A high rate of vegetation clearing around the upper stream of Kali Bekasi watershed currently causes various environmental problems, such as floods. The impacts occur predominantly in downstream area, mostly affecting cities, due to a disruption of the ecosystem in the upper stream. The main function of the upper stream to humans is acting as a buffer to protect downstream areas from flooding, run-off, as well as biodiversity protection. To achieve this, many varieties of plant are grown including bamboo plantations, which serve as a buffer plants on critical land especially with steep contours. In this study we aim to provide a better understanding of the effectiveness of different bamboo stands buffering to improve information for making management recommendation. We examine different points along the stream by mapping bamboo distribution, analyzing bamboo and non-bamboo (tree) stands diversity and biomass, and provide recommendations for bamboo management based on combining our findings with local ecological knowledge. We implemented image classification analysis for classifying bamboo and non-bamboo land use cover. We also measured bamboo and non-bamboo diversity by using Shannon's-Wienner diversity index. Our results showed that bamboo occupies approximately 5,360.89 ha or 11.39% of total area with six bamboo species. The highest bamboo diversity index was in the upper part of the Kali Bekasi watershed (0.62). In contrary, the highest bamboo biomass index was found in the lower part of the upper stream of Kali Bekasi watershed (98.96 ton ha -1 ). We also discovered about 29 species of tree (230 trees) and 27 above-ground plant species in the surveyed area. As a result of our findings, we propose a shift towards bamboo agroforestry management in a mixed garden of talun form, where the community implement their local knowledge on bamboo cultivation and management to maintain the bamboo. This option could improve cooperation among farmers and the local community in order to conserve bamboo and tree species diversity in harmony to local wisdom.
The Harau Valley Natural Tourism Park is one of the most popular destinations for local, domestic and foreign tourists in West Sumatra. The number of Harau Valley Natural Tourism Park tourists in 2018 increased by 37% from the previous year, which was 358,827 tourists. Increasing demand for tourism objects, especially in the Harau Valley Natural Tourism Park will unavoidably leads to further development of the park for tourism activities. Thus, consequently it will affect the park and its surroundings ecologically, socially, and economically. Studies on the carrying capacity of tourist areas are needed to minimize the impact caused by tourism activities. The purpose of this study is to identify and analyze the carrying capacity of the tourist area in the Harau Valley Natural Tourism Park. Data was collected using a survey method, namely field observations and literature studies which were then analyzed using the Douglass 1975 formula to identify carrying capacity for each tourist attraction. The results of the analysis of the carrying capacity of each tourist attraction were (1) natural recreation 85,056 people/year, (2) Boating is 7,802 people/year, (3) Swimming is 12,217 people/year, (4) Camping is 25,460 people/year, (5) Education and Research is 99,744 people/year, and (6) Photo hunting is 84,890 people /year. Hopefully, the results of the study can be used as future considerations in the development of the Harau Valley natural tourism park.
As landmass of the world is covered by vegetation, taking into account phenology when performing land cover classification may yield more accurate maps. The availability of no-cost Moderate Resolution Imaging Spectrometer (MODIS) NDVI dataset that provides high-quality continuous time series data is representing a potentially significant source of land cover information especially for detection natural forest distribution. This study intends to assess the advantage of MODIS 250 m Normalized Difference Vegetation Index (NDVI) multi-temporal imagery for detection of densely vegetation cover distribution in Java and then for identification of remaining natural forest in Java from densely vegetation cover distribution. Result of this study successfully demonstrated the contribution of MODIS NDVI 250 m for detection the natural forest distribution in Java Island. Therefore, the approach described herein provided classification accuracy comparable to those of maps derived from higher resolution data and will be a viable alternative for regional or national classifications.
Study of Biodiversity Value and Distribution in The Upper Stream of Ciliwung Watershed Watershed is one of the ecological boundary that has important role related to landscape sustainability. Ciliwung Watershed is categorized as super-priority watershed in Indonesia and provides landscape services particularly mentioned in this study is biodiversity value.The study sites were located in three sub-districts in the upper stream of Ciliwung Watershed (Ciawi, Megamendung, Cisarua), Bogor District of West Java. The main objectives of this study were to analyze value and distribution of biodiversity using six indicators of landscape sustainability. The six indicators (presence of threatened species, land use and land cover change, land use intensity of organic agricultural, population, occupation, and infrastructure) were analyzed using geographical information system (GIS). Based on the result, it can be shown that Cisarua and Megamendung sub-district are the first and second largest area with high biodiversity value (4.022 Ha and 2.099 Ha). Meanwhile, Ciawi sub-district has no area with high biodiversity value. Finally, we propose six recomendations of landscape management for biodiversity sustainability in the upper stream of Ciliwung Watershed. Keywords:biodiversity value, Ciliwung Watershed, landscape service, GIS, landscape management
Livestock is one of the leading commodities in Indonesia. Transportation costs and the quality of the produced are the problems that lead to the lack of competitiveness of these commodities. The main focus of this paper, is the transportation factor with the driver component as the object, the Knowledge Data Discovery we use as a methodology, and Naïve Bayes as one of the algorithms that can classify the driver and provide knowledge to the transportation managers, K-Means, and PCA we use as a supporting model. The results showed that the use of Naïve Bayes with a small number of datasets resulted in accuracy values of 0.80, the precision of 83%, recall 80%, and 81% of F1score, this value indicates that the resulting classification is in a good category, and this can be the basis of the manager’s decision in the selection of drivers.
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