Search Results (5)

Monitoring dust on plant canopies around open-pit coal mines is crucial to assessing environmental pollution and developing effective dust suppression strategies. This research focuses on the Ha’erwusu open-pit coal mine in Inner Mongolia, China, using measured dust content on plant canopies and UAV-borne VNIR hyperspectral data as the data sources. The study employed five spectral transformation forms—first derivative (FD), second derivative (SD), logarithm transformation (LT), reciprocal transformation (RT), and square root (SR)—alongside the competitive adaptive reweighted sampling (CARS) method to extract characteristic bands associated with canopy dust. Various regression models, including extreme learning machine (ELM), random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), were utilized to establish dust inversion models. The spatial distribution of canopy dust was then analyzed. The results demonstrate that the geometric and radiometric correction of the UAV-borne VNIR hyperspectral images successfully restored the true spatial information and spectral features. The spectral transformations significantly enhance the feature information for canopy dust. The CARS algorithm extracted characteristic bands representing 20 to 30% of the total spectral bands, evenly spread across the entire range, thereby reducing the estimation model’s computational complexity. Both feature extraction and model selection influence the inversion accuracy, with the LT-CARS and RF combination offering the best predictive performance. Canopy dust content decreases with increasing distance from the dust source. These findings offer valuable insights for canopy dust retention monitoring and offer a solid foundation for dust pollution management and the development of suppression strategies. Full article

Monitoring the dust retention content in grassland plants around open-pit coal mines is of significant importance for environmental pollution monitoring and the development of dust control strategies. This paper focuses on the HulunBuir grassland in the Inner Mongolia Autonomous Region, China. UAV-borne hyperspectral data and measured dust retention content in plant canopies are used as data sources. The spectral response characteristics of canopy dust retention are analyzed, and four types of optimized spectral indices are constructed, including the difference index (DI), ratio index (RI), normalized difference index (NDI), and inverse difference index (IDI). The spectral index with the highest absolute value of the correlation coefficient with the canopy dust retention is selected as the feature variable for each spectral index. In addition, machine learning methods such as the partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) methods are used to develop models for the inversion of canopy dust retention. The results show that as the dust retention content increases, the canopy reflectance in the visible wavelength initially increases and then decreases, while the reflectance in the near-infrared wavelength gradually decreases. The spectral reflectance values at different dust retention levels exhibit significant differences in the 400–420 nm, 579–698 nm, and 714–1000 nm ranges. The four types of spectral indices constructed exhibit high correlations with the canopy dust retention content, and the spectral index with the highest absolute value of the correlation coefficient is composed of near-infrared bands. The dust retention inversion model established using the RF method is more accurate than those established using the PLSR and SVM methods and yields a higher prediction accuracy. The high canopy dust retention areas are mainly distributed within 900 m of the mining area, and the dust retention gradually decreases with distance. In addition, with increasing dust retention, the fractional vegetation cover (FVC) decreases. The results of this study provide a theoretical basis and technical support for monitoring dust retention in grassland plant canopies and for dust control measures. Full article

Air pollution poses a significant threat to human health, especially in urban areas. Urban parks function as natural biofilters, and examining the factors influencing dust retention—specifically PM2.5 and PM10 concentrations—across different spatial scales can enhance air quality and resident well-being. This study investigates the factors affecting dust retention in urban parks at both the site and vegetation community scales, focusing on Xi’an Expo Park. Through on-site measurements and a land use regression (LUR) model, the spatial and temporal distributions of PM2.5 and PM10 concentrations were analyzed. The indications of the findings are as follows. (1) The LUR model effectively predicts factors influencing PM2.5 and PM10 concentrations at the site scale, with adjusted R² values ranging from 0.482 to 0.888 for PM2.5 and 0.505 to 0.88 for PM10. Significant correlations were found between particulate matter concentrations and factors such as the distance from factories, sampling area size, distance from main roads, presence of green spaces, and extent of hard pavements. (2) At the plant community scale, half-closed (30%–70% canopy cover), single-layered green spaces demonstrated the superior regulation of PM2.5 and PM10 concentrations. Specifically, two vegetation structures—the half-closed single-layered mixed broadleaf-conifer woodland (H1M) and the half-closed single-layered broad-leaved woodland (H1B)—exhibited the highest dust-retention capacities. (3) PM2.5 and PM10 concentrations were highest in winter, followed by spring and autumn, with the lowest levels recorded in summer. Daily particulate matter concentrations peaked between 8:00 and 10:00 a.m. and gradually decreased, reaching a minimum between 4:00 and 6:00 p.m. The objective of this study is to evaluate the impact of urban green spaces on particulate matter (PM) concentrations across multiple scales. By identifying and synthesizing key indicators at these various scales, the research aims to develop effective design strategies for urban green spaces and offer a robust theoretical framework to support the creation of healthier cities. This multi-scale perspective deepens our understanding of how urban planning and landscape architecture can play a critical role in mitigating air pollution and promoting public health. Full article

With the accelerated process of urbanization, air pollution has become increasingly severe. Garden plants can trap atmospheric particulate matter, which is of great significance for improving the urban ecological environment and promoting sustainable development. To investigate the dust retention effect of typical transportation green spaces in Nanjing, this study focuses on thirteen garden plants on Xuanwu Avenue in Nanjing. The dust retention capacity of these plants was determined using the wash-off method, while the microstructure of their leaf surfaces was observed using scanning electron microscopy. The results are as follows: Firstly, per unit leaf area, Liriope spicata, Ophiopogon japonicus, and Viburnum odoratissimum demonstrate solid dust retention abilities. Additionally, Viburnum odoratissimum, Prunus serrulata var. Lannesiana, and Liriope spicata show strong dust retention abilities per single leaf. Moreover, Platanus acerifolia, Viburnum odoratissimum, and Cinnamomum camphora have strong dust retention abilities per plant. Viburnum odoratissimum, Platanus acerifolia, and Prunus serrulata var. Lannesiana exhibit the most substantial dust retention capacities. Secondly, there is a significant negative correlation between dust retention per plant and the potassium content, while a significant positive correlation is observed with plant height, canopy height, and leaf width. Furthermore, there is a highly significant positive correlation between dust retention per unit leaf area and stomatal length and a highly significant negative correlation with leaf length. The surface microstructure of the blade mainly increases the dust retention capacity of the blade by increasing the friction of the leaf surface. Lastly, specific leaf surface microstructures, such as grooved epidermis and trichomes, enhance plants’ dust retention capacity. Consequently, for the future configuration of road green spaces in Nanjing, a mixed planting mode of trees, shrubs, and grass is recommended. Priority should be given to selecting plants with strong overall dust retention capabilities, such as Platanus acerifolia, Viburnum odoratissimum, and Prunus serrulata var. Lannesiana, to alleviate air pollution, improve the urban ecological environment, and achieve sustainable development. Full article

Accurate monitoring of plant dust retention can provide a basis for dust pollution control and environmental protection. The aims of this study were to analyze the spectral response features of grassland plants to mining dust and to predict the spatial distribution of dust retention using hyperspectral data. The dust retention content was determined by an electronic analytical balance and a leaf area meter. The leaf reflectance spectrum was measured by a handheld hyperspectral camera, and the airborne hyperspectral data were obtained using an imaging spectrometer. We analyzed the difference between the leaf spectral before and after dust removal. The sensitive spectra of dust retention on the leaf- and the canopy-scale were determined through two-dimensional correlation spectroscopy (2DCOS). The competitive adaptive reweighted sampling (CARS) algorithm was applied to select the feature bands of canopy dust retention. The estimation model of canopy dust retention was built through random forest regression (RFR), and the dust distribution map was obtained based on the airborne hyperspectral image. The results showed that dust retention enhanced the spectral reflectance of leaves in the visible wavelength but weakened the reflectance in the near-infrared wavelength. Caused by the canopy structure and multiple scattering, a slight difference in the sensitive spectra on dust retention existed between the canopy and leaves. Similarly, the sensitive spectra of leaves and the canopy were closely related to dust and plant physiological parameters. The estimation model constructed through 2DCOS-CARS-RFR showed higher precision, compared with genetic algorithm-random forest regression (GA-RFR) and simulated annealing algorithm-random forest regression (SAA-RFR). Spatially, the amount of canopy dust increased and then decreased with increasing distance from the mining area, reaching a maximum within 300–500 m. This study not only demonstrated the importance of extracting feature bands based on the response of plant physical and chemical parameters to dust, but also laid a foundation for the rapid and non-destructive monitoring of grassland plant dust retention. Full article