Indonesia's forests in different periods have been deforested at different levels. Deforestation caused carbon emissions. The purposes of this study were :1) to measure deforestation and carbon emissions in period of [2005][2006][2007][2008][2009][2010] in Indonesia and 2) to find out the incentive value to be paid by the government. One method for measuring emissions from deforestation and forest degradation is GeOSIRIS model. A modeled GeOSIRIS policy used a carbon payment system to incentivize emission reductions. Data used in this study were maps of forest cover in 2005 and 2010, map of deforestation 2005-2010, carbon and agricultural price and driver variables for deforestation such as slope, elevation, logarithmic distance to the nearest road, logarithmic distance to the nearest provincial capital, the amount of area per pixel included in a national park, a timber plantation. The result of this study showed rate of deforestation was 4.65 million ha/5 years. The REDD policy could decrease deforestation in Indonesia by 0.66 million ha (17.45 %).
The forest destruction, climate change and global warming can reduce an indirect forest benefit because forest is the largest carbon sink and it plays a very important role in global carbon cycle. To support reducing emissions from deforestation and forest degradation (REDD+) program, there is a need to understand the characteristics of existing Land Use/Cover Change (LUCC) modules. The aims of this study are 1) to calculate the rate of deforestation at Poso Regency; and 2) to compare the performance of LCM and GM for simulating baseline deforestation of multiple transitions based on model structure and predictive accuracy. The data used in this study are : 1) Indonesia tophographic map scale 1; 50.000, produced by Geospatial Information Agency (BIG), 2) Landcover maps (1990, 2000, and 2011) which were collected from the Director General of Forestry Planning, Ministry of Environment and Forestry. Meanwhile independent variables (environmental variables) such as : distance from the edge of the forest, the distance from roads, the distance from streams, the distance from settlement, elevation and slope. Landcover changes analysis was assessed by using Idrisi Terrset software and Geomod software. Landcover maps from 1990 and 2000 were used to simulate land-cover of 2011. The resulting maps were compared with an observed land-cover map of 2011. The predictive accuracy of multiple transition modeling was calculated by using Relative Operating Characteristics (ROC). The results show that the deforestation on the period of 1990-2011 reached 19,944 ha (3.55 %) or the rate of deforestation 949 ha year 1 . Based on the model structure and predictive accuracy comparisons, the LCM was more suitable than the GM for the asssement of deforestation.
This article assesses the feasibility of generating the geospatial data from a national classification standard. In this case, the National Standardization Agency (Badan Standardisasi Nasional) of Indonesia created and published a national seabed cover classification standard called SNI 7987-2014 but has not developed corresponding geospatial data. Geospatial data on seabed cover can be generated by integrating related thematic data, such as those on seafloor surficial sediments, coastal ecosystems, and coastal infrastructure. With consideration for these issues, this research evaluated the feasibility of using SNI 7987-2014 as a means of generating seabed cover geospatial data at scales of 1:250,000 and 1:50,000. To this end, the documentation accompanying the standard was evaluated via descriptive quantitative analysis through weighted scoring, and logical testing, after which overlay, feature selection based on the scored method and remote sensing analysis were carried out to develop the geospatial data prototypes. Results showed that the feasibility levels of using the prototypes for generating data at scales of 1:250,000 and 1:50,000 were 87.5% and 86.5%, respectively, indicating that SNI 7987-2014 can be fully used as the basis for generating geospatial data on seabed cover.
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.
Ministry of Agriculture have targeted production of 1.718 million tons of dry grain harvest during period of 2016-2021 to achieve food self-sufficiency, through optimization of special commodities including paddy, soybean and corn. This research was conducted to develop a sustainable paddy field zone delineation model using logistic regression and multicriteria land evaluation in Indramayu Regency. A model was built on the characteristics of local function conversion by considering the concept of sustainable development. Spatial data overlay was constructed using available data, and then this model was built upon the occurrence of paddy field between 1998 and 2015. Equation for the model of paddy field changes obtained was: logit (paddy field conversion) = - 2.3048 + 0.0032*X1 – 0.0027*X2 + 0.0081*X3 + 0.0025*X4 + 0.0026*X5 + 0.0128*X6 – 0.0093*X7 + 0.0032*X8 + 0.0071*X9 – 0.0046*X10 where X1 to X10 were variables that determine the occurrence of changes in paddy fields, with a result value of Relative Operating Characteristics (ROC) of 0.8262. The weakest variable in influencing the change of paddy field function was X7 (paddy field price), while the most influential factor was X1 (distance from river). Result of the logistic regression was used as a weight for multicriteria land evaluation, which recommended three scenarios of paddy fields protection policy: standard, protective, and permissive. The result of this modelling, the priority paddy fields for protected scenario were obtained, as well as the buffer zones for the surrounding paddy fields.
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