CN110685747B - Remote sensing extraction method for coal mining subsidence water body of high diving space - Google Patents
Remote sensing extraction method for coal mining subsidence water body of high diving space Download PDFInfo
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Abstract
The invention provides a remote sensing extraction method for a high-diving-level coal mining subsidence water body, which comprises the following steps: 1) calculating a remote sensing index according to the remote sensing images of the research area for years, identifying a water body in the remote sensing images, calculating the annual water frequency index of each pixel in the water body, and removing pseudo water bodies, seasonal ponding and permanent ponding in the water body according to the annual water frequency index to obtain a sunk water body or an artificial water body; constructing annual water frequency index time sequence track data of each pixel in the sunken water body or the artificial water body; 2) judging the years of the subsidence water body or the engineering water body on a remote sensing image of a research area by utilizing a time window according to the annual water frequency index time sequence track data to obtain accumulated water year pattern spots; 3) and removing the engineering water body in the accumulated water year pattern spots by using the morphological characteristic difference of the engineering water body and the subsidence water body in time and space to obtain the subsidence water body and realize the remote sensing extraction of the subsidence water body. The method is simple to operate, high in identification efficiency and beneficial to restoration and treatment of the ecological environment of the mining area.
Description
Technical Field
The invention belongs to the technical field of mining technology, land utilization and remote sensing, and particularly relates to a remote sensing extraction method of a sunk water body caused by high-phreatic water mining.
Background
China is one of the largest coal producing and consuming countries worldwide, however, coal mining may cause serious environmental problems such as soil pollution, surface subsidence, landscape changes, etc., resulting in a large amount of mining area land being wasted. Among them, surface subsidence is the main source of the abandoned land of the mining area. Disturbance of the earth's surface by underground coal resource development is a global problem. The yield of Chinese coal is more than 90% from underground mining, and most of the Chinese coal is mined by adopting a long-wall total collapse method, so that the land is easy to sink, and after the ground surface sinks, underground water rises to collect a large amount of water to form sinking accumulated water. In order to realize the sustainable ecological environment of the mining area, the dynamic change of the ponding in the subsidence area of the mining area needs to be monitored.
Remote sensing technology has matured very well in monitoring changes in natural resources of the earth's surface. The remote sensing image-based detection of land cover change is widely applied to mining industry, agriculture, forestry, cities and the like. The land change detection of the mining area can be divided into open-pit mining and underground mining, and the open-pit mining area mainly focuses on detecting the disturbance of mining activities to the earth surface by using vegetation indexes; for underground mining, it is common to manually interpret different periods of time and then compare the interpretation to detect disturbances of the mining activity to the earth's surface. However, the manual interpretation and classification method for multi-phase images is labor-intensive and the classification errors are very accumulated. Particularly in high-bay mines, the subsidence ponding is affected by seasons greatly, so that dynamic mapping of the subsidence ponding is challenging.
The extraction of surface water by the water body remote sensing index method is mature, but surface subsidence ponding is a gradual expansion process in time and space. The water body index is used simply, and under the condition of lacking underground mining information, natural water bodies, artificially excavated water bodies (such as ponds) and sunk water bodies are difficult to distinguish.
Disclosure of Invention
The invention aims to solve the problems and provides a remote sensing extraction method for a high-phreatic water mining subsidence water body.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a remote sensing extraction method for a coal mining subsidence water body of a high diving ground, which is characterized by comprising the following steps of:
1) calculating a remote sensing index according to remote sensing images from the beginning year to the ending year of a research area, identifying a water body in the remote sensing images through the remote sensing index, calculating an annual water frequency index of each pixel in the remote sensing images water body of the research area, further classifying the identified water body according to the annual water frequency index, and removing pseudo water bodies, seasonal accumulated water bodies and permanent accumulated water bodies from the water body after identifying the pseudo water bodies, the seasonal accumulated water bodies and the permanent accumulated water bodies to obtain a sunk water body or an artificial water body; respectively constructing time sequence track data of the annual water frequency indexes of corresponding pixels by the annual water frequency indexes of each pixel in the subsided water body or the artificial water body from the initial year to the final year, and smoothing the time sequence track data;
2) judging the years of the subsided water body or the engineering water body on a remote sensing image of a research area by using a time window according to the smoothed annual water frequency index time series track data of each pixel of the subsided water body or the engineering water body obtained in the step 1) to obtain accumulated water year pattern spots;
3) and (3) removing the engineering water body in the accumulated water year pattern spots obtained in the step 2) by utilizing the morphological characteristic difference of the engineering water body and the subsidence water body in time and space to obtain the subsidence water body, and realizing the remote sensing extraction of the subsidence water body.
Further, the specific process of step 1) is as follows:
11) processing the Landsat remote sensing image in the research area from the initial year to the final year through a cloud mask, and then respectively calculating the normalized vegetation index NDVI, the enhanced vegetation index EVI and the improved normalized difference water body index MNDWI of the remote sensing image through the following formulas:
in the formula, the values of the vegetation index NDVI, the enhanced vegetation index EVI and the improved normalized difference water body index MNDWI are all-1; NIR is the reflectivity of the remote sensing image in the near infrared band; RED is the RED light waveband reflectivity of the remote sensing image; BLUE is the reflectivity of the BLUE light wave band of the remote sensing image; GREEN is the remote sensing image GREEN light wave band reflectivity; MIR is the reflectivity of an infrared band in a remote sensing image;
12) extracting the water body of each image in the remote sensing image after cloud mask processing by using the following formula:
(MNDWI>NDVI or MNDWI>EVI)and EVI<0.1 (4)
wherein, the pixel satisfying the formula (4) in each scene image is a water body, and the pixel not satisfying the formula (4) is a non-water body;
13) according to the water body of each image, calculating the annual water frequency index AWFI of each pixel in the research area by using the following formula:
in the formula, the effective image number in the annual image is the scene number of the remote sensing image obtained by processing all transit Landsat remote sensing images in the research area in each year through a cloud mask, wherein the scene number is acquired through a GEE platform; the number of image scenes of the water body on each pixel in the year is the number of water body identifications in the year; the value of AWFI is between 0 and 1;
14) classifying the water body identified in the step 12) according to the annual water frequency index AWFI of each pixel in the research area calculated in the step 13), and removing the water body identified in the step 12) after identifying pseudo water bodies, seasonal accumulated water and permanent accumulated water to obtain a sunk water body or an artificial water body; the annual water frequency indexes AWFI of the pseudo water body are all located in a first preset interval, the annual water frequency indexes AWFI of seasonal ponding are all located in a second preset interval, the annual water frequency indexes AWFI of the permanent ponding are all located in a third interval, the first preset interval, the second preset interval and the third preset interval all belong to intervals within 0-1, values in the first preset interval are all smaller than values in the second preset interval, values in the second preset interval are all smaller than values in the third preset interval, and the first preset interval is equal to the second preset interval and the third preset interval is equal to (0, 1);
15) constructing time series track data of the annual water frequency indexes of each pixel respectively by the annual water frequency indexes of each pixel in the subsided water body or the artificial water body obtained in the step 14) from the initial year to the final year, and smoothing the time series track data.
Further, the specific process of step 2) is as follows:
screening out the water body with the mutation characteristic and the year in the time sequence track data of each pixel of the subsided water body or the engineering water body after the smoothing treatment in the step 1) by adopting a Landtrendr algorithm, wherein the water body with the mutation characteristic is represented as a pattern spot of sudden increase and sudden decrease of the water body in a remote sensing image; the specific screening process is as follows: analyzing the annual water frequency index time sequence track data of each pixel after smoothing processing in the step 1) by using a time window with the step length of N years, and if the average value of the annual water frequency in the first half of the time window length range of any year is smaller than the minimum value of a second preset interval and the annual water frequency value in the second half of the time window length range of the year is continuously larger than the minimum value of the second preset interval, taking the year as the beginning year of the sunk ponding of the corresponding pixel and obtaining a ponding year map spot corresponding to the year; half of the length N of the time window is equivalent to the time required for converting the accumulated water in the subsidence area caused by underground coal mining from seasonal accumulated water to permanent accumulated water, or half of the length N of the time window is equivalent to the time required for completing 90% of the total subsidence amount of the land subsidence.
Further, the specific process of step 3) is as follows:
removing the small-area pattern spots in the accumulated water year pattern spots obtained in the step 2), namely removing the water body which does not influence the research area in the accumulated water year pattern spots; then, extracting a year duration which is less than or equal to a preset period from the time dimension in the ponding year pattern spots with the small-area pattern spots removed, wherein the preset period is equivalent to the construction period of the artificial pond; calculating the fractal dimension of the extracted pattern spot, wherein the calculation formula is as follows:
wherein D is the fractal dimension of the extracted pattern spots; p is the perimeter of the extracted image spot, and A is the area of the extracted image spot;
if the fractal dimension of the extracted pattern spots belongs to [1,1.1 ], taking the extracted pattern spots as an engineering water body; and removing the pattern spots corresponding to the engineering water body from the accumulated water year pattern spots with the small-area pattern spots removed, and finally obtaining subsided accumulated water year pattern spots, thereby realizing the extraction of the coal mining subsided water body.
The invention has the characteristics and beneficial effects that:
the invention utilizes the long-term sequence remote sensing images of years to calculate the remote sensing water frequency index to realize the extraction of the water body in the research area, and adopts the time track change detection and the morphological method to realize the acquisition of the space position and the occurrence time of the sinking water body. The method is simple to operate and high in identification efficiency, and greatly realizes identification of the subsidence ponding caused by underground mining of the high diving place under the condition of lacking of underground mining information and detection of subsequent repair.
Drawings
Fig. 1 is a variation curve of annual water frequency index AWFI of various water bodies with the age in the embodiment of the present invention.
Fig. 2 is time series trace data representing an annual water frequency index AWFI for any pixel in a backfill repaired subsidence water in an embodiment of the invention.
Fig. 3 (a), (b), and (c) are plan and sectional views of the settled water and artificial pond, respectively, according to an embodiment of the present invention.
Fig. 4 is an overlay of the year of ponding pattern spot and the surface subsidence prediction area obtained in the embodiment of the present invention.
Detailed Description
The invention provides a remote sensing determination method for mining subsidence ponding of a high diving space, which is described in detail by combining the attached drawings and an embodiment as follows:
the invention provides a remote sensing extraction method for a high-diving-level coal mining subsidence water body, which is used for detecting the dynamic change of accumulated water based on remote sensing change and specifically comprises the following steps:
1) calculating a remote sensing index according to remote sensing images from the beginning year to the ending year of a research area, identifying a water body in the remote sensing images through the remote sensing index, calculating an annual water frequency index of each pixel in the remote sensing images water body of the research area, further classifying the identified water body according to the annual water frequency index, and removing pseudo water bodies, seasonal accumulated water bodies and permanent accumulated water bodies from the water body after identifying the pseudo water bodies, the seasonal accumulated water bodies and the permanent accumulated water bodies to obtain a sunk water body or an artificial water body; constructing a corresponding pixel annual water frequency index time sequence track by each annual water frequency index of each pixel in the subsided water body or the artificial water body between the initial year and the final year, and smoothing the time sequence track data, wherein the specific implementation process is as follows:
11) the Landsat remote sensing images of the research area from A1 of the beginning year to A2 of the ending year (A1-A2 period) are processed by cloud masks, and then normalized vegetation indexes NDVI, enhanced vegetation indexes EVI and improved normalized difference water body indexes MNDWI of the remote sensing images are respectively calculated by the following formulas:
in the formula, the values of the vegetation index NDVI, the enhanced vegetation index EVI and the improved normalized difference water body index MNDWI are all-1; NIR is the reflectivity of the remote sensing image in the near infrared band; RED is the RED light waveband reflectivity of the remote sensing image; BLUE is the reflectivity of the BLUE light wave band of the remote sensing image; GREEN is the remote sensing image GREEN light wave band reflectivity; MIR is the reflectivity of the infrared band in the remote sensing image.
12) In order to reduce the influence of plants on the water body, the water body of each image in the remote sensing image after cloud mask processing is extracted by the following formula:
(MNDWI>NDVI or MNDWI>EVI)and EVI<0.1 (4)
wherein, the pixel satisfying the formula (4) in each scene image is a water body, and the pixel not satisfying the formula (4) is a non-water body.
13) According to the water body of each image, calculating the annual water frequency index AWFI of each pixel in the research area by using the following formula:
in the formula, the effective image number in the annual image is the scene number of the remote sensing image obtained by processing all transit Landsat remote sensing images in the research area in each year through a cloud mask, wherein the transit Landsat remote sensing images are acquired through a GEE (Google Earth Engine) platform; the number of image scenes of the water body on each pixel in the year is the number of water body identifications in the year; the AWFI is used for representing the ratio of the number of images of water bodies on each pixel in each year to the number of effective images in each year, namely the ratio of the number of water bodies concentrated in the annual images on each pixel to the number of effective images, and the value of the AWFI is 0-1.
14) Classifying the water body identified in the step 12) according to the annual water frequency index AWFI of each pixel in the research area calculated in the step 13), and removing the water body from the water body identified in the step 12) after identifying pseudo water body, seasonal accumulated water and permanent accumulated water so as to eliminate the influence of the water body on the subsequent subsidence accumulated water identification and obtain a subsidence water body or an artificial water body; the annual water frequency indexes AWFI of the pseudo water body are all located in a first preset interval, the annual water frequency indexes AWFI of seasonal ponding are all located in a second preset interval, the annual water frequency indexes AWFI of the permanent ponding are all located in a third interval, the first preset interval, the second preset interval and the third preset interval all belong to intervals of 0-1, values in the first preset interval are all smaller than values in the second preset interval, values in the second preset interval are all smaller than values in the third preset interval, and the first preset interval U is equal to the second preset interval U (0,1) and the third preset interval U is equal to (0, 1). Referring to fig. 1, in the present embodiment, according to a plurality of exemplary in-region tests, it is preferable that a first predetermined interval is (0, 0.25) (considering that there may be interference factors such as clouds and shadows in each image, the interference factors are removed from the water body through the first predetermined interval), that is, the range below the horizontal line e (AWFI is 0.25) in fig. 1, a second predetermined interval is [0.25,0.75 ], that is, the range between the horizontal lines e (AWFI is 0.75) and f in fig. 2, a third predetermined interval is [0.75,1), that is, the range above the horizontal line f in fig. 1, wherein each annual water frequency index AWFI of seasonal ponding (as shown by a curve d in fig. 1) is located in the second predetermined interval, each annual water frequency index AWFI of permanent ponding (as shown by a curve c in fig. 1) is located in the third predetermined interval, and the repaired ponding is divided into a curve of settled ponding (as shown in fig. 1) and a curve of water body (as shown by backfilling water body 1) or water body not repaired by backfilling water body 1) The annual water frequency index AWFI of the sunk water body subjected to backfill restoration is sequentially located in a first preset interval, a second preset interval, a third preset interval, a second preset interval and the first preset interval along with annual migration, and the annual water frequency index AWFI of the sunk water body or the artificial water body not subjected to backfill restoration is sequentially located in the first preset interval, the second preset interval and the third preset interval along with annual migration.
15) Constructing time series track data of the annual water frequency indexes of each pixel in the sunken water body or the artificial water body obtained in the step 14) between the beginning year A1 and the ending year A2 (during the period of A1-A2) by each pixel, and smoothing the time series track data by adopting a Savitzky-Golay (S-G) algorithm.
2) Judging the years of the subsidence water body or the engineering water body on a remote sensing image of a research area by utilizing a time window according to the annual water frequency index time series track data of each pixel of the subsidence water body or the engineering water body after smoothing treatment obtained in the step 1), and obtaining accumulated water year pattern spots:
screening out the water body with the mutation characteristic and the year in the time sequence track data of each pixel of the subsided water body or the engineering water body after the smoothing treatment in the step 1) by adopting a Landtrendr algorithm, wherein the water body with the mutation characteristic is represented as a pattern spot (the pattern spot is composed of a plurality of pixels representing the water body) with the sudden increase and sudden decrease of the water body in a remote sensing image. The screening process comprises the following steps: analyzing annual water frequency index time sequence track data of each pixel after smoothing treatment in the step 1) by using a time window with the step length of N years (half of the length N of the time window is equivalent to the time required by converting seasonal water into permanent water in a subsidence area caused by underground coal mining, or half of the length N of the time window is equivalent to the time required by completing 90% of the total subsidence amount of the land subsidence), and if the average value of the annual water frequency in the first half of the time window length range of any year is less than the minimum value of a second preset interval and the annual water frequency value in the second half of the time window length range of the year is continuously greater than the minimum value of the second preset interval, taking the year as the beginning year of the subsidence water of the corresponding pixel and obtaining a water accumulation year graph spot corresponding to the year.
3) And (3) removing the engineering water body in the accumulated water year pattern spots obtained in the step 2) by utilizing the morphological characteristic difference of the engineering water body and the subsidence water body in time and space to obtain the subsidence water body, and realizing the remote sensing extraction of the subsidence water body:
judging the accumulated water year pattern spots obtained in the step 2) by adopting year and fractal dimension based on the morphological characteristic difference between the engineering water body and the subsidence water body in time and space to obtain subsidence pattern spots and the generation years of the subsidence pattern spots. Because the engineering water body is mostly formed by an artificial pond, the artificial excavation period is short, the area is relatively small and does not expand along with time, and the engineering water body exists in an island form. The specific screening steps are as follows: firstly, removing the small-area pattern spots in the accumulated water year pattern spots obtained in the step 2), namely removing the water body which does not influence the research area in the accumulated water year pattern spots; then, extracting the year duration from the ponding year pattern spots with the small-area pattern spots removed in the time dimension, wherein the year duration is less than or equal to a preset period (the preset period is equivalent to the construction period of the artificial pond, the construction period of the artificial pond is short and generally less than 2 years, and the period of water body appearance caused by surface subsidence caused by underground coal mining is longer than the construction period of the artificial pond, so that the engineering water body and the subsided water body are further distinguished from each other in the time dimension). Calculating the fractal dimension of the extracted pattern spot, wherein the calculation formula is as follows:
wherein D is the fractal dimension of the extracted pattern spots; p is the perimeter of the extracted image spot, and A is the area of the extracted image spot;
if the fractal dimension of the extracted pattern spots belongs to [1,1.1 ], taking the extracted pattern spots as the engineering water body, wherein the value range of the fractal dimension is generally [1,2], when the fractal dimension value is equal to 1, the pattern spots are square, the larger the fractal dimension value is, the more complex the pattern spot shape is, and the pattern spots of the engineering water body are relatively regular and simple in shape, so that the fractal dimension value can be used for distinguishing the engineering water body; and removing the pattern spots corresponding to the engineering water body from the accumulated water year pattern spots with the small-area pattern spots removed, and finally obtaining subsided accumulated water year pattern spots, thereby realizing the extraction of the coal mining subsided water body.
Example (b):
this embodiment is a certain high diving place mining area, and this mining area relief is flat, and the relief elevation is at +30m to +40m, and the dive buries deeply about 1.5m, means when the ground sinks to reach 1.5 meters, can appear the ponding of subsiding. The mining area is rich in coal, and has been found to contain 9-18 layers of mined coal in 1200 meters underground, with the total thickness of the coal layer averaging about 20-30 meters. The mining area was built in the 80 s, and mining activities lasted for more than 30 years. Due to long-term coal mining, large-area subsidence areas are formed on the ground, and the high diving level causes large-area seasonal or long-term water accumulation in the subsidence areas.
The remote sensing extraction method for the coal mining subsidence water body of the high diving space comprises the following steps:
1) calculating a remote sensing index according to a remote sensing image in a research area between 1986 and 2018, identifying a water body in the remote sensing image through the remote sensing index, calculating an annual water frequency index of each pixel in the water body of the remote sensing image in the research area, further classifying the identified water body according to the annual water frequency index, and removing a pseudo water body, a seasonal accumulated water body and a permanent accumulated water body from the water body after identifying the pseudo water body, the seasonal accumulated water body and the permanent accumulated water body to obtain a sunk water body or an artificial water body; the method comprises the following steps of constructing a corresponding pixel annual water frequency index time sequence track by each annual water frequency index of each pixel in the sunken water body or the artificial water body between 1986 and 2018, and smoothing time sequence track data, wherein the specific implementation process is as follows:
11) the Landsat remote sensing images of the research area from 1986 to 2018 are obtained, and the data are available in 1420 scenes. Removing high cloud pixels of which the cloud amount is higher than 50% through a cloud mask, and calculating a normalized vegetation index NDVI, an enhanced vegetation index EVI and an improved normalized difference water body index MNDWI of the remote sensing image according to the formulas (1), (2) and (3);
12) extracting the water body of each image in the remote sensing image after cloud mask processing by using the formula (4);
13) and (4) calculating the annual water frequency index AWFI of each pixel in the research area by using the formula (5) according to the water body of each image.
14) Classifying the water bodies identified in the step 12) according to the annual water frequency index AWFI of each pixel in the research area, wherein the water bodies in the mining area are mainly divided into: natural water, subsidence ponding and engineering water (such as an artificial pond) caused by human activities, wherein the natural water consists of seasonal rivers (corresponding to the seasonal ponding, AWFI is more than or equal to 0.25 and less than or equal to 0.75) and permanent rivers (corresponding to the permanent ponding, AWFI is more than or equal to 0.75 and less than or equal to 1); starting the process; the engineering water body is obtained by excavating the earth surface through engineering, 0< AWFI < 1; the subsidence water body (0< AWFI <1) is divided into a subsidence water body which is backfilled by recovery and a subsidence water body which is backfilled by non-recovery, and the change trend of the AWFI time sequence track data of the subsidence water body which is backfilled by non-recovery is the same as that of the engineering water body; for a water body with 0< AWFI <0.25, invalid data such as cloud and shadow are likely to be required to be discharged from the identified water body, and the identified water body is defined as a pseudo water body. Therefore, the pseudo water body, the seasonal ponding and the permanent ponding are removed from the identified water body so as to eliminate the influence of the pseudo water body, the seasonal ponding and the permanent ponding on the subsequent subsidence ponding identification, and the subsidence water body or the artificial water body, namely the water body with variation in time sequence, is obtained.
15) And (3) constructing time series trajectory data of the annual water frequency index of each pixel from the annual water frequency index of each pixel in the sunken water body or the artificial water body in 1986 to 2018, and smoothing the trajectory data by adopting Savitzky-Golay (S-G).
2) And (3) screening out the water body with mutation characteristics including the pattern spots of suddenly increased and suddenly reduced water bodies in the time sequence track data of each pixel of the subsided water body or the engineering water body after the smoothing treatment in the step 1) by adopting a Landtrendr algorithm. The screening process comprises the following steps: and analyzing the annual water probability time sequence track data of each pixel after smoothing treatment by using a time window with the step length of 6 years. And (3) extracting the years of subsidence ponding: if the average value of the water frequency of the first 3 years of any year is less than 0.25, and the water frequency value of the last 3 years is continuously more than 0.25, the year is determined as the beginning year of sinking ponding, and the ponding year pattern spot corresponding to the year is obtained. The typical case of annual water probability time series data segmentation of a single pixel of subsided ponding repaired by backfilling is shown in fig. 2, wherein o in the figure is an annual water frequency index value, ● is a mutation point of track data, and a broken line segment is a track line of the annual water frequency index value after smoothing processing.
3) Based on the morphological feature differences of the artificial water body and the subsided water body in time and space, as shown in fig. 3, the underground coal mining space form is divided into longitudinal multi-coal-seam mining and transverse single-coal-seam multi-face mining (shown in (a) and (b) in fig. 3), the disturbance space form to the earth surface is expressed as outward expansion of a single center and spreading of a strip along the mining trend, the strip form is stronger than the single center, and the disturbance time is expressed as continuous occurrence of multiple cycles. The pattern spots of the years of the subsidence ponding show the characteristics of various time, large-area continuous appearance in space, irregular outline and the like. The engineering water body is mostly an artificial pond, the artificial excavation period is short, the area is relatively small and does not expand along with time (shown in (c) in fig. 3), and the engineering water body mostly exists in an island form. In fig. 3, a-B represent the original ground, WT the diving depth, and LSB the final surface subsidence boundary. In the graph (a), Si represents a mined coal seam i (i is 1,2, …, n, n represents the total number of coal seams), a curve Li represents a subsidence basin range due to the mining completion of the coal seam i, Pi represents a surface water range after the mining completion of the coal seam i, and Ti represents water accumulation start time due to the mining of the coal seam i. In the diagram (b), S represents a coal seam, Gj represents the j-th (j is 1,2, …, m, m represents the total number of working faces) working face, a curve Kj represents the mining end hidden surface subsidence basin range of the j-th working face, a region Qj represents the mining end surface water range of the working face j, and Tj represents the water start time caused by mining of the coal seam j. In the graph (c), P represents the water accumulation range of the artificial pond, T represents the water accumulation start time, and Depth represents the excavation Depth of the artificial pond.
3) Judging the patch with the suddenly increased water frequency index by the year and the fractal dimension to obtain the patch sinking the ponding and the year of the patch. Because the engineering water body is mostly an artificial pond, the artificial excavation period is short, the area is relatively small and does not expand along with time, and the artificial pond mainly exists in an island form. So the steps of distinguishing are: firstly, removing patches with the area smaller than 4 pixels; then, extracting patches with the year duration less than or equal to 2 from the time dimension, and calculating the fractal dimension of the extracted patches, wherein the fractal dimension belongs to [1,1.1) water body as engineering water body; and removing the engineering water body to obtain the age pattern spots of the subsided accumulated water, thereby realizing the extraction of the coal mining subsided water body.
The invention has validity verification:
and predicting the surface subsidence area of the Mining area by using subsidence prediction software (Mining subsurface prediction system MSPS) of an integrated probability integration method, superposing the calculation result with the water accumulation year plaque, and verifying the position accuracy of the method in the coal Mining disturbance area identification, as shown in FIG. 4. The overlapping degree of the spot and the subsidence area of the waterlogging year reaches 98.12%, which proves the effectiveness of the method of the invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A remote sensing extraction method for a coal mining subsidence water body of a high diving space is characterized by comprising the following steps:
1) calculating a remote sensing index according to remote sensing images from the beginning year to the ending year of a research area, identifying a water body in the remote sensing images through the remote sensing index, calculating an annual water frequency index of each pixel in the remote sensing images water body of the research area, further classifying the identified water body according to the annual water frequency index, and removing pseudo water bodies, seasonal accumulated water bodies and permanent accumulated water bodies from the water body after identifying the pseudo water bodies, the seasonal accumulated water bodies and the permanent accumulated water bodies to obtain a sunk water body or an artificial water body; respectively constructing time sequence track data of the annual water frequency indexes of corresponding pixels by the annual water frequency indexes of each pixel in the subsided water body or the artificial water body from the initial year to the final year, and smoothing the time sequence track data;
2) judging the years of the subsided water body or the engineering water body on a remote sensing image of a research area by using a time window according to the smoothed annual water frequency index time series track data of each pixel of the subsided water body or the engineering water body obtained in the step 1) to obtain accumulated water year pattern spots;
3) and (3) removing the engineering water body in the accumulated water year pattern spots obtained in the step 2) by utilizing the morphological characteristic difference of the engineering water body and the subsidence water body in time and space to obtain the subsidence water body, and realizing the remote sensing extraction of the subsidence water body.
2. The remote sensing extraction method for the coal mining subsidence water body of the high diving ground according to claim 1, wherein the specific process of the step 1) is as follows:
11) processing the Landsat remote sensing image in the research area from the initial year to the final year through a cloud mask, and then respectively calculating the normalized vegetation index NDVI, the enhanced vegetation index EVI and the improved normalized difference water body index MNDWI of the remote sensing image through the following formulas:
in the formula, the values of the vegetation index NDVI, the enhanced vegetation index EVI and the improved normalized difference water body index MNDWI are all-1; NIR is the reflectivity of the remote sensing image in the near infrared band; RED is the RED light waveband reflectivity of the remote sensing image; BLUE is the reflectivity of the BLUE light wave band of the remote sensing image; GREEN is the remote sensing image GREEN light wave band reflectivity; MIR is the reflectivity of an infrared band in a remote sensing image;
12) extracting the water body of each image in the remote sensing image after cloud mask processing by using the following formula:
(MNDWI>NDVI or MNDWI>EVI)and EVI<0.1 (4)
wherein, the pixel satisfying the formula (4) in each scene image is a water body, and the pixel not satisfying the formula (4) is a non-water body;
13) according to the water body of each image, calculating the annual water frequency index AWFI of each pixel in the research area by using the following formula:
in the formula, the effective image number in the annual image is the scene number of the remote sensing image obtained by processing all transit Landsat remote sensing images in the research area in each year through a cloud mask, wherein the scene number is acquired through a GEE platform; the number of image scenes of the water body on each pixel in the year is the number of water body identifications in the year; the value of AWFI is between 0 and 1;
14) classifying the water body identified in the step 12) according to the annual water frequency index AWFI of each pixel in the research area calculated in the step 13), and removing the water body identified in the step 12) after identifying pseudo water bodies, seasonal accumulated water and permanent accumulated water to obtain a sunk water body or an artificial water body; the annual water frequency indexes AWFI of the pseudo water body are all located in a first preset interval, the annual water frequency indexes AWFI of seasonal ponding are all located in a second preset interval, the annual water frequency indexes AWFI of the seasonal ponding are all located in a third interval, the first preset interval, the second preset interval and the third preset interval all belong to intervals within 0-1, values in the first preset interval are all smaller than values in the second preset interval, values in the second preset interval are all smaller than values in the third preset interval, and the first preset interval, the second preset interval and the third preset interval (0,1) are equal;
15) constructing time series track data of the annual water frequency indexes of each pixel respectively by the annual water frequency indexes of each pixel in the subsided water body or the artificial water body obtained in the step 14) from the initial year to the final year, and smoothing the time series track data.
3. The remote sensing extraction method for the coal mining subsidence water body of the high diving ground according to claim 2, wherein the step 14), the first preset interval is (0,0.25 ]; the second preset interval is [0.25,0.75 ]; the third preset interval is [0.75,1 ].
4. The remote sensing extraction method for the coal mining subsidence water body of the high diving ground according to claim 2, wherein the step 15) is to smooth the annual water frequency index time series track data of each pixel in the subsidence water body or the artificial water body through an S-G algorithm.
5. The remote sensing extraction method for the coal mining subsidence water body of the high diving ground according to claim 1, wherein the specific process of the step 2) is as follows:
screening out the water body with the mutation characteristic and the year in the time sequence track data of each pixel of the subsided water body or the engineering water body after the smoothing treatment in the step 1) by adopting a Landtrendr algorithm, wherein the water body with the mutation characteristic is represented as a pattern spot of sudden increase and sudden decrease of the water body in a remote sensing image; the specific screening process is as follows: analyzing the annual water frequency index time sequence track data of each pixel after smoothing processing in the step 1) by using a time window with the step length of N years, and if the average value of the annual water frequency in the first half of the time window length range of any year is smaller than the minimum value of a second preset interval and the annual water frequency value in the second half of the time window length range of the year is continuously larger than the minimum value of the second preset interval, taking the year as the beginning year of the sunk ponding of the corresponding pixel and obtaining a ponding year map spot corresponding to the year; half of the length N of the time window is equivalent to the time required for converting the accumulated water in the subsidence area caused by underground coal mining from seasonal accumulated water to permanent accumulated water, or half of the length N of the time window is equivalent to the time required for completing 90% of the total subsidence amount of the land subsidence.
6. The remote sensing extraction method for the coal mining subsidence water body of the high diving ground according to claim 1, wherein the specific process of the step 3) is as follows:
removing the small-area pattern spots in the accumulated water year pattern spots obtained in the step 2), namely removing the water body which does not influence the research area in the accumulated water year pattern spots; then, extracting a year duration which is less than or equal to a preset period from the time dimension in the ponding year pattern spots with the small-area pattern spots removed, wherein the preset period is equivalent to the construction period of the artificial pond; calculating the fractal dimension of the extracted pattern spot, wherein the calculation formula is as follows:
wherein D is the fractal dimension of the extracted pattern spots; p is the perimeter of the extracted image spot, and A is the area of the extracted image spot;
if the fractal dimension of the extracted pattern spots belongs to [1,1.1 ], taking the extracted pattern spots as an engineering water body; and removing the pattern spots corresponding to the engineering water body from the accumulated water year pattern spots with the small-area pattern spots removed, and finally obtaining subsided accumulated water year pattern spots, thereby realizing the extraction of the coal mining subsided water body.
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