Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review DOI Creative Commons
Rui Mota, José Pacheco, Adriano Pimentel

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(10), P. 1789 - 1789

Published: May 18, 2024

Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting implementing preventive measures. This review article aims identify the most common remote sensing methods volcanic clouds. To achieve this, we conducted a systematic literature scientific articles indexed in Web Science database published between 2010 2022, using multiple query strings across all fields. The were reviewed based on research topics, methods, practical applications, case studies, outcomes Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based approaches are cost-efficient accessible, allowing at various spatial scales. Brightness temperature difference commonly used method detecting specified threshold. Approaches apply machine learning techniques help overcome limitations traditional methods. Despite constraints imposed by temporal resolution optical sensors, multiplatform can these improve accuracy. explores clouds, identifies gaps, lays foundation future research.

Language: Английский

Multispectral and LiDAR-Derived Vegetation Indicators of Water Table Dynamics in Forested Wetlands DOI Creative Commons

Ambika Paudel,

Murray Richardson, Douglas J. King

et al.

Canadian Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: 51(1)

Published: March 11, 2025

Language: Английский

Citations

0
UAV for Segmentation and Object Detection: A Systematic Review Using PRISMA Method DOI Open Access
Kiki Ahmad Baihaqi, Eko Sediyono, Indrastanti Ratna Widiasari

et al.

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 2998(1), P. 012016 - 012016

Published: April 1, 2025

Abstract The purpose of this literature review study is to examine the extent object prediction and detection approaches carried out by previous researchers identify research gaps. YOLO CNN algorithms are used group articles using several open source tools process data in form images. databases include taken from Google Scholar, indexed IEEE Elsevier keywords “UAV”, “CNN”, “YOLO” “remote sensing.” were last 5 years only included Q1 Q2 journals. results mostly algorithm (80%), followed machine learning. Of selected articles, 87% discussed detection, clustering. showed that optimizations important research, such as maximizing image quality, adding parameters, detecting edges, other companion for better results. In-depth analysis NDVI GNDVI still rarely agricultural sector, all journals obtained 2 in-depth color differences. This identified next gap there has been no discusses specifications pest disease spread remote sensing-based objects, especially drones, need use or plant health levels even ready-to-harvest plants.

Language: Английский

Citations

0
Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review DOI Creative Commons
Rui Mota, José Pacheco, Adriano Pimentel

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(10), P. 1789 - 1789

Published: May 18, 2024

Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting implementing preventive measures. This review article aims identify the most common remote sensing methods volcanic clouds. To achieve this, we conducted a systematic literature scientific articles indexed in Web Science database published between 2010 2022, using multiple query strings across all fields. The were reviewed based on research topics, methods, practical applications, case studies, outcomes Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based approaches are cost-efficient accessible, allowing at various spatial scales. Brightness temperature difference commonly used method detecting specified threshold. Approaches apply machine learning techniques help overcome limitations traditional methods. Despite constraints imposed by temporal resolution optical sensors, multiplatform can these improve accuracy. explores clouds, identifies gaps, lays foundation future research.

Language: Английский

Citations

0