Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations DOI Creative Commons

Nishanth Pushparaj,

Luis Cormier, Chantal Cappelletti

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(12), P. 1543 - 1543

Published: Dec. 23, 2024

Contrails, or condensation trails, left by aircraft, significantly contribute to global warming trapping heat in the Earth’s atmosphere. Despite their critical role climate dynamics, environmental impact of contrails remains underexplored. This research addresses this gap focusing on use CubeSats for real-time contrail monitoring, specifically over major air routes such as Europe–North Atlantic Corridor. The study proposes a 3 × CubeSat constellation highly eccentric orbits, designed maximize coverage and data acquisition efficiency. Simulation results indicate that configuration can provide nearly continuous monitoring with optimized satellite handovers, reducing blackout periods ensuring robust multi-satellite visibility. A machine learning-based system integrating space-based humidity temperature predict formation inform flight path adjustments is proposed, thereby mitigating impact. findings emphasize potential constellations revolutionize atmospheric practices, offering cost-effective solution aligns sustainability efforts, particularly United Nations Sustainable Development Goal 13 (Climate Action). represents significant step forward understanding aviation’s non-CO2 demonstrates feasibility mitigation through technology.

Language: Английский

Obtaining a Land Use/Cover Cartography in a Typical Mediterranean Agricultural Field Combining Unmanned Aerial Vehicle Data with Supervised Classifiers DOI Creative Commons

Ioannis A. Nikolakopoulos,

George P. Petropoulos

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 643 - 643

Published: March 18, 2025

The mapping of land use/cover (LULC) types is a crucial tool for natural resource management and monitoring changes in both human physical environments. Unmanned aerial vehicles (UAVs) provide high-resolution data, enhancing the capability accurate LULC representation at potentially very high spatial resolutions. In present study, two widely used supervised classification methods, namely Maximum Likelihood Classification (MLC) Mahalanobis Distance (MDC), were applied to analyze image data collected by UAVs from typical Mediterranean site located Greece. study area, characterized diverse uses (urban, agricultural, areas), served as an ideal field comparing methods. Although methods produced comparable results, MLC outperformed MDC, with overall accuracy 96.58% Kappa coefficient 0.942, compared MDC which 92.77% 0.878 reported. This highlights advantages using produce robust information on geospatial variability given area resolution cost-efficient, timely, on-demand manner. Such can help decision- policy-making ensuring more sustainable environment. study’s limitations, including small relatively homogeneous are acknowledged. Future research could focus exploring use advanced techniques, such deep learning landscapes, would assist present’s approach applicability.

Language: Английский

Citations

0
Coastal zones vulnerability evaluation in the southern Baltic Sea: Shoreline dynamics and land use/land cover changes over five decades DOI Creative Commons
Kamran Anwar Tanwari, Paweł Terefenko, Xiangjun Shi

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 976, P. 179345 - 179345

Published: April 8, 2025

Over the past century, coastal zones have experienced significant population growth and rapid development, often conflicting with these environments' dynamic sensitive nature. The present study investigated five decades (1972-2023) of shoreline dynamics land-use/land-cover (LULC) transformations along three sectors located on a 47 km stretch Southern Baltic coastline. research employed eleven multispectral Landsat MSS/TM/OLI images within geographic information system (GIS) framework to analyze coastline variations LULC patterns. Results showed accretion in Sector I (Usedom), while Sectors II III (Wolin) marked erosion. entire period, 29.59 % (3.21 km), 39.90 (4.51 67.54 (9.45 km) shorelines Sector-I, Sector-II, Sector-III distance correlation that hydrometeorological variables associated wind-wave dynamics, exerted stronger influence changes. change analysis highlighted decline forest cover (-846.86 ha) increased built-up areas (+1137.86) across all sectors. These results enabled identification four vulnerability zones-one Usedom Wolin-characterized by pronounced erosion, degradation, urban expansion. findings can inform management strategies identifying high-risk zones, guiding sustainable development practices, prioritizing for conservation intervention.

Language: Английский

Citations

0
Integration of Hyperspectral Imaging and AI Techniques for Crop Type Mapping: Present Status, Trends, and Challenges DOI Creative Commons
Mohamed Bourriz, Hicham Hajji, Ahmed Laamrani

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1574 - 1574

Published: April 29, 2025

Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly effective in the precise discrimination types. This systematic review examines evolution platforms, from Unmanned Aerial Vehicle (UAV)-mounted sensors space-borne satellites (e.g., EnMAP, PRISMA), explores recent scientific advances AI methodologies mapping. A protocol was applied identify 47 studies databases peer-reviewed publications, focusing on sensors, input features, classification architectures. analysis highlights significant contributions Deep Learning (DL) models, particularly Vision Transformers (ViTs) hybrid architectures, improving accuracy. However, also identifies critical gaps, including under-utilization limited multi-sensor need modeling approaches such as Graph Neural Networks (GNNs)-based methods geospatial foundation (GFMs) large-scale type Furthermore, findings highlight importance developing scalable, interpretable, transparent maximize potential imaging (HSI), underrepresented regions Africa, where research remains limited. provides valuable insights guide future researchers adopting HSI reliable mapping, contributing sustainable agriculture global

Language: Английский

Citations

0
Early and high-throughput plant diagnostics: strategies for disease detection DOI
Abdullah Bukhamsin, Jǘrgen Kosel, Matthew F. McCabe

et al.

Trends in Plant Science, Journal Year: 2024, Volume and Issue: 30(3), P. 324 - 337

Published: Nov. 6, 2024

Language: Английский

Citations

1
Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations DOI Creative Commons

Nishanth Pushparaj,

Luis Cormier, Chantal Cappelletti

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(12), P. 1543 - 1543

Published: Dec. 23, 2024

Contrails, or condensation trails, left by aircraft, significantly contribute to global warming trapping heat in the Earth’s atmosphere. Despite their critical role climate dynamics, environmental impact of contrails remains underexplored. This research addresses this gap focusing on use CubeSats for real-time contrail monitoring, specifically over major air routes such as Europe–North Atlantic Corridor. The study proposes a 3 × CubeSat constellation highly eccentric orbits, designed maximize coverage and data acquisition efficiency. Simulation results indicate that configuration can provide nearly continuous monitoring with optimized satellite handovers, reducing blackout periods ensuring robust multi-satellite visibility. A machine learning-based system integrating space-based humidity temperature predict formation inform flight path adjustments is proposed, thereby mitigating impact. findings emphasize potential constellations revolutionize atmospheric practices, offering cost-effective solution aligns sustainability efforts, particularly United Nations Sustainable Development Goal 13 (Climate Action). represents significant step forward understanding aviation’s non-CO2 demonstrates feasibility mitigation through technology.

Language: Английский

Citations

0