Study on Spectral Response and Estimation of Grassland Plants Dust Retention Based on Hyperspectral Data

Zhao, Yibo; Lei, Shaogang; Yang, Xingchen; Gong, Chuangang; Wang, Cangjiao; Cheng, Wei; Li, Heng; She, Changchao

doi:10.3390/rs12122019

Cited by 11 publications

(7 citation statements)

References 57 publications

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“…Second, coal mining and grazing cause land degradation and priority areas for ecological restoration need to be identified from the perspective of biodiversity conservation. Some studies have analyzed the impact range of coal mining on a grassland ecosystem from the perspectives of soil heavy metal pollution (Fang et al, 2021), underground water quality deterioration (Singha et al, 2022), dust diffusion (Zhao et al, 2020), and soil desertification (Li et al, 2018). However, few studies have been conducted from the perspective of plant diversity, and the coexistence of coal mining and grazing has largely been neglected.…”

Section: Introductionmentioning

confidence: 99%

Impacts of open‐pit coal mining and livestock grazing on plant diversity in a steppe: From the perspective of remote sensing

et al. 2023

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Coal mining and grazing cause land degradation. Plant diversity loss is one of the characteristics. Ground surveys cannot analyze the effects of mining and grazing on plant diversity in large areas, and remote sensing offers a novel solution. The aims of this study were as follows: (1) to analyze the impacts of mining and grazing on plant diversity composition by considering α‐, β‐, and γ‐ diversity comprehensively; (2) to determine the impacts of mining and grazing on the spatial distribution of α‐diversity; (3) to identify priority areas for ecological restoration. We applied a novel remote sensing method to obtain plant α‐, β‐, and γ‐ diversity maps and performed spatial analysis. The results were as follows: (1) mining reduced the proportion of α‐diversity in the total diversity (γ‐diversity) but increased that of β‐diversity. Grazing increased the proportion of α‐diversity but decreased that of β‐diversity. Mining increased the heterogeneity among plant communities, whereas grazing increased the homogeneity. (2) Both mining and grazing reduced α‐diversity. With increasing distance from the mine, α‐diversity increased logarithmically. (3) The severely affected range of mining on plant diversity is approximately 325 m. When there is grazing around the mine, the severely affected range is approximately 400 m. Severely affected areas are priority areas for ecological restoration. Improving the soil quality can be an effective measure. This study gains new insights into the effects of coal mining and grazing on plant diversity. These findings can provide some guidance for governments to formulate ecological restoration measures in mining areas.

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Section: Introductionmentioning

confidence: 99%

Impacts of open‐pit coal mining and livestock grazing on plant diversity in a steppe: From the perspective of remote sensing

et al. 2023

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“…Landscape plants play a crucial role in urban vegetation communities, providing diverse forms and species that facilitate the creation of aesthetically pleasing urban environments [4]. In addition to their ornamental function, plants have ecological functions, including carbon sequestration, oxygen release [5][6][7], dust retention, and noise reduction [8][9][10]. The leaves of plants serve as the primary site for energy conversion and material exchange with the external environment, and their functional traits can respond sensitively to environmental changes.…”

Section: Introductionmentioning

confidence: 99%

Response of Common Garden Plant Leaf Traits to Air Pollution in Urban Parks of Suzhou City (China)

Yang,

Zhang,

et al. 2023

Forests

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In this study, to comprehensively investigate the impact of garden plants on air quality, we measured the leaves of 18 common garden plants in three different areas, namely, Suzhou industrial parks (clean air area (CAA)), Xiangcheng district parks (lightly polluted area (LPA)), and Huqiu district parks (highly polluted area (HPA)). We also measured the leaf functional traits of different life-types of plants. To explore the trade-off strategies of the leaf traits of common garden plants in response to air pollution and to assess the adaptive capacity of different life types of plants to air pollution. The results show that plants in the polluted area had higher leaf dry matter content (LDMC) and leaf nitrogen content per unit mass (Nmass), and a lower specific leaf area (SLA), maximum net photosynthetic rate per unit area (Aarea), transpiration rate (Tr), stomatal conductance (Gs), and chlorophyll value (SPAD). Pearson correlation analysis showed that SLA was significantly positively correlated with Nmass, Tr, photosynthetic use efficiency (PNUE), and SPAD, and significantly negatively correlated with LDMC, while Aarea was significantly positively correlated with chlorophyll value. Redundancy analysis revealed that the correlation between each leaf functional trait and atmospheric pollution factors was as follows: LDMC > Nmass > SLA > LA > Aarea > Tr > PNUE > SPAD. The results suggest that different plant types have varying levels of adaptability to environmental conditions. Trees were found to be the most adaptable, followed by shrubs, herbs, and lianas. Additionally, under the stress of air pollution, herbs and lianas exhibited characteristics of “fast investment-return” on the leaf economic spectrum, meaning they were able to quickly allocate resources to maximize their return. However, trees and shrubs displayed traits of “slow investment-return”, indicating a more conservative approach to resource allocation. These results provide valuable insights into the leaf trade-off strategies of plants in Suzhou Park under air pollution stress and can guide the selection of suitable plant species in similar environments.

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“…Hyperspectral remote sensing uses hyperspectral images to monitor ground objects in the coal dust pollution area and analyzes the coal dust diffusion law using a combination of dynamic monitoring and field measurements. This method has a good monitoring effect from the point to surface scales [20,21], but it has a low ability for spatial intuitive interpretation of coal dust pollution. The aforementioned mechanism focuses on the hazard assessment of coal dust pollution and ignores the expression of the spatial information.…”

Section: Introductionmentioning

confidence: 99%

Identification and monitoring of coal dust pollution in Wucaiwan mining area, Xinjiang (China) using Landsat derived enhanced coal dust index

et al. 2022

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Coal dust is the main pollutant in coal mining areas. Such pollutants easily diffuse and are difficult to monitor, which increases the cost of environmental pollution control. Remote sensing technology can be used to dynamically monitor mining areas at a low cost, and thus, this is a common means of mining area management. According to the spectral characteristics of various ground objects in remote sensing images, a variety of remote sensing indexes can be constructed to extract the required information. In this study, the Wucaiwan open-pit coal mine was selected as the study area, and the Enhanced Coal Dust Index (ECDI) was established to extract the coal dust pollution information for the mining area. A new mining area pollution monitoring method was developed, which can provide technical support for environmental treatment and mining planning in Zhundong. The results of this study revealed the following: (1) Compared with the normalized difference coal index, the ECDI can expand the difference between the spectral information about the coal dust and the surrounding features, so it has a significant recognition ability for coal dust information. (2) From 2010 to 2021, the coal dust pollution in the study area initially increased and then decreased. With the continued exploitation of the coal mines in the study area, the coal dust pollution area increased from 14.77 km2 in 2010 to 69.49 km2 in 2014. After 2014, the local government issued various environmental pollution control policies, which had remarkable results. The coal dust pollution area decreased to 36.85 km2 and 17.85 km2 in 2018 and 2021, respectively. (3) There was a great deal of pollution around mines and roads, around which the pollution was more serious. Various factors, such as wind, coal type, and the mining, processing, and transportation modes, affect the distribution of the coal dust pollution.

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Study on Spectral Response and Estimation of Grassland Plants Dust Retention Based on Hyperspectral Data

Cited by 11 publications

References 57 publications

Impacts of open‐pit coal mining and livestock grazing on plant diversity in a steppe: From the perspective of remote sensing

Impacts of open‐pit coal mining and livestock grazing on plant diversity in a steppe: From the perspective of remote sensing

Response of Common Garden Plant Leaf Traits to Air Pollution in Urban Parks of Suzhou City (China)

Identification and monitoring of coal dust pollution in Wucaiwan mining area, Xinjiang (China) using Landsat derived enhanced coal dust index

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