Search

Net primary productivity (NPP) and chlorophyll-a (Chl-a) are indicators of water productivity. In this study, an analysis of NPP and Chl-a characteristics in the Banda Sea was carried out using the Hovmöller diagram and Pearson’s correlation. The NPP data used comes from VGPM and Chl-a from Aqua MODIS satellite. The results of data analysis from January 2003-December 2020, NPP and Chl-a reached highest concentrations in dry season and lowest in wet season. For monthly data, the highest concentrations occurred in August and the lowest in April and December. The waters of the Banda Sea include mesotrophic waters with monthly average of NPP 429 mg C/m2/day and Chl-a 0.24 mg/m3. During La Niña and El Niño, there was a change (decrease/increase) the concentration of NPP and Chl-a in dry season and transition period II. NPP and Chl-a have a high correlation and a strong linear relationship. NPP and Chl-a have almost the same pattern/tendency temporally. The change of NPP concentration temporally corresponded to change of Chl-a concentration. Seasonal factors, La Niña and El Niño have a strong influence in influencing the variability of NPP and Chl-a concentrations. High productivity based on NPP and Chl-a didn’t affect for skipjack and tuna seasons (big pelagic), that occurs in wet season and transition period II. High productivity affects to flying fish season (small pelagic) that occurs in dry season.

Pertanian adalah bidang yang sangat penting di Indonesia, sektor ini di tahun 2017 mencatat penyerapan tenaga kerja sebanyak 29.68% dari total seluruh pekerja (BPS, 2018), namun pentingnya sektor pertanian ini berbanding terbalik dengan data pertanian yang tersedia. Tahun 1998 Badan Pusat Statistik (BPS) bersama Japan International Cooperation Agency (JICA) telah mengisyaratkan overestimasi luas panen sekitar 17,07 persen. Ketidakuratan data pertanian ini kemudian diperbaiki pada tahun 2018 melalui kerjasama para stakeholder dengan menyusun suatu metodologi baru dalam menghitung luas lahan yang diberi nama kerangka sampel area. Selain metodologi yang sudah diperbarui, kemajuan teknologi dan teknik analisis di bidang ilmu pengetahuan juga mendukung perbaikan data pertanian. Citra satelit dan teknik klasifikasi menggunakan ensemble learning dapat dimanfaatkan dalam mengklasifikasikan jenis lahan padi. Pada penelitian ini digunakan citra satelit yang berasal dari United States Geological Survey (USGS) yaitu Landsat 8 dan teknik klasifikasi ensemble learning. Citra satelit dimanfaatkan untuk mengekstrak indeks vegetatif dari koordinat koordinat yang diteliti, sedangkan ensemble learning yang digunakan dalam penelitian ini yaitu Random Forest dan Boosting. Hasil pengolahan data menunjukkan Random Forest memiliki akurasi yang lebih tinggi daripada Boosting yaitu dengan nilai 76,52 untuk Random Forest dan 75,60 untuk Boosting. Keunggulan Random Forest terhadap Boosting tidak hanya dari sisi tingkat akurasi saja namun juga dari kestabilan model yang dibentuk.

This study aims to know prediction the productivity of rice by using Landsat Satellite data 8 in Karawang District. The research method used was descriptive, infrensial and modeling. For a presumes the productivity of the rice being estimated by using the Enhanced Vegetation Index (EVI). Prediction of productivity based on linear regression models between EVI from satellite imagery analysis results with the highest productivity of the rice plant of the Department of Agriculture Karawang District. The results showed that the analysis of Landsat 8 Satellite images obtained the average EVI value from 2017 and 2018, in 2017 the average EVI value was 0.36. while in the year 2018 average value of EVI was 0.48. Estimates of rice productivity in Karawang District 2017 and 2018 were obtained by using the regression equation model the relationship between EVI value and rice productivity yielding Anova obtained Sig = 0.000

Indonesia located at the confluence of Eurasian tectonic plate, Australian tectonic plate and the Pacific tectonic plate. Therefore, Indonesia has big geothermal potential. One of the areas that has geothermal potential is Ungaran Mount. Remote sensing technology can have a role in geothermal exploration activity to map the distribution of land surface temperatures associated with geothermal manifestations. The advantages of remote sensing are able to get information without having to go directly to the field with a large area, and it takes quick, so that the information can be used as an initial reference exploration activities. This study aimed to obtain the distribution of land surface temperature as a regional analysis of geothermal potential. The method of this research was a correlation of brightness temperature (BT) Landsat 8 with land surface temperature (LST) MODIS. The results of correlation analysis showed the R2 value was equal to 0.87, it shows that between BT Landsat 8 and LST MODIS has a very high correlation. Based on Landsat 8 LST imagery correction, the average of fumarole temperature and hot spring is 240C. Fumarole and hot spring are located in dense vegetation land which has average temperature around 26.90C. Land surface temperature Landsat 8 can not be directly used to identify geothermal potential, especially in the dense vegetation area, due to the existence of dense vegetation which can absorb heat energy released by geothermal surface feature.

The drought phenomena often occurs in summer season at paddy field of Java island. The drought phenomena causes decrease in rice production. This research was aimed to develop a model of land moisture (LM) estimation at agricultural field, especially for paddy field based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data which has seven reflectance and two thermal bands. The method used in this study included data correction, advance processing of MODIS data (land indices transformation), extraction of land indices value at location of field survey, and regression analysis to make the best model of land moisture estimation. The result showed that reflectance of 2nd channel (NIR) and rasio of Enhanced Vegetation Index (EVI) with Land Surface Temperature (LST) had high correlation with surface soil moisture (% weight) at 0 – 20 cm depth with formula: LM = 15.9*EVI/LST – 0.934*R2 – 16.8 (SE=9.6%; R2 =76.2%). Based on the model, land moisture was derived spatially at the agricultural field, especially at paddy field to detect andmonitor drought events. Information of land moisture can be used as an indicator to detect drought condition and early growing season of paddy crop