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

Wu, Zhenbang; Li, Hengkai; Wang, Yuqing

doi:10.1007/s11356-021-15480-3

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…(1) Elimination of coupling impact. Compared to direct observations of vegetation indices (e.g., [9][10][11][12][13][14]), the quantified contribution of the mining factor eliminates the impact of other factors. The vegetation in the study area is impacted by the coupling of multiple factors, so it is more rational to obtain the results of SRMD in 2020 with this study method.…”

Section: Discussionmentioning

confidence: 99%

“…For example, the trend of temperature vegetation dryness index (TVDI) was fitted using a linear model [8]. The trend of NDVI based on the coefficient of variation (CV) [9][10][11] and the LandTrendr algorithm [12] was fitted. The other category is to identify the SRMD based on a comparative area.…”

Section: Introductionmentioning

confidence: 99%

“…However, existing studies still have the following shortcomings. First, the identification of SRMD was based on direct observations of vegetation indices, which attributed the changes in vegetation exclusively to mining activities [9][10][11][12][13][14]. Nevertheless, the change of vegetation in the real situation is the result of coupling multiple factors (e.g., temperature, precipitation, topography, mining activities, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…Second, the trend of the vegetation index is fitted in sample areas selected from different directions or in buffers generated at different distances. The value of the stable trend is determined as the threshold for extracting the SRMD [8][9][10][11][12]. However, errors have been brought by the artificial determination of thresholds.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

Zhang

Zheng

et al. 2022

IJERPH

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Identifying the spatial range of mining disturbance on vegetation is of significant importance for the plan of environmental rehabilitation in mining areas. This paper proposes a method to identify the spatial range of mining disturbance (SRMD). First, a non-linear and quantitative relationship between driving factors and fractional vegetation cover (FVC) was constructed by geographically weighted artificial neural network (GWANN). The driving factors include precipitation, temperature, topography, urban activities, and mining activities. Second, the contribution of mining activities (Wmine) to FVC was quantified using the differential method. Third, the virtual contribution of mining activities (V-Wmine) to FVC during the period without mining activity was calculated, which was taken as the noise in the contribution of mining activities. Finally, the SRMD in 2020 was identified by the significance test based on the Wmine and noise. The results show that: (1) the mean RMSE and MRE for the 11 years of the GWANN in the whole study area are 0.0526 and 0.1029, which illustrates the successful construction of the relationship between driving factors and FVC; (2) the noise in the contribution of mining activities obeys normal distribution, and the critical value is 0.085 for the significance test; (3) most of the SRMD are inside the 3 km buffer with an average disturbance distance of 2.25 km for the whole SRMD, and significant directional heterogeneity is possessed by the SRMD. In conclusion, the usability of the proposed method for identifying SRMD has been demonstrated, with the advantages of elimination of coupling impact, spatial continuity, and threshold stability. This study can serve as an early environmental warning by identifying SRMD and also provide scientific data for developing plans of environmental rehabilitation in mining areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

Zhang

Zheng

et al. 2022

IJERPH

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“…The last stage of the mining life cycle [36] is post-mining land use, which is also related to the reclamation of the affected area. Addressing the importance of land and vegetation reclamation during mining activities [37] is of continuing interest to keep an eye on the future development of the landscape also from the perspective of ecological research [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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.

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

Yang

Shen²,

et al. 2021

Environ Sci Pollut Res

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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 thresholdbased 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.

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

Cited by 11 publications

References 28 publications

A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

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

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

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