Remote Sensing Monitoring and Analytical Evaluation of Grasslands in the Muli Region of Qinghai, China from 2000 to 2021

Jiang, Lu; Cui, Tengfei; Liu, Hui; Xue, Yong

doi:10.3390/land11101733

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…Meanwhile, the issue of illegal mining has come to light, and ecological and environmental problems in the region have attracted public attention. Relevant monitoring results show that the increase in industrial and mining land in key areas of the Qilian Mountains is the most significant problem [36][37][38][39], and the soil environmental problems caused by mineral exploration, mining, and smelting activities cannot be ignored [40][41][42][43][44]. Therefore, it is highly essential to assess the contamination properties and risks of heavy metals in the soil surrounding mining areas.…”

Section: Description Of the Study Areamentioning

confidence: 99%

Characteristics and Potential Ecological Risks of Heavy Metal Content in the Soil of a Plateau Alpine Mining Area in the Qilian Mountains

Zhang,

Cao,

Cao

et al. 2023

Land

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In recent years, the ecological and environmental problems caused by mining in the Qilian Mountains have attracted considerable attention, and the government has carried out a number of comprehensive ecological environment remediation projects there, among which ecological restoration in the Qilian Mountain alpine mining area is an essential task. As a result, heavy metals have been studied in the soil of the Qilian Mountain alpine mining area. This can provide a scientific basis and data support for the establishment of a demonstration index for monitoring ecological environmental restoration in mining areas. In order to understand the content and contamination status of heavy metals in the soil surrounding the alpine mining area of Qilian Mountain, 56 soil samples were collected to determine the levels of eight heavy metals, including Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni. The spatial distribution of heavy metals in the soil of the study area was analyzed based on a statistical approach. The single-factor pollution index (Pi), Nemerow comprehensive pollution index (PN), geoaccumulation index (Igeo), and potential ecological risk index (RI) were used to evaluate soil heavy metal pollution and potential ecological risk. Principal component analysis (PCA), positive matrix factorization (PMF) models, and geostatistical analysis were also used to investigate the source of heavy metals. The results show that the average Cd, As, Cr, Cu, Zn, and Ni content of the grassland soil around the mining area exceeds the soil background values in both Qinghai Lake Basin and Qinghai Province. The spatial distribution of the eight heavy metal elements in soil showed an island-like pattern, with high-value areas of each metal element appearing, indicating that human activities in the study area had negative effects on the soil environment. The value of the single pollution index showed that levels of Ni, Cd, Cu, Cr, Hg, and As pollution were low, while there was no Pb or Zn pollution. The Nemerow integrated pollution index had an average value of 1.39, indicating a slight pollution trend. The average values of Cr and Zn in the geoaccumulation index ranged from 0 to 1, indicating mild to moderate contamination in the studied region. The average value of the integrated ecological risk index in the study area was 135.43, which is in the intermediate ecological risk range. In descending order of size, the average ecological risk index of each heavy metal element was Hg > Cd> As > Ni > Cu > Pb > Cr > Zn. From the perspective of the spatial distribution pattern of ecological risk, the two high-value discriminants were in the western part of the study area, close to the mining area. Zn, Cu, Pb, and Cd in soils were mainly affected by human activity, while Cr and Ni were mainly affected by soil geochemistry. Cd is the main contaminant in the study area, and soil Cd contamination of the grassland in the study area must be considered.

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Section: Description Of the Study Areamentioning

confidence: 99%

Characteristics and Potential Ecological Risks of Heavy Metal Content in the Soil of a Plateau Alpine Mining Area in the Qilian Mountains

Zhang,

Cao,

Cao

et al. 2023

Land

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“…The unique environmental conditions, extreme weather variations, and specific vegetation types found in highaltitude and high-latitude regions pose distinct challenges for accurate AGB estimation (Gao et al, 2020). Remote sensing technology can provide high spatial-and temporal-resolution remote sensing image data, and by establishing a quantitative relation model between remote sensing images and grassland AGB, rapid, accurate, and large-scale inversion and prediction of grassland AGB can be achieved (Jiang L. et al, 2022;Liu et al, 2022). Fan et al (2022) used Sentinel-2 images to estimate the grassland AGB of the Qinghai-Tibetan Plateau, and Chapungu et al (2020) assessed grassland biomass in northeastern Zimbabwe by hyperspectral remote sensing data using the relation between vegetation indices and grassland organisms.…”

Section: Introductionmentioning

confidence: 99%

Estimation of grassland aboveground biomass and its response to climate changes based on remote sensing inversion in Three-River-Source National Park, Tibet Plateau, China

He,

Yu,

Rong

et al. 2023

Front. Ecol. Evol.

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Three-River-Source (TRS) National Park stands as one of China’s earliest established national parks, dedicated to significant ecological responsibilities that include conserving soil and water resources in the Tibetan Plateau region. Research on climate change’s influence on the TRS region’s grasslands is of great significance in our efforts to comprehend and conserve the grassland ecosystem. The most effective random forest (RF) model was chosen to invert the aboveground biomass (AGB) of grassland in the previous 6 years (2015−2020) and predict the grassland AGB in the following 20 years (2021−2040) by comparing linear regression and multivariate nonlinear regression models such as RF, support vector machine, decision tree, and artificial neural network. A Theil–Sen median trend analysis and a Mann–Kendal test were then used to examine the trends of grassland AGB. The results showed that (1) RF outperformed other models in estimating grassland AGB, with a test set decision coefficient of multiple determination (R2) of 0.722, a root mean square error of 42.596 g/m2, and a mean absolute error of 35.619 g/m2; (2) over 6 years, the grassland AGB in TRS National Park had a spatial trend of a steady rise from the northwest to the southeast. The average annual grassland AGB was 247.333 g/m2, with averages of 44.836 g/m2, 92.601 g/m2, and 120.217 g/m2 in the Yangtze River, Yellow River, and Lancang River source parks respectively. The trend of the grassland AGB was primarily stabilized and slightly recovered, with a small portion of the slightly deteriorated areas; (3) climate change significantly affected grassland AGB, and when temperature and precipitation conditions were adequate, grassland AGB values increased with temperature and precipitation. In the scenarios of ssp119, ssp245, and ssp585, grassland AGB is projected to exhibit a dynamic upward trend over the next 20 years. Global warming is expected to boost grassland AGB. Comprehensive measures are essential to maintain grassland health and ensure a positive impact on global carbon and ecological balance. The study’s findings hold great importance for the ecological security of the TRS region and contribute to our global understanding of sustainable grassland development.

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Ecological Risk Evaluation and Ecological Restoration Model of Mining in the Source Area of the Yellow River Basin

Weiling

Zhang

et al. 2023

Land

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Finding out about the ecosystem damaged by mining development and carrying out ecological risk diagnoses are important prerequisites for formulating mine ecological restoration strategies. This study established an integrated approach to quantitatively analyze mining ecological risks by combining water conservation and biodiversity conservation ecosystem service functions with natural ecological conditions, and based on these, proposes appropriate mine ecological restoration strategies. Results show that: (1) A total of 14,874.80 hm2 of ecosystems were damaged in the Qinghai section of the Yellow River Basin, caused by mining excavation, crushing and hollow collapse, and of which 52.10% were located in national important ecological function areas and National Nature Reserves, which caused a decrease of the important ecosystem service functions of water conservation and biodiversity conservation in the area, and aggravated the ecological risks of the river source area; (2) The areas of high ecological risk and comparatively high ecological risk in the research area are 1,093,800 hm2 and 902,100 hm2, which accounted for 7.27% and 6.00% of the land area, respectively. Ecological risk hotspot areas are mainly distributed in the Qilian Mountains, Hehuang Valley, Sanjiangyuan and other key water systems and water sources; (3) According to the principle of “one mine, one policy”, we propose five mine ecological restoration models: ecological reconstruction, artificial assistance and protection and conservation, artificial assistance, protection and conservation and natural restoration. This study provides a reliable basis for exploring the status of mining ecological risk at the source of the Yellow River and scientifically carrying out ecological restoration and risk management.

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Remote Sensing Monitoring and Analytical Evaluation of Grasslands in the Muli Region of Qinghai, China from 2000 to 2021

Cited by 3 publications

References 46 publications

Characteristics and Potential Ecological Risks of Heavy Metal Content in the Soil of a Plateau Alpine Mining Area in the Qilian Mountains

Characteristics and Potential Ecological Risks of Heavy Metal Content in the Soil of a Plateau Alpine Mining Area in the Qilian Mountains

Estimation of grassland aboveground biomass and its response to climate changes based on remote sensing inversion in Three-River-Source National Park, Tibet Plateau, China

Ecological Risk Evaluation and Ecological Restoration Model of Mining in the Source Area of the Yellow River Basin

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