Estimation of Turbidity in the Cheney Reservoir Using Landsat 8 Images: A Comparison of Regression, MARS, and TreeNet Methods

Year 2024, Volume: 5 Issue: 2, 172 - 185, 26.09.2024

Ömer Dilmen , Sinan Nacar , Esra Tunç Görmüş , Adem Bayram

https://doi.org/10.48123/rsgis.1451338

Abstract

Monitoring water quality in reservoirs is crucial for determining the suitability of water for its intended use and protecting aquatic life. One of the most commonly used indicators of reservoir water quality is turbidity. As a cost-effective and quick alternative to traditional monitoring methods, studies with remote sensing have gained traction. This study aims to develop a model to estimate turbidity in the Cheney Reservoir (Kansas, USA) using Landsat 8 Operational Land Imager (OLI) images. In total 99 Landsat 8 images were matched with turbidity data monitored in the reservoir between 2014 and 2022 with a time difference of at most 20 minutes. Estimation models were developed using regression analysis, multivariate adaptive regression splines (MARS), and TreeNet gradient boosting machine (TreeNet) methods. The success of the models was compared with the performance statistics of mean squared error, root mean squared error, mean absolute error, and Nash-Sutcliffe (NS) efficiency coefficient. The MARS and TreeNet methods were found to have equal predictive ability for the test dataset (NS = 0.61). The most significant parameter was determined as B4/B1 (red/coastal aerosol) with the MARS method, while B4/B2 (red/blue) was determined with the TreeNet model.

Keywords

Turbidity, Cheney reservoir, Landsat 8 OLI, Regression analysis

Landsat 8 Görüntüleri ile Cheney Rezervuarında Bulanıklık Tahmini: Regresyon, MARS ve TreeNet Yöntemlerinin Karşılaştırılması

Abstract

Rezervuarlardaki su kalitesi takibi, suyun kullanım amacına uygunluğu ve su canlılarının korunması için önemlidir ve su kalitesinin belirlenmesinde en yaygın kullanılan değişkenlerden biri de bulanıklıktır. Bu değişkenin takibinde kullanılan geleneksel yöntemlerin maliyetli ve zaman alıcı olması, su kalitesi takibi için daha ekonomik ve hızlı bir alternatif olan uzaktan algılama çalışmalarını ön plana çıkarmıştır. Bu çalışmada, Landsat 8 Operational Land Imager (OLI) görüntüleri kullanılarak Cheney Rezervuarında (Kansas, ABD) bulanıklık değişkenini tahmin edebilecek bir model kurulması amaçlanmıştır. Bu amaçla 99 Landsat 8 OLI görüntüsü, 2014-2022 yılları arasında rezervuarda takibi yapılan bulanıklık verileriyle aralarındaki zaman farkı 20 dakikadan az olacak şekilde eşleştirilmiştir. Tahmin modellerinin kurulmasında regresyon analizi, çok değişkenli uyarlanabilir regresyon eğrileri (MARS) ve TreeNet gradyan arttırma makinesi (TreeNet) yöntemleri kullanılmıştır. Kurulan modellerin performansları, ortalama karesel hata, ortalama karesel hatanın karekökü, ortalama mutlak hata ve Nash-Sutcliffe (NS) verimlilik katsayısı performans istatistikleri ile kıyaslanmıştır. MARS ve TreeNet yöntemlerinin tahmin gücünün test veri seti için birbirine eşit olduğu görülmüştür (NS = 0.61). En önemli parametrenin MARS yöntemi kullanılarak oluşturulan modelde B4/B1 (kırmızı/kıyı aerosol), TreeNet yöntemiyle oluşturulan modelde ise B4/B2 (kırmızı/mavi) olduğu belirlenmiştir.

Keywords

Bulanıklık, Cheney rezervuarı, Landsat 8 OLI, Regresyon analizi

Thanks

Yazarlar, veri izleme, işleme ve yönetimi için USGS personeline ve NASA/USGS’ye ücretsiz olarak sağladıkları veriler için teşekkür ederler.

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