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Updated: Sep 23, 2025
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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and
Chengye Zhang1, Huiyu Zheng1, Jun Li1
1College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
Summary
This study introduces a new method to map mining disturbance zones using a geographically weighted artificial neural network (GWANN). The approach accurately identifies the spatial range of mining disturbance (SRMD) for environmental rehabilitation planning.
Area of Science:
- Environmental Science
- Remote Sensing
- Geospatial Analysis
Background:
- Environmental rehabilitation in mining areas requires accurate identification of the spatial range of mining disturbance (SRMD).
- Existing methods may struggle with the complex interplay of factors affecting vegetation cover.
- Fractional vegetation cover (FVC) is a key indicator of ecological health and disturbance.
Purpose of the Study:
- To propose and validate a novel method for identifying the spatial range of mining disturbance (SRMD).
- To quantify the impact of mining activities on vegetation cover while accounting for other environmental factors.
- To provide a scientific basis for environmental rehabilitation planning in mining regions.
Main Methods:
- Constructed a non-linear relationship between driving factors (precipitation, temperature, topography, urban, and mining activities) and FVC using geographically weighted artificial neural network (GWANN).
- Quantified the contribution of mining activities (W) to FVC using a differential method.
- Identified SRMD through a significance test comparing mining's contribution (W) against calculated noise (V-W).
Main Results:
- The GWANN model demonstrated high accuracy (mean RMSE 0.0526, MRE 0.1029) in predicting FVC across 11 years.
- The identified SRMD was largely within a 3 km buffer, with an average disturbance distance of 2.25 km, showing directional heterogeneity.
- The noise in mining's contribution followed a normal distribution, with a critical value of 0.085 for the significance test.
Conclusions:
- The proposed method effectively identifies the spatial range of mining disturbance (SRMD) with advantages in eliminating coupling impacts, spatial continuity, and threshold stability.
- The method provides a reliable tool for early environmental warnings and data generation for rehabilitation strategies.
- This research supports informed environmental management and restoration efforts in areas affected by mining.
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