Remote sensing monitoring of land damage and restoration in rare earth mining areas in 6 counties in southern Jiangxi based on multisource sequential images

An investigation into the disturbance effects of coal mining on groundwater and surface ecosystems

Coal mining disturbs surface ecosystems in coal mining subsidence areas. Based on the groundwater-surface composite ecosystem analysis, we constructed an ecological disturbance evaluation index system (18 indices) in a coal mining subsidence area using the analytic hierarchy process (AHP). Taking the Nalinhe mining area in Wushen Banner, China, in 2018-2020 as an example, the weight, ecological disturbance grade and correlation of different indicators were determined by implementing fuzzy mathematics, weighting method, and correlation analysis method. The major conclusions of this review were: (i) After two years of mining, ecological disturbance was the highest in the study area (Grade III) and the lowest in the non-mining area (Grade I). (ii) Coal mining not only directly interfered with the environment, but also strengthened the connection of different ecological indicators, forming multiple ecological disturbance chains such as "mining intensity-mining thickness-buried depth/Mining thickness", "coal mining-surface subsidence-soil chemical factors", and "natural environment-soil physical factors". The disturbance chain that controls the ecological response factors in the region remains to be determined. However, the ecological response factors are the most important factor that hinders the restoration of the ecology in a coal mining subsidence area. (iii) The ecological disturbance in the coal mining subsidence area continued increasing over two years due to coal mining. The ecological disturbance by coal mining cannot be completely mitigated by relying on the self-repair capability of the environment. This study is of great significance for ecological restoration and governance of coal mining subsidence areas.

Pollution and health risk assessment of rare earth elements in Citrus sinensis growing soil in mining area of southern China

Background

Analyzing the pollution and health risk of rare earth elements (REEs) in crop-growing soils around rare earth deposits can facilitate the improvement of REE mining-influenced area. In this study, pollution status, fraction and anomaly, plant accumulation characteristics, and potential risks of REEs (including heavy and light rare earth elements, HREEs and LREEs) in C. sinensis planting soil near ion-adsorption deposits in southern Ganzhou were analyzed. The influence of the soil environment on REEs in soil and fruit of C. sinensis was also explored.

Methods

The geo-accumulation index (Igeo) and ecological risk index(RI) were used to analyze the pollution potential and ecological risks of REEs in soils, respectively. Health risk index and translocation factor (TF) were applied to analyze the accumulation and health risks of REEs in fruit of C. sinensis. The influence of soil factors on REEs in soil and fruit of C. sinensis were determined via correlation and redundancy analysis.

Results

Comparison with background values and assessment of Igeo and RI indicated that the soil was polluted by REEs, albeit at varying degrees. Fractionation between LREEs and HREEs occurred, along with significant positive Ce anomaly and negative Eu anomaly. With TF values < 1, our results suggest that C. sinensis has a weak ability to accumulate REEs in its fruit. The concentrations of REEs in fruit differed between LREEs and HREEs, with content of HREE in fruit ordered as Jiading > Anxi > Wuyang and of LREE in fruit higher in Wuyang. Correlation and redundancy analysis indicated that K2O, Fe2O3 and TOC are important soil factors influencing REE accumulation by C. sinensis, with K2O positively related and Fe2O3 and TOC negatively related to the accumulation process.

Evaluation of Remote Sensing Ecological Index Based on Soil and Water Conservation on the Effectiveness of Management of Abandoned Mine Landscaping Transformation

Abandoned mines are typical areas of soil erosion. Landscape transformation of abandoned mines is an important means to balance the dual objectives of regional ecological restoration and industrial heritage protection, but the secondary development and construction process of mining relics require long-term monitoring with objective scientific indicators and effective assessment of their management effectiveness. This paper takes Tongluo Mountain Mining Park in Chongqing as an example and uses a remote sensing ecological index (RSEI) based on Landsat-8 image data to assess the spatial and temporal differences in the dynamic changes in the ecological and environmental quality of tertiary relic reserves with different degrees of development and protection in the park. Results showed that: ① The effect of vegetation cover, which can significantly improve soil and water conservation capacity. ② The RSEI is applicable to the evaluation of the effectiveness of ecological management of mines with a large amount of bare soil areas. ③ The mean value of the RSEI in the region as a whole increased by 0.090, and the mean values of the RSEI in the primary, secondary and tertiary relic reserves increased by 0.121, 0.112 and 0.006, respectively. ④ The increase in the RSEI in the study area is mainly related to the significant decrease in the dryness index (NDBSI) and the increase in the humidity index (WET). The remote sensing ecological index can objectively reflect the difference in the spatial and temporal dynamics of the ecological environment in tertiary relic protection, and this study provides a theoretical reference for the ecological assessment of secondary development-based management under difficult site conditions.

A Remote-Sensing Ecological Index Approach for Restoration Assessment of Rare-Earth Elements Mining

In order to meet the requirements for comprehensive and multidimensional generalization of ecological management effectiveness evaluation indexes in the context of ecological restoration advocating comprehensive management by multiple means, this paper explores the rationality of using RSEI as an ecological management effectiveness evaluation index to adapt to the systematic transformation of the management goal of abandoned mine restoration from ecological restoration to regional socioeconomic sustainable development. Based on Landsat-8 image data, the remote sensing ecological index (RSEI) was used to evaluate the dynamic changes and spatial and temporal differences of the ecological environment in the study area under the long-term multimeans comprehensive management. The RSEI is suitable for evaluating the effectiveness of comprehensive ecological management in mining areas with a large amount of bare soil. The regional RSEI mean value increased by 0.029 in the early stage and 0.051 in the later stage by fragmentation management, indicating a better effect of multimeans comprehensive management. The remote sensing ecological index can objectively reflect the difference of spatial distribution characteristics of ecological environment in the four “Ecological+” governance regions. It can both objectively reflect the ecological status of the study area and reflect the differentiated spatial distribution characteristics of the ecological environment in different treatment areas, which is of long-term practical significance to the ecological construction of the study area. This study provides a theoretical reference for ecological assessment of complex situation under difficult site conditions.

Isolation and Nitrogen Removal Efficiency of the Heterotrophic Nitrifying-Aerobic Denitrifying Strain K17 From a Rare Earth Element Leaching Site

K17, an indigenous and heterotrophic nitrifying-aerobic denitrifying bacterium, was isolated from the soil of a weathered crust elution-deposited rare earth ore leaching site in Longnan County, China. Strain K17 was identified as Pseudomonas mosselii. In this study, the morphological characteristics of strain K17 were observed and the optimal ammonia nitrogen removal conditions for the strain were studied using a single-factor experiment. Key enzyme activities were determined, and we also explored the ammonia nitrogen removal process of strain K17 on simulated leaching liquor of the rare earth element leaching site. Based on the determination of ammonia nitrogen removal and enzyme activity, it was found that strain K17 has both heterotrophic nitrifying and aerobic denitrifying activities. In addition, single-factor experiments revealed that the most appropriate carbon source for strain K17 was sodium citrate with a C/N ratio of 10 and an initial NH₄⁺-N concentration of 100 mg/l. Furthermore, the optimal initial pH and rotation speed were 7 and 165 r/min, respectively. Under optimal conditions, the ammonia nitrogen removal efficiency of strain K17 was greater than 95%. As an indigenous bacterium, strain K17 has great potential for treating residual ammonium leaching solutions from rare earth element leaching sites.

Hyperspectral characterization and chlorophyll content inversion of reclaimed vegetation in rare earth mines

The special physical and chemical properties of the reclaimed land caused by the disturbance of rare earth mining and the environmental stress caused by the mining of rare earth lead to the inhibition of the physiological functions of the reclaimed vegetation and the severe challenge of vegetation ecological restoration. This study focuses on the Lingbei rare earth mining area in Dingnan County, Jiangxi Province, and investigates the original spectrum, derivative spectrum, and the continuum-removed spectrum of reclaimed vegetation. The spectral characteristics and trends and the typical reclaimed vegetation are analyzed, the correlation between the chlorophyll content and the spectral indices of the reclaimed vegetation is determined, and the sensitive spectral parameters are extracted. Partial least squares algorithm, a back propagation neural network algorithm, and a sparse autoencoder network are used to estimate the chlorophyll content, and the model's accuracies are compared. The vegetation spectrum of the reclaimed vegetation is characterized by high reflectance in the visible region, a redshift of the green peak and red valley positions, and a blueshift of the red edge positions, a relatively low spectral variation in. The variability of the sensitive spectral parameters of different vegetation type is extracted. The sparse autoencoder network is the optimal estimation model (the R² value of the three vegetations are 0.9117, 0.7418, and 0.9815, respectively). The results provide a scientific basis for monitoring and managing the growth of different types of reclaimed vegetation in rare earth mining areas under environmental stress and can guide the ecological restoration of reclaimed mining areas.

Unsupervised monitoring of vegetation in a surface coal mining region based on NDVI time series

Surface coal mining causes vegetation disturbance while providing an energy source. Thus, much attention is given to monitoring the vegetation of surface coal mining regions. Multitemporal satellite imagery, such as Landsat time-series imagery, is an operational environment monitoring service widely used to access vegetation traits and ensure vegetation surveillance across large areas. However, most of the previous studies have been conducted with change detection models or threshold-based methods that require multiple parameter settings or sample training. In this paper, we tried to analyze the change traits of vegetation in surface coal mining regions using shape-based clustering based on Normalized Difference Vegetation Index (NDVI) time series without multiple parameter settings and sample training. The shape-based clustering used in this paper applied shape-based distance (SBD) to obtain the distance between time series and used Dynamic Time Warping Barycenter Averaging (DBA) to generate cluster centroids. We applied the method to a stack of 19 NDVI images from 2000 to 2018 for a surface coal mining region located in North China. The results showed that the shape-based clustering used in this paper was appropriate for monitoring vegetation change in the region and achieved 79.0% overall accuracy in detecting disturbance-recovery trajectory types.

Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.

Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

It is of great significance for the monitoring and protection of the original ecological environment in coal mining areas to identify the ground subsidence and quantify its influence on the surface vegetation. The surface deformation and vegetation information were obtained by using spaceborne SAR and Landsat OLI images in the Xishan Coalfield. The relative change rate, coefficient of variation, and trend analysis methods were used to compare the vegetation growth trends in the subsidence center, subsidence edge, and non-subsidence zones; and the vegetation coverage was predicted by the pixel dichotomy and grey model from 2021 to 2025. The results indicated that the proportions of vegetation with high fluctuation and serious degradation were 6.60% and 5.64% in the subsidence center, and its NDVI values were about 10% lower than that in the subsidence edge and non-subsidence zones. In addition, vegetation coverage showed a wedge ascending trend from 2013 to 2020, and the prediction values of vegetation coverage obtained by GM (1,1) model also revealed this trend. The residuals of the predicted values were 0.047, 0.047, and 0.019 compared with the vegetation coverage in 2021, and the vegetation coverage was the lowest in the subsidence center, which was consistent with the law obtained by using NDVI. Research suggested that ground subsidence caused by mining activities had a certain impact on the surface vegetation in the mining areas; the closer to the subsidence center, the greater the fluctuation of NDVI, and the stronger the vegetation degradation trend; conversely, the smaller the fluctuation, and the more stable the vegetation growth.

Land Cover and Vegetation Coverage Changes in the Mining Area—A Case Study from Slovakia

Dealing with landscape changes in space and time is an important activity in terms of the process of future development of the selected area. In particular, it is necessary to focus on territories that are exposed to the effects of extraction activities. The main objective of the paper was the mapping of spatio-temporal changes in the landscape in connection with the extraction of minerals due to mining activities on the landscape using satellite images and data from the Corine land cover (CLC) database in the environment of geographic information systems. The selected study area is specific to the presence of four mineral deposits (three of which are under active mining). The Rohožník-Konopiská deposit was abandoned and the area was subsequently reclaimed. The study used Corine land cover (CLC) data and Landsat 5, 7, 8 satellite images for selected years in the period 1990–2021. The Normalized Difference Vegetation Index (NDVI) was calculated for vegetation cover analysis, which was further combined with the forest spatial division units (FSDU) layer. Areas in the immediate vicinity of the open-pit mine were selected for detailed analysis of vegetation changes. Using the FSDU data, an average NDVI index value was calculated using the Zonal statistics function for each plot. The results showed that over the selected period there have been changes indicating an improvement in the landscape condition by reclamation operations at two deposits, Rohožník-Konopiská (inactive) and Sološnica-Hrabník (active). The analyzed CLC data detected the change at the Rohožník-Konopiská deposit, but the active deposit Sološnica-Hrabník was not detected in these data. The loss of vegetation on the other two deposits is mainly due to pre-mining preparatory work, which causes the removal of soil and vegetation layers.

Mapping annual land disturbance and reclamation in rare-earth mining disturbance region using temporal trajectory segmentation

Rare-earth mining has caused extensive damage to soil, vegetation, and water, significantly threatening ecosystems. Monitoring environmental disturbance caused by rare-earth mining is necessary to protect the ecological environment. A spatiotemporal remote sensing monitoring method for mining to reclamation processes in a rare-earth mining area using multisource time-series satellite images is described. In this study, the normalized difference vegetation index (NDVI) is used to evaluate the mining impact. Regression analysis is conducted to relate the HJ-1B CCD and Landsat 5/8 data to reduce the NDVI error related to sensor differences between different datasets. The analysis method of NDVI trajectory data of ground objects is proposed, and areas of environmental disturbance caused by rare-earth mining are identified. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and a temporal trajectory segmentation method is established based on the vegetation changes in different disturbance stages. The temporal trajectory of the rare-earth disturbance points is segmented to extract features in each stage to obtain the disturbance year, recovery year, and recovery cycle and evaluate the vegetation recovery after rare-earth mining disturbance. We applied the method to a stack of 20 multitemporal images from 2000 to 2019 to analyze vegetation disturbance due to rare-earth mining and vegetation recovery in the upper reaches of the Guangdong-Hong Kong-Macao Greater Bay Area, China. The results show the following. (1) Mining industry in the study area experienced rapid expansion before 2008, but growth slowed since the policies implemented by the government since 2009 to restrict rare-earth mining. (2) The continuous influence to the land caused by rare-earth mining can last for decades; however, the reclamation activities shorten the recovery cycle of mining land from 5 to 3 years.

Soil characteristics and microbial community response in rare earth mining areas in southern Jiangxi Province, China

The microbial community and functional flora in rare earth mining areas are correlated, but the characteristics and metabolic pathways of pollutant in such mining areas are still poorly known. The heavy metals, rare earth elements, and microorganisms present after mining of rare earth mine sites were analyzed. After mining, all sampling sites exhibited low pH and low total organic carbon levels, accompanied by high iron and aluminum concentrations. The development of vegetation is closely related to the development of microorganisms. In the complex environment of rare earth mining areas, Proteobacteria exhibit an absolute competitive advantage. During mine environmental recovery, the relative abundances of Acidobacteria and Chloroflexi will increase markedly, and with further restoration the relative abundance of Firmicutes will gradually decrease. Many genera of bacteria related to the N cycle and heavy metal metabolism were detected in the study area, indicating the important metabolic pathways for ammonia nitrogen and heavy metals in rare earth mining areas. Bacterial genera that promote plant nitrogen fixation also occur in the area, further revealing the nitrogen cycle. This research is important for health assessment and recovery of rare earth mines.

Impact of urban expansion on vegetation: The case of China (2000-2018)

Extensive urbanization leads to the degradation of vegetation, which aggravates the deterioration of many ecological environments. However, the research on the impact of urbanization on vegetation change mainly focuses on cities. But the research on urban agglomeration is relatively scarce. The impact of urbanization on vegetation is explored by quantifying the changes in construction land and normalized difference vegetation index (NDVI) in China's urban agglomerations from 2000 to 2018. Results showed that in China, 72.73% of the regional NDVI presented a significant increasing trend, and 2.05% of the regional NDVI presented a significant downward trend. Vegetation degradation occurred in urban areas, but there was an improvement in vegetation in the urban centers of 2000. In urban agglomerations, the shift of the center of gravity of construction land can affect the direction of the transfer of NDVI cold spots or hot spots. Urbanization intensity in most urban agglomerations was negatively correlated with vegetation cover and showed a downward trend along the intensity gradient. However, NDVI in areas covered by complete vegetation showed an upward trend. Based on these findings, we suggest that vegetation protection and restoration should be strengthened, and effective urban landscape planning should be carried out to promote vegetation greening.

Spatiotemporal changes in desertified land in rare earth mining areas under different disturbance conditions

Special mining methods and red soil lead to large-scale land degradation and desertification in ion-type rare earth (RE) mining areas. Therefore, it is crucial for ecological management and restoration of mining areas to accurately understand the evolution process of desertification. In this study, remote sensing Landsat images from 1986 to 2019 were used to extract desertified land information from the Lingbei mining areas, Dingnan County, Ganzhou, China. To improve the reliability of the experiment, samples selected from Google images were used for verification to compare the accuracy of the desertification difference index (DDI) model and random forest (RF) algorithm for extracting land desertification information. The results showed that compared with the DDI model, the overall accuracy and kappa coefficient of the RF model based on multiple features were improved by 7% and 9.37%, respectively, indicating its higher applicability. Spatiotemporal change analysis of desertification in the mining area showed that the total area of desertification in the mining area increased most rapidly during 1986-1994 and reached 60.75 km². The area of desertified land increased continuously from 1994 to 2004 and reached a maximum of 143.08 km² in 2004. The area of desertified land decreased by 50.27 km², but the severe desertified land (SDL) area increased by 1.69 km² during 2004-2011. The area of desertified land gradually declined and stabilized from 2011 to 2019. Analysis of the desertification process in mining areas under different disturbance conditions showed that the desertified land disturbed by RE mining was most severely damaged. There is still an area of 16.77 km² in the process of restoration, of which 2.24 km² belongs to the SDL level. Moderate desertified land (MDL) and light desertified land (LDL) have not been completely contained and require the attention of the relevant departments to ensure their timely reclamation.