The mangrove forest is a very productive ecosystem, both economically and ecologically. Mangrove forests can protect settlements, buildings, and agriculture on the coast from strong winds or seawater intrusion, support the livelihoods of coastal communities and store high carbon stocks. Regardless of its role, the Taman Hutan Raya Ngurah Rai Mangrove Forest continues to be threatened by human activities. This study aims to monitor changes in the area, health, and water quality of mangrove forests. The data used are Landsat 7 ETM + imagery and Landsat 8 OLI in 2010-2020. We used NDVI vegetation indices to analyze mangrove health and Total Suspended Solid (TSS) to investigate the water quality of the mangrove forest. Temporal water quality parameters are obtained by using the TSS algorithm on Landsat imagery. The results showed variations in mangrove forest area changes, NDVI values, and water quality. In 2010 - 2015 Mangrove Forest Area increase by 117.72 ha but decrease in 2015-2020 by 96.21 ha. Mangrove health was also improved in 2010-2015 but the decline in 2015-2020. Meanwhile, mangrove forest water quality seen from the TSS distribution tends to grow from 2010 to 2020.
This study aimed to propose a tool for measuring the research performance of researchers, institutions, and journals in Indonesia based on bibliometrics. Specifically, the output of this measurement tool, referred to as the S-score, is described, as well as its implementation on the main database portal in Indonesia. The S-score was developed by a focus group discussion. The following 8 evaluation items for journal accreditation were analyzed in the development process: journal title, aims and scope; publisher; editorial and journal management; quality of articles; writing style; format of PDF and e-journal; regularity; and dissemination. The elements of the S-score are as follows: number of journal article documents in Scopus, number of non-journalarticle in Scopus, number of citations in Scopus, number of citations in Google Scholar, the hindex in Scopus, and the h-index in Google Scholar. The S-score yields results ranging from S1 to S6. The above metrics were implemented on the Science and Technology Index, a database portal in Indonesia. The measurement tool developed through the focus group discussion was successfully implemented on the database portal. Its validity and reliability should be monitored consistently through regular assessments of S-scores. The S-score may be a good example of a metric for measuring the performance of researchers, institutions, and journals in countries where most journals are not indexed by Scopus.
In recent years, unmanned aerial vehicles (UAVs) have been actively applied in the agricultural sector. Several UAVs equipped with multispectral cameras have become available on the consumer market. Multispectral data are informative and practical for evaluating the greenness and growth status of vegetation as well as agricultural crops. The precise monitoring of rice paddy, especially in the Asian region, is crucial for optimizing profitability, sustainability, and protection of agro-ecological services. This paper reports and discusses our findings from experiments conducted to test four different commercially available multispectral cameras (Micesense RedEdge-M, Sentera Single NDVI, Mapir Survey3, and Bizworks Yubaflex), which can be mounted on a UAV in monitoring rice paddy. The survey has conducted in the typical paddy field area located in the alluvial plain in Tottori Prefecture, Japan. Six different vegetation indices (NDVI, BNDVI, GNDVI, VARI, NDRE and MCARI) captured by UAVs were also compared and evaluated monitoring contribution at three different rice cropping phases. The results showed that the spatial distribution of NDVI collected by each camera is almost similar in paddy fields, but the absolute values of NDVI differed significantly from each other. Among them, the Sentera camera showed the most reasonable NDVI values of each growing phase, indicating 0.49 in the early reproductive phase, 0.62 in the late reproductive stage, and 0.38 in the ripening phase. On the other hand, compared to the most commonly used NDVI, VARI which can be calculated from only visible RGB bands, can be used as an easy and effective index for rice paddy monitoring.
Background: The Indonesian government has evaluated the research performance of universities, whose measurement process is projected into resources, management, outputs, and revenues to determine the provision of incentives, grants, and program funding to universities. However, efficiency calculations have shown that the outputs and competition-based incentives that drive scientific productivity are more complex. The most competitive systems must also be the most productive when considering resources. Objectives: This study aimed to analyze the research efficiency in the Indonesian higher education system. Research Designs: The efficiency was analyzed by maximizing the 13 product outputs from the research budget and university staff. The result was then compared with the existing performance measurement analysis. Data Envelopment Analysis (DEA) was used to evaluate the efficiency based on the data of 47 universities in the Mandiri cluster and 144 in the Utama cluster for the 2014–2018 period. Results: These findings showed that about 68% of universities have an efficiency value of 1 for the Mandiri group, almost 40% in the Utama group, and 41% for the two groups combined. Additionally, this study compared the efficiency analysis and the impact of the performance evaluation. Conclusions: The comparison showed that adding efficiency or productivity factors in the performance evaluation assessment produced a more accurate result.
Development and economic growth in an area can cause land cover changes. Penajam Paser Utara Regency, as a new capital candidate, is also predicted to experience in land cover changes. Land cover changes that are not following the land’s potential will cause environmental problems, so it is necessary to predict land cover changes by looking at patterns of land cover changes in the past and the factors that influence it. The purpose of this study is to analyze and predict the land cover change in Penajam Paser Utara Regency in 2031. The method used in this study is modeling using Cellular Automata - Markov. The driving factor of land cover change is used in making prediction models such as distance from the center of activity, distance from the road, distance from the river, elevation, and slope. The prediction land cover changes show that there has been an increase in plantation area and a decrease in forest area, while the development of the built-up area is not visible. The kappa test for predicted land cover showed perfect results. The resulting land cover model can be used to formulate land-use policies.
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