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  • 1
    In: Land, MDPI AG, Vol. 11, No. 8 ( 2022-08-04), p. 1232-
    Abstract: Terrestrial carbon sequestration capacity is an important indicator of ecosystem service function, and the carbon storage value can reflect the climate regulation capacity of the regional ecological environment. The Zoigê alpine grassland is a representative area of the Qinghai-Tibet Plateau grassland ecosystem, with carbon sequestration types such as alpine grassland and marsh meadow and also an important water-conserving area in the upper reaches of the Yangtze River and the Yellow River. In this study, based on the land use/cover change pattern of the Zoigê alpine grassland region from 2000 to 2020, the carbon density coefficients corrected by the regional average annual precipitation and temperature factors were used to assess the carbon stocks of the Zoigê alpine grassland for three periods from 2000 to 2020 using the InVEST model. The results showed that the carbon stocks of the Zoigê alpine grassland region were 786.19 Tg, 780.02 Tg, and 775.22 Tg in 2000, 2010, and 2020, respectively, with a cumulative loss of 10.97 Tg and carbon densities of 183.70 t/ha, 182.26 t/ha, and 181.14 t/ha, showing a decreasing trend year by year. The carbon stock of the grassland ecosystem is the absolute contributor to the regional carbon stock, and the carbon stock accounts for 75.28% of the total carbon stock. The increase in the cultivated land area with a lower carbon density and the decrease in the grassland area with a higher carbon density are the main factors leading to the decrease in the carbon stock in the regional ecosystem of the Zoigê alpine grassland.
    Type of Medium: Online Resource
    ISSN: 2073-445X
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2682955-1
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  • 2
    Online Resource
    Online Resource
    Frontiers Media SA ; 2022
    In:  Frontiers in Ecology and Evolution Vol. 10 ( 2022-12-6)
    In: Frontiers in Ecology and Evolution, Frontiers Media SA, Vol. 10 ( 2022-12-6)
    Abstract: The vegetation in mountainous areas is abundant, and its ecological carbon sequestration ability is of great significance to maintain the sustainable and healthy development of the ecological environment. However, when estimating the carbon sequestration of mountain vegetation, the Carnegie-Ames-Stanford Approach (CASA) model assigns a uniform value to the maximum light energy utilization (ε max = 0.389 gC/MJ), ignoring the influence of vegetation types and topographic factors on ε max , resulting in the low accuracy of the CASA model in estimating the carbon sequestration of mountain vegetation. In this paper, the improved CASA model was combined with Landsat 8 Operational Land Imager (OLI) remote sensing image data to improve the estimation accuracy of carbon sequestration of mountain vegetation. The first was the establishment of a linear link between the terrain characteristics (slope and aspect), vegetation types, and ε max in mountainous locations. The second was the improvement of the CASA model’s calculation method for key parameters. The different distributions of the estimation results from the two techniques in 2015 and 2016 are then compared using Landsat 8 data as the data source, and the impact of the terrain factors in the improved CASA model on the estimation results is confirmed. Finally, the improved CASA model and the CASA model are used to estimate the Net Primary Productivity (NPP) of the study area from 2000 to 2020, and the estimated results of the two models are compared with the computation results of the MODIS data NPP product. The findings indicate that the improved CASA model’s estimation results have a higher degree of fit and a better correlation. The improved CASA model aids in precisely understanding the ecological carbon sequestration potential of mountain areas and increases the estimation accuracy of vegetation carbon sequestration in mountainous areas.
    Type of Medium: Online Resource
    ISSN: 2296-701X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2745634-1
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  • 3
    In: Ore Geology Reviews, Elsevier BV, Vol. 138 ( 2021-11), p. 104359-
    Type of Medium: Online Resource
    ISSN: 0169-1368
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2029106-1
    SSG: 13
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  • 4
    Online Resource
    Online Resource
    American Chemical Society (ACS) ; 2020
    In:  Journal of Agricultural and Food Chemistry Vol. 68, No. 19 ( 2020-05-13), p. 5483-5495
    In: Journal of Agricultural and Food Chemistry, American Chemical Society (ACS), Vol. 68, No. 19 ( 2020-05-13), p. 5483-5495
    Type of Medium: Online Resource
    ISSN: 0021-8561 , 1520-5118
    Language: English
    Publisher: American Chemical Society (ACS)
    Publication Date: 2020
    detail.hit.zdb_id: 1483109-0
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  • 5
    In: Minerals, MDPI AG, Vol. 12, No. 6 ( 2022-05-30), p. 692-
    Abstract: The harsh environment of high-latitude areas with large amounts of snow and ice cover makes it difficult to carry out full geological field surveys. Uranium resources are abundant within the Ilimaussaq Complex in the Narsaq region of Greenland, where the uranium ore body is strictly controlled by the Lujavrite formation, which is the main ore-bearing rock in the complex rock mass. Further, large aggregations of radioactive minerals appear as thermal anomalies on remote sensing thermal infrared imagery, which is indicative of deposits of highly radioactive elements. Using a weight-of-evidence analysis method that combines machine-learned lithological classification information with information on surface temperature thermal anomalies, the prediction of radioactive element-bearing deposits at high latitudes was carried out. Through the use of Worldview-2 (WV-2) remote sensing images, support vector machine algorithms based on texture features and topographic features were used to identify Lujavrite. In addition, the distribution of thermal anomalies associated with radioactive elements was inverted using Landsat 8 TIRS thermal infrared data. From the results, it was found that the overall accuracy of the SVM algorithm-based lithology mapping was 89.57%. The surface temperature thermal anomaly had a Spearman correlation coefficient of 0.63 with the total airborne measured uranium gamma radiation. The lithological classification information was integrated with surface temperature thermal anomalies and other multi-source remote sensing mineralization elements to calculate mineralization-favorable areas through a weight-of-evidence model, with high-value mineralization probability areas being spatially consistent with known mineralization areas. In conclusion, a multifaceted remote sensing information finding method, focusing on surface temperature thermal anomalies in high-latitude areas, provides guidance and has reference value for the exploration of potential mineralization areas for deposits containing radioactive elements.
    Type of Medium: Online Resource
    ISSN: 2075-163X
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2655947-X
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