Elsevier BVActa Medica Okayama0273-11777692025Spatiotemporal evolution of ecosystem carbon storage under land use/land cover dynamics in the coastal region of Central Vietnam48154837ENViet HoangHoGraduate School of Environmental and Life Science, Okayama UniversityHidenoriMoritaGraduate School of Environmental, Life, Natural Science and Technology, Okayama UniversityThanh HaHoUniversity of Agriculture and Forestry, Hue UniversityFelixBachoferGerman Aerospace Center (DLR), Earth Observation CenterEcosystem carbon storage is a cost-effective strategy for global climate change mitigation, and its fluctuation is markedly shaped by land use/land cover (LULC) dynamics. Taking Danang city as an example of Central Coastal Vietnam, this study aims to assess LULC changes and analyze the spatiotemporal evolution of carbon storage from 2023 to 2050 under four LULC change scenarios, including natural trend scenario (NTS), ecological protection scenario (EPS), economic development scenario (EDS), and cropland protection scenario (CPS), by integrating the support vector machine-cellular automata-Markov (SVM-CA-Markov) model and the InVEST model. The Optimal Parameters-based Geographical Detector (OPGD) model was subsequently employed to elucidate the impacts of driving factors on the spatial distribution of carbon storage. The results showed that, from 2007 to 2023, Danang city experienced a dramatic back-and-forth transformation between LULC types, with the predominant transitions being from natural forest to acacia tree-dominated plantation forest (6492.31 ha), and from cropland to settlements, acacia tree-dominated plantation forest, and other land (5483.05 ha, 3763.66 ha, 2762.35 ha, respectively). Between 2023 and 2050, LULC transformations in Danang city are projected to yield varying degrees of carbon storage levels across different scenarios. Specifically, carbon storage is anticipated to dwindle by 0.221 Mt, 0.223 Mt, and 0.298 Mt under NTS, EDS, and CPS, respectively, while enhancing by 0.141 Mt under EPS. Regarding the spatial distribution of carbon storage, high values will be chiefly found in the western high-elevation mountainous region, while low values will be concentrated mostly in the eastern lower-lying areas of the city. Additionally, elevation and temperature acted as the two most significant driving factors influencing the spatial distribution of carbon storage, with Q values of 0.88 and 0.86 (p-value < 0.05), respectively. For interaction detection, the combination of elevation and soil exhibited a synergistic reinforcement effect on the spatial partitioning of carbon storage, with a high Q value of 0.9566 (p-value < 0.05). Our study highlights the necessity of ecological conservation measures in Danang city in the on-track pursuit of national net-zero carbon emissions by 2050.No potential conflict of interest relevant to this article was reported.Springer Science and Business Media LLCActa Medica Okayama1439-01082552025Comparison of geostatistics, machine learning algorithms, and their hybrid approaches for modeling soil organic carbon density in tropical forests15541577ENViet HoangHoGraduate School of Environmental and Life Science, Okayama UniversityHidenoriMoritaGraduate School of Environmental, Life, Natural Science and Technology, Okayama UniversityThanh HaHoUniversity of Agriculture and Forestry, Hue UniversityFelixBachoferGerman Aerospace Center (DLR), Earth Observation CenterThi ThuongNguyenUniversity of Agriculture and Forestry, Hue UniversityPurpose Understanding the spatial variability of soil organic carbon density (SOCD) in tropical forests is necessary for efficient climate change mitigation initiatives. However, accurately modeling SOCD in these landscapes is challenging due to low-density sampling efforts and the limited availability of in-situ data caused by constrained accessibility. In this study, we aimed to explore the most suitable modeling technique for SOCD estimation in the context of tropical forest ecosystems.<br>
Methods To support the research, thirty predictor covariates derived from remote sensing data, topographic attributes, climatic factors, and geographic positions were utilized, along with 104 soil samples collected from the top 30 cm of soil in Central Vietnamese tropical forests. We compared the effectiveness of geostatistics (ordinary kriging, universal kriging, and kriging with external drift), machine learning (ML) algorithms (random forest and boosted regression tree), and their hybrid approaches (random forest regression kriging and boosted regression tree regression kriging) for the prediction of SOCD. Prediction accuracy was evaluated using the coefficient of determination (R2), the root mean squared error (RMSE), and the mean absolute error (MAE) obtained from leave-one-out cross-validation.<br>
Results The study results indicated that hybrid approaches performed best in predicting forest SOCD with the greatest values of R2 and the lowest values of MAE and RMSE, and the ML algorithms were more accurate than geostatistics. Additionally, the prediction maps produced by the hybridization showed the most realistic SOCD pattern, whereas the kriged maps were prone to have smoother patterns, and ML-based maps were inclined to possess more detailed patterns. The result also revealed the superiority of the ML plus residual kriging approaches over the ML models in reducing the underestimation of large SOCD values in high-altitude mountain areas and the overestimation of low SOCD values in low-lying terrain areas.<br>
Conclusion Our findings suggest that the hybrid approaches of geostatistics and ML models are most suitable for modeling SOCD in tropical forests.No potential conflict of interest relevant to this article was reported.岡山大学環境理工学部Acta Medica Okayama2187-69402212017タイのアスパラガス産地における圃場の選好条件6571ENHidenoriMoritaGraduate School of Environmental and life Science, Okayama UniversityShoyaHironakaYukiMatsumoto10.18926/fest/54864A questionnaire survey by interview to the farmhouse in Nakhon Pathom Province in Thailand had been carried out, where the asparagus farming as the cash crop have spread rapidly. Valid response from 24 farmers were obtained and Asparagus had been planted in 30 plots within 74 plots (40.5%). As the result of statistical analysis on the geospatial preference for asparagus fields by logit model in which the geospatial characteristics of plots such as elevation, slope, curvature, acreage and distance from farmhouse were adapted as the explanatory variables while the category of the cultivated crops in there was adapted as the independent variable, it became clear that as for the asparagus plots, small lots were more preferred. On the other hand, the geospatial preference for asparagus plots such as elevation, slope, curvature and distance from farmhouse did not became clear.No potential conflict of interest relevant to this article was reported.岡山大学環境理工学部Acta Medica Okayama2187-69402212017岡山地方気象台観測露場移転による気温低下量の推定6164ENTakeshiMiuraGraduate School of Environmental and life Science, Okayama UniversityYuUedaGraduate School of Environmental and life Science, Okayama UniversityHidenoriMoritaGraduate School of Environmental and life Science, Okayama UniversityHidetakaChikamoriGraduate School of Environmental and life Science, Okayama UniversityMasahiroKurokawaYoshitakaNakashimaGraduate School of Environmental and life Science, Okayama UniversityYokoOkiGraduate School of Environmental and life Science, Okayama University10.18926/fest/54863Statistical change in official temperature records at Okayama City caused by relocation of meteorological observation field of Meteorological Agency was evaluated. The observation field of Okayama local meteorological observatory was moved to the Tsushima Campus of Okayama University from the downtown area of Okayama City in March, 2015. Comparison between the air temperature records measured at meteorological agency station and the records at Tanjo Pond in Tsushima Campus, showed 0.56 ℃ drop in annual average before and after relocation. Moreover, comparison between the records of Okayama local meteorological observatory and that at the surrounding meteorological observing 9 stations showed 0.55 ℃ drop in annual average. Those results suggest that the relocation dropped annual average of air temperature by about 0.6 ℃.No potential conflict of interest relevant to this article was reported.Acta Medica Okayama2014Simplified approach to evaluate total external loading to Kojima Lake7692ENShin-ichiNISHIMURAKenjiOKUBOHidenoriMORITAJariKOSKIAHOSirrkaTATTARINo potential conflict of interest relevant to this article was reported.