelligent transportation systems [140]- [143] Connection between ground vehicles [144]- [147] Areas without network deployment Farms Survey of UAV in agriculture [63], [148] Imagery analysis of crops [149]- [153] Deserts Disaster monitoring [154]- [156] Geomorphological analysis [61], [155], [157] Military detection [158] Forests Trees and plants monitoring [159]- [162] Forest growing volume prediction [163], [164] Oceans Coastal environment analysis [165]- [168] Ocean environment monitoring [169]- [171] Marine science and observation [18]...…”

Section: B Uav-enabled Ioementioning

confidence: 99%

“…[125] Emergency networks construction [126]- [129] Urban coverage blind spots Enhanced coverage in urban area [29], [80], [130]- [133] Patrolling and surveillance [134]- [139] Transportation systems Intelligent transportation systems [140]- [143] Connection between ground vehicles [144]- [147] Areas without network deployment Farms Survey of UAV in agriculture [63], [148] Imagery analysis of crops [149]- [153] Deserts Disaster monitoring [154]- [156] Geomorphological analysis [61], [155], [157] Military detection [158] Forests Trees and plants monitoring [159]- [162] Forest growing volume prediction [163], [164] Oceans Coastal environment analysis [165]- [168] Ocean environment monitoring [169]- [171] Marine science and observation [18], [172]- [174] Fig. 8.…”

Section: B Uav-enabled Ioementioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

179

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The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

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“…Penginderaan jauh dan sistem informasi geografis menjadi pilihan yang tepat. Namun, metode penginderaan jauh di Indonesia yang beriklim tropis seringkali membatasi kualitas citra yang diperoleh melalui satelit karena tingginya tutupan awan (Ruwaimana et al, 2016;2017;2018;Sriyana, 2020). Kendala ini tentu tidak menjadi masalah bagi kegiatan dengan pendanaan besar, karena citra satelit berkualitas tinggi juga dapat diakses secara komersil.…”

Section: Introductionunclassified

Identifikasi Spesies Mangrove dengan Menggunakan Sistem Pesawat Udara Kecil Tanpa Awak di Kawasan Ekosistem Mangrove Sedari, Kabupaten Karawang, Jawa Barat

Sewiko

Sagala

Yulandhita

et al. 2022

NEKTON: J. Perikan. dan Ilmu Kelaut.

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Remote sensing is one of the effective methods of monitoring mangrove ecosystems. One of the challenges in implementing this method is image resolution. Access to large-resolution imagery as a basic material for spatial analysis is not cheap. Unmanned small aircraft systems (SPUKTA) or drones are able to answer these challenges. Orthophotos obtained from the acquisition of drones are capable of producing large-resolution imagery. This method is then implemented in conservation areas, to facilitate the process of identifying mangrove species in the area. The drone was flown at an altitude of 150 m with a pavement value of 85% for 4 flying missions. The result of processing 1614 aerial photos into orthophotos produced images with a GSD resolution of 4.75 cm/pix. These images are then analyzed with on-screen digitization techniques and visual interpretation. From the total area of the study area of 46.48 ha obtained the digitization results of Rhizophora sp. with a total area of 24.68 ha, Avicennia sp. 7.64 ha, dead mangroves 0.19 ha, and non-vegetation 13.97 ha.

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The Study of Utilization and Precision Based on the Comparison and Analysis of Drone-Based Coastal Hazard Data and Its Application in the Ocean Environment

Cited by 6 publications

References 0 publications

Monitoring Coastline Dynamics Using Satellite Remote Sensing and Geographic Information Systems: A Review of Global Trends

Monitoring Coastline Dynamics Using Satellite Remote Sensing and Geographic Information Systems: A Review of Global Trends

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Identifikasi Spesies Mangrove dengan Menggunakan Sistem Pesawat Udara Kecil Tanpa Awak di Kawasan Ekosistem Mangrove Sedari, Kabupaten Karawang, Jawa Barat

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