Driving force analysis of LUCC and forecast method based on remote sensing in coal mining area

2009 ◽  
Author(s):  
Hong-quan Xie ◽  
Xiang-wei Gao ◽  
Xia Lu
Keyword(s):  
Remote Sensing ◽  
Coal Mining ◽  
Driving Force ◽  
Mining Area ◽  
Force Analysis ◽  
Forecast Method ◽  
Coal Mining Area ◽  
Driving Force Analysis

scholarly journals Experience of the application of remote sensing data in the problems of geo-ecological monitoring of the coal mining area

2019 ◽  
Vol 75 ◽  
pp. 02001
Author(s):  
Olga Giniyatullina ◽  
Evgeniy Schastlivtsev ◽  
Vladimir Kovalev
Keyword(s):  
Remote Sensing ◽  
Coal Mining ◽  
Remote Sensing Data ◽  
Ecological Monitoring ◽  
Mining Area ◽  
The State ◽  
Sensing Data ◽  
Coal Mining Area ◽  
Mining Region ◽  
Dust Load

The experience of solving problems of geoecological monitoring of coal mining region with the use of remote sensing data is presented. The results of control over the boundaries of coal-mining enterprises, assessment of the degree of self-growth of dumps, monitoring of the state of vegetation near objects of coal mining and dust load of the area are shown.

scholarly journals Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

2020 ◽  
Vol 12 (4) ◽  
pp. 1626
Author(s):  
Hongfen Zhu ◽  
Ruipeng Sun ◽  
Zhanjun Xu ◽  
Chunjuan Lv ◽  
Rutian Bi
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Coal Mining ◽  
Partial Least Squares ◽  
Influencing Factors ◽  
Soil Nutrients ◽  
Partial Least Squares Regression ◽  
Mining Area ◽  
Least Squares Regression ◽  
Coal Mining Area

(1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSROri or PLSROri), partial least squares regression based on bi-dimensional intrinsic mode function (PLSRBIMF), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMDPM). (3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMDPM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.

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