Analysis of spatial and temporal changes of vegetation cover and its driving forces in the Huainan mining area

Zhang, Xuyang; Liu, Ying; Chen, Xiaoyang; Long, Linli; Su, Yongdong; Yu, Xiaokun; Zhang, Hongmei; Chen, Yongchun; An, Shuhong

doi:10.1007/s11356-022-19921-5

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…Therefore, the prediction of vegetation cover can be added in the later stage to provide data support for managers. ②In analyzing the spatial changes of vegetation cover, it is also effective to reflect the spatial aggregation and migration characteristics of vegetation in the study area in spatial and temporal changes by using the centre of gravity migration model [41] , while this study mainly analyzes the vegetation cover by interpreting remote sensing images, and the accuracy can be improved by combining multiple models in the later stage. ③When analyzing the causes of vegetation cover trend changes, this study did not consider topography and human activities, etc.…”

Section: Recommendations and Discussionmentioning

confidence: 99%

Dynamic monitoring of vegetation restoration in coal mining subsidence area: a case in Jiawang District, Xuzhou City

Jiang,

Gao,

et al. 2024

Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023)

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Section: Recommendations and Discussionmentioning

confidence: 99%

Dynamic monitoring of vegetation restoration in coal mining subsidence area: a case in Jiawang District, Xuzhou City

Jiang,

Gao,

et al. 2024

Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023)

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“…Human activities in mining areas primarily consist mainly of mining activities and ecological restoration. Among natural factors, temperature and precipitation exert relatively significant influences on vegetation [5,6].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Human Activities on Vegetation Restoration in Mining Areas Based on the GTWR

Guo,

Li,

Zhang

et al. 2024

IJGI

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The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and Digital Elevation Model (DEM). Furthermore, residual analysis was performed to remove the impact of natural factors and separately assess the impact of human activities on vegetation restoration. The experiment was carried out in Shangwan Mine, China, and following results were obtained: (1) During the period of 2000 to 2020, intensified huan activities corresponded to positive vegetation changes (NDVI-HA) that exhibited an upward trend over time. (2) The spatial heterogeneity of vegetation restoration was attributed to the DEM. It is negatively correlated with NDVI in natural conditions, while under the environment of mining activities, there is a positive correlation between NDVI-HA and DEM. (3) The contribution of human activities to vegetation restoration in mining areas has been steadily increasing, surpassing the influences of temperature and precipitation since 2010. The results of this study can provide important references for the assessment of vegetation restoration to some extent in mining areas.

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“…Various methods are commonly employed for driving force analysis, including factor analysis, principal component analysis, regression analysis, Mann-Kendall trend method, geographic detector, and so on. Many scholars have utilized these methods to identify the factors most closely associated with vegetation change, such as climate variables and human activities (e.g., precipitation, temperature, and population density) [23][24][25]. These factors can be considered potential driving forces behind vegetation change.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Pattern and Driving Forces of Fractional Vegetation Coverage in Xiong’an New Area of China from 2005 to 2019

Huang

Qiu²,

Cao

et al. 2023

Sustainability

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The Xiong’an New Area was officially established in 2018 to construct a new, intelligent, and efficient urban area to alleviate Beijing’s non-capital functions. Using Landsat satellite images, we employed the dimidiate pixel model, band operation, and transition matrix to analyze the temporal and spatial variations in FVC (Fractional Vegetation Coverage) within the Xiong’an New Area in 2005, 2013, and 2019, respectively. Urbanization rate, precipitation, temperature, and population were considered potential driving forces, which we analyzed using grey relational analysis and linear regression to explore the correlation between FVC and these factors. The findings are as follows: from 2005 to 2019, overall improvement and significant degradation have been observed. In Baiyangdian, a part of the national key ecological area, water bodies and FVC have increased. Grey relational analysis revealed that precipitation had the highest grey relational value of 0.76. The average correlation among natural factors was 0.67, while that among human factors was 0.60. Generally, the Xiong’an New Area vegetation exhibited instability, while Baiyangdian demonstrated relatively stable FVC. Grey relational analysis indicates a strong potential for social and economic development in the Xiong’an New Area.

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Analysis of spatial and temporal changes of vegetation cover and its driving forces in the Huainan mining area

Cited by 13 publications

References 37 publications

Dynamic monitoring of vegetation restoration in coal mining subsidence area: a case in Jiawang District, Xuzhou City

Dynamic monitoring of vegetation restoration in coal mining subsidence area: a case in Jiawang District, Xuzhou City

Evaluating the Impact of Human Activities on Vegetation Restoration in Mining Areas Based on the GTWR

Spatial-Temporal Pattern and Driving Forces of Fractional Vegetation Coverage in Xiong’an New Area of China from 2005 to 2019

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