Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they require near-real-time geospatial information on palm stands. We developed a semi-automated classification scheme for mapping sago palm using machine learning within an object-based image analysis framework with Pleiades-1A imagery. In addition to spectral information, arithmetic, geometric, and textural features were employed to enhance the classification accuracy. Recursive feature elimination was applied to samples to rank the importance of 26 input features. A support vector machine (SVM) was used to perform classifications and resulted in the highest overall accuracy of 85.00% after inclusion of the eight most important features, including three spectral features, three arithmetic features, and two textural features. The SVM classifier showed normal fitting up to the eighth most important feature. According to the McNemar test results, using the top seven to 14 features provided a better classification accuracy. The significance of this research is the revelation of the most important features in recognizing sago palm among other similar tree species.

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Macrobenthos are organisms that live crawling, sessile, and digging holes in substrates such as sand, silt, rock, coral fragments, or dead coral. Benthos have low mobility, easy to catch, have a long life, and sensitive to some pollutants so that it can mean that benthic community structure can be used as a parameter of condition the current ecosystem of a particular area and also as information about wealth contained in the aquatic area. The study was conducted on 6-14 September 2018 in the Pramuka Island area, Kepulauan Seribu district, Indonesia. The collection of ecological data was carried out at seven different observation stations. Along with the rapid development of facilities on this island, there are threats both directly and indirectly to the preservation of the coral reef ecosystem around the Pramuka Island. The method used in collecting macrobenthos data is the Belt Transect method and collecting diversity index, evenness index, dominance index, and density. During the observation at the seven observation stations, 94 species of the seven macrobenthos phylum with the highest phylum were found, namely the Chordata phylum. The species most commonly found is Atriolum robustum. Diversity Index at each station in shallow depth and in the medium category. The evenness value obtained indicates a stable community condition. The highest index of dominance occurred at Pramuka Island Dock. Macrobenthos conditions from seven observation stations on Pramuka Island and its surroundings are still relatively good.