Rubber is a perennial plant grown for natural rubber production, which is used in various global products. Ensuring the sustainability of rubber cultivation is crucial for smallholder farmers and economic development. Accurately predicting rubber yields is necessary to maintain price stability. Remote sensing technology is a valuable tool for collecting spatial data on a large scale. However, for smaller plots of land owned by smallholder farmers, it is necessary to process productivity estimates from high-resolution satellite data that are accurate and reliable. This study examines the impact of spatial factors on rubber yield and evaluates the technical suitability of using grouping analysis with the forest classification and regression (FCR) method. We developed a high-density variable using spatial data from rubber plots in close proximity to each other. Our approach incorporates eight environmental variables (proximity to streamlines, proximity to main river, soil drainage, slope, aspect, NDWI, NDVI, and precipitation) using an FCR model and GIS. We obtained a dataset of 1951 rubber yield locations, which we split into a training set (60%) for model development and a validation set (40%) for assessment using area under the curve (AUC) analysis. The results of the alternative FCR models indicate that Model 1 performs the best. It achieved the lowest root mean square error (RMSE) value of 19.15 kg/ha, the highest R-squared (R²) value (FCR) of 0.787, and also the highest R² (OLS) value of 0.642. The AUC scores for Model 1, Model 2, and Model 3 were 0.792, 0.764, and 0.732, respectively. Overall, Model 4 exhibited the highest performance according to the AUC scores, while Model 3 performed the poorest with the lowest AUC score. Based on these findings, it can be concluded that Model 1 is the most effective in predicting FCR compared to the other alternative models.

Khon Kaen District is in the central, north-east part of Thailand and is being developed to handle the country’s growth. Khon Kaen District is undertaking the project of building a light rail as a facility for the people. Consequently, one of the problems is ensuring adequate parking for people using the light rail service. In general, the symmetry concept naturally used in decision making to finding an optimal solution for decision and optimization problems. In this paper, multi-criteria decision analysis (MCDA) and multi-objective decision making (MODM) were used to solve the parking site selection problem, which made the decision easier. This paper proposed an analytic hierarchy process (AHP) technique, combined with the geographical information system (GIS), to evaluate the weight of the criteria used in the analysis and find potential parking solutions. Furthermore, this paper proposed the application of a linguistic technique with fuzzy TOPSIS methods to analyze the appropriateness of parking site selections from potential candidates to support use of the light rail. The results of the MCDA show that the most suitable parking lot location is along the light rail and closest to the business area. The results of the fuzzy TOPSIS method, both positive and negative ideal decisions, can help inform decision makers in selecting which candidate site is optimal for parking.

Infection with liver flukes (Opisthorchis viverrini) is partly due to their ability to thrive in habitats in sub-basin areas, causing the intermediate host to remain in the watershed system throughout the year. Spatial modeling is used to predict water source infections, which involves designing appropriate area units with hexagonal grids. This allows for the creation of a set of independent variables, which are then covered using machine learning techniques such as forest-based classification regression methods. The independent variable set was obtained from the local public health agency and used to establish a relationship with a mathematical model. The ordinary least (OLS) model approach was used to screen the variables, and the most consistent set was selected to create a new set of variables using the principal of component analysis (PCA) method. The results showed that the forest classification and regression (FCR) model was able to accurately predict the infection rates, with the PCA factor yielding a reliability value of 0.915. This was followed by values of 0.794, 0.741, and 0.632, respectively. This article provides detailed information on the factors related to water body infection, including the length and density of water flow lines in hexagonal form, and traces the depth of each process.

Forests are the largest carbon sinks in the world. They play a crucial role in mitigating climate change through carbon storage. Assessing carbon storage in forests is essential for policy formulation, management planning, and as a strategy to reduce the impacts of global warming. The aims of this research were to explore plant diversity, assess tree biomass, and evaluate carbon storage and carbon credits in urban forestry areas under the Thailand Voluntary Emission Reduction Program (T-VER). The study was conducted in the forested area of Rajamangala University of Technology Isan, Surin Campus, Thailand, and encompassed 60.96 ha. The methodology involved the creation of 10 temporary sample plots, each measuring 40 × 40 m. We then surveyed the species types and measured tree diameter at breast height (DBH) and the total height. Biomass was calculated using allometric equations and the stored carbon was quantified. In this study, we identified 85 species of plants. The analysis of tree biomass averaged 23,1781.25 kg/ha or 231.81 ton/ha. The carbon storage was estimated to be 108.94 tC/ha. When aggregating the data for the entire project, the total carbon storage amounted to 6851.55, with an equivalent carbon sequestration capacity of 25,122 tCO₂e in the base year (baseline). We calculated that the carbon storage capacity could increase to 28,741.00 tCO₂e with proper maintenance of the urban forest area over a 10-year period, equivalent to a carbon sequestration capacity of 3619 tCO₂e. This would result in a carbon credit value equivalent to USD 90,475.

Silky Oak or Silver Oak (Grevillea robusta A.Cunn. ex R.Br.) is classified as a high-value economic wood in Thailand, it is also considered to be a plant that can grow rapidly, and it has the potential to efficiently reduce greenhouse gases emitted into the atmosphere. This research aimed to study and develop an allometric equation to evaluate the biomass of F1 Silky Oak, which was imported to Thailand from Australia, and grown in Thailand’s economic woods in Silky Oak sites in Pak Chong District, Nakhon Ratchasima Province. The sample group consisted of trees of different ages (i.e., of 2 years, 3–4 years, and 7 years). An allometric equation was used to determine the tree biomass, based on mathematical models that describe the relationship between tree biomass and diameter at breast height (DBH). It was developed in the form of a quadratic equation by multiplying the square DBH by the total height (DBH² × Ht). Subsequently, the equation was separated into different components, which corresponded with different parts of the tree (i.e., stem, branches, leaves, and roots). The following equations were obtained for the stem: Ws = 0.0721 (D²H) 0.8297 R² = 0.998. The following equations were obtained for the branches: Wb = 0.0772 (D²H) 0.7027 R² = 0.977. The following equations were obtained for the leaves, Wl = 0.2085 (D²H) 0.4313 R² = 0.990. The following equations were obtained for the roots: Wr = 0.3337 (D²H) 0.4886 R² = 0.957. The results of a laboratory elemental analysis of the carbon sequestration in the biomass, using a CHN elemental analyzer, showed that the mean percentage of carbon content in the stems, branches, leaves, and roots was 45.805. Applying the developed allometric equation for evaluating carbon sequestration, using the survey data from the sample sites of Silky Oak, it was found that the amount of carbon sequestration for the aboveground biomass in three sites was 130.63 tCO₂eq. When the amount was converted into carbon dioxide, which was absorbed in the three sites, we obtained a value of 478.99 tCO₂eq. The results of the application of the allometric equation showed that there was substantial carbon sequestration potential in the surveyed sites, emphasizing the role of Silky Oak plantations for climate change mitigation and sustainable land management. This study advances our understanding of Silky Oak growth and carbon storage dynamics, offering valuable tools for biomass estimation and promoting environmentally beneficial land use practices.