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This study analyses the characteristics of the Indonesian seas using satellite remote-sensing data for the 10-year period from 1998 to 2007. Statistical properties and monthly average data or climatological data of sea surface temperature (SST), wind speed (WS), wind direction (WD), chlorophyll (CH) and sea surface height anomaly (SSHA) for the Ind...
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... It is urgent to carry out fundamental research in order to build low-carbon emission societies generally in Indonesia. Especially it must to uses over the top technology to solve fundamental problem, such as satellite remote sensing technology [9] [10]. This paper is one part of publication series with theme light threshold for tourism resort in the role of low-carbon emissions community, and energy savings using nighttime satellite data. ...
Bali is the most excellent tourism resort through precious supporting facility. The structure creates with art that inspires from culture and blends with environment. The original concept is not much usable energy for conditioner and light. Modern and comport facilities are more attractive now. Electricity supply becomes issue. It is important to control uses of excessive energy that emitted carbon and cost. This paper presents results of radiance threshold method that can be used as a system to monitor above issue. The variability of light sources aspects that deplete energy had defined. Development of business support tourism privately is a key of this case. For sustainable of energy, a mutual agreement among stakeholder is urgency. Areas that define as class criteria spread widely, mostly at Kuta and Nusadua city. Development of moderate class light is key to understand spatial-temporal of dynamical many aspects that relate to urban cycles. Nocturnal light trend spread up to northern part of island. Notice for stakeholder to attention of diminishes green space. Long-term dynamical trend of nocturnal light requires inter calibration between nighttime satellite mission. The nonlinearity relationship of those data pays attentions to many factors. Possibility of model function approved a power model. Linearity validation results shows agreement.
... In Indonesia, remote sensing satellites have also been used to determine the characteristics of the seas. Climatological data used as parameters include sea surface temperature, wind direction and speed, chlorophyll, and the anomaly of sea surface height (Swardika et al. 2012). Sea surface temperature data is derived from the TRMM satellite while wind speed data is derived from the DMSP satellite. ...
Aiming to lessen degradations and effects on marine ecosystems, Marine Spatial Planning is used as a management method with a purpose to guide development and use within the marine environment. The stages of Marine Spatial Planning are spatial data collection, data management, data analysis, and decision support systems. This method requires tools to be able to achieve the goals effectively. Geographic Information System (GIS) and remote sensing can easily and effectively be used to access and summarize spatial data into information for evaluating the Marine Spatial planning projects. GIS and remote sensing obviously have an essential function in Marine Spatial Planning in terms of its opportunities and its capabilities for development and projections in the future. This review is intended to produce critical description on the study of GIS for marine spatial planning. Furthermore, this review is intended to foresee the challenges faced in GIS and remote sensing implementation in Indonesia.
... Seasonal rainfall variability (Fig. 7) was proportional to SST variability, as previously observed by Swardika et al. (2012). In the equatorial region, the stable monthly average SST pattern was similar to the monthly average rainfall pattern, and warm SST condition during JJA in the northern region also created heavy rainfall; the similar events tend to occur in the southern region during DJF. ...
... The man-made environmental changes cause climate to changes and warming. In order to understand the changes of our climate, the world's space agency in 1980's initiated together the mission to planet Earth program to emphasize free distribution of satellite remote sensing data for global warming and climate changes studies (Swardika et al., 2012). ...
We analyzed human activities on the night of silence day or Nyepi Day in March, 2015 over Bali Island. The Nyepi Day is a unique activity of Balinese Hindu to celebrate New Year of Çaka Calendar with no light at night. The human activities at night Nyepi Day were extracted from night time satellite data of the Visible Infrared Imager Radiometer Suite (VIIRS) Day Night Band (DNB) product onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. The Suomi NPP project is part of the mission to planet Earth program of the world's space agency to emphasize free distribution of satellite remote sensing data for global warming and climate changes studies. The Nyepi Day was introduced as local action against global warming and climate changes in the 14 th United Nations Climate Change Conferences in Bali. Two-day scenes (March 20-21, 2015) of the night before and on the night of Nyepi Day of the VIIRS DNB radiance images and Imaging VIIRS Cloud Cover Type/Layers (IVICC) product were analyzed and compared. Cloud information from IVICC was used to remove the VIIRS DNB pixels taking the form of clouds. The background noise pixels were removed using maximum of histogram radiance image as a threshold. Detection of human activities from light information on a lit pixel was divided into six categories i.e. absolutely no-light, become no-light, become light, no-changes, increasing and decreasing of light intensity. Results indicated that there were dramatic decreases in nocturnal light intensity at night of Nyepi Day over Bali Island than usual. Other condition was also observed e.g. light from fishing boat appeared wider on southern part of Bali province, some pixels lit on rural areas, light was observed to extend on east Java province, west side of Bali Island. In detail, only over main city-regency of Bali province light intensity was observed to decrease significantly. Only two from the six categories of light information were observed significantly i.e. decreasing or increasing of light intensity. Common intensity of lit pixels decreased over main city-regency of Bali province but not over rural areas. Results of analysis were strongly affected by the cloud.
... In addition, range of SSC inversely proportional with SST. Every year, higher concentrations of SSC occur in June to September and lower concentrations in December, January and February (Hendiarti et al., 2005;Swardika et al., 2012). However, ocean environmental parameter in the southern waters off Java and Bali has high vulnerability to the phenomena of oceanatmosphere interaction, such as the El Niño-Southern Oscillation (ENSO) and the Indian 3 Ocean Dipole Mode (IODM; Terray et al., 2007;Ningsih et al., 2013). ...
Southeast Indian Ocean, particularly in the Southern waters off Java-Bali was identified as potential fishing ground of bigeye tuna (Thunnus obesus). We used satellite remote sensing data of Sea Surface Temperature (SST) and Sea Surface Chlorophyll (SSC) and daily fish catch data from PT Perikanan Nusantara, Bali during 2006-2010. To understand the preferred habitat of bigeye tuna in the current study area, the relationship between SST, SSC and the number of bigeye tuna are important to investigate. To understand the relationship between SST, SSC and bigeye tuna, fisheries data classification and polynomial regression were used. This study considers to evaluate which parameter will influence the abundance of bigeye tuna, and to determine the range value of each parameter that is optimum for bigeye tuna. The results clearly show that both of SST and SSC, which derived from satellite observation confirmed a strong relationship with the abundance of bigeye tuna. Statistical analysis showed the optimum of SST for bigeye tuna is less than 29.1°C and more than 27.4°C. In addition, the optimum value of SSC was 0.055 to 0.175 mg m-3. Moreover, meteorological data and ocean dynamic analysis seems to be other parameters that affect the abundance of bigeye tuna.
... Extensive space and time coverage of satellite images has made them indispensable to the study of oceanographic dynamics of marine ecosystems (Borzelli et al. 1999;Vantrepotte and Mélin 2011;Swardika, Tanaka, and Ishida 2012). Most satellite remote-sensing products are retrieved using spectral data from the visible and infrared bands, e.g. ...
A data-interpolating empirical orthogonal function DINEOF method was applied to 8 day composited satellite-derived chlorophyll-a chl-a images to produce a long-term, cloud-free chl-a data set over the Bohai Sea and Yellow Sea from 1997 to 2010. In this study, two additional procedures, a depth subdivision scheme and a new process of outlier detection and removal, improved the overall performance of this interpolating technique. The whole chl-a data set was divided into three subsets according to 20 and 50 m isobaths and the DINEOF reconstruction was performed on each subset. This subdivision scheme can significantly improve the accuracy of reconstruction, but is achieved with loss of computational efficiency due to the increased number of iterations required for reconstruction of the three subsets. A simple and new outlier detection method based on standardized residuals theory was developed to eliminate the spurious values outliers from the chl-a data set. The accuracy of the DINEOF reconstruction was significantly improved by the application of the outlier detection and removal process.
... The general characteristic of the Indonesian seas was reported with the satellite remote-sensing dataset (Swardika et al., 2012). It concluded that the ocean characteristics, the sea surface temperature (SST), zonal-component of wind speed (U-WS) and rain rate (RR), are very stable in the Equatorial areas, while their seasonal variability appears as increasing the latitude in both the north and south hemispheres. ...
... We reveal the ocean characteristics from the monthly SST, U-WS and RR in 10 years (December 1999-November 2009) by satellites observation. Those three data show the clear cyclic variability of the ocean characteristics (Swardika et al., 2012;Nurweda & Tanaka, 2012). ...
... This is the monsoon wind pattern of the inner Indonesian Seas. Saveral studies have described the monsoon pattern of the inner Indonesian Seas (Wyrtki, 1962;Susanto et al., 2006), but the pattern was unclear (Swardika et al., 2012). ...
The overall objective of this study is to investigate the local characteristics of the Indonesian Seas in response to the 6 months, seasonal and inter-annual variations. A moving averages method with 3, 6 and 12 months of lengths has been used to show the temporal variability. Using satellite observed datasets for the 10 year period from December 1999-November 2009, the results of moving averages indicate the 6 months and seasonal variabilities of sea surface temperature (SST), zonal-component of wind speed (U-WS) and rain rate (RR) in most of the Indonesian Seas area. It is also interesting to note that, although in the same latitude with the strong seasonal variability area, a stable condition of SST occurs in some particular areas. The cross-lag correlation analysis shows the relationship among indices with several month time lags. After removing the seasonal variability, the possible connection of indices with the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) phenomena in the Indonesian Seas can be detected by the 6 month moving averages method.
This study explores to reveal the characteristic (variability and climatic long term trend) of The West Sumatra and South Java Sea Significant Wave Height (SWH) and wave power density for the period of 1988–2011 using hindcasting data from Simulating WAve Nearshore (SWAN). The wind data input originates from Cross-Calibrated Multi-Platform (CCMP) with grid resolution 0.25°×0.25°. The Unstructured grid is implemented for better flexibility to illustrate the coastline of the domain model
Results show that (1) significant wave height output from simulation gives good agreement with buoy and satellite observation in Indonesian inner sea. The verification result at the main study area is not as good as Indonesian inner sea because the size of the domain model does not include incoming swell from high latitude area, (2) highest seasonal mean of significant wave height and wave power density occurs in east monsoon, (3) West Sumatra and South Java Sea generally exhibits an increasing trend in SWH 1.04 cm/year and wave power density 94.6 W/m/year. (4) there is a noticeable great potential of SWH and wave power density in South of west Sumatranean waters and West of South Java Sea with average SWH of 1.21 m and wave power density of 4.42 kW/m, The trend of SWH and wave power density in this region also exhibits a significantly increasing trend with value of SWH is 1.39 cm/year and wave Power density trend 0.10 [(kW/m)/year], (5) El-Nino Phenomenon can decrease the average value of significant wave height and wave power density in study area, (6) the increasing wind speed trend is possibly happened because the duration of the research is coincide with transition time from warm phase to cold phase in Pasific Decadal Oscillation (PDO) index, (7) there is an increasing of SWH and wave power density in most part of West Sumatra and South Java Sea with the most noticeable trend occuring in DJF and MAM rather than JJA and SON.
Finding out the possible connections of two coupled ocean-atmosphere phenomena in the Indo-Pacific Oceans, El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), on sea surface temperature (SST), zonal-sea surface wind speed (U-WS) and rain rate (RR) of Indonesian Seas are challenging due to the location of Indonesian Seas that separates those two oceans. The characteristics of indices in the Indonesian Seas are studied based upon data analysis of satellite observed datasets for the 10 year period from December 1999-November 2009. The results show that the indices are easily disturbed by a cyclic temporal variability, the 6 months variability; seasonal variability; and variability longer than 12 months. This cyclic temporal variability becomes a difficulty in separating the ENSO and IOD signals from the original signal of indices. The traditional method that used the threemonths averagesof SST anomaliesto detect the ENSO signal is more suitable for the area that free from a cyclic temporal variability. For this case, we have developed a method that generates ENSO and IOD signals by removing the cyclic temporal variability from the indices. This proposed method improves the detection of ENSO and IOD signal especially for the areas that show a cyclic temporal variability in the indices.
