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All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES)
Komang Iwan Suniada, Komang Iwan
SEACORM
Earth & Planetary Sciences

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ROLLING MOSAIC METHOD TO SUPPORT THE DEVELOPMENT OF POTENTIAL FISHING ZONE FORECASTING FOR COASTAL AREAS Suniada, Komang Iwan; Susilo, Eko; Siwi, Wingking Era Rintaka; Widagti, Nuryani
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1544.839 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3252

Abstract

The production of the Indonesian Institute for Marine Research and Observation’s mapping of forecast fishing areas (peta prakiraan daerah penangkapan ikan or PPDPI) based on passive satellite imagery is often constrained by high-cloud-cover issues, which lead to sub-optimal results. This study examines the use of the rolling mosaic method for providing geophysical variables, in particular, seasurface temperature (STT) together with minimum cloud cover, to enable clearer identification of oceanographic conditions. The analysis was carried out in contrasting seasons: dry season in July 2018 and rainy season in December 2018. In general, the rolling mosaic method is able to reduce cloud cover for sea-surface temperature (SST) data. A longer time range will increase the coverage percentage (CP) of SST data. In July, the CP of SST data increased significantly, from 15.3 % to 30.29% for the reference 1D mosaic and up to 84.19 % to 89.07% for the 14D mosaic. In contrast, the CP of SST data in December tended to be lower, from 4.93 % to 13.03% in the 1D mosaic to 41.48 % to 51.60% in the14D mosaic. However, the longer time range decreases the relationship between the reference SST data and rolling mosaic method data. A strong relationship lies between the 1D mosaic and 3D mosaics, with correlation coefficients of 0.984 for July and 0.945 for December. Furthermore, a longer time range will decrease root mean square error (RMSE) values. In July, RMSE decreased from 0.288°C (3D mosaic) to 0.471°C (14D mosaic). The RMSE value in December decreased from 0.387°C (3D mosaic) to 0.477°C (14D mosaic). Based on scoring analysis of CP, correlation coefficient and RMSE value, results indicate that the 7D mosaic method is useful for providing low-cloud-coverage SST data for PPDPI production in the dry season, while the 14D mosaic method is suitable for the rainy season.
VARIABILITY OF SEA SURFACE TEMPERATURE AT FISHERIES MANAGEMENT AREA 715 IN INDONESIA AND ITS RELATION TO THE MONSOON, ENSO AND FISHERY PRODUCTION Suniada, Komang Iwan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 2 (2020)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3370

Abstract

Sea surface temperature (SST) is one of the important oceanographic and climatemparameters. Its variability and anomaliesmoften influence the environment and organisms, both in the oceansand on land. This study aims to identifythe variability of SST and help the fisheries community to understand how climate phenomena such as ENSOand monsoonal phases (represented by wind speed) are related to SST and fishery production in Fisheries Management Area (FMA)715.SST was measured at Parimo, which represents conditionsinthe western partof the areainside Tomini Bay,and at Bitung, which represents SST in the open ocean,with a more exposuredgeographical position. SST wasderived from MODIS satellite imagery, downloaded from the ocean color database (https://oceancolor.gsfc.nasa.gov/) with a4 km spatial resolution, from January 2009 to December 2018. Wind speed data, historical El Niño or La Niña events,and fish production data were also used in the study. Pearson’s correlation (Walpole, 1993) was used to test the relationship between SST variability or anomaly and ENSO and monsoons. The results show that the SST characteristics and variability of the Parimo and Bitung watersare very different, although they bothliein the same FMA 715. SST in Parimo waters is warmer,but with lower variability than in Bitung waters. SST in Parimo has a lowcorrelation with ENSO (r=0.06, n=66), low correlation with wind speed (r=-0.29, n=120),with also a lowcorrelation between SST anomaly and ENSO (r=0.05, n=66). SST in Bitung has a higher, but inverse, correlation with ENSO (r=-0.53, n=66), highcorrelation with wind speed (r=-0.60, n=119), with also a high correlation between SST anomaly and ENSO (r=-0.74, n=66). Unlike in other parts of Indonesia, fishery production in Parimo,or the western part inside Tomini Bay,is not affected by ENSO events
Co-Authors Eko Susilo Nuryani Widagti, Nuryani Siwi, Wingking Era Rintaka