Burapha Science Journal

Variations of Sediment Organic Matter and Nutrients in Nam Oun Dam Reservoir, Sakon Nakhon Province

Kritsana Akkharaphat — 2026-01-07

Background and Objectives : Nam Oun Dam is a large reservoir, serves water storage, irrigation, flood control and drainage. Nam Oun Dam Reservoir covers the areas of many districts in Sakon Nakhon province. Land use in the areas surrounding the reservoir, such as residential communities, tourist sites, fisheries, agriculture (crop cultivation and livestock farming), fisheries conservation zones, and water inflow and outflow areas. These areas are important for the generation of sediments and nutrients within the reservoir. This reservoir is currently facing the problem of Giant Salvinia infestation (Salvinia molesta D.S. Mitchell), which creates a risk to accelerate the accumulation of sediments and may affect the water quality in the long-term of the reservoir. This study aimed to investigate the spatial and seasonal variations of organic matter and nutrient contents in the sediments of Nam Oun Dam Reservoir, including sediment water content and particle size.

Methodology : Study sites in Nam Oun Dam Reservoir were 10 stations (S1-S10) such as S1 (water outlet-left gate), S2 (water outlet-right gate), S3 (tourist attraction and community), S4 (raft-ecotourist attraction and fisheries conservation zone), S5 (pumping station for tap water production and agriculture areas), S6 (Huai Lek Fai inlet and community areas), S7 (fisheries conservation zone), S8-S9 (fisheries and agriculture area) and S10 (Nam Oun River inlet area). Surface sediment samples were collected using an Ekman Grab sampler (15x15 cm) from 10 stations (S1-S10) located in areas with different land uses surrounding the reservoir. Sampling was conducted seasonally within a year, three sampling periods were summer (late March 2023), rainy (mid- August 2023) and winter (early January 2024). The sediment samples were analyzed to determine the contents of organic carbon (OC), organic matter (OM), ammonium-nitrogen (NH₄⁺-N), nitrate-nitrogen (NO₃^--N), phosphate-phosphorous (PO₄^3--P), sediment water content (WC) and percentage (%) of sediment particles (clay, silt and sand). The data were statically analyzed using One-Way Analysis of Variance (ANOVA), the averages were compared by Duncan’s Multiple Range Test, and the relationships between parameters were analyzed by Pearson’s Correlation.

Main Results :The contents of OC and OM ranged from 0.85-4.93% and 1.46-8.50%, with the averages of 2.22±0.97% and 3.83±1.66%, respectively. There were not significant seasonal differences (p>0.05), whereas significant spatial differences were found among the study sites (p<0.05). Site S7 was the highest OC and OM contents, while the lowest contents were found at S1, S2, S3, S6, S9 and S10. The averages of OM content in most areas of the reservoir indicated a low level of organic matter in the sediments. The averages of NH₄⁺-N, NO₃^--N and PO₄^3--P contents ranged from 3.68-58.69 mg/kg, 10.94-160.23 mg/kg and 0.07-0.72 mg/kg, with the averages of 17.55±12.99 mg/kg, 50.17±40.73 mg/kg and 0.36±0.20 mg/kg, respectively. There were not significant spatial differences for nutrients (p>0.05). However, significant seasonal differences were observed for NO₃^--N and PO₄^3--P (p<0.05), with the highest averages recorded in winter and rainy seasons, respectively. In contrast, NH₄⁺-N was not significant seasonal difference (p>0.05). The NH₄⁺-N content was significantly correlated with OM content (p<0.05, r=0.41) and highly significantly correlated with NO₃^--N content (p<0.001, r=0.68). Furthermore, PO₄^3--P content was also found to be very significantly correlated with OM content (p<0.01, r=0.49). These indicated that the organic matter content in the sediments had positive effect on the nutrient contents. WC ranged from 41.47-77.11%, with the averages of 58.45±9.03%. WC was not significant differences between seasons (p>0.05), but significant difference was found among study sites (p<0.05). The highest contents were found at S7 and S8, with the lowest content was found at S3. WC showed highly significant positive correlation with the contents of OM (p<0.001, r=0.83), very significant positive correlation with NH₄⁺-N (p<0.01, r=0.48), and PO₄^3--P (p<0.01, r=0.55) and significant positive correlation with NO₃^--N (p<0.05, r=0.39). Therefore, WC could be the important parameter for preliminary indicator of the organic matter and nutrient conditions in the sediments. The results of the sediment particle size were found that clay, silt and sand ranged from 19.96-54.27%, 14.72-44.57% and 22.63-62.09%, with the averages of 29.06±8.50%, 26.83±8.28% and 44.17±11.60%, respectively. Particle size was not statically significant seasonal difference (p>0.05), but it was statically significant spatial difference (p<0.05). The results of sediment texture analysis showed that sandy clay loam was mostly found in the study areas of the reservoir (S2, S4, S7 and S8), followed by loam (S1, S3 and S5), clay loam (S6 and S9) and clay (S10), respectively. This research did not detect a correlation in the particle size and the contents of organic matter and nutrients.

Conclusions : The land use areas surrounding the Nam Oun Dam Reservoir had a significant effect (p<0.05) on the variations in sediment organic matter contents (OC and OM), water content (WC) and Particle size (clay, silt and sand) within the reservoir, but had no significant effect (p>0.05) on the sediment nutrient contents. In contrast, the season had a significant effect (p<0.05) on the variations of OC, OM and particle size, but had no significant effect (p>0.05) on the variations of most sediment nutrients. Further study on water current velocity could provide more explanation of sediment transport direction. The data from this research can be used as baseline for sustainable management of Nam Oun Dam Reservoir.

Prediction of above Ground Carbon Sequestration from Landuse/Landcover Changes in the Upper Northern Thailand Using CASA-Biosphere Model

Jirapon Samsen — 2026-01-07

Background and Objectives : Aboveground carbon sequestration, or Net Primary Productivity (NPP), is an important ecological indicator that reflects the potential growth of vegetation in absorbing and storing carbon dioxide from the atmosphere through photosynthesis. This process plays a critical role in offsetting carbon emissions resulting from human activities. Therefore, assessing aboveground carbon sequestration in a specific region is essential for understanding carbon balance within ecosystems and promoting ecological restoration. This study selected the Upper Northern Region of Thailand as the study area due to several unique characteristics, including high ecological diversity, the presence of various forest and agricultural landscapes, topographical variation ranging from highlands to watershed areas, and seasonal climatic fluctuations. Land use/land cover (LULC) in this region directly affects the carbon sequestration potential of each land type. However, systematic studies on the relationship between LULC types and carbon sequestration in the Upper Northern Region of Thailand remain limited, especially those employing remote sensing technology for detailed spatial analysis. This study aims to (1) Analyze the relationship between carbon sequestration and changes in land use/land cover, and (2) Predict future carbon sequestration and land use/land cover changes.

Methodology : The study begins with an analysis of the relationship between land use/land cover and aboveground carbon sequestration by classifying land use types for three time periods 2016, 2019, and 2024 using the Random Forest model. The CASA-Biosphere Model is then applied to estimate carbon sequestration, and the estimated results are validated using field data through Pearson’s correlation coefficient. Subsequently, future aboveground carbon sequestration is predicted using the hybrid Markov-CA model to simulate future LULC changes. Regression equations are then developed to predict the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). These predicted datasets, together with projected LULC data, are incorporated into the CASA-Biosphere Model to estimate future NPP. Finally, the accuracy of NPP prediction is assessed using Pearson’s correlation coefficient, Correlation Coefficient, Root Mean Square Error (RMSE), and Mean Absolute Error (MAPE).

Main Results : The analysis using the CASA-Biosphere Model revealed distinct variations in aboveground carbon sequestration across different land use and land cover (LULC) types, with forest areas exhibiting the highest sequestration potential (0.249–1.256 gC/m²) due to dense vegetation, complex canopy structures, and robust root systems, followed by perennial crops (0.245–0.836 gC/m²), while rice paddies and field crops showed moderate values (up to 0.596 gC/m² and 0.637 gC/m², respectively), reflecting differences in species composition, management practices, and growing seasons. In contrast, urban and industrial areas showed very low or negative sequestration (0.057 to –0.287 gC/m²), indicating their role as net carbon sources as a result of limited vegetation cover and high anthropogenic emissions. Model validation using three sets of field data produced Pearson’s R² values of 0.663, 0.710, and 0.994, confirming strong agreement between modeled and observed NPP. Future projections for 2033 indicate increasing carbon sequestration across most LULC categories, with forests remaining the highest (up to 1.326 gC/m²), alongside rising values for rice paddies (0.569 gC/m²) and perennial crops (0.778 gC/m²), a trend potentially influenced by climatic conditions that enhance vegetation productivity. The accuracy of NPP predictions, validated using historical data, achieved a high correlation coefficient (R² = 0.924), demonstrating the model’s strong capability to capture spatial–temporal patterns and its suitability for long-term carbon sequestration forecasting and environmental planning

Conclusions : The assessment of aboveground carbon sequestration under land use/land cover changes using the CASA-Biosphere Model reveals that forest areas possess the highest carbon sequestration potential, followed by perennial crops. Agricultural areas show moderate potential, with field crops performing similarly to rice paddies. Urban and industrial areas exhibit very low or negative sequestration, acting instead as carbon sources. The CASA-Biosphere Model demonstrates high accuracy when validated with field data. Forecasts for the year 2033 indicate increasing carbon sequestration for all LULC categories, with forests remaining the strongest contributors, followed by perennial crops and agricultural areas. The high predictive accuracy confirms the suitability of this approach for assessing carbon sequestration trends within the study region. The findings support forest conservation and sustainable agricultural practices to enhance carbon sequestration and mitigate climate change impacts. The results can guide policy-making on forest resource conservation by identifying priority areas for preservation and restoration, as well as informing national greenhouse gas reduction targets under the Paris Agreement. Moreover, the projected carbon sequestration trends can serve as a foundation for sustainable land management, low-carbon agriculture, and the development of agricultural carbon credit mechanisms to enhance regional and national carbon sequestration efficiency.

Investigation of Land Use and Land Cover Change and Urban Heat Island Phenomenon in Nakhon Ratchasima Province

Anake Srisuwan — 2026-01-07

Background and Objectives: Urbanization constitutes a significant physical and sociodemographic transformation of the 21st century, exerting considerable influence on global ecosystems and local microclimates. Human-induced alterations in the previously vegetated and permeable soil structure of natural landscapes, along with their transformation into concrete, asphalt, and decorative architectural developments, significantly affect the Surface Energy Budget (SEB). These alterations generate an artificial environment that disrupts the natural equilibrium of the Latent Heat Flux. Latent Heat Flux decreases exponentially because of insufficient evapotranspiration, concurrently leading to an increase in the storage and re-release of Sensible Heat Flux. The Urban Heat Island (UHI) Effect refers to the phenomenon where urban areas experience higher temperatures than their rural surroundings due to human activities and alterations in land cover. The Urban Heat Island (UHI) Effect refers to a temperature phenomenon where urbanized areas exhibit markedly elevated atmospheric and surface temperatures compared to nearby rural regions. The Urban Heat Island (UHI) Effect refers to a temperature phenomenon in which urbanized areas exhibit markedly elevated atmospheric and surface temperatures compared to nearby rural regions. The UHI Effect has significant implications, including increased energy demands for air conditioning, deterioration of air quality due to heightened ground-level ozone production, and substantial risks to human health and urban living conditions. Nakhon Ratchasima Province is strategically located as the Gateway of Isan, serving as the biggest economic, industrial, and transportation center of Northeastern Thailand. The province has undergone significant structural urbanization in the last two decades. The expansion has been driven by major infrastructure assets and an increase of industrial areas. The city is experiencing thermal challenges due to its expansion. The previous study on the UHI phenomenon in this domain has been mainly descriptive. Literature often exhibits insufficient statistical validation to quantify temperature variations among various Land Use and Land Cover (LULC) types and is frequently hindered by methodological limitations concerning satellite image data calibration. Addressing these significant studies problems. This study aims to: (1) analyze the spatiotemporal changes in land use and land cover (LULC) from 2006 to 2021 and (2) assess the impact on structural changes in urban heat island (UHI) intensity and dynamics, by using high-quality radiometric data and robust statistical methods to verify the accuracy and reliability of the results.

Methodology: This study employed a Hybrid Classification method for the analysis of Land Use and Land Cover (LULC) for optimal accuracy of data and consistency. The 2006 LULC database published by the Land Development Department (LDD) was used as the baseline for this study. In 2021, LULC classification occurred by using multispectral imagery from the Landsat 8 Operational Land Imager (OLI) sensor that was acquired during the dry season in February to avoid cloud influence and variations in the season. The classification process used the Maximum Likelihood Classification (MLC) algorithm, that is a parametric supervised learning method based on the assumption of a normal distribution of image pixels within each training class. The classification accuracy was assessed through Stratified Random Sampling involving 205 reference points, results in an Overall Accuracy of 95% and a Kappa Coefficient of 0.90, which refers to an excellent level of agreement between the LULC classified map and in-situ data. The temperature evaluation focused on capturing Land Surface Temperature (LST) and identified data quality through the selection of USGS Collection 2 Level-2 Science Products (L2SP). The measurements of temperatures have been acquired from Landsat 5 Thematic Mapper (TM) for February 2007 and from Landsat 8 Thermal Infrared Sensor (TIRS) for February 2021. A selection was taken to apply data from the same temporal season (dry/cool season), and the selection of 2007 LST as a baseline estimate effectively reduced discrepancies associated with seasonal variability and atmospheric absorption. This study uses advanced statistical methods to evaluate the relationship between LST and various LULC types, expanding higher than simple descriptive statistics. Welch’s One-way ANOVA was used to improve its resistance against violations of homogeneity of variance assumptions, which are often present in environmental datasets. Post-hoc pairwise comparisons were performed utilizing the Games-Howell method. The spatial impact of heat island variation has been evaluated by using the Urban Heat Island Ratio Index (URI) and the Temperature Grade Change Index (TGCI) to show and quantify heat island expansion.

Main Results: The Transition Matrix analysis showed that the city's urban morphology changed a lot in terms of structure throughout the 15 years of research. Urban and built-up areas grew by a net of 73.66 square kilometers, which is a huge growth rate of 34.46% compared to the baseline. This urban and built-up area expansion primarily occurred on agricultural land (55.32 sq. km.) while other of LULC (23.35 sq. km.). Additionally, the analysis found that forest land decreased by 49.83%, which shows how significantly people are placing stress on natural resources and their buffers. Statistical analysis of the 2021 LST data found important proof of the UHI effect. The average surface temperature in urban and built-up areas was 28.4°C, which was significantly greater than the average surface temperature in forests land (25.40°C), with a statistical significance of p < .001. The average temperature difference of about 2.99°C shows how urbanization impacts the temperature. The result is distinct from the baseline LULC data from 2007, that showed a lack of statistical significance between urban and built-up areas and forest area temperatures. This indicates that the city has changed from a temperature balanced condition to a distinct island. In 2021, the mean temperature in the area decreased into slightly because of the La Niña phenomenon, which decreases the region. However, the location inside thermal structure became more severe. The URI analysis showed that the number of regions classified as "High Temperature Grade" increased from 7.30% to 7.39% of the whole area studied. This indicates that the increase due to urbanization is strong enough to cause local cooling patterns in the surrounding environment. The Linear Regression study also showed a strong positive relation between the Normalized Difference Built-up Index (NDBI) and LST. The coefficient of slope increasing higher in 2021 shows that the current urban surface elements in Nakhon Ratchasima are becoming increasingly prone to heat accumulation and retention.

Conclusions : This study shows that the fast urbanization of Nakhon Ratchasima during the past fifteen years has greatly exacerbated the UHI phenomena. The city has transitioned from thermal equilibrium to a fully developed heat island, mostly due to alterations in urban morphology rather than regional climatic influences. The results have serious effects on urban planning and policy. Statistical evidence indicates that there is a major negative relationship between LST and NDVI and MNDWI. Therefore, city planners require that they quickly add "Blue-Green Infrastructure" to the city's overall strategy. Several actions that should be implemented are to protect the original forest areas, build green spaces in cities, and restore water bodies. These actions have been shown to be the most effective approaches to decrease UHI intensity, increase Nakhon Ratchasima more comfortable in hot weather, as well as making it more resilient to climate change as the future urban expansion.

Synergistic Antibacterial and Antibiofilm Activity of Glochidonol Isolated from Glochidion acuminatum with Antibiotics against Opportunistic Bacteria

Wisatre Kongcharoensuntorn — 2026-01-08

Background and Objectives: Glochidonol, a bioactive compound isolated from Glochidion acuminatum, has been traditionally recognized for its analgesic, antipyretic, and diuretic properties. In recent years, scientific investigations have revealed that Glochidonol also possesses a broad spectrum of biological activities, including anti-inflammatory, antibacterial, antioxidant, and anticancer effects. These properties make it a promising candidate in the search for novel agents to combat emerging threats from drug-resistant microorganisms. Opportunistic bacterial pathogens such as Pseudomonas aeruginosa, Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus are increasingly responsible for healthcare-associated infections, especially among immunocompromised individuals. Infections caused by this group of bacteria are often severe and tend to develop into bloodstream infections (septicemia), which may lead to serious complications or death. This condition reduces the effectiveness of antibiotics in the tetracycline group (such as Oxytetracycline) and the penicillin group (such as Ampicillin), rendering them less effective or ineffective.Opportunistic bacteria have developed various mechanisms of antibiotic resistance, such as producing drug-degrading enzymes, reducing membrane permeability, and most importantly, forming biofilms, complex structures composed of extracellular polymeric substances (EPS) secreted by the bacteria to encapsulate themselves and neighboring cells within the community. This protective matrix enables the bacteria to survive under unfavorable environmental conditions and adhere firmly to surfaces such as wounds or medical devices, making biofilm formation a key factor that accelerates the high incidence of antibiotic resistance among opportunistic bacteria. As conventional antibiotics such as Oxytetracycline) and Ampicillin) become less effective due to these resistance mechanisms, the development of new therapeutic strategies is urgently needed. One promising approach is the combination of natural bioactive compounds with existing antibiotics to restore or enhance antibacterial efficacy. Natural products like Glochidonol are of particular interest because they may disrupt biofilm formation, increase bacterial membrane permeability, or inhibit resistance-related enzymes, thereby potentiating the activity of conventional antibiotics. Therefore, the use of Glochidonol in combination with antibiotics may represent a new therapeutic alternative and constitutes the first study of this compound. Given this context, this study aims to evaluate the potential synergistic effects of Glochidonol, in combination with standard antibiotics, against opportunistic bacterial pathogens, with a particular focus on its ability to inhibit bacterial growth and biofilm formation. By investigating these combined effects, the research seeks to provide a scientific basis for utilizing Glochidonol as a complementary agent in antimicrobial therapy, offering a novel and sustainable strategy to combat drug-resistant infections and reduce the environmental impact of antibiotic-resistant bacteria.

Methodology : This study investigates the efficacy of Glochidonol in inhibiting the growth of opportunistic bacteria by determining the Minimal Inhibitory Concentration (MIC) using the broth microdilution assay. Glochidonol was prepared in various concentrations in Mueller Hinton Broth (MHB) and mixed with bacterial suspensions adjusted to 1 × 10⁸ CFU/mL. The samples were incubated at 37°C for 18–24 hours, and bacterial growth was conducted by measuring optical density at 600 nm (OD_₆₀₀) to determine the MIC. To evaluate the synergistic antibacterial activity, the Fractional Inhibitory Concentration Index (FICI) was calculated using the checkerboard microdilution assay. Glochidonol was combined with antibiotics, and the mixtures were tested against bacterial suspensions at 1 × 10⁸ CFU/mL, following the same incubation protocol as the MIC assay. The FICI was calculated to determine potential synergistic effect between Glochidonol and antibiotics. The anti-biofilm activity of Glochidonol was determined using the Crystal Violet Assay. Bacterial cultures were incubated with Glochidonol or antibiotics for 24 hours, after which non-adherent cells were removed by washing with Phosphate Buffered Saline (PBS). Bacterial biofilms were stained with 0.1% crystal violet, fixed with 100%methanol, and the bacterial bound dye was solubilized using 33% glacial acetic acid. Biofilm biomass was quantified by measuring optical density at 600 nm.

Main results : The purified compound Glochidonol (GE-12) was found to inhibit the growth of opportunistic bacteria, including both Gram-positive and Gram-negative bacteria. GE-12 exhibited its strongest antibacterial activity against Gram-negative bacteria, particularly against A. baumannii and K. pneumoniae, with a MIC of 128 µM. When compared with conventional antibiotics, GE-12 demonstrated comparable efficacy to Ampicillin and Oxytetracycline. Moreover, GE-12 showed the best synergistic effect with Oxytetracycline against Escherichia coli, with a FICI value of 0.56. In contrast, it showed antagonistic effect against Pseudomonas aeruginosa (FICI = 32) and had no synergistic effect against A. baumannii. The time-kill assay revealed that GE-12 most effectively inhibited E. coli growth at 8 hours when used at 16 µM (1/64 MIC) in combination with Oxytetracycline at 256 µM (1/8 MIC). For P. aeruginosa, the best inhibitory effect was observed when GE-12 at 16 µM (1/32 MIC) was combined with Oxytetracycline at 2048 µM (1× MIC). Additionally, GE-12 at a concentration of 2048 µM was most effective in inhibiting biofilm formation of A. baumannii, E. coli, and P. aeruginosa over a 2–24hour period, with inhibition rates ranging from 25% to 68%.

Conclusions : Glochidonol isolated from G. acuminatum demonstrated stronger inhibitory activity against Gram-negative bacteria compared to Gram-positive bacteria. It also exhibited synergistic effects in inhibiting bacterial growth and biofilm formation in both drug-resistant and non-resistant strains, particularly E. coli and P. aeruginosa.