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EN
Many countries, including Indonesia, face severe water scarcity and groundwater depletion. Monitoring and evaluation of water resources need to be done. In addition, it is also necessary to improve the method of calculating water, which was initially based on a biophysical approach, replaced by a socio-ecological approach. Water yields were estimated using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. The Ordinary Least Square (OLS) and geographic weighted regression (GWR) methods were used to identify and analyze socio-ecological variables for changes in water yields. The purpose of this study was: (1) to analyze the spatial and temporal changes in water yield from 2000 to 2018 in the Citarum River Basin Unit (Citarum RBU) using the InVEST model, and (2) to identify socio-ecological variables as driving factors for changes in water yields using the OLS and GWR methods. The findings revealed the overall annual water yield decreased from 16.64 billion m3 year-1 in the year 2000 to 12.16 billion m3 year-1 in 2018; it was about 4.48 billion m3 (26.91%). The socio-ecological variables in water yields in the Citarum RBU show that climate and socio-economic characteristics contributed 6% and 44%, respectively. Land use/Land cover (LU/LC) and land configuration contribution fell by 20% and 40%, respectively.The main factors underlying the recent changes in water yields include average rainfall, pure dry agriculture, and bare land at 28.53%, 27.73%, and 15.08% for the biophysical model, while 30.28%, 23.77%, and 10.24% for the socio-ecological model, respectively. However, the social-ecological model demonstrated an increase in the contribution rate of climate and socio-economic factors and vice versa for the land use and landscape contribution rate. This circumstance demonstrates that the socio-ecological model is more comprehensive than the biophysical one for evaluating water scarcity.
EN
Land suitability assessment is an important stage in land use planning that guides the direction of optima land use. The objective of this study was to select a suitable location for settlements in earthquake-prone areas using the integration of the Analytical Hierarchy Process (AHP) and Geographical Information System (GIS). In total, six maps were considered to determine a suitable location for settlements, namely topography, soil, geology, land cover/land use, a regional spatial planning pattern map, and an earthquake vulnerability map. The results showed that in medium earthquake-prone areas, the suitable land area which are available for settlement was 90.25 km2 (46.36% of the total land area available - 194.68 km2). Whereas in highly earthquake-prone areas, the suitable and available land area was 528.11 km2 (70.25% of the total land area in the high vulnerability zone - 751.81 km2). The research proved that AHP and GIS integration is very effective and robust for mapping land suitability in earthquake-prone areas. The results of the analysis can be used by planners to prioritize settlement development in the Sukabumi regency. The methodology developed is recommended to be applied in selecting locations for settlements in other parts of Indonesia.
EN
This article discusses the ability of the Cellular Automata (CA) Markov method to project rice sufficiency by considering the conversion of massive rice fields, such as the ones in Indonesia. The conversion of rice fields into land use for non-farming due to the rapidly growing population, industry and economic needs is increasingly affecting the rice self-sufficiency. With the development of remote sensing techniques, such as CA Markov, which has been used for years in spatial change projection, there is a need to assess the rice field conversion and its impact on the rice field self-sufficiency. The process is not solely based on CA Markov but also includes an object-based classification method utilising multi-temporal spot image data to derive land use maps, CA Markov for rice field conversion projection and rice self-sufficiency assessment, which was developed by assessing the availability of rice, consumption and production. Using the Indramayu district as the study area, the results indicate that within the next 20 years, the rice field area will decrease, and the impact on rice self-sufficiency will be 5.34 for Business as usual (BAU) and 0.47 when considering population growth. The previous research validated the results and indicated the efficiency of this method for rice self-sufficiency projection. Moreover, a management assessment was also conducted and indicated that in order to maintain rice self-sufficiency, innovation in the planting and seed systems as well as in farmers’ welfare management, such as incentives and subsidies, local food diversification systems and innovative food technique development to support food diversification, should be considered.
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