Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan

Osman Karakoç; İlkay Buğdaycı

doi:10.28948/ngumuh.1603421

Araştırma Makalesi

Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan

Yıl 2025, Cilt: 14 Sayı: 2, 1 - 1

Osman Karakoç , İlkay Buğdaycı

Öz

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

Anahtar Kelimeler

Landsat-8, Remote sensing, Deep learning, Chlorophyll-a, Spectral indices

Kaynakça

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A. Kulkarni, Water quality retrieval from Landsat TM imagery. Procedia Computer Science, 6, 475-480, 2011. https://doi.org/10.1016/j.procs.2011.08.088.
J. Kravitz, M. Matthews, S. Bernard and D. Griffith, Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges. Remote Sensing of Environment, 237, 111562, 2020. https://doi.org/10.1016/j.rse.2019.111562.
H. Huang, W. Wang, J. Lv, Q. Liu, X. Liu, S. Xie, F. Wang and J. Feng, Relationship between chlorophyll a and environmental factors in lakes based on the random forest algorithm. Water, 14, 19, 3128, 2022. https://doi.org/10.3390/w14193128.
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A. Sudradjat, B. S. Muntalif, N. Marasabessy, F. Mulyadi and M. I. Firdaus, Relationship between chlorophyll-a, rainfall, and climate phenomena in tropical archipelagic estuarine waters, Heliyon, 10, 4, 2024. https://doi.org/10.1016/j.heliyon.2024.e25812.
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K. Dörnhöfer, P.Klinger, T. Heege and N. Oppelt, Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake. Science of The Total Environment, 612, 1200-1214, 2018. https://doi.org/10.1016/j.scitotenv.2017.08.219.
M. W. Matthews, A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. International Journal of Remote Sensing, 32, 21, 6855-6899, 2011. https://doi.org/10.1080/01431161.2010.512947
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Landsat-8 uydu verileri ve derin öğrenme teknikleri ile klorofil-a parametresinin mekansal modellenmesi: Mogan Gölü örneği

Yıl 2025, Cilt: 14 Sayı: 2, 1 - 1

Osman Karakoç , İlkay Buğdaycı

Öz

Su, yaşamın sürdürülebilirliği ve ekosistemlerin sağlıklı işleyişi için kritik öneme sahiptir. Artan çevresel kirlilik, dünyadaki su kütlelerine yönelik ciddi tehditler oluşturmakta olup, su kalitesinin izlenmesi ve korunmasını stratejik bir zorunluluk haline getirmiştir. Klorofil-a, su ekosistemlerinin yaşam kaynağı olan fotosentetik aktivenin ve fitoplankton yoğunluğunun bir göstergesi olarak, su kalitesini ve ekosistem sağlığını şekillendiren en kritik göstergelerden biridir. Uzaktan algılama tabanlı veri setleri, göl ekosistemlerindeki klorofil-a değişimlerini analiz etmek için eşsiz bir fırsat sunmaktadır. Bu çalışmada, Mogan Gölü'nün 2018-2024 yılları arasındaki klorofil-a ölçümleri, Landsat-8 yüzey yansıma değerleri ve spektral indeksleri kullanılarak klorofil-a konsantrasyonu makine ve derin öğrenme teknikleri ile modellenmiştir. RF, ANN ve CNN modelleri sırasıyla 0.84, 0.85 ve 0.92 R2 değerlerine ulaşmıştır. Spektral ilişkileri öğrenme kapasitesi, karmaşık veri setlerindeki desenleri tanımlama becerisi ve uzaktan algılama görüntülerini işleme konusundaki üstün yetenekleriyle, araştırmada en iyi performansı gösteren CNN modeli kullanılarak tematik haritalar üretilmiştir. Çalışma sonuçları, klorofil-a parametresinin izlenmesinde uzaktan algılama tabanlı derin öğrenme yaklaşımlarının potansiyelini ortaya koymaktadır. Bu çalışma, yüksek doğruluklu sonuçlar üretme yeteneği ile gelecekteki stratejik izleme çalışmaları için literatüre etkili bir araç sunmaktadır.

Anahtar Kelimeler

Landsat-8, Uzaktan algılama, Derin öğrenme, Klorofil-a, Spektral indeks

Kaynakça

S. Prashant, M. Ramandeep, P. Prem, A. Akash, S. Prachi, P. Manish and G. Ayushi, Revisiting hyperspectral remote sensing: Origin, processing, applications and way forward. Hyperspectral Remote Sensing, Elsevier, 3-21, 2020. https://doi.org/10.1016/B978-0-08-102894-0.00001-2.
M. I. H. Zaidi Farouk, Z. Jamil, M.F. Abdul Latip, Towards online surface water quality monitoring technology: A review. Environmental research, 238, 117147, 2023. https://doi.org/10.1016/j.envres.2023.117147.
S. G. Virdis, W. Xue, E. Winijkul, V. Nitivattananon and P. Punpukdee, Remote sensing of tropical riverine water quality using sentinel-2 MSI and field observations. Ecological Indicators, 144, 109472, 2022. https://doi.org/10.1016/j.ecolind.2022.109472.
A. Kulkarni, Water quality retrieval from Landsat TM imagery. Procedia Computer Science, 6, 475-480, 2011. https://doi.org/10.1016/j.procs.2011.08.088.
J. Kravitz, M. Matthews, S. Bernard and D. Griffith, Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges. Remote Sensing of Environment, 237, 111562, 2020. https://doi.org/10.1016/j.rse.2019.111562.
H. Huang, W. Wang, J. Lv, Q. Liu, X. Liu, S. Xie, F. Wang and J. Feng, Relationship between chlorophyll a and environmental factors in lakes based on the random forest algorithm. Water, 14, 19, 3128, 2022. https://doi.org/10.3390/w14193128.
S.T.P. Phu, Research on the correlation between chlorophyll-a and organic matter BOD, COD, phosphorus, and total nitrogen in Stagnant Lake Basins. In: Kaneko, Sustainable Living with Environmental Risks, Springer, Tokyo, 2014. https://doi.org/10.1007/978-4-431-54804-1_15.
A. Sudradjat, B. S. Muntalif, N. Marasabessy, F. Mulyadi and M. I. Firdaus, Relationship between chlorophyll-a, rainfall, and climate phenomena in tropical archipelagic estuarine waters, Heliyon, 10, 4, 2024. https://doi.org/10.1016/j.heliyon.2024.e25812.
C. Kislik, I. Dronova, T. E. Grantham and M. Kelly, Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine, Ecological Indicators, 140, 109041, 2022. https://doi.org/10.1016/j.ecolind.2022.109041.
J. Wang and X. Chen, A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data. Science of The Total Environment, 906, 167631, 0048-9697, 2024. https://doi.org/10.1016/j.scitotenv.2023.167631.
K. Dörnhöfer, P.Klinger, T. Heege and N. Oppelt, Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake. Science of The Total Environment, 612, 1200-1214, 2018. https://doi.org/10.1016/j.scitotenv.2017.08.219.
M. W. Matthews, A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. International Journal of Remote Sensing, 32, 21, 6855-6899, 2011. https://doi.org/10.1080/01431161.2010.512947
U. Tarı and N. Olğun Kıyak, Uydu verisi ve CBS ile Van Gölü klorofil-a dinamiklerinin izlenmesi [English Translation: Monitoring the chlorophyll-a dynamics of Van Lake with satellite data and GIS]. Journal of Advanced Research in Natural and Applied Sciences, 10, 1, 60-79, 2024. https://doi.org/10.28979/jarnas.1317247.
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Toplam 68 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Derin Öğrenme, Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme, Fotogrametri ve Uzaktan Algılama, Uydu Tabanlı Konumlama
Bölüm	Makaleler
Yazarlar	Osman Karakoç 0000-0003-0351-7075 İlkay Buğdaycı 0000-0001-8361-1306
Erken Görünüm Tarihi	25 Mart 2025
Yayımlanma Tarihi
Gönderilme Tarihi	18 Aralık 2024
Kabul Tarihi	7 Mart 2025
Yayımlandığı Sayı	Yıl 2025 Cilt: 14 Sayı: 2

Kaynak Göster

APA	Karakoç, O., & Buğdaycı, İ. (2025). Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 14(2), 1-1. https://doi.org/10.28948/ngumuh.1603421
AMA	Karakoç O, Buğdaycı İ. Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan. NÖHÜ Müh. Bilim. Derg. Mart 2025;14(2):1-1. doi:10.28948/ngumuh.1603421
Chicago	Karakoç, Osman, ve İlkay Buğdaycı. “Spatial Modeling of Chlorophyll-a Parameter by Landsat-8 Satellite Data and Deep Learning Techniques: The Case of Lake Mogan”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14, sy. 2 (Mart 2025): 1-1. https://doi.org/10.28948/ngumuh.1603421.
EndNote	Karakoç O, Buğdaycı İ (01 Mart 2025) Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14 2 1–1.
IEEE	O. Karakoç ve İ. Buğdaycı, “Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan”, NÖHÜ Müh. Bilim. Derg., c. 14, sy. 2, ss. 1–1, 2025, doi: 10.28948/ngumuh.1603421.
ISNAD	Karakoç, Osman - Buğdaycı, İlkay. “Spatial Modeling of Chlorophyll-a Parameter by Landsat-8 Satellite Data and Deep Learning Techniques: The Case of Lake Mogan”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14/2 (Mart 2025), 1-1. https://doi.org/10.28948/ngumuh.1603421.
JAMA	Karakoç O, Buğdaycı İ. Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan. NÖHÜ Müh. Bilim. Derg. 2025;14:1–1.
MLA	Karakoç, Osman ve İlkay Buğdaycı. “Spatial Modeling of Chlorophyll-a Parameter by Landsat-8 Satellite Data and Deep Learning Techniques: The Case of Lake Mogan”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 14, sy. 2, 2025, ss. 1-1, doi:10.28948/ngumuh.1603421.
Vancouver	Karakoç O, Buğdaycı İ. Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan. NÖHÜ Müh. Bilim. Derg. 2025;14(2):1-.

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