A grassland open-pit mine space-air-ground integrated ecological environment monitoring method

Abstract

The present application relates to a kind of grassland open-pit mine space-time integration ecological environment monitoring method, comprising: mining area scale vegetation cover monitoring;Including: obtaining mining area satellite remote sensing data;Extracting mining area normalized vegetation difference index;Determine the NDVI variation trend and significant situation of mining area;Mine scale vegetation cover monitoring;Including: design the route of unmanned aerial vehicle in mine;Obtain satellite remote sensing data of mine and carry out pretreatment;Calculate the normalized vegetation difference index NDVI value and vegetation cover FVC value of mine;Determine the vegetation cover condition of mine;Ground vegetation investigation and sampling;Including: vegetation quadrat layout;Quadrat field measurement;Plant aboveground biomass sampling.The present application gives full play to the advantage of remote sensing technology, combined with the vegetation cover characteristics of different scales of open-pit mine area, mine and dump, form a set of multi-scale, accurate and comprehensive grassland open-pit mine ecological environment monitoring method, provide more accurate and comprehensive method and means for the monitoring of grassland open-pit mine ecological environment.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring technology in mining areas, specifically to an integrated air-ground-space ecological environment monitoring method for grassland open-pit mines. Background Technology

[0002] Coal resources are an important pillar of my country's economic development. Open-pit mining is one of the main forms of coal mining. Open-pit mining has advantages such as high resource utilization, high output and convenient working conditions. However, this method strips away topsoil over a large area, which seriously damages the original ecological environment of the mining area as resources are extracted.

[0003] Currently, vegetation cover monitoring in grassland mining areas mainly relies on traditional manual field surveys and remote sensing methods. In the field of remote sensing monitoring of grassland mining areas, research methods are mostly adopted according to different application needs, and there is no unified and systematic method for monitoring vegetation cover in mining areas.

[0004] Compared to satellite remote sensing technology and UAV low-altitude remote sensing technology, manual field surveys can investigate the coverage and habitat information of various vegetation types, providing more comprehensive information, including vegetation types that are not easily distinguishable from remote sensing data.

[0005] In comparison, manual field surveys can obtain higher quality and more accurate vegetation data, rather than relying solely on remote sensing images or model results. However, when acquiring information over large areas, manual field surveys require significant manpower and time, potentially leading to high costs and time consumption. Therefore, under large-scale conditions, manual field surveys are not suitable for acquiring vegetation information and are not conducive to observing long-term dynamic changes.

[0006] Compared to UAV low-altitude remote sensing and manual surveys, satellite remote sensing technology can rapidly collect information, providing digitized data that can be directly processed into images. More importantly, it allows for long-term continuous observation of large-scale areas and easily generates time-series information. This effectively compensates for the limitations of UAV low-altitude remote sensing, such as limited observation range, weak endurance, and small coverage area at one time. In summary, the advantages of satellite remote sensing are high efficiency, non-contact and easy data acquisition, and low cost, making up for the shortcomings of manual surveys. However, its resolution is relatively low, limiting the accuracy of vegetation interpretation.

[0007] Therefore, there is an urgent need for a long-term, dynamic, multi-scale, accurate, and efficient method for ecological and environmental monitoring. Summary of the Invention

[0008] In view of the shortcomings of the prior art, the main objective of this invention is to provide an integrated air-space-ground ecological environment monitoring method for grassland open-pit mines, so as to solve one or more problems in the prior art.

[0009] The technical solution of the present invention is as follows:

[0010] This invention proposes an integrated air-space-ground ecological environment monitoring method for grassland open-pit mines, comprising the following steps:

[0011] S10: Vegetation cover monitoring at the mining area scale; including:

[0012] S101: Acquire long-term satellite remote sensing images of the mining area and extract the Normalized Difference Vegetation Index (NDVI) value of the mining area;

[0013] S102: Analyze the NDVI variation trend in the mining area to achieve dynamic monitoring of the ecological environment at the mining area scale;

[0014] S20: Mine-scale vegetation cover monitoring; including:

[0015] S201: Design flight paths for drones in the mine;

[0016] S202: Acquire real-time remote sensing data using drones and preprocess the acquired remote sensing data;

[0017] S203: Calculate the Normalized Difference Vegetation Index (NDVI) and the Free Variable Vegetation (FVC) values ​​for the mine;

[0018] S204: Classify the vegetation coverage of mines to determine the vegetation coverage status of mines and achieve dynamic and accurate monitoring of the mine's ecological environment.

[0019] S30: Ground vegetation survey and sampling; including:

[0020] S301: Vegetation quadrat layout;

[0021] S302: Field measurement of quadrats;

[0022] S303: Sampling of aboveground plant biomass to enable regular monitoring of the ecological environment of reclaimed spoil heaps.

[0023] In some embodiments, S101, acquiring satellite remote sensing images of the mining area and extracting the Normalized Difference Vegetation Index (NDVI) value of the mining area includes:

[0024] First, long-term time-series image data of the target mining area are selected through a cloud computing platform;

[0025] Then calculate the NDVI value and merge them to extract the maximum value.

[0026] In some embodiments, S102, the analysis of the NDVI change trend in the mining area includes:

[0027] First, the Theil-Sen slope method was used to calculate the trend of NDVI variation;

[0028]

[0029] In the formula, Median() represents taking the median value; β greater than 0 indicates that vegetation growth has a positive trend, and β less than 0 indicates that vegetation growth has a negative trend; i and j are the year sequence.

[0030] Then, the Mann-Kendall test was used to determine the significance of the NDVI trend in the mining area. The standardized test statistic Z was defined and used to perform a significance trend test. The Z value was calculated as follows:

[0031]

[0032]

[0033]

[0034]

[0035] In the formula, S is the Mann-Kendall statistic; sgn() is the sign function; x i and x j For time series data; n is the number of data points; Var is the variance to be calculated.

[0036] In some embodiments, the data are further tested using a two-sided test; specifically including:

[0037] The significance test results of the MK test at the 0.05 confidence level are divided into two categories: |Z| < 1.96 (no significant change) and |Z| ≥ 1.96 (significant change).

[0038] Using a grid calculator, the Sen trend value and its MK significance analysis results were multiplied, and the trend was divided into five categories: severe degradation (β≤-0.0005, Z≤-1.96), slight degradation (β≤-0.0005, -1.96<Z<1.96), stable (-0.0005<β<0.0005, -1.96<Z<1.96), slight improvement (β≥0.0005, -1.96<Z<1.96), and significant improvement (β≥0.0005, Z≥1.96).

[0039] Based on the above classification results of the significant change trend of NDVI in the mining area, the changes in vegetation growth in the mining area can be accurately monitored within a given time period.

[0040] In some embodiments, in S203, calculating the Normalized Difference Vegetation Index (NDVI) and the Free Variable Vegetation Cap (FVC) of the mine includes:

[0041] The vegetation cover factor (FVC) is calculated using the normalized difference in vegetation index (NDVI) through a pixel-based binary model. The expression for the NDVI is as follows:

[0042]

[0043] The expression for vegetation cover (FVC) is:

[0044]

[0045] In the formula, NIR is the reflectance value in the near-infrared band; R is the reflectance value in the red band; NDVI veg Indicates areas completely covered by vegetation; NDVI soil It represents areas with bare soil or no vegetation cover.

[0046] In some embodiments, S204, the step of classifying the vegetation coverage of the mine and determining the vegetation coverage status of the mine includes:

[0047] Using ArcGIS software, the real-time data collected by drones on the vegetation cover in the mine was divided into 5 levels, namely:

[0048] Extremely low coverage (0-20%), low coverage (20-40%), medium coverage (40-60%), high coverage (60-80%), and extremely high coverage (80-100%) are used to achieve dynamic and precise monitoring of the mine's ecological environment.

[0049] In some embodiments, S301, the vegetation quadrat layout includes:

[0050] Test areas were set up according to different slope aspects of open-pit mine spoil heaps, and each test area was divided into four slope positions: downhill, middle slope, uphill, and platform.

[0051] Three 1m×1m plant experimental plots were randomly selected at each slope. At the same time, 1m×1m plots of grassland flat land around the mine on different slopes of the spoil heap were selected as control plots. The plant community of each plot was investigated.

[0052] In some embodiments, S302, the field measurement of the quadrat includes:

[0053] Record the names and quantities of various plant species in the experimental and control quadrats, and measure the vegetation cover (FVC) using photographic methods. The expression for FVC is:

[0054]

[0055] In the formula, PX1 and PX2 represent the selected pixel value and all pixel values, respectively.

[0056] At the same time, the height of the plants in the quadrat was measured, and the growth status of the plants in the spoil heap was judged by comparing the experimental quadrat and the control quadrat.

[0057] In some embodiments, S303, the aboveground biomass sampling of plants includes:

[0058] Plant samples were collected from the quadrat, placed in a sealed container, and then placed in a 105℃ oven for 24 hours.

[0059] Then, the dried samples were removed and immediately weighed using a 0.01 g / L balance, and the dry weight of each sample plot was recorded.

[0060] By comparing the vegetation dry weight of experimental and control quadrats on the same slope aspect, the nutrient level obtained by plants in the spoil heap can be inferred.

[0061] The dry weight of multiple vegetation quadrats was measured to provide an overall description and interpretation of the vegetation growth within the experimental study area.

[0062] Regularly monitor vegetation changes in the sample plots, compare vegetation composition and structure at different time points, assess the restoration status of grassland mining area reclaimed vegetation, the degree of biodiversity and ecological function restoration, and provide a basis for the management and protection of grassland mining area reclamation.

[0063] The advantages of this invention over the prior art are:

[0064] (1) This invention proposes an integrated air-space-ground ecological environment monitoring method for grassland open-pit mines. This method fully leverages the advantages of remote sensing technology to improve the accuracy and timeliness of vegetation cover monitoring, providing a more precise and comprehensive method and means for monitoring the ecological environment of grassland open-pit mines, and providing data and basis for carrying out comprehensive mine environmental remediation, mine ecological environment restoration and reconstruction, and mine environmental supervision and management.

[0065] (2) This invention combines satellite remote sensing technology, UAV remote sensing technology and manual field investigation to achieve complementary advantages and realize a "space-air-ground integrated" monitoring method that combines satellite remote sensing, UAV remote sensing and manual field investigation.

[0066] (3) This invention utilizes the characteristics of satellite remote sensing at the mining area scale, which allows for long-term continuous observation of large-scale areas and easy generation of time-series information, to monitor the dynamic changes of the ecological environment of the entire mining area. At the mine scale, it utilizes the advantages of UAV low-altitude remote sensing at the mesoscale, which allows for high-precision data acquisition, to conduct dynamic and precise monitoring of the ecological environment of a single mine. Finally, it utilizes the characteristics of manual field investigation, which allows for obtaining higher quality, more accurate and comprehensive information, to conduct regular monitoring of the ecological environment of open-pit mine reclamation spoil heaps, thereby achieving a focus from large scale to mesoscale and then to small scale.

[0067] (4) This invention first uses satellite remote sensing to monitor the vegetation cover of the mining area on a large scale, then uses UAV remote sensing to monitor the vegetation cover of the mine on a medium scale, and finally conducts ground vegetation surveys and aboveground biomass sampling on small-scale reclamation spoil heaps. Thus, by combining the vegetation cover characteristics of different scales, a set of multi-scale, accurate and comprehensive grassland open-pit mine ecological environment monitoring methods are formed.

[0068] It should be understood that the implementation of any embodiment of the present invention does not mean that it will simultaneously possess or achieve multiple or all of the above-mentioned beneficial effects. Attached Figure Description

[0069] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0070] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0071] Figure 1 This is a flowchart of the monitoring method steps of the present invention;

[0072] Figure 2 This is a schematic diagram of the significant trend change of vegetation cover in the Baiyinhua mining area according to an embodiment of the present invention, wherein (a) represents the Baiyinhua mining area, (b) represents Baiyinhua No. 1 Mine and No. 2 Mine, (c) represents Baiyinhua No. 3 Mine, and (d) represents Baiyinhua No. 4 Mine.

[0073] Figure 3 This is a schematic diagram of the route range of Baiyinhua No. 1 and No. 2 mines according to an embodiment of the present invention;

[0074] Figure 4 This is a schematic diagram of images of Baiyinhua No. 1 and No. 2 mines according to an embodiment of the present invention, wherein (a) is an orthophoto image and (b) is a DSM image;

[0075] Figure 5 This is a schematic diagram of the layout of sample plots for Baiyinhua No. 1 and No. 2 mines according to an embodiment of the present invention, wherein (a) represents Baiyinhua No. 1 mine and (b) represents Baiyinhua No. 2 mine. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention.

[0077] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0078] It should be understood that the terms "comprising / including," "consisting of," or any other variations are intended to cover non-exclusive inclusion, such that a product, apparatus, process, or method that comprises a list of elements includes not only those elements but may also include, where necessary, other elements not expressly listed, or elements inherent to such a product, apparatus, process, or method. Without further limitation, an element defined by the phrases "comprising / including," "consisting of," does not exclude the presence of additional identical elements in the product, apparatus, process, or method that includes said element.

[0079] It should also be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device, component or structure referred to must have a specific orientation, be constructed or operated in a specific orientation, and should not be construed as a limitation of the present invention.

[0080] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0081] Satellite remote sensing technology can quickly collect information, and the acquired data is digitized and can be directly processed into images. More importantly, it can conduct long-term continuous observation of large-scale areas and easily form time-series information. This also makes up for the shortcomings of UAV low-altitude remote sensing, such as limited observation range, weak endurance and small one-time coverage.

[0082] Low-altitude drone remote sensing can automatically acquire high-resolution, large-scale remote sensing images with high quality and provide more detailed vegetation cover information. Drones are fast and flexible in operation, have low requirements for flight sites, and can operate in areas with complex terrain that are inaccessible by human personnel, effectively compensating for the limitations of manual field surveys in terms of terrain selection. At the same time, drone remote sensing effectively solves the limitations of traditional satellite remote sensing imagery in acquiring high-quality, large-scale images for monitoring small areas.

[0083] This invention combines satellite remote sensing technology, UAV remote sensing technology, and manual field surveys to achieve complementary advantages and realize an "integrated air-space-ground" monitoring method that integrates satellite remote sensing, UAV remote sensing, and manual field surveys.

[0084] The implementation of the present invention will be described in detail below with reference to preferred embodiments.

[0085] See Figure 1 This invention proposes an integrated air-ground-space ecological environment monitoring method for grassland open-pit mines, including vegetation cover monitoring at the mining area scale, vegetation cover monitoring at the mine scale, and ground vegetation survey and sampling. The specific steps are as follows:

[0086] S10: Monitoring of vegetation cover at the mining area scale.

[0087] S10 specifically includes:

[0088] S101: Acquire satellite remote sensing images of the mining area and extract the Normalized Difference Vegetation Index (NDVI) value of the mining area;

[0089] In this step, long-term time-series imagery data of the target area, such as Landsat, MODIS, or Sentinel, is selected on the GEE (Google Earth Engine) cloud platform. Then, the NDVI value is calculated and merged to extract the maximum value.

[0090] S102: Analysis of the significant trend of NDVI changes in the mining area;

[0091] First, the Theil-Sen slope method was used to calculate the trend of NDVI variation.

[0092]

[0093] In the formula, Median() represents taking the median value; β greater than 0 indicates that vegetation growth has a positive trend, and β less than 0 indicates that vegetation growth has a negative trend; i and j are the year sequence.

[0094] The β results were divided into three levels: β≤-0.0005 was a vegetation degradation area, -0.0005<β<0.0005 was a stable area, and 0.0005≤β was a vegetation improvement area.

[0095] Then, the Mann-Kendall method was used to determine the significance of the NDVI change trend in the mining area. The standardized test statistic Z was defined and used to perform a significance trend test. The Z value was calculated as follows:

[0096]

[0097]

[0098]

[0099]

[0100] In the formula, S is the inspection statistic; sgn() is the sign function; x i and x j For time series data; n is the number of data points; Var is the variance to be calculated.

[0101] The data were tested using a two-sided test. The significance of the MK test at the 0.05 confidence level was divided into two categories: no significant change (|Z|<1.96) and significant change (|Z|≥1.96). Using a grid calculator, the Sen (Theil-Sen) trend value and the MK (Mann-Kendall) significance analysis result were multiplied, and the trend was categorized into five types: severe degradation (β≤-0.0005, Z≤-1.96), slight degradation (β≤-0.0005, -1.96<Z<1.96), stable (-0.0005<β<0.0005, -1.96<Z<1.96), slight improvement (β≥0.0005, -1.96<Z<1.96), and significant improvement (β≥0.0005, Z≥1.96).

[0102] Based on the above-mentioned requirements for classifying the NDVI significance trend of mining areas, the target mining area is classified into significance trend levels. This enables precise monitoring of changes in vegetation growth in the mining area within a given time period, timely understanding of the impact of open-pit mining activities on the ecological environment of the mining area, and provides rational suggestions for subsequent work.

[0103] S20: Monitoring of vegetation cover at the mine scale.

[0104] S20 specifically includes:

[0105] S201: Design flight paths for drones in the mine;

[0106] In this step, firstly, using an Earth browser such as Google Earth, Google Maps, or Google Maps Mobile, the location of the target area to be flown is determined. Then, the pre-flight area is further divided, and finally a KML file is generated. Next, the KML file is imported into the flight path planning software that comes with the DJI M300 drone. Select the KML file of the flight to be flown, click on the flight path to enter the "Edit" page, select the camera carried by the flight, and proceed with "Flight Altitude Setting (Design Flight Altitude)", "Flight Speed ​​Setting (Design Speed)", and "Completion Maneuver Setting (Return Home)". Then, click "Advanced Settings" to perform "Side / Heading Overlap Setting (Parameter Setting)", "Main Flight Path Angle Setting (Minimize Turns)", "Margin Setting", and "Shooting Mode Setting (Equal Interval Shooting)". Finally, save the flight path.

[0107] S202: Acquire real-time remote sensing data using drones and preprocess the acquired remote sensing data;

[0108] This step includes: (1) Image matching: using Pix4Dmapper software to process the feature point image ratio of the data obtained by the UAV in the target mine.

[0109] Specifically, open the "Pix4Dmapper" software, create a new project, and import the photos taken by the drone. Then, in the "Image Properties" box, select the default coordinate system of pos. When selecting the output coordinate system, it should be consistent with the control point coordinate system. If the control point coordinate system is different from the final required coordinate system, then coordinate transformation is required. If the control points are in an independent coordinate system, select any coordinate system. Then, select the high-quality orthophoto image from "3D Maps" under "Standard Template". It's easy to understand that in field operations, to check the image quality on-site and whether reshooting is needed, the usual experience is to quickly detect a low-quality quick mosaic for verification. Then, select "Processing Options" to perform "Initialization Processing" to process the feature point image ratio.

[0110] (2) Aerial Triangulation Encryption: Aerial triangulation ray piercing method is used for rapid and accurate piercing.

[0111] Specifically, the aerial triangulation method is used to quickly and accurately puncture points. Click "Project" "GCP Management" "Import Control Points" in sequence, and use the "Aerial Triangulation Editor" tool to select the control points to be punctured for rematching and optimization.

[0112] (3) Triangular mesh construction and texture mapping: Perform "point cloud and texture" processing, and finally output DSM and orthophoto.

[0113] S203: Calculate the Normalized Difference Vegetation Index (NDVI) and the vegetation cover (FVC) of the mine;

[0114] Specifically, first open the UAV remote sensing image in ENVI software, and then use the "Select Input to Subset via ROI" tool to input the vector boundary of the study area and crop out the study area data.

[0115] In this step, the Normalized Difference Vegetation Index (NDVI) is:

[0116]

[0117] In the formula, NIR is the reflectance value in the near-infrared band; R is the reflectance value in the red band.

[0118] Specifically, first search for the "NDVI" tool in the "Toolbox" search box of the ENVI software, select the sensor corresponding to the remote sensing image and the corresponding band of the sensor, enter the formula in the "Band Math" tool, click "Choose" to select a storage path, and then click "OK" to obtain the NDVI result of the target area. When saving, you can directly add the .tif suffix to save it as a .tif format.

[0119] In this step, vegetation cover (FVC) estimation requires a pixel-based binary model, followed by extraction using the Normalized Difference Vegetation Index (NDVI). The basic principle is to assume that the information of a pixel is contributed only by vegetation and soil. Then, based on the NDVI grayscale distribution in the image, confidence intervals are used to extract the upper and lower thresholds of NDVI to represent vegetation and soil, respectively. The calculation formula is as follows:

[0120]

[0121] In the formula, NDVI veg The maximum value within the confidence interval represents the area with complete vegetation cover; NDVI soil It represents the minimum value within the confidence interval, indicating a bare soil or unvegetated area.

[0122] Specifically, first open the ENVI software, use the "Band Math" tool to manually input the corresponding band formula to calculate the NDVI value, then use the "Compute Statistics" tool to count the NDVI value. The DN value is the NDVI, and Acc Pct is the cumulative percentage. Take the cumulative percentage at 5% (DN value minimum) and 95% (DN value maximum) as a confidence interval. Next, insert the vegetation cover formula in "Band Math" to calculate, and finally add the .tif suffix to the output and save it as a .tif format, and then put it into ArcGIS to generate the map.

[0123] S204: Classify the vegetation coverage of the mine to determine the vegetation coverage status of the mine;

[0124] In ArcGIS, select the "Reclassification Tool" to classify the mine vegetation coverage collected in real time by drones into five levels: extremely low coverage (0-20%), low coverage (20-40%), medium coverage (40-60%), high coverage (60-80%), and extremely high coverage (80-100%).

[0125] Based on the above-mentioned requirements for the classification of mine vegetation coverage, classifying mine vegetation coverage into different levels can achieve the goal of monitoring the dynamics of the mine's ecological environment and provide rational suggestions for adjusting and revising the next steps in vegetation protection measures and technologies.

[0126] S30: Ground vegetation survey and sampling. By conducting on-site investigations, more detailed and accurate ground condition data can be obtained.

[0127] Specifically, S30 includes:

[0128] S301: Vegetation quadrat layout;

[0129] In this step, experimental areas were set up according to different slope aspects of the open-pit mine spoil heap. Each experimental area had four slope positions: the lower slope, the middle slope, the upper slope, and the platform. Three 1m×1m plant experimental plots were randomly selected at the lower slope position of each slope position. At the same time, 1m×1m plots of grassland flat land around the mine on different slope aspects of the spoil heap were selected as control plots. The plant community of each plot was investigated and the latitude and longitude coordinates were recorded.

[0130] S302: Field measurement of quadrats;

[0131] In this step, the names and quantities of various plant species in the experimental and control quadrats are recorded, and the vegetation cover is measured by photography. At the same time, the height of the plants in the quadrats is measured to compare and judge the growth status of the plants in the spoil heap.

[0132] When taking photographic photos, shadows should be avoided within the photo area. Because the grassland quadrats are small, a tripod is used to mount the camera and shoot vertically downwards. To reduce the impact of geometric distortion at the edges of the photo on the observation results, the photo coverage area needs to be larger than the quadrat. The photo is cropped during the measurement. After obtaining the standard photo of the quadrat, Adobe Photoshop software is used to judge and count the vegetation pixels in the quadrat photo (first, record all pixel values ​​in the quadrat, then set the tolerance, extract the selected pixel values, and finally obtain the vegetation coverage within the photo area).

[0133] The formula for calculating the vegetation cover of this vegetation quadrat is:

[0134]

[0135] In the formula, PX1 is the selected pixel value; PX2 is all pixel values ​​within the sample plot.

[0136] S303: Sampling of plant ground biomass

[0137] Plant samples (including stems, leaves, flowers, and fruits) were collected from the quadrats and placed in sealed containers. These containers were then placed in an oven at 105°C for 24 hours. The dried samples were then removed and immediately weighed using a 0.01% balance, and the dry weight of each quadrat was recorded. By comparing the dry weight of vegetation in experimental and control quadrats on the same slope aspect, the nutrient levels acquired by plants in the spoil heap can be inferred. By measuring the dry weight of multiple vegetation quadrats, an overall description and interpretation of vegetation growth within the experimental study area can be provided.

[0138] This invention monitors vegetation cover across the entire mining area by acquiring remote sensing images at different time phases using long-term satellite data at the mining area scale. At the mine scale, it utilizes the high accuracy of data acquired at the mesoscale using low-altitude remote sensing by unmanned aerial vehicles (UAVs) to monitor vegetation cover in the mine and its surrounding grasslands. Finally, it leverages the higher quality, more accurate, and more comprehensive information obtained through manual field surveys to conduct regular monitoring of the ecological environment of open-pit mine reclamation spoil heaps, thus achieving a focus from large-scale to mesoscale and then to small-scale.

[0139] By combining the vegetation cover characteristics at different scales, a multi-scale, accurate, and comprehensive method for monitoring the ecological environment of grassland open-pit mines is formed.

[0140] Engineering Examples

[0141] This embodiment selects the Baiyinhua Coal Mine in Xilingol League, Inner Mongolia for monitoring. The Baiyinhua Coalfield is located on the western slope of the southern section of the Greater Khingan Mountains in central and eastern Inner Mongolia, in the heart of the vast Xilingol Grassland. The terrain is relatively flat with few valleys. The terrain slopes from north to south and from east to west, and is characterized by plateau, low mountains, gentle slopes, and hills. Over the years, human mining activities have created artificial terrain such as open-pit mines and spoil heaps.

[0142] The natural landscape of this mining area is natural grassland, and the vegetation belongs to the transitional zone between temperate meadow steppe and typical steppe. Meadow vegetation is widely developed, with *Chrysanthemum lineare*, *Leymus chinensis*, and *Stipa baikarana* being predominantly found in the steppe. The vegetation communities are mainly composed of *Chrysanthemum lineare* + *Stipa baikarana*, *Chrysanthemum lineare* + miscellaneous grasses, *Stipa baikarana* + miscellaneous grasses, *Leymus chinensis* + *Stipa baikarana*, and *Leymus chinensis* + miscellaneous grasses, with grass height generally ranging from 40 to 60 cm. Large-scale mining activities began in this area after 2005. This study uses the study area in 2000 as the background area and the development period from 2005 to 2020 as the research period.

[0143] In this embodiment, S10, vegetation cover monitoring at the mining area scale includes:

[0144] S101, acquire satellite remote sensing data of Baiyinhua mining area and extract NDVI value;

[0145] In this step, Landsat series satellite images covering the Baiyinhua Mine Cluster research area from June 1, 2000 to September 30, 2020, with cloud cover less than 20%, were selected using programming on the GEE cloud platform. Then, the NDVI values ​​were calculated and the maximum value was extracted by merging.

[0146] S102, Determine the trend and significance of NDVI changes in the mining area;

[0147] See Figure 2 In the Baiyinhua mining area, due to the impact of mining activities, the vegetation in the mining area generally shows a trend of degradation, with the vegetation degradation trend gradually decreasing from the open-pit mines of the four open mines as the center to the surrounding areas.

[0148] In S20, mine-scale vegetation cover monitoring includes:

[0149] S201, designed the flight path of the drone in Baiyinhua No. 1 and No. 2 mines;

[0150] In this embodiment, Baiyinhua Mine No. 1 and No. 2 were selected as the pre-flight area for the UAV. The pre-flight area was then divided into corresponding transects, as detailed below. Figure 3 As shown, a encompasses the spoil heaps of mines No. 1 and No. 2, and b represents the spoil heap of mine No. 2 and the surrounding area.

[0151] S202 uses drones to acquire remote sensing data of the spoil heaps and surrounding grasslands of Baiyinhua No. 1 and No. 2 mines and to preprocess the acquired remote sensing data.

[0152] See Figure 4 The orthophotos of Baiyinhua Mine No. 1 and No. 2 were preprocessed using Pix4Dmapper software to obtain DSM images, including the orthophotos of Baiyinhua Mine No. 1 and No. 2 as shown below. Figure 4 As shown in (a) above, the DSM image is as follows Figure 4 As shown in (b) of the diagram.

[0153] In S203, the Normalized Difference Vegetation Index (NDVI) and vegetation cover (FVC) of Baiyinhua No. 1 and No. 2 spoil heaps are calculated.

[0154] In S204, the UAV remote sensing data of Baiyinhua No. 1 and No. 2 mines are processed for vegetation coverage classification to determine the vegetation coverage status.

[0155] In S30, the ground vegetation survey and sampling included:

[0156] In S301, vegetation plots were constructed at Baiyinhua Mine No. 1 and No. 2, and the layout method is as follows: Figure 5 As shown, where Figure 5 In the diagram, (a) represents Baiyinhua Mine No. 1 and (b) represents Baiyinhua Mine No. 2, and the sample plots of Baiyinhua Mine No. 1 and No. 2 are both 1m × 1m in size.

[0157] In S302, on-site measurements were conducted on Baiyinhua No. 1 Mine and Baiyinhua No. 2 Mine.

[0158] In S303, biomass sampling was conducted on vegetation quadrats set up at Baiyinhua No. 1 Mine and Baiyinhua No. 2 Mine.

[0159] It will be readily understood by those skilled in the art that, without conflict, the above-mentioned preferred solutions can be freely combined and superimposed.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for integrated air-space-ground ecological environment monitoring in grassland open-pit mines, characterized in that, Includes the following steps: S10: Vegetation cover monitoring at the mining area scale; including: S101: Acquire long-term satellite remote sensing images of the mining area and extract the Normalized Difference Vegetation Index (NDVI) value of the mining area; S102: Analyze the NDVI variation trend in the mining area to achieve dynamic monitoring of the ecological environment at the mining area scale; wherein the analysis of the NDVI variation trend in the mining area includes: First, the Theil-Sen slope method was used to calculate the trend of NDVI variation; (1) In the formula, Median() represents taking the median value; β greater than 0 indicates that vegetation growth has a positive trend, and β less than 0 indicates that vegetation growth has a negative trend; i and j are the year sequence. Then, the Mann-Kendall test was used to determine the significance of the NDVI trend in the mining area. The standardized test statistic Z was defined and used to perform a significance trend test. The Z value was calculated as follows: (2) (3) (4) (5) In the formula, S is the Mann-Kendall statistic; sgn() is the sign function; x i and x j For time series data; n is the number of data points; Var is the variance to be calculated; S20: Mine-scale vegetation cover monitoring; including: S201: Design flight paths for drones in the mine; S202: Acquire real-time remote sensing data using drones and preprocess the acquired remote sensing data; S203: Calculate the Normalized Difference Vegetation Index (NDVI) and the Free Variable Vegetation Cap (FVC) value of the mine; wherein the calculation of the NDVI and FVC values ​​of the mine includes: The vegetation cover factor (FVC) is calculated using the normalized difference in vegetation index (NDVI) through a pixel-based binary model. The expression for the NDVI is as follows: (6) The expression for vegetation cover (FVC) is: (7) In the formula, NIR is the reflectance value in the near-infrared band; R is the reflectance value in the red band; NDVI veg Indicates areas completely covered by vegetation; NDVI soil Characterizes areas with bare soil or no vegetation cover; S204: Classify the vegetation coverage of mines to determine the vegetation coverage status of mines and achieve dynamic and accurate monitoring of the mine's ecological environment. S30: Ground vegetation survey and sampling; including: S301: Vegetation quadrat layout; S302: Field measurement of quadrats; S303: Sampling of aboveground plant biomass to enable regular monitoring of the ecological environment of reclaimed spoil heaps.

2. The integrated air-space-ground ecological environment monitoring method for grassland open-pit mines according to claim 1, characterized in that, In S101, acquiring satellite remote sensing images of the mining area and extracting the Normalized Difference Vegetation Index (NDVI) value of the mining area includes: First, long-term time-series image data of the target mining area are selected through a cloud computing platform; Then calculate the NDVI value and merge them to extract the maximum value.

3. The integrated air-space-ground ecological environment monitoring method for grassland open-pit mines according to claim 1, characterized in that, This also includes using two-sided tests to examine the data, specifically including: The significance test results of the MK test at the 0.05 confidence level are divided into two categories: |Z| < 1.96 (no significant change) and |Z| ≥ 1.96 (significant change). Using a grid calculator, the Sen trend value and its MK significance analysis results were multiplied, and the trend was divided into 5 categories: Severe degradation, β≤-0.0005, Z≤-1.96; Slight degradation, β≤-0.0005, -1.96<Z<1.96; Stable and unchanged, -0.0005 < β < 0.0005, -1.96 < Z < 1.96; Slight improvement, β≥0.0005, -1.96<Z<1.96; and Significant improvement, β≥0.0005, Z≥1.96; Based on the above classification results of the significant change trend of NDVI in the mining area, the changes in vegetation growth in the mining area can be accurately monitored within a given time period.

4. The integrated air-space-ground ecological environment monitoring method for grassland open-pit mines according to claim 1, characterized in that, In S204, the step of classifying the vegetation coverage of the mine and determining the vegetation coverage status of the mine includes: Using ArcGIS software, the real-time data collected by drones on the vegetation cover in the mine was divided into 5 levels, namely: Extremely low coverage (0–20%), low coverage (20–40%), medium coverage (40–60%), high coverage (60–80%), and extremely high coverage (80–100%) are used to achieve dynamic and precise monitoring of the mine's ecological environment.

5. The integrated air-space-ground ecological environment monitoring method for grassland open-pit mines according to claim 1, characterized in that, In S301, the vegetation quadrat layout includes: Test areas were set up according to different slope aspects of open-pit mine spoil heaps, and each test area was divided into four slope positions: downhill, middle slope, uphill, and platform. Three 1m×1m plant experimental plots were randomly selected at each slope. At the same time, 1m×1m plots of grassland flat land around the mine on different slopes of the spoil heap were selected as control plots. The plant community of each plot was investigated.

6. The integrated air-space-ground ecological environment monitoring method for grassland open-pit mines according to claim 5, characterized in that, In S302, the field measurement of the quadrat includes: Record the names and quantities of various plant species in the experimental and control quadrats, and measure the vegetation cover (FVC) using a photographic method. The expression for FVC is: (8) In the formula, PX1 and PX2 represent the selected pixel value and all pixel values, respectively. At the same time, the height of the plants in the quadrat was measured, and the growth status of the plants in the spoil heap was judged by comparing the experimental quadrat and the control quadrat.

7. The integrated air-space-ground ecological environment monitoring method for grassland open-pit mines according to claim 5, characterized in that, In S303, the aboveground biomass sampling of plants includes: Plant samples were collected from the quadrat, placed in a sealed container, and then placed in a 105℃ oven for 24 hours. Then, the dried samples were taken out and weighed immediately using a balance of 0.01 g / cm³, and the dry weight of each sample plot was recorded. By comparing the vegetation dry weight of experimental and control quadrats on the same slope aspect, the nutrient level obtained by plants in the spoil heap can be inferred. The dry weight of multiple vegetation quadrats was measured to provide an overall description and interpretation of vegetation growth within the experimental study area; Regularly monitor vegetation changes in the sample plots, compare vegetation composition and structure at different time points, assess the restoration status of grassland mining area reclaimed vegetation, the degree of biodiversity and ecological function restoration, and provide a basis for the management and protection of grassland mining area reclamation.

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