Conference proceedings

Displaying 1 - 3 of 3 in CEST2021 (remove filter), sensing (remove filter), Environmental data analysis and modelling (remove filter)

CEST Proceedings are published under the ISSN 2944-9820.

The potential of UAV multispectral imagery to estimate chlorophyll content of vine leaves

(Corresponding) Tepanosyan G., Muradyan V., Hovsepyan A., Ayvazyan G., Avetisyan R., Asmaryan S.

Topic:

Environmental data analysis and modelling

As is known, chlorophyll is an important biophysical parameter used to monitor the overall physiological status of plants. The aim of this research was to study the potential of UAV multispectral images for estimating the contents of leaf chlorophyll in vineyards. For this purpose, a UAV flight was...Read more

Keywords:

Vineyards, UAV, Chlorophyll content, PLSR, Remote Sensing

DOI:

https://doi.org/10.30955/gnc2021.00131

Paper ID:

cest2021_00131

Exploitation of Crowdsourcing Tools and Earth Observation data: A Systematic Literature Review

(Corresponding) Tsiakou D., Tsimiklis G., Tsiakos V., Karagiannopoulou K., Amditis A.

Topic:

Environmental data analysis and modelling

Crowdsourcing is a method gaining ever wider use in practice and leverages human intelligence to solve problems in a considerable number of study fields. Howe (Howe 2006) coined the concept defining: “Crowdsourcing represents the act of a company or institution taking a function once performed by...Read more

Keywords:

Remote Sensing, Earth Observation, Citizen Science, Crowdsourcing tools

DOI:

https://doi.org/10.30955/gnc2021.00182

Paper ID:

cest2021_00182

A data-driven approach to predict phytoplankton blooms using satellite-derived water quality and hydrometeorological drivers

(Corresponding) Kandris K., Romas E., Tzimas A., Bresciani M., Giardino C., Bauer P., Pechlivanidis I., Dessena M.

Topic:

Environmental data analysis and modelling

The present work leverages simulated hydrometeorological factors and satellite-derived chlorophyll-a to predict phytoplankton dynamics for Mulargia reservoir (Sardinia, Italy). A Random Forest (RF) model was (a) calibrated to minimize out-of-bag errors of chlorophyll-a predictions for a 5-year-long...Read more

Keywords:

Machine learning; forecasting; phytoplankton blooms; remote sensing; hydrometeorological predictions

DOI:

https://doi.org/10.30955/gnc2021.00420

Paper ID:

cest2021_00420