scholarly journals Trophic state in a tropical lake based on Chlorophyll‐a profiler data and Sentinel‐2 images: the onset of an algal bloom event.

2021 ◽  
Author(s):  
Diego A. Pantoja ◽  
Néstor A. Vega‐Álvarez ◽  
Tzitlali Gasca‐Ortiz
Keyword(s):  
Chlorophyll A ◽  
Trophic State ◽  
Algal Bloom ◽  
Tropical Lake ◽  
Profiler Data ◽  
Sentinel 2

scholarly journals Determinação da Concentração de Clorofila-a por Sensoriamento Remoto no Reservatório de Chapéu d’Úvas (Mg), Brasil

2021 ◽  
Vol 14 (6) ◽  
pp. 3561
Author(s):  
Larissa Ferreira Serbeto ◽  
George Mendes ◽  
Celso Bandeira de Melo Ribeiro ◽  
Renata De Oliveira Pereira
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Chlorophyll A ◽  
Trophic State ◽  
The Body ◽  
Component Concentration ◽  
Public Water Supply ◽  
Trophic State Index ◽  
Chlorophyll A Concentration ◽  
Sentinel 2

Na atualidade, um grande impacto nos reservatórios de água doce é a eutrofização, que afeta diretamente o tratamento e uso da água para abastecimento público, navegação, fauna e flora aquática e impacto visual. A clorofila-a é um dos indicadores de estado trófico da água e pode ser determinada utilizando sensoriamento remoto. Desta forma, este estudo objetivou determinar a concentração de clorofila-a na barragem de Chapéu d’Uvas em Juiz de Fora (Brasil), um dos principais mananciais de abastecimento público da cidade. Através de um modelo utilizando imagens do satélite Sentinel-2 foi avaliado o comportamento espaço-temporal da concentração do componente, foi correlacionado com dados de pluviosidade, temperatura, evaporação e uso e ocupação do solo em torno da barragem. Também foi aplicado um modelo para determinar o índice de estado trófico, que apresenta o grau de trofia que o corpo hídrico se encontra, classificando Chapéu d’Uvas como estado mesotrófico. Os resultados se mostraram satisfatórios para a espacialização e análise temporal da concentração de clorofila-a, a correlação com os dados de evaporação nos permitiu observar uma compatibilidade direta com a concentração do componente e verificou-se valores de clorofila-a acima da média do reservatório nas regiões de entradas de água pelos riachos e próximos as margens com menos cobertura de vegetação.Determination of Chlorophyll-a Concentration from Remote Sensing in Chapéu d’Úvas Reservoir (State of Minas Gerais, Brazil) A B S T R A C TCurrently, one of the great impacts on the freshwater reservoirs is eutrophication, which directly affects the treatment and use of water for public water supply, navigation, aquatic fauna and flora and visual impact. Chlorophyll-a is one of the water trophic state indicators and it can be determined using remote sensing. Thus, this study aimed to determine chlorophyll-a concentration in Chapéu d’Uvas dam, in Juiz de Fora (Brazil), one of the main water sources of public water supply for the city. From a model that uses Sentinel-2 satellite images, the spatial-temporal behavior of that component concentration was evaluated and correlated with data regarding rainfall, temperature, evaporation, and soil use and occupation around the dam. A model was also applied to determine trophic state index, which presents the body of water trophic state, classifying Chapéu d’Uvas as mesotrophic state. The results were satisfactory regarding spatialization and temporal analysis of chlorophyll-a concentration. The correlation with evaporation data permitted us to observe a direct correspondence with the component concentration. Chlorophyll-a values higher than the reservoir average were found in creek inlets and near the shore with lower vegetal cover.Keywords: eutrophication, Sentinel-2, quality of water, inland waters, trophic state

APLIKASI CITRA LANDSAT 8 OLI UNTUK IDENTIFIKASI STATUS TROFIK WADUK GAJAH MUNGKUR WONOGIRI, JAWA TENGAH

2017 ◽  
Vol 19 (2) ◽  
pp. 113
Author(s):  
Kusuma Wardani Laksitaningrum ◽  
Wirastuti Widyatmanti
Keyword(s):  
Chlorophyll A ◽  
Total Phosphorus ◽  
Trophic State ◽  
Water Clarity ◽  
Coefficient Of Determination ◽  
Physical Factors ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Landsat 8 Oli ◽  
State Index

<p align="center"><strong>ABSTRAK</strong></p><p class="abstrak">Waduk Gajah Mungkur (WGM) adalah bendungan buatan yang memiliki luas genangan maksimum 8800 ha, terletak di Desa Pokoh Kidul, Kecamatan Wonogiri, Kabupaten Wonogiri. Kondisi perairan WGM dipengaruhi oleh faktor klimatologis, fisik, dan aktivitas manusia yang dapat menyumbang nutrisi sehingga mempengaruhi status trofiknya. Tujuan dari penelitian ini adalah mengkaji kemampuan citra Landsat 8 OLI untuk memperoleh parameter-parameter yang digunakan untuk menilai status trofik, menentukan dan memetakan status trofik yang diperoleh dari citra Landsat 8 OLI, dan mengevaluasi hasil pemetaan dan manfaat citra penginderaan jauh untuk identifikasi status trofik WGM. Identifikasi status trofik dilakukan berdasarkan metode <em>Trophic State Index</em> (TSI) Carlson (1997) menggunakan tiga parameter yaitu kejernihan air, total fosfor, dan klorofil-a. Model yang diperoleh berdasar pada rumus empiris dari hasil uji regresi antara pengukuran di lapangan dan nilai piksel di citra Landsat 8 OLI. Model dipilih berdasarkan nilai koefisien determinasi (R<sup>2</sup>) tertinggi. Hasil penelitian merepresentasikan bahwa nilai R<sup>2</sup> kejernihan air sebesar 0,813, total fosfor sebesar 0,268, dan klorofil-a sebesar 0,584. Apabila nilai R<sup>2 </sup>mendekati 1, maka semakin baik model regresi dapat menjelaskan suatu parameter status trofik. Berdasarkan hasil kalkulasi diperoleh distribusi yang terdiri dari kelas eutrofik ringan, eutrofik sedang, dan eutrofik berat yaitu pada rentang nilai indeks 50,051 – 80,180. Distribusi terbesar adalah eutrofik sedang. Hal tersebut menunjukkan tingkat kesuburan perairan yang tinggi dan dapat membahayakan makhluk hidup lain.</p><p><strong>Kata kunci: </strong>Waduk Gajah Mungkur, citra Landsat 8 OLI, regresi, TSI, status trofik</p><p class="judulABS"><strong>ABSTRACT</strong></p><p class="Abstrakeng">Gajah Mungkur Reservoir is an artificial dam that has a maximum inundated areas of 8800 ha, located in Pokoh Kidul Village, Wonogiri Regency. The reservoir’s water conditions are affected by climatological and physical factors, as well as human activities that can contribute to nutrients that affect its trophic state. This study aimed to assess the Landsat 8 OLI capabilities to obtain parameters that are used to determine its trophic state, identifying and mapping the trophic state based on parameters derived from Landsat 8 OLI, and evaluating the results of the mapping and the benefits of remote sensing imagery for identification of its trophic state. Identification of trophic state is based on Trophic State Index (TSI) Carlson (1997), which uses three parameters there are water clarity, total phosphorus, and chlorophyll-a. The model is based on an empirical formula of regression between measurements in the field and the pixel values in Landsat 8 OLI. Model is selected on the highest value towards coefficient of determination (R<sup>2</sup>). The results represented that R<sup>2</sup> of water clarity is 0.813, total phosphorus is 0.268, and chlorophyll-a is 0.584. If R<sup>2</sup> close to 1, regression model will describe the parameters of the trophic state better. Based on the calculation the distribution consists of mild eutrophic, moderate eutrophic, and heavy eutrophic that has index values from 50.051 to 80.18. The most distribution is moderate eutrophication, and it showed the high level of trophic state and may harm other living beings.</p><p><strong><em>Keywords: </em></strong><em>Gajah Mungkur Reservoir, </em><em>L</em><em>andsat 8 OLI satellite imagery, regression, TSI, trophic state</em></p>

scholarly journals Effect of red algal bloom on growth and production of carps

Our Nature ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 48-54
Author(s):  
Ram Bhajan Mandal ◽  
Sunila Rai ◽  
Madhav Kumar Shrestha ◽  
Dilip Kumar Jha ◽  
Narayan Prasad Pandit
Keyword(s):  
Water Quality ◽  
Body Weight ◽  
Chlorophyll A ◽  
Total Nitrogen ◽  
Total Phosphorus ◽  
Algal Bloom ◽  
Total Dissolved Solids ◽  
Dissolved Solids ◽  
Aquaculture Farm ◽  
Red Algal

An experiment was carried to assess the effect of red algal bloom on growth and production of carp, water quality and profit from carp for 120 days at Aquaculture Farm of Agriculture and Forestry University, Chitwan. The experiment included two treatments: carp polyculture in non-red pond and carp polyculture in red pond with algal bloom each with three replicates. Carp fingerlings were stocked at 1 fish/m2 and fed with pellet containing 24% CP at 3% body weight. Net yield of rohu was found significantly higher (p<0.05) in non-red ponds (0.38±0.01 t ha-1) than red ponds (0.24±0.05 t ha-1). Survival of rohu (84.9±1.4%), bighead (95.2±2.0%) and mrigal (88.1±14.4%) were also significantly higher (p<0.05) in non-red ponds than red ponds. Red algal bloom affected DO, nitrate and chlorophyll-a, nitrite, total nitrogen, total phosphorus, total dissolved solids and conductivity. However, overall carp production and profit from carp remained unaffected.

scholarly journals Detection and Analysis of the Causes of Intensive Harmful Algal Bloom in Kamchatka Based on Satellite Data

2021 ◽  
Vol 9 (10) ◽  
pp. 1092
Author(s):  
Valery Bondur ◽  
Viktor Zamshin ◽  
Olga Chvertkova ◽  
Ekaterina Matrosova ◽  
Vasilisa Khodaeva
Keyword(s):  
Chlorophyll A ◽  
Suspended Matter ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Simulated Data ◽  
Region Of Interest ◽  
Chlorophyll A Concentration ◽  
Ocean Level ◽  
Radar Satellite ◽  
Sentinel 2A

In this paper, the causes of the anomalous harmful algal bloom which occurred in the fall of 2020 in Kamchatka have been detected and analyzed using a long-term time series of heterogeneous satellite and simulated data with respect to the sea surface height (HYCOM) and temperature (NOAA OISST), chlorophyll-a concentration (MODIS Ocean Color SMI), slick parameters (SENTINEL-1A/B), and suspended matter characteristics (SENTINEL-2A/B, C2RCC algorithm). It has been found that the harmful algal bloom was preceded by temperature anomalies (reaching 6 °C, exceeding the climatic norm by more than three standard deviation intervals) and intensive ocean level variability followed by the generation of vortices, mixing water masses and providing nutrients to the upper photic layer. The harmful algal bloom itself was manifested in an increase in the concentration of chlorophyll-a, its average monthly value for October 2020 (bloom peak) approached 15 mg/m3, exceeding the climatic norm almost four-fold for the region of interest (Avacha Gulf). The zones of accumulation of a large amount of biogenic surfactant films registered in radar satellite imagery correlate well with the local regions of the highest chlorophyll-a concentration. The harmful bloom was influenced by river runoff, which intensively brought mineral and biogenic suspensions into the marine environment (the concentration of total suspended matter within the plume of the Nalycheva River reached 10 mg/m3 and more in 2020), expanding food resources for microalgae.

scholarly journals Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors

2022 ◽  
Vol 14 (1) ◽  
pp. 229
Author(s):  
Jiarui Shi ◽  
Qian Shen ◽  
Yue Yao ◽  
Junsheng Li ◽  
Fu Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Water Bodies ◽  
Gradient Boosting ◽  
Learning Models ◽  
Small Water ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models ◽  
Sentinel 2 ◽  
Small Water Bodies

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.

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