Correlation Analysis of Chlorophyll-a and Sea Surface Temperature with Fish Production in Baron Beach Gunungkidul Using Remote Sensing Technology

An exploratory study was conducted using Landsat-8 (L8) and Sentinel-2 (S2) satellite images for the extraction of chlorophyll-a and SST, followed by determining their relationship with fish catch at Baron Beach due to the increasing fish catch in certain months. L8 and S2 can observe chlorophyll-a but not as optimum as low-resolution imagery such as MODIS due to the complex optical characteristics of seawater and their limited band types. Aside from observing the chlorophyll-a, L8 can observe SST value but S2 cannot because it currently has no thermal infrared band. Therefore, both images need to be compared to know their capability of extracting chlorophyll-a and SST. Data processing for chlorophyll-a and SST extraction used Google Earth Engine (GEE) and QGIS. Data extraction preparation involved cloud masking with four scenarios. Chlorophyll-a extraction used Ocean Color (OC) algorithm, while SST extraction at L8 used thermal-infrared band and optical band approach at S2. Differences in extraction results were analyzed using a non-parametric significance test with α = 0.05. The relationship between chlorophyll-a, SST and fish catch was assessed using Catch per Unit Effort (CPUE) values in the Spearman correlation test. The extraction results showed changes in chlorophyll-a and SST values each month in 2022 where both images show an increasing chlorophyll-a within June until October and decreasing within those months. However, the extraction results from both images are significantly different. Aside from the significantly different extraction results, there is a positive correlation between chlorophyll-a and fish catch, but the SST correlation varied between L8 and S2 images. This difference is thought to be caused by image characteristics, cloud masking, and extraction models that are not yet suitable for the Baron coastal area, which is characterized by high sedimentation coastal areas. In this context, correlation analysis showed a relationship between chlorophyll-a concentration and SST with fish production, but direct comparison data at Baron Beach is needed for further analysis.

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Publication Details

Journal
Engineering headway
Published
2025-10-13
DOI
https://doi.org/10.4028/p-carjq3
Primary Topic
Water Quality Monitoring Technologies
Type
article
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article

Correlation Analysis of Chlorophyll-a and Sea Surface Temperature with Fish Production in Baron Beach Gunungkidul Using Remote Sensing Technology

Leni Sophia Heliani, Hyatma Adikara Ajrin
Engineering headway
Water Quality Monitoring Technologies
article

Correlation Analysis of Chlorophyll-a and Sea Surface Temperature with Fish Production in Baron Beach Gunungkidul Using Remote Sensing Technology

Leni Sophia Heliani, Hyatma Adikara Ajrin
article en

Abstract

An exploratory study was conducted using Landsat-8 (L8) and Sentinel-2 (S2) satellite images for the extraction of chlorophyll-a and SST, followed by determining their relationship with fish catch at Baron Beach due to the increasing fish catch in certain months. L8 and S2 can observe chlorophyll-a but not as optimum as low-resolution imagery such as MODIS due to the complex optical characteristics of seawater and their limited band types. Aside from observing the chlorophyll-a, L8 can observe SST value but S2 cannot because it currently has no thermal infrared band. Therefore, both images need to be compared to know their capability of extracting chlorophyll-a and SST. Data processing for chlorophyll-a and SST extraction used Google Earth Engine (GEE) and QGIS. Data extraction preparation involved cloud masking with four scenarios. Chlorophyll-a extraction used Ocean Color (OC) algorithm, while SST extraction at L8 used thermal-infrared band and optical band approach at S2. Differences in extraction results were analyzed using a non-parametric significance test with α = 0.05. The relationship between chlorophyll-a, SST and fish catch was assessed using Catch per Unit Effort (CPUE) values in the Spearman correlation test. The extraction results showed changes in chlorophyll-a and SST values each month in 2022 where both images show an increasing chlorophyll-a within June until October and decreasing within those months. However, the extraction results from both images are significantly different. Aside from the significantly different extraction results, there is a positive correlation between chlorophyll-a and fish catch, but the SST correlation varied between L8 and S2 images. This difference is thought to be caused by image characteristics, cloud masking, and extraction models that are not yet suitable for the Baron coastal area, which is characterized by high sedimentation coastal areas. In this context, correlation analysis showed a relationship between chlorophyll-a concentration and SST with fish production, but direct comparison data at Baron Beach is needed for further analysis.

Engineering headwayVol. 27
Universitas Gadjah Mada (ID)
Openalex Percentile: Top 73%
Water Quality Monitoring Technologies
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