Dataset Information

Comparison of two methods for indirect measurement of atmospheric dust deposition: Street-dust composition and vegetation-health status derived from hyperspectral image data.

ABSTRACT: This study presents a statistical comparison between the in situ measurements of the elemental composition of street dust and a forest health status classification derived from aerial hyperspectral image data (HyMap). Combining these two methods allowed us to indirectly pinpoint at a high spatial resolution the atmospheric dust emissions and its effects in a study area around the open-pit lignite mine in Sokolov, Czech Republic. The results reveal a statistically significant relationship between increased Al, Na, Li and Sr levels in street dust and decreased forest health status, and the highest number of statistically significant correlations within a 100 m distance from the street-dust sampling points. Differences in lithological composition were unable to sufficiently explain these changes, therefore anthropogenic factors like dust emissions from coal mining and coal combustion, as well as urbanisation and other industries might be the reason for this link. Such studies are a crucial step in developing new high spatial resolution methods for determining atmospheric dust deposition and their effects.

SUBMITTER: Zibret G

PROVIDER: S-EPMC6411812 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Comparison of two methods for indirect measurement of atmospheric dust deposition: Street-dust composition and vegetation-health status derived from hyperspectral image data.

Žibret Gorazd G Kopačková Veronika V

Ambio 20180825 4

This study presents a statistical comparison between the in situ measurements of the elemental composition of street dust and a forest health status classification derived from aerial hyperspectral image data (HyMap). Combining these two methods allowed us to indirectly pinpoint at a high spatial resolution the atmospheric dust emissions and its effects in a study area around the open-pit lignite mine in Sokolov, Czech Republic. The results reveal a statistically significant relationship between ...[more]

PMID: 30145733

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Project description:Atmospheric deposition is a source of inorganic nutrients and organic matter to the ocean, and can favor the growth of some planktonic species over others according to their nutrient requirements. Atmospheric inputs from natural and anthropogenic sources are nowadays increasing due to desertification and industrialization, respectively. While the impact of mineral dust (mainly from the Saharan desert) on phytoplankton and bacterial community composition has been previously assessed, the effect of anthropogenic aerosols on marine bacterial assemblages remains poorly studied. Since marine bacteria play a range of roles in the biogeochemical cycles of inorganic nutrients and organic carbon, it is important to determine which taxa of marine bacteria may benefit from aerosol fertilization and which not. Here, we experimentally assessed the effect of Saharan dust and anthropogenic aerosols on marine bacterioplankton community composition across a spatial and temporal range of trophic conditions in the northwestern Mediterranean Sea. Results from 16S rDNA sequencing showed that bacterial diversity varied significantly with seasonality and geographical location. While atmospheric deposition did not yield significant changes in community composition when all the experiments where considered together, it did produce changes at certain places and during certain times of the year. These effects accounted for shifts in the bacterial community's relative abundance of up to 28%. The effect of aerosols was overall greatest in summer, both types of atmospheric particles stimulating the groups Alphaproteobacteria, Betaproteobacteria, and Cyanobacteria in the location with the highest anthropogenic footprint. Other bacterial groups benefited from one or the other aerosol depending on the season and location. Anthropogenic aerosols increased the relative abundance of groups belonging to the phylum Bacteriodetes (Cytophagia, Flavobacteriia, and Sphingobacteriia), while Saharan dust stimulated most the phytoplanktonic group of Cyanobacteria and, more specifically, Synechococcus.

Project description:BackgroundFractional vegetation coverage (FVC) is a crucial parameter in determining vegetation structure. Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement.Methods and resultsBased on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of [Formula: see text], [Formula: see text], and [Formula: see text] ([Formula: see text], [Formula: see text], and [Formula: see text] are the actual pixel digital numbers from the images based on each RGB channel, [Formula: see text] is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses [Formula: see text] ([Formula: see text] is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses [Formula: see text] and [Formula: see text] to separate the plants from the background. [Formula: see text] is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, - 2.86, - 7.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, - 6.39, - 20.67, 7.30, and 24.49%, respectively.ConclusionsGiven that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of [Formula: see text], [Formula: see text], and [Formula: see text]) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements.

Dataset Information

Comparison of two methods for indirect measurement of atmospheric dust deposition: Street-dust composition and vegetation-health status derived from hyperspectral image data.

Publications

Comparison of two methods for indirect measurement of atmospheric dust deposition: Street-dust composition and vegetation-health status derived from hyperspectral image data.

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