Analysis of Mining Waste Dump Site Stability Based on Multiple Remote Sensing Technologies

Wei, Lianhuan; Zhang, Yun; Zhao, Zhanguo; Zhong, Xiao-yu; Liu, Shanjun; Mao, Yachun; Li, Jiayu

doi:10.3390/rs10122025

Cited by 24 publications

(9 citation statements)

References 59 publications

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“…However, the FLCC of CMALs has not been investigated. This is significant for the scientific management of mines and agricultural development.Three significant landscape-specific characteristics seriously limit the accuracy of FLCC of CMALs [12][13][14]: significant three-dimensional terrain [15,16]; strong spatio-temporal variability [17,18]; and spectral-spatial homogeneity. Methods to reduce these characteristics were identified.…”

mentioning

confidence: 99%

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

Tang

Chen

et al. 2019

Remote Sensing

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Land cover classification (LCC) of complex landscapes is attractive to the remote sensing community but poses great challenges. In complex open pit mining and agricultural development landscapes (CMALs), the landscape-specific characteristics limit the accuracy of LCC. The combination of traditional feature engineering and machine learning algorithms (MLAs) is not sufficient for LCC in CMALs. Deep belief network (DBN) methods achieved success in some remote sensing applications because of their excellent unsupervised learning ability in feature extraction. The usability of DBN has not been investigated in terms of LCC of complex landscapes and integrating multimodal inputs. A novel multimodal and multi-model deep fusion strategy based on DBN was developed and tested for fine LCC (FLCC) of CMALs in a 109.4 km 2 area of Wuhan City, China. First, low-level and multimodal spectral–spatial and topographic features derived from ZiYuan-3 imagery were extracted and fused. The features were then input into a DBN for deep feature learning. The developed features were fed to random forest and support vector machine (SVM) algorithms for classification. Experiments were conducted that compared the deep features with the softmax function and low-level features with MLAs. Five groups of training, validation, and test sets were performed with some spatial auto-correlations. A spatially independent test set and generalized McNemar tests were also employed to assess the accuracy. The fused model of DBN-SVM achieved overall accuracies (OAs) of 94.74%± 0.35% and 81.14% in FLCC and LCC, respectively, which significantly outperformed almost all other models. From this model, only three of the twenty land covers achieved OAs below 90%. In general, the developed model can contribute to FLCC and LCC in CMALs, and more deep learning algorithm-based models should be investigated in future for the application of FLCC and LCC in complex landscapes.

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mentioning

confidence: 99%

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

Tang

Chen

et al. 2019

Remote Sensing

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“…The projection of

in LOS direction can be calculated directly according to the incidence angle

. In order to resolve the projection of

in LOS direction,

should be first projected to the vertical plane where LOS is in [ 30 ]. Since the LOS directions in descending and ascending orbits are different, the projections would be discussed separately.…”

Section: Methodsmentioning

confidence: 99%

“…By substituting Equations (1) and (6) into Equation (7), the relationship between

and

in ascending geometry can be described, which is identical to Equation (4). Therefore, for ascending data, the conversion of displacement from LOS to slope direction is the same as Equation (5) [ 30 ]. In this study, the heading angles

(assuming clockwise with reference to the North is positive) and incidence angles

of the two datasets are shown in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Precise Topographic Model Assisted Slope Displacement Retrieval from Small Baseline Subsets Results: Case Study over a High and Steep Mining Slope

Wei

Feng

Liu

et al. 2020

Sensors

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Due to the intrinsic side-looking geometry of synthetic aperture radar (SAR), time series interferometric SAR is only able to monitor displacements in line-of-sight (LOS) direction, which limits the accuracy of displacement measurement in landslide monitoring. This is because the LOS displacement is only a three dimensional projection of real displacement of a certain ground object. Targeting at this problem, a precise digital elevation model (DEM) assisted slope displacement retrieval method is proposed and applied to a case study over the high and steep slope of the Dagushan open pit mine. In the case study, the precise DEM generated by laser scanning is first used to minimize topographic residuals in small baseline subsets analysis. Then, the LOS displacements are converted to slope direction with assistance of the precise DEM. By comparing with ground measurements, relative root mean square errors (RMSE) of the estimated slope displacements reach approximately 12–13% for the ascending orbit, and 5.4–9.2% for the descending orbit in our study area. In order to validate the experimental results, comparison with microseism monitoring results is also conducted. Moreover, both results have found that the largest slope displacements occur on the slope part, with elevations varying from −138 m to −210 m, which corresponds to the landslide area. Moreover, there is a certain correlation with precipitation, as revealed by the displacement time series. The outcome of this article shows that rock mass structure, lithology, and precipitation are main factors affecting the stability of high and steep mining slopes.

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“…Tao et al used fiber optic sensing technology to monitor the stability of the dumping slope with a height of 300 m in Nanfen Iron Mine, and determined the deformation rule of the soil under different loading loads and rainfall conditions [15]. Wei et al delineated the landslide dangerous zones of the dump slope, and analyzed the stability of the dump slope by comprehensive utilization of multi-source, collaborative remote sensing technology and numerical method [16]. Wang and Chen analyzed the instability failure mode of the dump slope by using the FLAC3D and floor friction model tests [17].…”

Section: State Of the Artmentioning

confidence: 99%

Stability of Step-shaped Dump Slope and Reinforcement Optimization Analysis of Anti-slide Piles

Wang¹,

Yan²,

Wu³

et al. 2019

JESTR

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The geotechnical material in slope engineering of mine dumps is loose, under severe conditions such as heavy rain, there is a risk of landslide in the dumping site. Therefore, to evaluation on the stability of the dump slope in the dumping site is essential to ensure the safety and property of the mine. Taking Jinhaili dumping slope in Chengde as engineering background, based on the site survey and laboratory test, a three-dimensional numerical calculation model of the dumping slope was constructed by using FLAC3D, and the loading process and the stability of the step-shaped dumping slope were carried out. Then the reinforcement optimization analysis of anti-slide piles for potential slip was analyzed. Based on the calculation results, the landslide mechanism of the dumping slope was revealed, and the key part of the potential slip of the slope during the step-by-step loading process was determined. The reinforcement scheme of the antislide piles for the potential sliding part was proposed also. The conclusions obtained in the study are of important theoretical value to similar dump slope engineering.

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Analysis of Mining Waste Dump Site Stability Based on Multiple Remote Sensing Technologies

Cited by 24 publications

References 59 publications

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

Precise Topographic Model Assisted Slope Displacement Retrieval from Small Baseline Subsets Results: Case Study over a High and Steep Mining Slope

Stability of Step-shaped Dump Slope and Reinforcement Optimization Analysis of Anti-slide Piles

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