Aplikasi Citra Sentinel-2 untuk Pemetaan Sebaran Material Padatan Tersuspensi Di Muara Sungai Wulan Demak

Prasetiyo, Bayu Adi; Rochaddi, Baskoro; Satriadi, Alfi

doi:10.14710/jmr.v8i4.25193

Cited by 7 publications

(9 citation statements)

References 1 publication

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“…However, the values are comparable. For example, the R 2 values of each model are 0.5140 and 0.5056 by using validation data in this study, while they are 0.4707 and 0.4735 by using validation data in other locations [28], [31] for TSS models 1 and 4, respectively (see 'This Study' and 'Other Validation Data' column for TSS model 1 and 4 in Nurgiantoro & Jaelani [30] Other Validation Data [28], [31] This Study Nurgiantoro et al [29] Nurgiantoro & Jaelani [30] Other Validation Data [28] Nurgiantoro & Jaelani [30] Other Validation Data [28], [31] This Study Nurgiantoro et al [29] Nurgiantoro & Jaelani [30] Other Validation Data [28] [13], [15], [16], [23], [26]- [28], [53], [54]. Moreover, it also provides favourable results in estimating TSS concentration.…”

Section: Evaluation and Comparison Between All Modelsmentioning

confidence: 99%

An Alternative Model to Estimate Total Suspended Solids Concentrations using Landsat 8 Imagery in Indonesia

Risky Panji Wijaya,

Haribowo,

E. Ball

2023

CIVENSE

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A regular monitoring program of water quality is generally performed using a direct measurement method, which requires substantial efforts and resources. These issues can be minimised using several options, one of which is Landsat 8 (L8). This imagery has been broadly used to measure several water quality parameters, especially Total Suspended Solids (TSS) concentrations, even in Indonesia. This paper will compare several models from previous studies and a modified model generated using data from various sites. The comparison is based on their competencies to estimate TSS concentrations. The competencies are determined by the coefficient of correlation (r), correlation of determination (R2), and residual standard error (RSE) parameters as these three parameters are strongly correlated, generally applied, and provide distinctive determinations. The best model should have the highest r and R2 values, while the RSE value should be the lowest. The results imply that TSS model 4 generated in this study provides comparable results with TSS model 1, which has been generally used in Indonesia and provided favourable results. Thus, it can be an alternative model to estimate TSS concentrations in Indonesia.

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Section: Evaluation and Comparison Between All Modelsmentioning

confidence: 99%

An Alternative Model to Estimate Total Suspended Solids Concentrations using Landsat 8 Imagery in Indonesia

Risky Panji Wijaya,

Haribowo,

E. Ball

2023

CIVENSE

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“…Distribusi dari kondisi TSS waduk dapat menunjukkan gambaran mengenai proses sedimentasi di waduk tersebut. Perairan waduk yang memiliki kadar TSS tinggi juga cenderung akan mengalami sedimentasi yang tinggi (Prasetiyo, Rochaddi, & Satriadi, 2019). Distribusi TSS sangat bergantung pada pola pemanfaatan waduk.…”

Section: Pendahuluanunclassified

“…Waduk Gondang memberikan manfaat yang besar bagi perkembangan daerah-daerah disekitarnya. Waduk ini dimanfaatkan sebagai pengairan atau irigasi pertanian, budidaya perikanan serta objek wisata (Prasetiyo et al, 2019). Namun, seiring berjalannya waktu Waduk Gondang mengalami beberapa permasalahan, seperti penyempitan area luasan waduk, pendangkalan waduk serta penurunan kualitas air waduk.…”

Section: Pendahuluanunclassified

Pemetaan distribusi Total Suspended Solid (TSS) di Waduk Gondang Lamongan menggunakan citra landsat multi temporal

Na’imah,

Taryana,

Purwanto

2023

JIHI3S

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Total Suspended Solid (TSS) is defined as a collection of suspended matter and particles in water with a size of less than 2 microns. High concentrations of TSS are dangerous because they interfere with light penetration into the water. This disrupts the photosynthesis process and causes reduced oxygen levels in the waters. Conventional TSS monitoring in laboratory tests is considered less efficient regarding time, effort and cost. Therefore, remote sensing technology is a more efficient alternative solution for TSS monitoring in reservoirs. One type of imagery that can be used to monitor TSS in reservoirs is Landsat 8 satellite imagery. This article aims to determine the appropriate empirical algorithm in estimating TSS concentrations in the Gondang Lamongan Reservoir, to examine the spatial distribution of TSS in the Gondang Reservoir with a 3-year time span, namely 2016, 2019, and 2022, and to analyze changes in reservoir TSS conditions in recent years. research year. The method used in this research is descriptive quantitative with the application of the Syarif, Parwati and Guzman & Santaella algorithms. The results showed that the condition of TSS from year to year continued to increase. The concentration of TSS is getting worse, which is indicated by an increase in the amount of TSS at the edge of the reservoir, especially at inlet area. The results of the accuracy test show a coefficient of determination of 0.71 for the syarif algorithm which indicates that the syarif algorithm method is quite accurate in estimating the TSS in the Gondang reservoir. Total Suspended Solid (TSS) didefinisikan sebagai kumpulan dari materi dan partikel yang tersuspense di dalam air dengan ukuran mencapai kurang dari 2 mikrometer. Konsentrasi TSS yang tinggi bersifat membahayakan karena mengganggu panetrasi cahaya yang masuk ke dalam air. Hal ini berakibat pada terganggunya proses fotosintesis dan menyebabkan berkurangnya kadar oksigen di perairan. Pemantauan TSS secara konvensional melalui uji laboratorium secara masif dinilai kurang efisien baik dari segi waktu, tenaga dan biaya. Oleh karena itu, teknologi penginderaan jauh menjadi solusi alternatif yang lebih efisien untuk melakukan pemantauan TSS di waduk. Salah satu jenis citra yang dapat digunakan untuk memantau TSS di waduk adalah citra satelit Landsat 8. Artikel ini bertujuan untuk menentukan algoritma empiris yang sesuai dalam mengestimasi konsentrasi TSS di Waduk Gondang Lamongan, mengkaji distribusi spasial TSS di Waduk Gondang dengan rentang waktu 3 tahunan, yakni di tahun 2016, 2019, dan 2022, serta menganalisis perubahan kondisi TSS waduk di tahun-tahun penelitian. Metode yang digunakan pada penelitian ini adalah deskriptif kuantitatif dengan penerapan algoritma Syarif, Parwati dan Guzman & Santaella. Hasil penelitian menunjukkan bahwa kondisi TSS dari tahun ke tahun terus mengalami peningkatan. Konsentrasi TSS semakin memburuk yang ditandai dengan peningkatan jumlah TSS di bagian tepi waduk yang merupakan area inlet. Hasil uji akurasi menunjukkan nilai koefisien determinasi sebesar 0,71 untuk algoritma syarif dimana hal tersebut menandakan bahwa metode algoritma syarif cukup akurat dalam mengestimasi TSS di Waduk Gondang.

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“…In Zainab et al [9], the relationship between RGB color and chlorophyll-a content was found; it is the best equation in the form of an exponential equation with a correlation coefficient of 0.767. In addition, in other studies [10], [11] studied the relationship between RGB colors for the distribution of solid materials, and the best equation was obtained, i.e., the exponential equation with a correlation coefficient of 0.9336 and 0.9075, respectively.…”

Section: Introductionmentioning

confidence: 99%

Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis

Abadi

Tahcfulloh²

2022

JOIV : Int. J. Inform. Visualization

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Pixel is the smallest element given by the image from a digital camera and is used as a data source in the digital image processing process. In this paper, two data collection processes are carried out, i.e. taking actual height data using a standard stature meter and taking sample photos using a camera placed from the sample with a distance of 160 cm and a height of 100 cm. The sample photos obtained are then processed for segmentation of the sample body against the surrounding environment using several digital image-processing techniques such as grayscale, blur, edge detection, and bounding box in order to obtain a pixel value that represents the height of the sample. The next stage is the regression analysis process by correlating actual height with pixel height using five regression equation analysis methods such as least squares, logarithmic powers, exponentials, quadratic polynomials, and cubic polynomials. This study analyzes the differences between these methods in terms of correlation coefficient, Root Mean Squared Error (RMSE), average error, and accuracy between height calculation data based on digital image processing and actual height measurement data. From the results obtained, the logarithmic power method produces the best analytical value compared to other methods with the correlation coefficient, RMSE, average error percentage, and percentage accuracy of 0.976, 1.3, 0.58%, and 99.42%, respectively. While the cubic polynomial is in the last position, the correlation coefficient, RMSE, average error percentage, and accuracy percentage are 0.978, 1.41, 0.64%, and 99.36%, respectively.

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Aplikasi Citra Sentinel-2 untuk Pemetaan Sebaran Material Padatan Tersuspensi Di Muara Sungai Wulan Demak

Cited by 7 publications

References 1 publication

An Alternative Model to Estimate Total Suspended Solids Concentrations using Landsat 8 Imagery in Indonesia

An Alternative Model to Estimate Total Suspended Solids Concentrations using Landsat 8 Imagery in Indonesia

Pemetaan distribusi Total Suspended Solid (TSS) di Waduk Gondang Lamongan menggunakan citra landsat multi temporal

Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis

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