Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

Subsurface hydrocarbon occurrences can be detected by clay mineral (CM) alteration at the surface as a consequence of hydrocarbon migration. This study analyzed CM alteration in an oil and gas (O&G) field in the West Tugu field, located in the northwest Java Basin of Indonesia. Landsat 8 OLI data acquired on 25 September 2015 and soil spectral reflectance (SSR) data recorded using analytical spectral devices (ASD) in March 2017 were processed using clay indices to analyze CM alteration. Soil samples from a field survey of the same area were also analyzed using X-ray diffraction (XRD) to identify their CM compositions. CM distribution was empirically modeled by integrating the SSR bands re-sampled to Landsat 8 OLI (SSRL) bands and the CM composition using the best subset regression method and regression analyses. The resulting model was applied to Landsat 8 OLI images to map the surface CM alteration. The results show that CM alteration observed in the O&G field is an indicator of hydrocarbon microseepage at the surface. These results can be used as a technique to identify prospective regions that contain hydrocarbons and differentiate from those that are barren, and could be useful for increasing the rate of drilling success.

Batimetri mempunyai peran penting dalam perencanaan wilayah pesisir sehingga pemetaan batimetri dangkal sangat diperlukan. Penginderaan jauh merupakan salah satu metode yang efisien, mudah dan murah untuk pemetaan tersebut. Penelitian ini bertujuan untuk menganalisis kanal-kanal terbaik pada Landsat 8 OLI untuk memetakan batimetri dan kedalaman optimum yang dapat dipetakan sehingga dapat digunakan sebagai rujukan dalam memanfaatkan data penginderaan jauh untuk pemetaan tersebut. Lokasi kajian dilakukan di pulau Putri, Kota Batam, Provinsi Kepulauan Riau. Analisis regresi linear menunjukkan kanal tunggal terbaik untuk pemetaan batimetri adalah kanal hijau (kanal 3), diikuti oleh kanal merah (kanal 4) dan kanal inframerah dekat (kanal 5). Namun pemetaan batimetri dengan kombinasi kanal menghasilkan koefisien determinasi yang lebih baik. Analisis best subset menunjukkan pemetaan batimetri pada kedalaman 0 – 20 m menggunakan kanal 2, 3, 5, dan 6 dengan koefisien determinasi (R²) 85,4%; kedalaman 0 – 25 m menggunakan kanal 1, 3, 5, 6, dan 7 dengan R² 75%; dan pemetaan kedalaman 0 – 50 m menggunakan kanal 1, 3, dan 4 dengan R² 49,1%. Hasil pemetaan batimetri menggunakan Landsat 8 OLI secara umum lebih efektif dan mempunyai akurasi tinggi pada kedalaman 0 – 20 m dan semakin berkurang kemampuannya pada kondisi perairan yang semakin dalam. Kata kunci: Batimetri, Landsat 8 OLI, kanal, algoritma. Bathymetry has an important role in planning coastal areas so that mapping of shallow bathymetry is needed. Remote sensing is one of the efficient, easy and inexpensive methods for mapping. This study aims to analyze the best channels in Landsat 8 OLI for mapping bathymetry and optimum depth that can be mapped so that it can be used as a reference in utilizing remote sensing data for mapping. The location of the study was conducted on the  Putri island, Batam City, Riau Islands Province. Linear regression analysis shows the best single channel for bathymetry mapping is the green channel (channel 3), followed by the red channel (channel 4) and the near infrared channel (channel 5). But bathymetry mapping with channel combinations produces a better coefficient of determination. Best subset analysis shows bathymetry mapping at depths of 0-20 m using channels 2, 3, 5, and 6 with a coefficient of determination (R2) of 85.4%; depth of 0 - 25 m using channels 1, 3, 5, 6, and 7 with R2 75%; and mapping depth 0 - 50 m using channels 1, 3, and 4 with R2 49.1%. The results of bathymetry mapping using Landsat 8 OLI are generally more effective and have a high accuracy at a depth of 0-20 m and are increasingly reduced in conditions of deeper water conditions.  Keywords: Bathymetry, Landsat 8 OLI, Band, Algorithm