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This paper examines the identification of key indicators that could be used to measure the success of reclamation plants in post-exploration oil and gas mining areas. The main objective of this research was to find key indicators or variables for evaluating the level success of reclamation results in the post-mining of oil and gas area. In this study, 44 environmental variables of the physical, biological, soil, water and air indicators were analyzed from 70 field plots of 6 reclamation and 2 natural forest sites. The analysis methods included (1) cluster analysis using the Agglomerative Hierarchical Clustering method with the Ward's method, and (2) quadratic discriminant analysis. The results of the clustering analysis showed that there were some clusters due to variation of biomass, water, soil and air conditions. The three clusters developed based on water and/or air variables provided high cophenetic correlation (0.80) with low within-cluster (14.5%) and high between-cluster variations (85.5%). Based on the multicollinearity analysis, average vector difference test, variance matrix variance test, unidimensional test of each variable and quadratic discriminant function, this study found that there were 3 key indicators determining variations of the quality of the reclamation plantations within the study sites, namely, biological indicator of biomass volume (Bio_B); soil indicator of P content in the soil (Tnh_P), saturation base of soil (Tnh_Kb), Manganese (Mn) content in the soil (Tnh_Mn), Sulfur content in the soil (Tnh_S), percentage of ash in the soil (Tnh_Ab), percentage of clay in the soil (Tnh_Li), and water indicator of chloride content in the surface water (Air_Cl). The examination on four classes of the reclamation quality showed that the classes were successfully classified having excellent crossvalidation error matrix with overall accuracy more than 90%. [ABSTRACT FROM AUTHOR]

Developing growth curve for forest and environmental management is a crucial activity in forestry planning. This paper describes a proposed technique for developing a growth curve based on the SPOT 6 satellite imageries. The most critical step in developing a model is on pre-processing the images, particularly during performing the radiometric correction such as reducing the thin cloud. The pre-processing includes geometric correction, radiometric correction with image regression, and index calculation, while the processing technique include training area selection, growth curve development, and selection. The study found that the image regression offered good correction to the haze-distorted digital number. The corrected digital number was successfully implemented to evaluate the most accurate growth-curve for predicting mangrove. Of the four growth curve models, i.e., Standard classical, Richards, Gompertz, and Weibull models, it was found that the Richards is the most accurate model in predicting the mean annual increment and current annual increment. The study concluded that the growth curve model developed using high-resolution satellite image provides comparable accuracy compared to the terrestrial method. The model derived using remote sensing has about 9.16% standard of error, better than those from terrestrial data with 15.45% standard of error. [ABSTRACT FROM AUTHOR]

Forest cover change is one of the environmental issues that continually gotten international attention. This study describes how to develop a spatial model of this change in each village-based typology by considering various biophysical and social-economic factors. The village typologies were investigated by applying the clustering analysis approach. The objective of this study was to develop the spatial model and to identify the driving forces of forest cover change by the village in Kubu Raya Regency of West Kalimantan. Based on the proportion of forest in 2015, the study found that there are two village typologies within the study area with 81% overall accuracy (OA). The Typology 1 (T1) which has low change rate of 5,001.8 ha year-1 consisted of 56 villages, while the Typology 2 (T2) which has high change rate about 8,050.6 ha year-1 covered 34 villages. The study also recognized that the most significant driving forces of change in T1 were the distance from rivers (X) and settlements (X), whereas in T2 were 2 3 the distance from roads (X) and the edge of the forest in 2015 (X). The best spatial model of the change are Y = - 1 9 0.01+0.0001X +0.0004X with OA of 83% and mean deviation (SR) 10.5% for T1 and Y = 0.02+0.0001X - 2 3 1 0.0002X with OA 53% and SR 13.3% for T2. The study concludes that the proximity from the center of the human 9 activities holds a significant influence on the behavior of forest cover changes. [ABSTRACT FROM AUTHOR]

At a landscape level, forest transitions have complex spatial heterogeneity characteristics, thus the causes, driving force, typology and specific profile characteristics need to be considered for managing and mitigating forest transition. This paper describes how the diversity of forest transition characteristics was grouped and how the characteristic of group was identified. Typology classes within water catchment areas in Riau, North Sumatera and West Sumatera Provinces, Indonesia were investigated by considering social, economic and biophysical aspects. The main study objective was to develop a forest transition typology at a landscape level. The model typology was derived from a clustering method with the Standardized Euclidean Distance. The study found that the most significant factor which successfully differentiated the typology of forest transition into two typologies was the population growth having approximately 92% of overall accuracy. The first typology (typology 1) could be categorized as rapid forest transition, while the typology 2 was categorized as slow forest transition. The study suggested that the management and mitigation of the impacts of the forest transition should be conducted by considering the landscape typology as a function of the profiles for each typology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The objective of the study is to identify and measure the relationships among stakeholders that influence the process of policy-making in defining legality of timber from private forests. The study focuses on the policy-making process of the Ministry of Forestry Regulation P.38/Menhut-II/2009 on Standard and Guidelines for Assessment of Sustainable Forest Management Performance and Timber Legality Verification of Concessionaire or of the Private Forest License Holder as the subject that has been implemented in several private forest management units as follow: Giri Mukti Wana Tirta in Lampung, Koperasi Serba Usaha APIK in Bali, Koperasi Hutan Jaya Lestari in South East Sulawesi, and Koperasi Wana Lestari Menoreh Kulonprogo in Yogyakarta. This research used a qualitative approach and the analysis method used in this research is a modified-stakeholder analysis that developed by ODA (1995), Reitbergen et al. (1998), and Mayer (2005). The stakeholder analysis shows that the interests and influences do not consider private forest farmers as primary stakeholder during the process of policy formulation. The strong national and international interests, supported by high authority could not be influnced by the role of the NGOs and academicians. The imbalance of responsibilities, rights, and revenues that was experienced by farmers as the manager of private forest when started implementing the policy was more as burdens, it means implementation of the policy was more as burdens. Strong relationships between the Ministry of Forestry with the state as a core could not empower the relationship with private forest farmers. As result, policy assumptions cannot be implemented properly. [ABSTRACT FROM AUTHOR]

The accurate information derived from high accuracy of remote sensing imagery analyses coupled with field observation data are required to develop a sound forest management. The study is mainly emphasized on assessment of the capabilities of remote sensing imageries to identify ecosystem types within the transitional ecosystem. Since, the predominant transition ecosystems found within the study area were secondary forest, rubber jungle, rubber, oil palm plantation, and also other land cover such as mixed plantation and shrubs, therefore, the models developed were focused for those ecosystem types. Prior to any further analysis, this study was initiated to develop the biomass estimation model using 50 m resolution of ALOS PALSAR image in transition ecosystem, Jambi Province. Biomass models were developed by analyzing the relationship between backscatter magnitude and field biomass. Backscatter magnitude from 1 polarization images, namely HH, HV, and one additional band of ratio of HH/HV were analyzed simultaneously with field biomass. The best models established are AGB = 42,069 exp (0.510 HV) and AGB = 1,610 exp (-0.02 HV²) with R² of 52.3% and 50,8%, respectively. The models are then used to map out the biomass distribution within the transition ecosystem and to identify the factors affecting the magnitude of biomass content for all transition ecosystem types. [ABSTRACT FROM AUTHOR]