Quantifying Mining-Induced Phenological Disturbance and Soil Moisture Regulation in Semi-Arid Grasslands Using HLS Time Series
Abstract
1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Sources and Preprocessing
3. Research Methodology
3.1. Construction of Initial Vegetation Index Time Series Dataset
3.2. Vegetation Index Time Series Reconstruction
3.2.1. Savitzky–Golay Filtering
3.2.2. Double Logistic Fitting
3.2.3. Asymmetric Gaussian Fitting
3.3. Extraction of Vegetation Phenology Parameters
3.4. Quantitative Method for Assessing the Impact of Mining Activities on Vegetation Phenology
4. Results and Analysis
4.1. Comparison of Time Series Reconstruction Methods
4.2. Delineation of the Impact Range of Mining Activities
4.3. Sensitivity Analysis of Vegetation Indices to Mining Activities
4.4. The Effect of Soil Moisture Content on Vegetation Phenology
5. Discussion
5.1. Analysis of Sensitivity Differences in Vegetation Indexes Under Mining Activities
5.2. Analysis of Phenological Variations in Mining Areas and Influencing Factors
- (1)
- Following large-scale coal extraction, the overlying strata undergo displacement, bending and deformation due to loss of support, ultimately forming subsidence basins and tensile fissures at the surface. Surface subsidence and fissures constitute the primary physical pathways for soil moisture loss [73]. Compared to undisturbed areas, subsidence zones exhibit significantly reduced soil bulk density and markedly increased porosity [74]. This alteration in soil physical structure substantially diminishes its water-holding capacity. Furthermore, by providing rapid pathways for surface water and precipitation, the vertical extension of surface fissures reduces surface runoff and effective infiltration, thereby intensifying water evaporation from the increased soil surface area. This physical water loss directly causes a sharp decline in surface soil moisture content, imposing severe water stress on vegetation [75].
- (2)
- Beyond direct surface impacts, large-scale underground mining has caused fundamental disruption to regional hydrogeological systems. Mining activities have compromised the integrity of aquifer systems, particularly shallow alluvial aquifers, leading to reduced aquifer pressure and hydrogeophysical leakage [76]. These alterations to the hydrogeological structure have altered groundwater flow pathways and may disrupt groundwater recharge sources. The substantial decline in groundwater levels induced by mining constitutes a key factor in vegetation water scarcity within semi-arid regions [77]. As native vegetation in these areas relies heavily on groundwater for survival, falling water tables directly sever deep-water supply sources to vegetation, triggering cascading effects on growth, spatial distribution and community succession processes. Case studies from other regions globally corroborate this perspective. For instance, mining-induced droughts have led to substantial reductions in groundwater reserves surrounding mining areas, resulting in a marked decline in vegetation community indicators [78,79,80].
- (3)
- In addition to alterations in physical structure and hydrological conditions, mining activities may induce changes in the physicochemical properties of surface soils [81], thereby further exacerbating vegetation stress. Although the physicochemical characteristics of surface soils vary across different subsidence areas, these alterations may indirectly affect vegetation growth by influencing soil nutrient availability and ionic equilibrium [82,83,84]. For instance, salts and heavy metals from mining waste may enter surrounding soils via runoff or seepage, leading to soil salinization or excessive heavy metal concentrations, thereby exerting toxic effects on vegetation physiology [85]. However, systematic research examining the interplay between different coal-mine subsidence patterns and soil physicochemical properties remains scarce and fragmented, highlighting topics that require further investigation in future studies.
5.3. Limitations and Future Work
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Type | Time Coverage | Spatial Resolution | Data Source | Purpose | Preprocessing Steps |
---|---|---|---|---|---|
HLS | 1 January–30 December 2020 | 30 m | NASA (https://hls.gsfc.nasa.gov/ accessed on 12 September 2024) | Time series vegetation monitoring | Mosaicking, clipping and QA filtering |
GLC_FCS30-2020 | 2020 | 30 m | CASEarth (https://data.casearth.cn/dataset/6523adf6819aec0c3a438252 accessed on 12 August 2024) | Land cover classification | ready-to-use classification product |
Soil Water Content | 1 January–30 December 2020 | 1 km | TPDC (https://data.tpdc.ac.cn/zh-hans/data/49b22de9-5d85-44f2-a7d5-a1ccd17086d2 accessed on 28 December 2024) | Analysis of hydrological influence on phenology | Spatial resampling and temporal alignment |
Vegetation Index Calculation Formula | Description |
---|---|
EVI accounts for the effects of atmospheric scattering and surface reflectance, enhancing its sensitivity to vegetation cover in areas with high vegetation density as well as in arid and semi-arid regions [29]. | |
GNDVI is an indicator of the photosynthetic activity of vegetation and is commonly used to assess the water content and nitrogen concentration in plant leaves based on multispectral data without extreme red channels [30]. | |
RECI is an efficient vegetation index that utilizes the spectral characteristics of the red-edge region. It shows a high sensitivity and linear relationship with leaf chlorophyll content, reduces the effects of background and canopy structure, and is less prone to saturation in areas with high biomass [31]. | |
MSAVI is an index proposed to address the issue of soil background interference in vegetated areas. By introducing a soil adjustment factor, it effectively reduces soil noise [32]. | |
NDVI is an important parameter for detecting vegetation growth status and mitigating certain radiometric errors. It reflects the background influences on the plant canopy, such as soil, wet surfaces, snow, dead leaves, and surface roughness [33]. |
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Zhao, Y.; Ren, S.; Tang, Y. Quantifying Mining-Induced Phenological Disturbance and Soil Moisture Regulation in Semi-Arid Grasslands Using HLS Time Series. Land 2025, 14, 2011. https://doi.org/10.3390/land14102011
Zhao Y, Ren S, Tang Y. Quantifying Mining-Induced Phenological Disturbance and Soil Moisture Regulation in Semi-Arid Grasslands Using HLS Time Series. Land. 2025; 14(10):2011. https://doi.org/10.3390/land14102011
Chicago/Turabian StyleZhao, Yanling, Shenshen Ren, and Yanjie Tang. 2025. "Quantifying Mining-Induced Phenological Disturbance and Soil Moisture Regulation in Semi-Arid Grasslands Using HLS Time Series" Land 14, no. 10: 2011. https://doi.org/10.3390/land14102011
APA StyleZhao, Y., Ren, S., & Tang, Y. (2025). Quantifying Mining-Induced Phenological Disturbance and Soil Moisture Regulation in Semi-Arid Grasslands Using HLS Time Series. Land, 14(10), 2011. https://doi.org/10.3390/land14102011