Carbon Sequestration Potential in Aboveground Biomass of Hybrid Eucalyptus Plantation Forest

Forests are a significant part of the global carbon cycle. Forests sequester carbon by conducting photosynthesis, which is the process of converting light energy to chemical energy and storing it in the chemical bonds of sugar. Carbon sequestration through forestry has the potential to play a significant role in ameliorating global environmental problems such as atmospheric accumulation of GHG's and climate change.  The present investigation was carried out to determine carbon sequestration potential of hybrid Eucalyptus. This study was conducted primarily to develop a prediction model of carbon storage capacity for plantation forest of hybrid Eucalyptus in Aek Nauli, Simalungun District, North Sumatera. Models were tested and assessed for statistical validity and accuracy in predicting biomass and carbon, based on determination coefficient (R) and correlation coefficient (r), aggregative deviation percentage (AgD), and the average deviation percentage (AvD). The best general model to estimate the biomass of hybrid Eucalyptus was Y = 1351,09x^0,876. e^(0,094).  Results showed that hybrid Eucalyptus had an average above-ground biomass in year 0 (the land without the eucalyptus trees) up to year 3 as large as 1.36, 11.56, 43.18, and 63.84 t ha. The carbon content of hybrid Eucalyptus were 0.61, 5.2, 19.43 t^(-1), and 28,73  t^(-1) C ha while the carbon sequestration potential were 2.23, 19.08, 71.31, and 105.43 t^(-1) CO ha^(-1) respectively

Resiliency of Singkil Coastal Vegetation due to Natural Catastrophes

Aceh Singkil west coast of Northern Sumatra was affected by natural catastrophes both tsunami and coastal deformation. Apparently most of the inter-tidal vegetation communities suffered because of the inundation intensity and duration changed. Investigation was carried on the structure and composition of littoral and mangrove forests in Singkil coast for 52 and 49 months after the 2004 and 2005 natural catastrophes, respectively. In each vegetation type, data were collected from four sampling plots, each measuring 30 m x 30 m. The sampling plots were separated into 10 m x 10 m sub-plots for matured trees and 5 m x 5 m sub-plots for smaller trees or shrubs. All plants within the subplots were identified and counted. Pure stand of littoral forests were dominated by Casuariana equisetifolia in the mature stage and Cerbera manghas in regeneration stages as natural regeneration. In the mangrove area, most of the mangrove trees such as Bruguiera gymnorrhiza, B. parviflora, and Rhizophora apiculata dead. Sonneratia caseolaris was higher survival rate compared than mangrove trees. B. gymnorrhiza seedlings were growing well. Mangrove palm Nypa fruticans populations were recorded growing well and with a good resiliency and persistence. In fact some of coastal vegetations both in coastal dry lands and in wetland forests have a good capacity to naturally restore and grow after the environmental destruction. From ecological point of view, these plant species should be selected for rehabilitation program in the natural catastrophes both tsunami and coastal subsidence as the impact of large earthquake could be reduced

Spatial Model of Deforestation in Sumatra Islands Using Typological Approach

High rate of deforestation occurred in Sumatra Islands had been allegedly triggered by various factors. This study examined how the deforestation pattern was related to the typology of the area, as well as how the deforestation is being affected by many factors such as physical, biological, and socio-economic of the local community. The objective of this study was to formulate a spatial model of deforestation based on triggering factors within each typology in Sumatra Islands.  The typology classes were developed on the basis of socio-economic factors using the standardized-euclidean distance measure and the memberships of each cluster was determined using the furthest neighbor method. The logistic regression method was used for modeling and estimating the spatial distribution of deforestation. Two deforestation typologies were distinguished in this study, namely typology 1 (regencies/cities with low deforestation rate) and typology 2 (regencies/cities with high deforestation rate). The study found that growth rate of farm households could be used to assign each regencies or cities in Sumatra Islands into their corresponding typology. The resulted spatial model of deforestation from logistic regression analysis were logit (deforestation) = 1.355 + (0.012*total of farm households) – (0.08*elevation) – (0.019*distance from road) for typology 1 and logit (deforestation) = 1.714 + (0.007*total of farm households) – (0.021*slope) – (0.051*elevation) – (0.038* distance from road) + (0.039* distance from river) for typology 2, respectively. The accuracy test of deforestation model in 2000–2006 showed overall accuracy of  68.52% (typology 1) and 74.49% (typology 2), while model of deforestation in 2006–2012 showed overall accuracy of 65.37% (typology 1) and 72.24% (typology 2), respectively

Detection of Deforestation Using Low Resolution Satellite Images in the Islands of Sumatra 2000-2012

In the last two decades, the international community has given great attention to the issues of deforestation and degradation. In Indonesia, these issues had been a very critical as they were related to the Indonesian government's commitment in reducing greenhouse gases by 2020 through the Reducing Emission from Deforestation and forest Degradation (REDD) mechanism. This paper describes the use of low resolution satellite imagery, i.e., MODIS (Moderate Resolution Imaging Spectroradiometer) for monitoring deforestation in Sumatra during the period of 2000-2012. The main objective of the study was to derive rapid forest and land cover change information from low resolution imageries in Sumatra between 2000 - 2012. This study used level 2 Terra MODIS (MOD13Q1) imageries, acquired in 2000, 2006 and 2012 as the main data source, where the 16-day composite imageries were derived from NAS

Kandungan Karbon Rawa Singkil dan Potensi Pengembangan Produk Jasa Lingkungan di Kabupaten Aceh Singkil dan Subulussalam

20 hal; Tabel; Peta; Grafi

Spatial Model of Deforestation in Sumatra Islands Using Typological Approach

High rate of deforestation occurred in Sumatra Islands had been allegedly triggered by various factors. This study examined how the deforestation pattern was related to the typology of the area, as well as how the deforestation is being affected by many factors such as physical, biological, and socio-economic of the local community. The objective of this study was to formulate a spatial model of deforestation based on triggering factors within each typology in Sumatra Islands. The typology classes were developed on the basis of socio-economic factors using the standardized-euclidean distance measure and the memberships of each cluster was determined using the furthest neighbor method. The logistic regression method was used for modeling and estimating the spatial distribution of deforestation.Two deforestation typologies were distinguished in this study, namely typology 1 (regencies/cities with low deforestation rate) and typology 2 (regencies/cities with high deforestation rate). The study found that growth rate of farm households could be used to assign each regencies or cities in Sumatra Islands into their corresponding typology. The resulted spatial model of deforestation from logistic regression analysis were logit (deforestation) = 1.355 + (0.012*total of farm households) – (0.08*elevation) – (0.019*distance from road) for typology 1 and logit (deforestation) = 1.714 + (0.007*total of farm households) – (0.021*slope) – (0.051*elevation) – (0.038* distance from road) + (0.039* distance from river) for typology 2, respectively. The accuracy test of deforestation model in 2000–2006 showed overall accuracy of 68.52% (typology 1) and 74.49% (typology 2), while model of deforestation in 2006–2012 showed overall accuracy of 65.37% (typology 1) and 72.24% (typology 2), respectively

Dynamic System for Silvofishery Pond Feasibility in North Sumatera, Indonesia

Silvofishery is the planting of mangroves in pond area. The commodities selected for silvofishery ponds include tiger shrimp, milkfish, and mangrove crab. The purpose of this research was to find the best silvofishery system based on the average net present value (NPV) using a dynamic model simulation and provide information about the effect of price changes or production of selected commodities on silvofishery. The results of this research showed that tiger shrimp and mud crab are the best and most feasible combinations for silvofishery, having an average NPV of $755.71/ha/year. Meanwhile, silvofishery using tiger shrimp has an average NPV of$332.28/ha/year, and the silvofishery combination of milkfish and tiger shrimp has an average NPV of $-216.45/ha/year. The effect of price increases the variable cost price by 63.3%, which indicates that the silvofishery combination of tiger shrimp and mud crab is still feasible to run. The decline in the selling price of the commodities of tiger shrimps and mud crab by 70% and 50%, respectively, makes this combination still feasible to operate. On the other hand, the surrounding community’s level of consumption greatly affects the level of sale of the silvofishery commodity. Environmental management must also be practiced as best as possible to maintain the functioning of the environment around the ponds to avoid major losses, and periodic maintenance must be done by managers to achieve the production targets. The present study suggested that pond farmers must be wise in making decisions to implement the appropriate combination of silvofishery

Evaluation of Plant Growth and Potential of Carbon Storage in the Restored Mangrove of an Abandoned Pond in Lubuk Kertang, North Sumatra, Indonesia

Mangrove forest in Lubuk Kertang Village, West Brandan sub-district has been converted around 20 ha annually (1996–2016) into various non-forest land use. Rehabilitation can be a solution to restore the condition of the ecosystem so that it can resume its ecological and economic functions. This paper discusses the evaluation of mangrove rehabilitation carried out by planting 6000 propagules in December 2015 and 5000 seedlings in May 2016 with Rhizophora apiculata species in abandoned ponds. Monitoring was carried out every 6 months from 2016 to 2022. In the restored area, 11 true mangrove species and 3 associated mangrove species were found. The percentage of plants that survived after seven years was 69.42% for planting using propagules and 86.38% for planting with seedlings. The total biomass carbon stocks stored by 7-year-old plants using propagules was 51.18 Mg ha−1, while the carbon stored by planting using seedlings was 56.79 Mg ha−1. Soil carbon stocks at the planted site with propagules were 506.89 ± 250.74 MgC ha−1, and at the planted site with seedlings were 461.85 ± 102.23 MgC ha−1. The total ecosystem carbon stocks (including aboveground carbon) in the planted site using propagules were 558.07 MgC ha−1, while planting using seedlings were 518.64 MgC ha−1. The dataset and findings on the carbon storage evaluation of mangrove rehabilitation will be useful for blue carbon research community and policymakers in the context of the climate change mitigation strategy for Indonesia