Abstrak. Masalah kuantitatif dalam metode potensial diri terkadang masih ditemukan. Beberapa asumsi dan metode inversi digunakan dalam proses pembuatan modelnya agar dicapai error yang sekecil mungkin sehingga makin mendekati kondisi sebenarnya. Telah dilakukan "Pemodelan Numerik Data Potensial Diri (Self Potential)" untuk menginterpretasi secara kuantitatif anomali dan parameter geometri dari anomalinya. Asumsi model yang digunakan adalah geometri sederhana dari data potensial diri. Program untuk pemodelan numerik ini dibuat di perangkat lunak MATLAB yang meliputi pemodelan kedepan dan pemodelan kebelakang. Metode inversi yang dipakai yaitu metode yang dikembangkan oleh El-Araby (2003). Program yang dibuat selanjutnya diuji pada data sintetik (3 data) dan data sekunder (3 data). Berdasarkan hasil penelitian yang sudah dilakukan, untuk data sintetik, hasil RMS error-nya sebagian besar dibawah 10%. Di samping itu, untuk data sekunder, hasilnya sesuai dengan referensi yang dipakai, dimana data sekunder 1 dan 2 saling memvalidasi karena berada pada daerah yang sama namun beda lintasan pengukuran yang jenis sumber anomalinya adalah bola karena berkaitan dengan mineral tembaga. Sedangkan untuk data sekunder 3, sumber anomalinya adalah vertical cylinder, yaitu rembesan yang berhubungan dengan potensial streaming yang terjadi di daerah bendungan. Abstract. Quantitative problems in self-potential method sometimes still be found. Some of the assumptions and methods used in the model inversion in order to get the smallest possible error so perilously close to actual conditions. It has been done "Numerical Modeling of Self Potential Data" to quantitatively interpret anomalies and geometry parameters of the anomaly. Assumptions model that are used is the simple geometry of the self potential data. Program for numerical modeling is created in the MATLAB software that includes forward modeling and inverse modeling. The inversion method that is used is the method developed by El-Araby (2003). The program that created subsequently tested on synthetic data (3 data) and secondary data (3 data). Based on the research that has been done, for synthetic data, the results of all RMS error mostly is below 10%. In addition, secondary data, the results are in accordance with the reference that was used, in which secondary data 1 and secondary data 2 validating each other because they are in the same area but different line measurement that type of source anomaly was associated with the sphere for a copper mineral. As for the secondary data 3, the source of the anomaly was a vertical cylinder, that is seepage associated with streaming potential that occurs in the area of the dam.

Although researchers have investigated the impact of Indian Ocean Dipole (IOD) phases on human lives, only a few have examined such impacts on fisheries. In this study, we analyzed the influence of negative (positive) IOD phases on chlorophyll a (Chl-a) concentrations as an indicator of phytoplankton biomass and small pelagic fish production in the eastern Indian Ocean (EIO) off Java. We also conducted field surveys in the EIO off Palabuhanratu Bay at the peak (October) and the end (December) of the 2019 positive IOD phase. Our findings show that the Chl-a concentration had a strong and robust association with the 2016 (2019) negative (positive) IOD phases. The negative (positive) anomalous Chl-a concentration in the EIO off Java associated with the negative (positive) IOD phase induced strong downwelling (upwelling), leading to the preponderant decrease (increase) in small pelagic fish production in the EIO off Java.

The Banda Sea region is one of the locations in Indonesian waters that have high Coastal Upwelling intensity which related to the monsoon pattern. The calculation of Upwelling Index (UI) based on Wind data show that the peak coastal upwelling is from June to September. Analysis of SST trends was carried out in the July-September period based on NOAA OISST data from 1982-2017. The results show that there are differences in the value of SST trends that occur in the Coastal and Oceanic regions. In general, the SST trend in the Banda Sea waters has a positive value (warming) in both the coastal and ocean areas. While the UI trend in coastal and oceanic regions in the Banda Sea tends to decline from the 1982-2017 period. This condition shows that external factors quite influence oceanographic conditions in Banda Sea waters. It is also thought to have caused a decrease in the intensity of the upwelling trend found in the Banda Sea. The results show that the SST trend in the Banda Sea tends to increase during the peak upwelling season (June-September). Meanwhile, the Upwelling intensity trend shows a decreasing pattern which is also confirmed by decreasing trend in chlorophyll concentration.

No abstract

Abstrak. Pemetaan dan pemantauan kondisi hutan mangrove diperlukan untuk rehabilitasi dan konservasi lingkungan. Mangrove Health Index (MHI) menggunakan analisis citra satelit merupakan pendekatan baru yang bisa digunakan untuk mengetahui kualitas lingkungan ekosistem hutan mangrove. Penelitian ini bertujuan untuk untuk mengetahui struktur komunitas hutan mangrove dan melakukan analisis spasial-temporal MHI di kawasan pesisir Surabaya dan Sidoarjo menggunakan citra satelit. Data yang digunakan untuk analisis struktur komunitas mangrove pada penelitian ini adalah hasil pengamatan lapang di 10 transek. Untuk analisis MHI menggunakan citra Sentinel 2 perekaman tahun 2015, 2018, 2021. Hasil analisis menunjukkan bahwa spesies mangrove yang paling dominan di lokasi penelitian adalah Avicennia marina. Analisis citra satelit mendeteksi pertambahan luas mangrove yang signifikan dari tahun 2015 hingga 2021 yaitu lebih dari 500 Ha. Berdasarkan analisis MHI, terjadi perubahan positif dari kondisi hutan mangrove dominansi buruk (MHI<33%) pada tahun 2015 menjadi sedang (33,4%<MHI< 66,67%) hingga baik (MHI > 66,68%). Pertambahan luas hutan mangrove diiringi dengan perbaikan kondisi ekosistem dengan indikator meningkatnya MHI.Abstract. Mapping and monitoring the condition of mangrove forests is needed for environmental rehabilitation and conservation. Mangrove Health Index (MHI) using satellite image analysis is a new approach that can be used to determine the environmental quality of mangrove forest ecosystems. This study aims to determine the structure of the mangrove forest community and conduct a spatial and temporal MHI analysis in the coastal areas of Surabaya and Sidoarjo. The data used in this study were the results of field observations on 10 transects. MHI analysis using Sentinel 2 imagery recorded in 2015, 2018, 2021. The results of the analysis show that the most dominant mangrove species in the research location is Avicennia marina. Analysis of satellite imagery detects a significant increase in mangrove area from 2015 to 2021, which is more than 500 Ha. Based on the MHI analysis, there was a positive change from poor dominant mangrove forest conditions (MHI <33%) in 2015 to moderate (33.4% < MHI < 66.67%) to good (MHI > 66.68%). The increase in the area of mangrove forests is accompanied by improvements in ecosystem conditions with indicators of increasing MHI.

No abstract

South Java Sea are regions that have quite complex dynamics because they are influenced by several factors, both regionally and globally. The influence certainly affects the variations in oceanographic features such as Sea Surface Temperature (SST), Sea Surface Height (SSH), and Chlorophyll-a concentration. Observation of oceanographic feature at this time has many methods, one of them by remote sensing. The purpose of this study is to calculate the variation of oceanographic conditions based on satellite data and its correlation with field data. The results show that the SPL and ATPL data with the field data have a fairly good relationship, where the value of R2 reaches 0.74 and 0.9. In general, the variation of oceanographic data has the same pattern that is changing seasonally. SST and SSH data are at their maximum in the January-March period, while the minimum is July-September. While the concentration of chlorophyll-a is at the maximum condition in July-September and minimum in January-March. This is thought to be an upwelling phenomenon that occurred in July-September due to the monsoon wind movement. Upwelling index calculation results based on wind data show that in the period June to September is the peak of the upwelling phenomenon.

Although researchers have investigated widely the impact of IOD phases on human lives, only a few have examined such impacts on fisheries. In this study, we analyzed the influence of negative (positive) of IOD on a chlorophyll a (Chl-a) concentration as an indicator of phytoplankton biomass and small pelagic fish production in the eastern Indian Ocean (EIO) off Java. We also conducted field surveys in the EIO off Palabuhanratu Bay at the peak (October) and the end (December) of the 2019 positive IOD phase. Our findings show that the Chl-a concentration had a strong and robust association with the 2016 (2019) negative (positive) IOD phases. The negative (positive) anomalous Chl-a concentration in the EIO off Java associated with the negative (positive) IOD phase induced strong downwelling (upwelling), leading to the preponderant decrease (increasing) of small pelagic fish production in the EIO off Java.