Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan DOI Creative Commons
Osman Karakoç, İlkay Buğdaycı

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Journal Year: 2025, Volume and Issue: 14(2), P. 615 - 629

Published: April 15, 2025

Water is essential for the sustainability of life and healthy functioning ecosystems. Increasing pollution poses a serious threat to world's waters, making monitoring protection water quality strategic imperative. Chlorophyll-a one most important indicators ecosystem health, as it measure photosynthetic activity phytoplankton density, lifeblood aquatic Remote sensed data provide unique opportunity analyse chlorophyll-a changes in lake In this study, concentration was modelled by machine deep learning techniques using measurements, Landsat-8 surface reflectance values spectral indices Lake Mogan between 2018 2024. The RF, ANN, CNN models achieved R² 0.84, 0.85, 0.92, respectively. With its ability learn relationships, identify patterns complex datasets, superior process remote sensing imagery, thematic maps were generated model, which performed best study. results study demonstrate potential sensing-based approaches chlorophyll-a. produce highly accurate results, provides literature with an effective tool future studies.

Language: Английский

Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan DOI Creative Commons
Osman Karakoç, İlkay Buğdaycı

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Journal Year: 2025, Volume and Issue: 14(2), P. 615 - 629

Published: April 15, 2025

Water is essential for the sustainability of life and healthy functioning ecosystems. Increasing pollution poses a serious threat to world's waters, making monitoring protection water quality strategic imperative. Chlorophyll-a one most important indicators ecosystem health, as it measure photosynthetic activity phytoplankton density, lifeblood aquatic Remote sensed data provide unique opportunity analyse chlorophyll-a changes in lake In this study, concentration was modelled by machine deep learning techniques using measurements, Landsat-8 surface reflectance values spectral indices Lake Mogan between 2018 2024. The RF, ANN, CNN models achieved R² 0.84, 0.85, 0.92, respectively. With its ability learn relationships, identify patterns complex datasets, superior process remote sensing imagery, thematic maps were generated model, which performed best study. results study demonstrate potential sensing-based approaches chlorophyll-a. produce highly accurate results, provides literature with an effective tool future studies.

Language: Английский

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