Weight-based mining vegetation ecological accumulation effect disturbance range identification method

Abstract

The invention discloses a weight-based mining vegetation ecological accumulation effect disturbance range identification method. The method comprises the following steps: A, collecting original data of a research area; B, constructing a driving factor data set and quantizing a plurality of driving factors; C, carrying out driving factor data set expansion in the M1-M2 period in a three-dimensional space and forming an ecological evolution big data cube; D, performing data extraction by adopting a sliding cube method, and constructing a geographic space-time weighted artificial neural network model; E, quantifying the weight of each driving factor; F, obtaining a mining driving factor virtual weight in the M0-M1 period; and G, obtaining an area which is obviously influenced by mining disturbance in the research area, and determining an influence range of mining on vegetation disturbance. The disturbance range of mining on vegetation can be finally identified, disturbance range identification errors caused by multi-factor coupling are avoided, and data support is provided for excavating an influence mechanism of mining area mining activities on the ecological environment, protecting the mining area ecological environment and the like.

Description

technical field [0001] The invention relates to the field of identification and processing of mining remote sensing data, in particular to a weight-based identification method for the disturbance range of ecological accumulation effect of mining vegetation. Background technique [0002] The mining of mineral resources has a strong impact on the vegetation in the mining area through the excavation and transportation of rock formations, which has an ecological cumulative effect and destroys the local natural ecosystem. This impact has a disturbed spatial and geographical range, that is, the disturbance range of the ecological cumulative effect of mining vegetation (hereinafter It is referred to as the disturbance range of mining to vegetation). Vegetation, as the producer of the ecosystem, plays a key role in the ecosystem, and it is of great practical significance to identify the disturbance range of mining activities to vegetation. The current identification method for the ...

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Problems solved by technology

However, existing studies on the disturbance range of mining areas (Fourier analysis, functional principal component analysis, buffer analysis, trend line fitting, also such as: Yang Y, Erskine P D, Lechner A M, etal. Detecting the dynamics of vegetation disturbance and recovery in surfacemining area via Landsat imagery and LandTrendr algorithm[J].Journal of Cleaner Production,2018,178(MAR.20):353-362) does not consider the influence of other factors such as temperature, terrain, grazing and other factors on vegetation, but only Simply analyzing the vegetation index and taking the result of multi-factor coupling as the result of the influence of mining factors leads to large errors in the identification of mining disturbance ranges

Second, the existing research is to select sample areas in certain directions of the research area, analyze the vegetation index of each sample area, and fit the trend line to find a point that tends to be stable as the threshold value of mining for vegetation disturbance, see figure 2 In practice, the point where the trend line tends to be stable cannot be determined, and it must be the threshold of vegetation disturbance by mining. The current method of determining the threshold still lacks certain rationality

Third, the existing research is to connect the threshold point positions of the sampling areas in all directions into a closed curve as the disturbance range of the mining area. However, the connection area of ​​the threshold point positions of adjacent sampling areas belongs to the blind area of ​​the research. There is a spatial discontinuity between the threshold points; and the existing research has no reasonable explanation for the connection mode between the threshold point and the threshold point; therefore, there is a certain error in the disturbance range of the mining area formed by the connection between the threshold point and the threshold point, such as image 3 shown

Fourth, the mining area of ​​the mining area will change with time. The existing methods only consider the range of influence in the year of the study, and do not consider the temporal heterogeneity of the impact of mining activities on vegetation in the mining area.

[0003] To sum up, the existing studies only analyze the vegetation index, without considering the influence of other factors such as temperature, terrain, grazing, etc. on the vegetation, and take the result of multi-factor coupling as the result of the influence of mining factors; the current common methods are all Unable to accurately obtain the disturbance range of vegetation caused by mining activities

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