start-ver=1.4
cd-journal=joma
no-vol=76
cd-vols=
no-issue=9
article-no=
start-page=4815
end-page=4837
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2025
dt-pub=202511
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Spatiotemporal evolution of ecosystem carbon storage under land use/land cover dynamics in the coastal region of Central Vietnam
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Ecosystem 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.
en-copyright=
kn-copyright=
en-aut-name=HoViet Hoang
en-aut-sei=Ho
en-aut-mei=Viet Hoang
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=
en-aut-name=MoritaHidenori
en-aut-sei=Morita
en-aut-mei=Hidenori
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=
en-aut-name=HoThanh Ha
en-aut-sei=Ho
en-aut-mei=Thanh Ha
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=
en-aut-name=BachoferFelix
en-aut-sei=Bachofer
en-aut-mei=Felix
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=
affil-num=1
en-affil=Graduate School of Environmental and Life Science, Okayama University
kn-affil=
affil-num=2
en-affil=Graduate School of Environmental, Life, Natural Science and Technology, Okayama University
kn-affil=
affil-num=3
en-affil=University of Agriculture and Forestry, Hue University
kn-affil=
affil-num=4
en-affil=German Aerospace Center (DLR), Earth Observation Center
kn-affil=
en-keyword=Carbon sequestration
kn-keyword=Carbon sequestration
en-keyword=Scenario-based modeling
kn-keyword=Scenario-based modeling
en-keyword=Remote sensing
kn-keyword=Remote sensing
en-keyword=Spatial autocorrelation analysis
kn-keyword=Spatial autocorrelation analysis
END
start-ver=1.4
cd-journal=joma
no-vol=25
cd-vols=
no-issue=5
article-no=
start-page=1554
end-page=1577
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2025
dt-pub=20250405
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Comparison of geostatistics, machine learning algorithms, and their hybrid approaches for modeling soil organic carbon density in tropical forests
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Purpose 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.
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.
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.
Conclusion Our findings suggest that the hybrid approaches of geostatistics and ML models are most suitable for modeling SOCD in tropical forests.
en-copyright=
kn-copyright=
en-aut-name=HoViet Hoang
en-aut-sei=Ho
en-aut-mei=Viet Hoang
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=
en-aut-name=MoritaHidenori
en-aut-sei=Morita
en-aut-mei=Hidenori
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=
en-aut-name=HoThanh Ha
en-aut-sei=Ho
en-aut-mei=Thanh Ha
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=
en-aut-name=BachoferFelix
en-aut-sei=Bachofer
en-aut-mei=Felix
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=
en-aut-name=NguyenThi Thuong
en-aut-sei=Nguyen
en-aut-mei=Thi Thuong
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=
affil-num=1
en-affil=Graduate School of Environmental and Life Science, Okayama University
kn-affil=
affil-num=2
en-affil=Graduate School of Environmental, Life, Natural Science and Technology, Okayama University
kn-affil=
affil-num=3
en-affil=University of Agriculture and Forestry, Hue University
kn-affil=
affil-num=4
en-affil=German Aerospace Center (DLR), Earth Observation Center
kn-affil=
affil-num=5
en-affil=University of Agriculture and Forestry, Hue University
kn-affil=
en-keyword=Digital soil mapping
kn-keyword=Digital soil mapping
en-keyword=Hybrid approaches
kn-keyword=Hybrid approaches
en-keyword=Kriging
kn-keyword=Kriging
en-keyword=Machine learning
kn-keyword=Machine learning
en-keyword=Soil organic carbon density
kn-keyword=Soil organic carbon density
en-keyword=Tropical forests
kn-keyword=Tropical forests
END
start-ver=1.4
cd-journal=joma
no-vol=22
cd-vols=
no-issue=1
article-no=
start-page=65
end-page=71
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2017
dt-pub=201703
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=Geospatial Preference for Asparagus Fields in Thailand
kn-title=タイのアスパラガス産地における圃場の選好条件
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=A 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.
en-copyright=
kn-copyright=
en-aut-name=MoritaHidenori
en-aut-sei=Morita
en-aut-mei=Hidenori
kn-aut-name=守田秀則
kn-aut-sei=守田
kn-aut-mei=秀則
aut-affil-num=1
ORCID=
en-aut-name=HironakaShoya
en-aut-sei=Hironaka
en-aut-mei=Shoya
kn-aut-name=弘中奨也
kn-aut-sei=弘中
kn-aut-mei=奨也
aut-affil-num=2
ORCID=
en-aut-name=MatsumotoYuki
en-aut-sei=Matsumoto
en-aut-mei=Yuki
kn-aut-name=松本雄樹
kn-aut-sei=松本
kn-aut-mei=雄樹
aut-affil-num=3
ORCID=
affil-num=1
en-affil=Graduate School of Environmental and life Science, Okayama University
kn-affil=岡山大学大学院環境生命科学研究科
affil-num=2
en-affil=
kn-affil=倉敷市役所
affil-num=3
en-affil=
kn-affil=復建調査設計株式会社
en-keyword=Land use
kn-keyword=Land use
en-keyword=GIS
kn-keyword=GIS
en-keyword=Thailand
kn-keyword=Thailand
en-keyword=Asparagus
kn-keyword=Asparagus
en-keyword=logit model
kn-keyword=logit model
END
start-ver=1.4
cd-journal=joma
no-vol=22
cd-vols=
no-issue=1
article-no=
start-page=61
end-page=64
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2017
dt-pub=201703
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=Evaluation of drop in official air temperature record at Okayama caused by relocation of observation field of Okayama Local Meteorological Observatory - By comparison with records observed at surrounding points -
kn-title=岡山地方気象台観測露場移転による気温低下量の推定
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Statistical 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 ℃.
en-copyright=
kn-copyright=
en-aut-name=MiuraTakeshi
en-aut-sei=Miura
en-aut-mei=Takeshi
kn-aut-name=三浦健志
kn-aut-sei=三浦
kn-aut-mei=健志
aut-affil-num=1
ORCID=
en-aut-name=UedaYu
en-aut-sei=Ueda
en-aut-mei=Yu
kn-aut-name=上田悠生
kn-aut-sei=上田
kn-aut-mei=悠生
aut-affil-num=2
ORCID=
en-aut-name=MoritaHidenori
en-aut-sei=Morita
en-aut-mei=Hidenori
kn-aut-name=守田秀則
kn-aut-sei=守田
kn-aut-mei=秀則
aut-affil-num=3
ORCID=
en-aut-name=ChikamoriHidetaka
en-aut-sei=Chikamori
en-aut-mei=Hidetaka
kn-aut-name=近森秀高
kn-aut-sei=近森
kn-aut-mei=秀高
aut-affil-num=4
ORCID=
en-aut-name=KurokawaMasahiro
en-aut-sei=Kurokawa
en-aut-mei=Masahiro
kn-aut-name=黒川正宏
kn-aut-sei=黒川
kn-aut-mei=正宏
aut-affil-num=5
ORCID=
en-aut-name=NakashimaYoshitaka
en-aut-sei=Nakashima
en-aut-mei=Yoshitaka
kn-aut-name=中嶋佳貴
kn-aut-sei=中嶋
kn-aut-mei=佳貴
aut-affil-num=6
ORCID=
en-aut-name=OkiYoko
en-aut-sei=Oki
en-aut-mei=Yoko
kn-aut-name=沖陽子
kn-aut-sei=沖
kn-aut-mei=陽子
aut-affil-num=7
ORCID=
affil-num=1
en-affil=Graduate School of Environmental and life Science, Okayama University
kn-affil=岡山大学大学院環境生命科学研究科
affil-num=2
en-affil=Graduate School of Environmental and life Science, Okayama University
kn-affil=岡山大学大学院環境生命科学研究科
affil-num=3
en-affil=Graduate School of Environmental and life Science, Okayama University
kn-affil=岡山大学大学院環境生命科学研究科
affil-num=4
en-affil=Graduate School of Environmental and life Science, Okayama University
kn-affil=岡山大学大学院環境生命科学研究科
affil-num=5
en-affil=
kn-affil=岡山大学環境理工学部
affil-num=6
en-affil=Graduate School of Environmental and life Science, Okayama University
kn-affil=岡山大学大学院環境生命科学研究科
affil-num=7
en-affil=Graduate School of Environmental and life Science, Okayama University
kn-affil=岡山大学大学院環境生命科学研究科
en-keyword=Okayama Local Meteorological Observatory
kn-keyword=Okayama Local Meteorological Observatory
en-keyword=relocation
kn-keyword=relocation
en-keyword=drop in air temperature
kn-keyword=drop in air temperature
END
start-ver=1.4
cd-journal=joma
no-vol=
cd-vols=
no-issue=
article-no=
start-page=76
end-page=92
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2014
dt-pub=201406
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Simplified approach to evaluate total external loading to Kojima Lake
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=
en-copyright=
kn-copyright=
en-aut-name=NISHIMURAShin-ichi
en-aut-sei=NISHIMURA
en-aut-mei=Shin-ichi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=
en-aut-name=OKUBOKenji
en-aut-sei=OKUBO
en-aut-mei=Kenji
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=
en-aut-name=MORITAHidenori
en-aut-sei=MORITA
en-aut-mei=Hidenori
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=
en-aut-name=KOSKIAHOJari
en-aut-sei=KOSKIAHO
en-aut-mei=Jari
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=
en-aut-name=TATTARISirrka
en-aut-sei=TATTARI
en-aut-mei=Sirrka
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=
affil-num=1
en-affil=
kn-affil=
affil-num=2
en-affil=
kn-affil=
affil-num=3
en-affil=
kn-affil=
affil-num=4
en-affil=
kn-affil=
affil-num=5
en-affil=
kn-affil=
END