Version 1
: Received: 5 April 2024 / Approved: 5 April 2024 / Online: 5 April 2024 (10:10:23 CEST)
How to cite:
Irie, M.; Manabe, Y.; Yamashita, M. Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images considering Turbid Water Distribution in a Reservoir. Preprints2024, 2024040428. https://doi.org/10.20944/preprints202404.0428.v1
Irie, M.; Manabe, Y.; Yamashita, M. Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images considering Turbid Water Distribution in a Reservoir. Preprints 2024, 2024040428. https://doi.org/10.20944/preprints202404.0428.v1
Irie, M.; Manabe, Y.; Yamashita, M. Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images considering Turbid Water Distribution in a Reservoir. Preprints2024, 2024040428. https://doi.org/10.20944/preprints202404.0428.v1
APA Style
Irie, M., Manabe, Y., & Yamashita, M. (2024). Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images considering Turbid Water Distribution in a Reservoir. Preprints. https://doi.org/10.20944/preprints202404.0428.v1
Chicago/Turabian Style
Irie, M., Yugen Manabe and Masafumi Yamashita. 2024 "Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images considering Turbid Water Distribution in a Reservoir" Preprints. https://doi.org/10.20944/preprints202404.0428.v1
Abstract
The causes of algal blooms in reservoirs are often complexly intertwined with chemical, physical, and biological factors such as the supply of nutrients. Observation of phytoplankton distribution with high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to wind and water temperature stratification. Observation from a UAV, which has excellent temporal and spatial resolution, is considered to be an effective method to obtain water quality information comprehensively. On the other hand, it is not only the growth of plankton that affects the color of the water surface but also turbidity. Furthermore, since the brightness value of passive sensors such as optical cameras changes depending on the amount of insolation, it is necessary to perform analysis after making corrections for this. In this study, we attempted to develop a method for estimating chlorophyll concentration from aerial images taken from UAVs using machine learning that takes into account brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
