Potensi Pengembangan Budidaya Silvofishery Di Area Mangrove Wonorejo Surabaya

Wijaya, Nirmalasari Idha; Trisyani, Ninis; Sulestiani, Aniek

doi:10.20886/jphka.2019.16.2.173-189

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…The mangrove area in the study area has various potentials that can be utilized by the local community (Wardhani, 2011;Bibin & Ardian, 2020). For example, local people use the area for silvofishery activities (Wijaya et al, 2019). On the other hand, the Ministry of Environment and Forestry (KLHK) and The Provincial Environment Service have made this area a natural tourism centre of mangroves (Putra, 2014).…”

Section: Resultsmentioning

confidence: 99%

Vegetation-Water-Built Up Index Combined: Algorithm Indices Combination for Characterization and distribution of Mangrove Forest through Google Earth Engine

Rahmawati

2022

CELEBES Agricultural

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Mangroves that live in ecotone areas have a fairly significant role in the economy and ecology. This strategic role requires spatial data to facilitate the management and development of mangrove areas. The mangrove mapping process usually uses a manual method, namely through software, and has shortcomings and limitations in image management that require massive data storage. Cloud computing-based Google Earth Engine (GEE) mapping platform can manage images with an extensive scope and spatiotemporal data processing. However, this platform requires index formulas or combinations to help classify and increase accuracy in mapping the earth’s surface. The innovation with the combined VWB-IC (Vegetation-Water-Built-up Index Combined) formula is projected to classify the characteristics of mangrove areas in Jakarta Bay. The combination consists of three types of indices, namely vegetation index (NDVI, GNDVI, ARVI, EVI, SLAVI, and SAVI), water (NDWI, MNDWI, and LSWI), and buildings (IBI and NDBI). This combination is used to translate the classification of mangroves using the Random Forest (RF) machine learning algorithm method with the Sentinel-2 MSI (Multispectral Instrument) satellite image source and through the GEE platform. This platform generates raster data for land use classification (including mangroves), and then the analysis is continued using ArcMap software. The obtained mangrove area is 220.43 ha, located in Jakarta Bay and divided into the Angke Kapuk Nature Tourism Park and the Pantai Indah Kapuk Mangrove Ecotourism Area. The data from this research is expected to provide a recommendation for a combination index formula for mapping mangrove areas in urban areas. The spatial distribution area can be used as an evaluation material in mangrove areas in Jakarta Bay

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Section: Resultsmentioning

confidence: 99%

Vegetation-Water-Built Up Index Combined: Algorithm Indices Combination for Characterization and distribution of Mangrove Forest through Google Earth Engine

Rahmawati

2022

CELEBES Agricultural

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“…This is in accordance with the statement by [15] that the growing mangrove vegetation has various benefits, including providing nutrients and natural food to media for non-cultivated ponds. Furthermore, according to a study by [16], crab cultivation is more profitable than fish cultivation in silvofishery ponds with a high percentage of mangrove coverage. The highest natural shrimp production was in pond 4, with a yield of 291.1 kg, while the lowest natural shrimp production was in pond 11, with a yield of 27 kg (Table 2).…”

Section: Resultsmentioning

confidence: 99%

Silvofishery: In What Mangrove Coverage Condition Can this System Provide Benefits for the Community?

Harefa¹,

Nasution²,

Mulya³

et al. 2022

ujar

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“…is considered good enough to support aquaculture activities. The concept of silvofishery with minimal waste pollution affects the stability of the ecosystem in pond waters (Wijaya et al, 2019). These good ecological conditions affect the performance of the farmed fish (Ariadi, 2023).…”

Section: Clinical Symptoms and Fish Growthmentioning

confidence: 99%

Relationship Between Abundance of Clamydomonas spp and Chlorella spp on Clinical Performance of Red Tilapia Oreochromis niloticus in Silvofishery Ponds

Linayati,

Nhi,

Ariadi

et al. 2024

Croatian Journal of Fisheries

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Chlamydomonas spp. and Chlorella spp. are plankton genera that can be used to determine the level of balance in the aquatic environment. The purpose of this study is to determine the abundance of plankton Chlamydomonas spp. and Chlorella spp. and its relation to the clinical performance of red tilapia in silvofishery ponds. The research method used is an ex post facto causal design with random sampling. The results showed that the water quality in the silvofishery ponds was very good, except for the nitrate parameters of 0.00-1.50 mg/L and salinity of 2-11 gr/L, which exceeded the quality standards. In the silvofishery ponds, 5 plankton classes were found, namely Chlorophyceae, Cyanophyceae, Chrysophyceae, Protozoa, and Dinophysis, consisting of 15 genera. The dominant class Chlorophyceae had an abundance of 2.88E+06 cells/ml. The class Chlorophyceae is dominated by Chlorella spp. (2.63E+06 cells/ml) and Chlamydomonas spp. (2.20E+05 cells/ml). The abundance of Chlamydomonas spp. in silvofishery ponds is closely related to phosphate solubility (0.988). Based on observations of clinical symptoms on the eyes, gills, and fins of fish, no disease infections or physical abnormalities were found in the fish cultured in silvofishery ponds. Fish tend to live well with a growth rate of 0.44 g/day. The aquatic ecosystem of the silvofishery pond, which is dominated by the plankton Chlorella spp. and Chlamydomonas spp., had a good effect on the performance and condition of the farmed fish. This study concludes that the abundance of Chlamydomonas spp. and Chlorella spp. was very dominant compared to other plankton genera. The presence of Chlamydomonas spp. and Chlorella spp. illustrates good and stable environmental conditions in silvofishery ponds, followed by no clinical signs of disease infection in the fish reared during the study period.

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Potensi Pengembangan Budidaya Silvofishery Di Area Mangrove Wonorejo Surabaya

Abstract: Kontribusi penulis: NIW: pengambilan data primer, mengolah data dan penulisan bidang ekologi mangrove dan kepiting bakau; NT: mengeolah data dan penulisan bidang biologi perikanan; AS: mengolah data dan penulisan bidang analisis finansial

Cited by 4 publications

References 2 publications

Vegetation-Water-Built Up Index Combined: Algorithm Indices Combination for Characterization and distribution of Mangrove Forest through Google Earth Engine

Vegetation-Water-Built Up Index Combined: Algorithm Indices Combination for Characterization and distribution of Mangrove Forest through Google Earth Engine

Silvofishery: In What Mangrove Coverage Condition Can this System Provide Benefits for the Community?

Relationship Between Abundance of Clamydomonas spp and Chlorella spp on Clinical Performance of Red Tilapia Oreochromis niloticus in Silvofishery Ponds

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