CN114863271A - Mining area ecological environment remote sensing method considering rock-soil difference and plant diversity - Google Patents
Mining area ecological environment remote sensing method considering rock-soil difference and plant diversity Download PDFInfo
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Abstract
The application provides a mining area ecological environment remote sensing method considering rock-soil difference and plant diversity, which is used for remotely sensing the ecological environment of a target area aiming at rock-soil difference, plant diversity and index right determination by calculating a remote sensing ecological index REM facing the mining area environment. Taking the western open-pit mine pacified by Liaoning as an example, the performance of the method is verified by combining remote sensing data of sentinel No. 2, global 10-meter land coverage data and field investigation data. The method is expected to provide an accurate and good remote sensing tool for dynamic monitoring of the ecological environment of the mining area, ecological restoration of the mining area or quality assessment of land reclamation.
Description
Technical Field
The application relates to a direct satellite remote sensing technology, in particular to a mining area ecological environment remote sensing method always considering rock-soil difference and plant diversity.
Background
National economic development and mineral resources which cannot be obtained in people's production and life are reported in "Chinese mineral resources report 2021 [1] In 2020, the total primary energy production amount of China is 40.8 hundred million tons of standard coal, which is increased by 2.8% compared with the last year; in 2020, the yield of iron ore in China is 8.7 hundred million tons, which is increased by 3.7 percent compared with the last year; the yield of the ten nonferrous metals is increased by 5.5 percent in the last year, so that the development of mineral resources still plays an important role in economic development. However, the problems of land digging loss, occupation, ground subsidence, vegetation growth stress, environmental pollution, ecological degradation and the like caused by mineral resource development activities are in conflict with the overall requirements of national ecological civilization construction. Therefore, the related theories and technologies of 'green mine', 'mining area ecological restoration', 'mining area land reclamation' and the like are prosperous and developed, and the prosperous and prosperous view! The mining area ecological environment remote sensing is a technical method for monitoring and evaluating the quality of the mining area ecological environment by fully relying on a remote sensing information technical means. The technical method can provide necessary basic data for the land reclamation or the monitoring and evaluation of ecological restoration activities in mining areas, can also provide a convenient supervision tool for natural resource management departments, and has important practical value and research significance [2] 。
The remote sensing monitoring method for the ecological environment of a mining area is roughly divided into four categories by scholars, namely a method based on the earth surface coverage type, a method based on a single remote sensing spectral index, a method based on a comprehensive remote sensing index and a method based on the service function of an ecological system [3] . The methods have advantages and disadvantages, and the method mainly focuses on the method based on the comprehensive remote sensing index. A typical representation of this method is RSEI (Remote Sensing-based Ecological Index) [4,5] The method is defined as the integration of four indexes, namely a remote sensing humidity index (WET obtained by remote sensing Thysanocap transformation), a greenness index (NDVI), a heat index (LST) and a dryness index (NDBSI), wherein the four indexes are the remote sensing normalized difference building and bare soil indexes, and the integration mode is the first principal component of Principal Component Analysis (PCA). The RSEI index has the characteristics of simplicity and easiness in use, and is widely used in mining area or mine ecological environment monitoring, such as northern river peak mining area [6] "Fujian" ever-fixed mine area [7] Shaanxi provinceShenfu mine area [8] Antimony mine area of tin mine in Hunan province [9] Zhengzhou mining area of Henan province [10] Fujian Zijin mountain area [11] And inner Mongolia Shendong mining area [12] And so on. However, the RSEI has the defect of insufficient representation of ecological quality, so that various novel comprehensive remote sensing indexes are proposed. For example, students introduce urban Impervious Surface Coverage (ISC) to construct an urban Surface ecological integrated remote sensing index (RSUSEI) [13] . Also, the scholars introduce the air quality index, provide a vegetation-soil-impervious bed-atmosphere (VISA) frame for ecological remote sensing monitoring, and construct an urban ecological comprehensive remote sensing index (RSUAE) [14] 。
The comprehensive remote sensing index has some obvious defects aiming at the ecological environment of a mining area or a mine. Firstly, in the evaluation of the land quality of a mining area, the existence of soil is a very important index [15] . Soil represents the capability of cultivating vegetation or crops, but bare rocks without soil do not have the capability, so the bare soil and the bare rocks have important difference (referred to as 'rock-soil difference problem') and cannot be regarded as equivalent in ecological quality evaluation. However, the problem of rock-soil variation is not reflected in most comprehensive indexes. Secondly, plant diversity represents the abundance and uniformity of plant species, and it plays an important role in promoting ecosystem service ability and maintaining ecosystem stability. The area with higher plant diversity and the single plant coverage area are not considered to be equal in ecological environment quality evaluation. Particularly, for the evaluation of the ecological restoration quality of a mining area, the restoration area with rich plant species is obviously superior to the restoration area with single plant species. However, the problem of plant diversity is likewise not reflected in most comprehensive indices (shortly "plant diversity problem"). Third, many of the combined remote sensing indices combine multiple single remote sensing indices in the form of the first principal component of PCA, resulting in inefficient information utilization. For the problem, the scholars perform weighted summation on the previous 2, 3 or even all 4 principal component components according to the contribution rate as the weight [16-18] . However, the original single remote ecological index passes through the PCAAfter the processing, the interpretation meaning of each principal component on the ecological quality is vague compared with the original index, and the positive and negative correlation of each principal component on the ecological quality is vague compared with the original index. How to assign the weight to a single remote sensing index by using the PCA method instead of assigning the weight to each PCA component becomes a problem to be solved urgently (the weight determining problem for short).
Disclosure of Invention
In view of the above problems, the present application provides a mining area ecological environment remote sensing method taking into account geotechnical differences and plant diversity.
According to the mining area ecological environment remote sensing method considering the rock-soil difference and the plant diversity, the mining area ecological environment is remotely sensed by calculating the remote sensing ecological index REM facing the mining area environment according to the rock-soil difference, the plant diversity and the index right confirmation; wherein:
REM＝I imper ×(α 1 ×I wet +α 2 ×I veg +α 3 ×I soil ) (formula 1);
in the formula 1, the value range of REM is 0-1, the larger the value is, the better the ecological environment is, otherwise, the worse is; i is imper Whether the water-proof layer or the bare rock exists or not is represented, if the value is 0, the value is 1; i is wet Represents the wetness index of vegetation or soil; i is veg A composite index representing the coverage of vegetation and the degree of diversity of the plants; i is soil A comprehensive index representing the degree of exposure and the flatness of the soil; alpha is alpha 1 、α 2 、α 3 Representing the weight coefficients.
Preferably, I imper Obtained by the following calculation formula,
in formula 2,. rho Blue 、ρ NIR The reflectivity of a blue wave band and a near infrared wave band; m and n are respectively blue band and near infrared band coefficients; c is a constant; usually, m is 0.905, n is 0.435, C is 0.019; sgn () represents a coincidence function, and when PII is greater than 0, it indicates that it is bare rockAnd returning a value of 0, and returning a value of 1 when the PII is less than 0.
Preferably, I wet Obtained by the following calculation formula,
in formula 3,. rho red 、ρ nir 、ρ blue 、ρ green 、ρ swir1 、ρ swir2 Respectively the reflectivity of the corresponding wave band; I.C. A wet Through normalization, the value range is 0-1, and the larger the value is, the better the hydrologic ecological quality is.
Preferably, I veg Obtained by the following calculation formula,
I veg ＝[(EVI+RSPD)+1](formula 10);
in the formula 10, I veg Through normalization, the value range is 0-1, and the larger the value is, the better the ecological quality of vegetation is;
EVI is used to characterize vegetation coverage or greenness, which is obtained by the following calculation,
in formula 4, G is 2.5, C 1 ＝6、C 2 ＝7.5、L＝1；ρ red 、ρ nir 、ρ blue Respectively the reflectivity of the corresponding wave band; the value range of the EVI is-1;
RSPD is the remote sensing index of plant diversity.
Preferably, I soil Obtained by the following calculation formula,
in formula 12, entopy is the normalized Entropy value; p i The probability of the occurrence of a pixel with a gray value i in the window is shown, and G is the gray level number; w is the number of pixels in the window; i is soil Through normalization, the value range is 0-1, and the larger the value is, the better the ecological quality of the soil is; SI is obtained by the following calculation:
in formula 11, [ rho ] red 、ρ nir 、ρ blue 、ρ green 、ρ swir1 Respectively, the reflectivities of the corresponding bands.
Preferably, the weight coefficient α 1 、α 2 、α 3 Obtained by the following calculation:
in formula 13, H i The communality factor variance of the ith evaluation index; lambda [ alpha ] i,j Load or correlation between the ith evaluation index and the jth PCA principal component; m is the first few components with a cumulative contribution rate of 80%; n is the number of evaluation indexes.
Aiming at three problems of rock-soil difference, plant diversity and index weight determination, a Mining area Ecological environment Remote sensing monitoring model considering the rock-soil difference and the plant diversity is constructed and named as a Mining area Ecological Remote sensing index (REM). The research takes the western open-pit mine pacified by Liaoning as an example, and the REMOTE performance of REM is evaluated and analyzed by combining remote sensing data of sentinel No. 2, global 10-meter land coverage data and field investigation data. The method is expected to provide an accurate and good remote sensing tool for dynamic monitoring of the ecological environment of the mining area, ecological restoration of the mining area or quality assessment of land reclamation.
Drawings
FIG. 1 shows the spatial positions and the distribution of sampling points in a research area;
FIG. 2 is a schematic diagram of a vegetation-impermeable layer-soil framework in a mining area;
FIG. 3 shows the results of annual monitoring of REM model in 2016-and 2021-pacified open West opencast mine;
FIG. 4 is a statistical result of ecological environment quality of different coverage types;
FIG. 5 compares the REM values in 2017 (a) - (f) and 2020 (g) - (l) with the type of cover, where (a), (d), (g) and (j) are the entire study area; (b) the (e), (h) and (k) are in a key repair area; (c) (f), (i) and (l) are all in the northern vegetation area;
FIG. 6 shows the correlation test results between the REM model results and the field survey data;
FIG. 7 is a comparison of REM model results with field survey results;
FIG. 8 is the statistical information of 2016-year 2021-pacified West opencast mine;
FIG. 9 shows the REM change in 2016-2021;
FIG. 10 shows the result of REM histogram distribution;
FIG. 11 is a REM level distribution plot for a study area;
FIG. 12 shows the ratio of different levels of REM in 2021 in the study area 2016-;
FIG. 13 shows the statistics (a) and spatial variations (b) of 2016-2021 equal-weight REM;
FIG. 14 shows the results of the correlation test between the equal-weight REM and the field survey data;
FIG. 15 shows the correlation test results of equal-weight REM by CV method and equal-weight REM by RSPD method;
FIG. 16 shows the correlation test results of CV-method equal-weight REM and field survey data.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings.
1 study area and data
1.1 overview of the study region
The pacifying west opencast mine area shown in fig. 1 is the study area herein, which belongs to the northern moderate temperature zone semi-humid continental monsoon climate, hot and rainy in summer, extremely cold in winter, and the annual average temperature is 5-6 ℃. The landform change in the mining area is large, the mining area is in a step-shaped artificial landform shape, the lowest elevation of a mine pit is-309 m, and the vertical height of the south side of the mine pit is 400-500 m. The geological structure is complex and is comprehensively influenced by various geological activities. The plants planted in the research area belong to natural secondary forests or artificial forests of the long and white plant area, the vegetation is flourishing, the plants mainly comprise arbors and shrubs, the trees mainly comprise acacia and poplar trees, and the shrubs mainly comprise wattles and amorpha fruticosa. Since 9 months in 2018, west opencast mines develop comprehensive treatment and integration of coal mining affected areas and gradually enter the work of comprehensive treatment and ecological restoration.
1.2 remote sensing data
(1) In the research, L1C-level multispectral remote sensing data (MSI) of Sentinel-2 issued by the European Bureau of America (ESA) is selected, and the acquisition time comprises 2016-. MSI provides 13 spectral bands in the wavelength range from visible to short-wave infrared. The spatial resolution of the different spectral bands is 10m, 20m and 60m, respectively. The band with a spatial resolution of 60m is excluded from the study due to the coarser resolution. The raw data is preprocessed by atmospheric correction, resampling, cutting and the like to generate 10-meter L2A-level earth surface reflectivity data.
(2) Esri published global 10 meter land cover data, which was constructed using the 2020 Sentinel-2 satellite imagery of the European Space Agency (ESA).
(3) FROM-GLC 10m land cover data [19] The data was plotted by Qinghua university based on 10-meter resolution Sentinel-2 data obtained in 2017. Table 1 shows the classification system of the land cover data.
TABLE 1 Classification system of land cover data
1.3 field survey data
In order to evaluate the rationality of the remote sensing monitoring model, field investigation is carried out on the field of research areas. Firstly, 95 sample points are selected in the mining area, the vegetation area and the bare soil area according to the basic situation of the research area, and the sample point selection result is shown in figure 1. To facilitate the ranking of late sample point data, a solid photograph is taken of the sampling area during the survey. Meanwhile, the land coverage type and the vegetation type condition of the sampling point area are recorded. And scoring all sample points according to the investigation result, and evaluating the ecological quality grade.
2 method
2.1 mining Vegetation-soil-impermeable layer VIS-M frame
The mine has essentially the following in common: based on mining, with the aim of developing economy, having production facilities and being able to carry out normal life, having regional characteristics [20] . Reference is made herein to a "vegetation-soil-impermeable layer (VIS) frame [21] ", the VIS-M framework for the mining ecological environment assessment as shown in fig. 2 was established. The basic principle of the framework is that the ground surface of the mining area is assumed to be composed of three basic components of vegetation, soil and impervious layers (or rocks), so the comprehensive evaluation index should integrate the biophysical characteristics of each basic component. To account for the geotechnical differences and plant diversity mentioned in the introduction, a Remote sensing Ecological index (REM) oriented to the Mining environment was constructed herein, expressed as:
REM＝I imper ×(α 1 ×I wet +α 2 ×I veg +α 3 ×I soil ) (formula 1)
In the formula, the value range of REM is 0-1, the larger the value is, the better the ecological environment is, otherwise, the worse is; i is imper Whether the water-proof layer or the bare rock exists or not is represented, if the value is 0, the value is 1; i is wet Represents the degree of wetting of vegetation or soil; i is veg Represents a combination of vegetation coverage and plant diversity; i is soil Represents the integration of the exposure degree and the flatness of the soil; alpha (alpha) ("alpha") 1 、α 2 、α 3 Representing the weight coefficients. Note that the water body region does not participate in the model evaluation, and can be excluded in advance in the data preprocessing, for example, the scene classification algorithm built in the Sentiel-2 data processing software Sen2Cor identifies the water body pixels [22] . The REM annual comprehensive index is obtained by taking the maximum value of REM calculated from remote sensing data of each period in the year and synthesizing.
2.2 impervious layers or bare rocks
0/1 mask data are constructed through the impervious layer index, and the impervious layer or the mixture of the bare rock and the vegetation soil is separated. Constructed in linear form in constructing the impervious bed indexThe index can self-adaptively adjust parameters according to soil lines in different research areas, and the noise influence brought by soil is weakened. Therefore, a linearity index PII (vertical impervious layer index) constructed by using a combination of a blue band and a near infrared band [23] . PII and I imper The calculation expression of (a) is as follows:
in the formula, ρ Blue 、ρ NIR The reflectivity of a blue wave band and a near infrared wave band; m and n are respectively blue band and near infrared band coefficients; c is a constant; usually, m ═ 0.905, n ═ 0.435, C ═ 0.019; sgn () represents a fitness function, returning a value of 0 for bare rock when PII is greater than 0 and returning a value of 1 for non-bare rock when PII is less than 0.
2.3 humidity of vegetation or soil
The humidity index (WET) is the third index component in the transformation of the tassel cap, can reflect the water content information in soil and vegetation, and is widely applied to the evaluation of the ecological environment quality. The study used the Tasselled Cap-wetness formula to calculate the humidity index for Sentinel-2A data. The histogram distribution shows that the numerical values are intensively distributed in the range of-0.15 to 0.05, and thus the normalization processing is performed by the following formula.
In the formula, ρ red 、ρ nir 、ρ blue 、ρ green 、ρ swir1 、ρ swir2 Respectively, the reflectivities of different wave bands. I is wet The value range is 0-1 for the normalized humidity index, and the larger the value is, the better the hydrologic ecological quality is.
2.4 Vegetation coverage and diversity
Enhanced Vegetation Index (EVI), incorporating both "anti-atmospheric vegetation index" and "soil-regulated vegetation index", improves vegetation monitoring through decoupling of canopy background signals and reduction of atmospheric effects, can easily capture evergreens and other vegetation types, and is therefore referenced to EVI to characterize vegetation coverage or greenness:
wherein G is 2.5 or C 1 ＝6、C 2 ＝7.5、L＝1；ρ red 、ρ nir 、ρ blue Respectively, the reflectivities of different wave bands. The EVI ranges from-1 to 1.
Plant diversity affects ecosystem productivity, community and ecosystem stability and nutrient utilization and retention, and plays an important role in maintaining the health function of the ecosystem. When the EVI is greater than 0.6, this pixel can be considered a dense vegetation pixel. Aiming at dense vegetation pixels, a plant diversity index RSPD is calculated to represent the abundance and uniformity conditions of vegetation so as to measure the growth stability of regional vegetation [24] . Firstly, calculating a spectral vegetation index SVI, wherein the spectral vegetation index SVI comprises 7 indexes including a normalized vegetation index NDVI, a red-edge vegetation index and a normalized infrared index, and the numerical ranges are [ -1,1]。
In the formula, ρ b1 、ρ b2 Respectively the wave band reflectivities corresponding to the spectrum vegetation indexes; SVI' is the normalization of the spectral vegetation index.
And combining each reflectivity waveband with the calculated SVI' to generate a new data set. At this time, each pixel value can be represented by a multi-dimensional vector:
X i ＝[v i,1 ,v i,2 ,v i,3 ,…,v i , n ](formula 6)
In the formula, X i Is the ith vegetation pixel; v is the value of each band with a fixed data range of [0, 1]](ii) a n is the total number of bands.
Opening up a 3X 3 window, assuming X 0 Is a slideThe vegetation pixel at the center of the movable window is the other vegetation pixel and X 0 The similarity between them can be expressed as:
wherein D i Is a measure of similarity between different vegetation pixels. Since v varies from 0 to 1, the theoretical minimum and maximum values of this similarity are 0 and 1, respectively
To determine a threshold for distinguishing between different plant species, D i From 0 to
Uniformly divided into Q sections. Vegetation pixels with the same similarity fall into the same segment, are considered to belong to the same plant, and are considered to be different species otherwise. Thus, the relative abundance of each segment can be calculated using the following formula:
wherein p is q The relative abundance of the q section is the proportion of the number of pixels of each section in the sliding window to the total number of implanted pixels in the window; w is the total number of pixels in the window; q is the number of segments (usually 100);
finally, referring to Shannon entropy principle, the plant diversity remote sensing index of the sliding window is calculated by using the following formula:
in the formula p q And the relative abundance of the q-th segment is the proportion of the number of pixels of each segment in the window to the total number of pixels in the window. The value range of RSPD is 0-1.
Aiming at a high coverage area (namely EVI is more than or equal to 0.6) in the vegetation, the area is subjected to accumulation calculation through RSPD, so that the vegetation change condition can be distinguished better.
I veg ＝[(EVI+RSPD)+1]/3 (formula 10)
In the formula I veg The vegetation quality index is normalized, the value range is 0-1, and the larger the value is, the better the vegetation ecological quality is.
2.5 soil exposure and evenness
The bare soil index SI is an important index for reflecting the bare degree of the surface soil, and the expression is as follows:
in the formula, ρ red 、ρ nir 、ρ blue 、ρ green 、ρ swir1 Respectively, the reflectivities of different wave bands.
When EVI is less than 0.2 and PII <0, the pixel is considered a bare-earth pixel. For bare soil pixels, the entropy of the leveled bare soil is smaller, and conversely, the entropy of the unploughed soil is larger. Therefore, the entropy value of SI is utilized to express the bare soil leveling condition. The distribution of the SI histogram shows that the values are concentrated in the range of-0.5 to 0.5, and thus the normalization process is performed by the formula (12).
Wherein, Encopy is a normalization Entropy value; p i The probability of the occurrence of a pixel with a gray value i in the window is shown, and G is the gray level number; w is the number of pixels in the window; i is soil And normalizing the value of the exposure degree and the flatness of the soil. I is soil The value range is 0-1, and the larger the value is, the better the ecological quality of the soil is.
2.6 weight coefficient determination
Weight coefficient alpha 1 、α 2 、α 3 Determined by the following formula:
in the formula H i The communality factor variance of the ith evaluation index; lambda [ alpha ] i,j Load or correlation between the ith evaluation index and the jth PCA principal component; m is the first few components with a cumulative contribution rate of 80%; n is the number of evaluation indexes. Note that the use of the PCA method to weight a single index is implemented here.
3 results
3.1 remote sensing monitoring results of ecological environment in mining area
Fig. 3 shows the annual comprehensive monitoring results of the REM model, the time span is 2016 to 2021, and it can be seen from the graph that the higher REM value of the vegetation coverage area around the mine pit represents better ecological quality, especially the best ecological quality in the southern area. The REM values of bare rocks and building areas inside and around the mine pit are low, which represents that the ecological environment quality is general. The REM value at the periphery of the pit in 2016-2021 is slightly increased, which represents that the ecological quality is improved year by year. The REM value of the western ecological restoration key area (namely the black frame area in the figure) of the mining area is changed greatly, which represents that the ecological quality changes violently. Fig. 3 shows that the REM monitoring result is consistent with the actual situation.
Further analysis on the ecological restoration area of the mining area (namely, the black frame area in the figure) shows that the area gradually changes from the mining area to the vegetation area in 2016-2021. In 2016, the plants are mainly mined and sporadically distributed with a few vegetations; 2016-2018 vegetation is gradually reduced, probably because of the ground surface damage caused by mining, waste residue accumulation and other behaviors in a mining area; the land surface is recuperated in 2019 until the restoration area is gradually enlarged in 2021, the coverage of the vegetation on the land surface is increased, and the quality of the ecological environment is gradually improved. The time variation process further shows that the monitoring result of the REM model is highly consistent with the actual situation.
3.2 comparison with surface coverage type data
To further analyze the reasonableness of the REM model, the earth surface coverage types in 2017 and 2020 were used for I veg 、WET、I soil And REM monitoring results were statistically analyzed, and the results are shown in fig. 4. The results show that: i is veg (vegetation State), WET (Wet status), and I soil The vegetation area, the bare soil area and the impervious layer are sequentially represented from good to bad, and the ecological quality represented by the REM value is high and low, and is also represented by the vegetation area, the bare soil area and the impervious layer. The result of the REM model is consistent with the result of the actual ecological environment quality, and the REM can distinguish the difference between bare soil and a impervious layer, namely the problem of rock-soil difference can be solved.
For a detailed evaluation of REM effect, fig. 5 is a graph comparing REM values with changes in land cover type in 2017 and 2020. As can be seen from fig. 5(b) and (h), the key restoration area in 2017 was distributed with a large area of bare soil, and in 2020, the area was changed into arable land and grassland. The corresponding REM results, fig. 5(e) and (k), show a gradual improvement trend in the ecological quality, consistent with the land cover type change. As can be seen from fig. 5(f) and (l), the northern vegetation area REM of the mining area shows a tendency of gradually improving in the years of 2017-2020. From (c), it can be seen that the corresponding areas are distributed with a large amount of grassland and woodland, and in (i), it can be seen that the woodland area is enlarged in 2020, proving that the ecological quality is gradually improved, consistent with the REM results. In summary, fig. 5 also demonstrates REM model results in rationality from a comparison of REM models with the time variation of land cover types.
3.3 comparison with field survey data
Fig. 6 compares field survey data with REM model values in the form of scatter plots. The results show that REM values have very significant correlation with field investigation data, Pearson correlation coefficient is 0.8994, and significance level P < 0.01. In addition, the map shows that the ecological quality levels of the REM model and the field survey data in different land cover types are basically consistent, and the ecological quality sequentially comprises the following steps from good to bad: dense vegetation is sparse, medium vegetation is bare soil is impervious. Therefore, the REM model has good consistency with field investigation data, and can represent the ecological environment quality of the research area.
In addition to the correlation test method, the study also analyzed the accuracy and rationality of the model effect by visual interpretation. Firstly, respectively selecting targets in eight land areas, namely a rock wall, a waste residue area, a bare soil area, a large-area ploughing area, a sparse blending area, a road and peripheral blending area, a small-area ploughing and blending area and a dense vegetation area, comparing the distribution condition of REM model results of 9-15 days in 2021 with a Sentinel-2 true color image and a field photo by adopting a visual interpretation method, and judging the performance of REM.
As can be seen from the true color image and the solid photograph in fig. 7, the ecological quality is, in order from good to bad: dense vegetation area, road and peripheral mixed forest area, sparse mixed forest area, large-area ploughing area, bare soil area, rock wall and waste residue area. The REM model results are substantially consistent with their distribution.
In order to judge the effect of the plant diversity index and the accuracy of REM, 6 sample points are selected for carrying out comparative analysis on the dense vegetation region. Comparing the true color images and the solid photographs of the sample points 105 and 131 shows that the greenness conditions of the two are basically consistent, but the vegetation uniformity at 105 is lower than that at 131, the degree of biodiversity is slightly lower, so that the ecological quality at 131 is better than that at 105 on the whole, and the REM result can accurately represent the difference between the two points. Second, at both points 101 and 152, REM results showed that point 152 was of higher ecological quality. 101, the point is positioned in a key ecological restoration area, and the main species is acacia; and the 152 point is located in the waste park in the north of the mining area, the vegetation is various, the diversity degree is higher than that of the 101 point, and therefore the ecological quality of the 152 point is higher than that of the 101 point on the whole and is consistent with the REM result. In addition, at the 21 point and the 1155 point, the vegetation coverage degree at the two points is basically consistent according to the influence of true color and the solid photograph, but the 1155 position vegetation species are richer, the biological diversity degree is higher, therefore, the quality of the ecological environment at the 1155 position is higher than that of the 21 sample point on the whole, and the REM model result conforms to the distribution situation. Therefore, the plant diversity problem is significantly reflected in the REM model, so that the REM model can distinguish the ecological quality difference caused by the plant diversity in the dense vegetation coverage area.
3.4 mining area ecological environment space-time variation analysis
In order to analyze the space-time change characteristics of ecological restoration quality of the pacific west strip mine, statistical analysis and difference operation are carried out on annual data. FIG. 8 shows the time-series changes of ecological restoration quality of the pacific West strip mine in the year 2016 and 2021. As can be seen, the REM median and mean values and the 10% fractional bit line become larger year by year in 2016-; the maximum value shows fluctuation change at 2016-2021, but shows a trend of becoming larger as a whole, which indicates that the overall ecological environment quality of the research area is improved.
FIG. 9 is a graph of the spatial distribution of the 2021-2016 difference values, with a minimum value of-0.83 and a maximum value of 0.91. In general, most of the area difference values are between-0.3 and 0.3, between-0.9 and-0.3 are mainly located in the edge area of the mine pit, and between 0.3 and 0.9 are mainly distributed in the ecological restoration key area in the west of the mine pit, so that the ecological environment quality improvement range of the area is large. Therefore, the ecological restoration quality of vegetation coverage areas around the mine pit is improved. And a part of area inside the pit is influenced by pit mining, waste residue accumulation and the like, so that the ecological quality is poor. But still, partial areas can be processed by surface regulation and other treatment measures to ensure that the surface of the ground object is smoother and the ecological quality is slightly improved.
4.1 hierarchical discussion of REM model
In order to more intuitively reflect the space-time distribution result of the ecological environment, judge the quality change condition of the ecological environment, and divide the REM value into 5 grades according to the distribution condition of the REM model histogram. Fig. 10 shows a distribution histogram of the values of the evaluation results of the REM model in the research area, and it can be seen that REM is mainly concentrated between 0.2 and 1, REM is divided into 5 levels (tables) according to the characteristics of data distribution, and the larger the level, the better the ecological environment quality.
TABLE 2 REM grading Standard
By taking 2017 and 2021 as examples, fig. 11 shows spatial distribution of REM at different levels, and it can be seen that the ecological environment quality of vegetation coverage areas around the mine pit is high, especially the ecological restoration quality of the southern area is the best. In addition, the ecological environment quality of the whole area is mainly in medium and good level. The poor and common grades are mostly positioned at the edge of a building group and the bare soil part of a main ecological restoration area at the west of a mining area, and the occupied area is small. The medium and good grades are mainly located in vegetation areas around the mine pit, the good grades are intensively distributed in vegetation dense areas, and the occupied area is increased year by year.
Statistics are carried out on the REM grading results of 2016 and 2021, and the statistical results are shown in FIG. 12, so that the medium grade ratio is the largest, the good grade and the general grade are the second, and the poor grade and the good grade ratio are the smallest. 2016-2021, the differential and general ratings increased slightly over the first reduction, but showed a decreasing trend overall; the medium-grade percentage is increased year by year in 2016 and 2018, and the fluctuation trend is shown in 2018 and 2021; the good grade accounts for the rising year by year in 2016-; the quality grade is increased year by year, and the increase range is the largest in 2021, which shows that the quality of the ecological environment is obviously improved. In general, from 2016-.
4.2 different methods of determining rights in REM model
The PCA principal component analysis method is adopted to determine the weight of each single index, and in order to make the model better usable, the weights are tried to be set to be equal, namely alpha in formula (1) 1 ＝α 2 ＝α 3 1/3, thereby constructing an equally weighted REM index and comparatively analyzing the influence of the two weight-determining methods on the results. Fig. 13(a) shows the maximum minimum, median and mean values of the equally weighted REM indices. The changes of the two are basically the same by comparing with the REM statistical information shown in FIG. 8. FIG. 13(b) is a graph showing the distribution of the spatial variation of the 2016- + -2021 equal-weight REM difference, which is substantially the same as the REM difference shown in FIG. 9.
In addition, the correlation test of the equal-weight method REM and the field survey data is performed, and the result is shown in fig. 14. The Pearson correlation coefficient of field survey data and the equal weight method REM is 0.8993, significance P<0.01, indicating that the two have strong correlation; the correlation is slightly lower than that between REM and field survey data, but the difference between REM and field survey data is very small(ii) a And the ecological quality level of the two is consistent in different land cover types. Therefore, the results of the two methods for determining the right have good consistency. Thus, in time series monitoring analysis, α can also be set directly 1 ＝α 2 ＝α 3 ＝1/3。
4.3 ReM model simplification application discussion
For the convenience of model application, the method for calculating plant diversity in the REM model is simplified, and here, a Coefficient of Variation (CV) method is used instead, and the formula is as follows:
wherein ρ λ Is the spectral reflectance of the λ band; Δ is the total number of spectral bands, i.e., 10 reflectance bands of Sentinel-2; std () is a function to obtain the standard deviation; mean () is a function that obtains an average value.
In the calculation process of the equal-weight REM model, CV is used for replacing RSPD to obtain a simplified REM model, and for convenience, the model is respectively called CV equal-weight REM and RSPD equal-weight REM. Fig. 5 compares the results of two REM estimates, in the form of scatter plots, for consistency over different years. FIG. 15 shows that the two REM methods have high consistency, Pearson correlation coefficient is larger than 0.9, and significance P is smaller than 0.01.
Correlation test is carried out on the CV method equal-weight REM and field investigation data, the result is shown in FIG. 16, the Pearson correlation coefficient is 0.8992, the significance P is less than 0.01, and the result shows that the two have a strong consistency relationship. Therefore, the RSPD model can be replaced by a coefficient of variation CV method so as to achieve the simplified application of the REM model.
The VIS-M framework for evaluating the ecological environment of the mining area is established, the framework considers that the ground surface of the mining area consists of three basic components, namely vegetation, soil and impervious layers (or bare rocks), and therefore the remote sensing comprehensive monitoring index should be fused with the biophysical characteristics of each basic component. Based on a VIS-M framework, a remote sensing ecological index (REM) facing to a mining area environment is constructed. The research uses Sentinel-2 multispectral data as a drive, uses the pacific West open mine as a research area, realizes REM monitoring with 10m resolution, and contrasts and analyzes the effectiveness of the REM model by combining field survey data and surface coverage type classification data. The research result shows that: 1) the REM value has stronger consistency with the ecological environment quality represented by the land covering type, and the REM can distinguish the difference between bare soil and a impervious layer, thereby taking the problem of rock-soil difference into consideration; 2) the REM value has stronger consistency with the ecological environment quality investigated by field sampling points (Pearson correlation coefficient is 0.8994, significance level P is less than 0.01), and REM can distinguish the ecological quality difference caused by the diversity of plants in a dense vegetation coverage area, so that the problem of 'plant diversity' is considered; 3) the REM value effectively represents the spatio-temporal pattern and the change of the ecological environment quality in a 2016-2021 year research area, and the quality change of an ecological restoration area is also effectively represented through the REM difference value between 2021 and 2016 years. Research also discusses the problem of grading REM values, and provides a simplified application strategy of the REM model. The rock-soil difference and the plant diversity are the problems that need to be considered in the mining area ecological environment remote sensing, and the REM model constructed in the method has great application potential, but needs more comparative analysis and improved research, such as exploration of the change rule and the remote sensing mechanism of the rock-soil difference and the plant diversity problems in different geographic areas, data conditions or mining area scenes.
Unless defined otherwise, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The materials, methods, and examples set forth in this application are illustrative only and not intended to be limiting.
Although the present invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the teachings of this application and yet remain within the scope of this application.
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Claims (6)
1. A mining area ecological environment remote sensing method considering rock-soil difference and plant diversity remotely senses the ecological environment of a target area by calculating a remote sensing ecological index REM facing the mining area environment according to the rock-soil difference, the plant diversity and index right; wherein:
REM＝I imper ×(α 1 ×I wet +α 2 ×I veg +α 3 ×I soil ) (formula 1);
in the formula 1, the value range of REM is 0-1, the larger the value is, the better the ecological environment is, otherwise, the worse is; i is imper Whether the water-proof layer or the bare rock exists or not is represented, if the value is 0, the value is 1; i is wet Represents the wetness index of vegetation or soil; i is veg A composite index representing the coverage of vegetation and the degree of diversity of the plants; i is soil A comprehensive index representing the degree of exposure and the flatness of the soil; alpha is alpha 1 、α 2 、α 3 Representing the weight coefficients.
2. The method of claim 1, wherein: i is imper Obtained by the following calculation formula,
in formula 2,. rho Blue 、ρ NIR The reflectivity of a blue wave band and a near infrared wave band; m and n are respectively blue band and near infrared band coefficients; c is a constant; usually, m is 0.905, n is 0.435, C is 0.019; sgn () represents a coincidence function, and when PII is greater than 0, it is a nullRocks return a value of 0, and non-nude rocks return a value of 1 when PII is less than 0.
3. The method of claim 1, wherein: i is wet Obtained by the following calculation formula,
in formula 3,. rho red 、ρ nir 、ρ blue 、ρ green 、ρ swir1 、ρ swir2 Respectively the reflectivity of the corresponding wave band; i is wet Through normalization, the value range is 0-1, and the larger the value is, the better the hydrologic ecological quality is.
4. The method of claim 1, wherein: i is veg Obtained by the following calculation formula,
I veg ＝[(EVI+RSPD)+1](formula 10);
in the formula 10, I veg Through normalization, the value range is 0-1, and the larger the value is, the better the ecological quality of vegetation is;
EVI is used to characterize vegetation coverage or greenness, which is obtained by the following calculation,
in formula 4, G is 2.5, C 1 ＝6、C 2 ＝7.5、L＝1；ρ red 、ρ nir 、ρ blue Respectively the reflectivity of the corresponding wave band; the value range of the EVI is-1;
RSPD is the remote sensing index of plant diversity.
5. The method of claim 1, wherein: i is soil Obtained by the following calculation formula,
in formula 12, entopy is the normalized Entropy value; p i The probability of the occurrence of a pixel with a gray value i in the window is shown, and G is the gray level number; w is the number of pixels in the window; i is soil Through normalization, the value range is 0-1, and the larger the value is, the better the ecological quality of the soil is; SI is obtained by the following calculation:
in formula 11, [ rho ] red 、ρ nir 、ρ blue 、ρ green 、ρ swir1 Respectively, the reflectivities of the corresponding bands.
6. The method of claim 1, wherein: weight coefficient alpha 1 、α 2 、α 3 Obtained by the following calculation:
in formula 13, H i The communality factor variance of the ith evaluation index; lambda [ alpha ] i,j Load or correlation between the ith evaluation index and the jth PCA principal component; m is the first few components with a cumulative contribution rate of 80%; n is the number of evaluation indexes.
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