Estimation model of dust deposition capacity of common vegetation based on spectral characteristics in Shanghai, China

Lin, Wenpeng; Sun, Yue; Wang, Dan; Li, Ying; Yu, Xumiao

doi:10.1016/j.scs.2021.102915

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…The process of dust deposition amount measurement followed the order of weighing, spectrum measuring, cleaning, reweighing, and spectrum re-measuring [49,50] to ensure the reflectance spectra and weight of leaves before and after dust deposition. Based on similar research [26], the reflectance of clean leaves was higher than that of dusty leaves in 750-1350 nm. Therefore, this study focused on the analysis of leaf reflectance spectrum under different dust deposition.…”

Section: Dust Deposition Amount Measurementmentioning

confidence: 79%

“…From a physical point of view, dust on the leaf surface will directly affect the reflectance spectroscopy measurements. The reflectance spectrum and spectral characteristic parameters of leaf surface will be different from the results measured from pure leaf surface [26]. From a biochemical point of view, dust deposition on leaf surface will affect photosynthesis and biological parameters of vegetation, especially the chlorophyll content.…”

Section: Introductionmentioning

confidence: 91%

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Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Lin

Xü

et al. 2021

Remote Sensing

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Using reflectance spectroscopy to monitor vegetation pigments is a crucial method to know the nutritional status, environmental stress, and phenological phase of vegetation. Defining cities as targeted areas and common greening plants as research objects, the pigment concentrations and dust deposition amounts of the urban plants were classified to explore the spectral difference, respectively. Furthermore, according to different dust deposition levels, this study compared and discussed the prediction models of chlorophyll concentration by correlation analysis and linear regression analysis. The results showed: (1) Dust deposition had interference effects on pigment concentration, leaf reflectance, and their correlations. Dust was an essential factor that must be considered. (2) The influence of dust deposition on chlorophyll—a concentration estimation was related to the selected vegetation indexes. Different modeling indicators had different sensitivity to dust. The SR705 and CIrededge vegetation indexes based on the red edge band were more suitable for establishing chlorophyll-a prediction models. (3) The leaf chlorophyll concentration prediction can be achieved by using reflectance spectroscopy data. The effect of the chlorophyll estimation model under the levels of “Medium dust” and “Heavy dust” was worse than that of “Less dust”, which meant the accumulation of dust had interference to the estimation of chlorophyll concentration. The quantitative analysis of vegetation spectrum by reflectance spectroscopy shows excellent advantages in the research and application of vegetation remote sensing, which provides an important theoretical basis and technical support for the practical application of plant chlorophyll content prediction.

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Section: Dust Deposition Amount Measurementmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 91%

Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Lin

Xü

et al. 2021

Remote Sensing

Self Cite

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“…Some studies make this type of relationship possible also by using satellite imagery and also web‐based applications to determine the capacity of trees (shrubs and herbaceous vegetation) to absorb some of the pollution generated by humans in daily life and industrial process facilities. [ 21,22,23,24,25 ] Similarly, for example, a recent study [ 26 ] has demonstrated a reduction of up to 3 kg PM 2 . 5 evaluating particulate matter concentrations in areas covered with approximately 80 trees in the Drumcondra tree alley in Dublin at different times of the day for 5 days representing rush and non‐rush hours.…”

Section: Introductionmentioning

confidence: 99%

Linking PM₁₀ and PM_2.5 Pollution Concentration through Tree Coverage in Urban Areas

Sierra-Porta

Solano‐Correa

Tarazona‐Alvarado

2023

CLEAN Soil Air Water

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Particulate matter, PM 10 and PM 2.5 , represents common air pollutants in cities and constitute a considerable threat to public health impacting daily activity of people living in city. In large cities, the main sources of PM 10 and PM 2.5 are diesel engine exhaust, brake dust, and particulate matter from vehicle tires. These particles can be deposited, filtered, and considerably reduced if there is a vegetative surface in the neighborhoods, thus eliminating a part of these particles and reducing their harmful footprint. This study evaluates the effect of tree coverage in urban areas on PM 10 and PM 2.5 removal considering air quality monitoring stations. Estimation of tree coverage is made by using high spatial and temporal resolution satellite images from Planet constellations. An empirical relationship between these two variables, with an acceptable correlation (R 2 = 0.478 and R 2 = 0.589 for PM 10 and PM 2.5 , respectively), is obtained. A higher abundance of green space is associated with significantly lower PM 10 and PM 2.5 values. Preliminary results suggest that the amount of tree coverage do cause some degree of air quality improvement and can be used to inform national clean air strategies aimed at reducing pollutant emissions.

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Extracting built-up areas from spectro-textural information using machine learning

et al. 2022

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Estimation model of dust deposition capacity of common vegetation based on spectral characteristics in Shanghai, China

Cited by 8 publications

References 19 publications

Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Linking PM₁₀ and PM_2.5 Pollution Concentration through Tree Coverage in Urban Areas

Extracting built-up areas from spectro-textural information using machine learning

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Estimation model of dust deposition capacity of common vegetation based on spectral characteristics in Shanghai, China

Cited by 8 publications

References 19 publications

Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Linking PM10 and PM2.5 Pollution Concentration through Tree Coverage in Urban Areas

Extracting built-up areas from spectro-textural information using machine learning

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Product

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Linking PM₁₀ and PM_2.5 Pollution Concentration through Tree Coverage in Urban Areas