NL2030915B1 - Time-space dynamic monitoring method of county soil erosion based on remote sensing and gis - Google Patents
Time-space dynamic monitoring method of county soil erosion based on remote sensing and gis Download PDFInfo
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- NL2030915B1 NL2030915B1 NL2030915A NL2030915A NL2030915B1 NL 2030915 B1 NL2030915 B1 NL 2030915B1 NL 2030915 A NL2030915 A NL 2030915A NL 2030915 A NL2030915 A NL 2030915A NL 2030915 B1 NL2030915 B1 NL 2030915B1
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- 238000004162 soil erosion Methods 0.000 title claims abstract description 76
- 238000012544 monitoring process Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000013507 mapping Methods 0.000 claims abstract description 28
- 230000003628 erosive effect Effects 0.000 claims abstract description 21
- 238000012706 support-vector machine Methods 0.000 claims abstract description 4
- 239000000284 extract Substances 0.000 claims abstract 3
- 238000005516 engineering process Methods 0.000 claims description 7
- 239000002689 soil Substances 0.000 abstract description 2
- 230000000875 corresponding effect Effects 0.000 description 14
- 238000012545 processing Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000013049 sediment Substances 0.000 description 2
- 230000002194 synthesizing effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003340 mental effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 238000013316 zoning Methods 0.000 description 1
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Abstract
A time—space dynamic monitoring method. of county soil erosion based on remote sensing and GIS. The present disclosure collects a remote sensing image and soil erosion history data in past few years; determines a map spot in the soil erosion map and corresponding soil erosion type and erosion intensity; establishes a soil erosion vector; then extracts the remote sensing image within. a to—be—monitored. scope, and. generates an interpretation map; for the interpretation map, extracts a feature of a mapping domain as a discriminant feature, determines a final division plan of a pre—divided block of the interpretation map; uses the soil erosion vector as an output to establish a support vector machine discriminative model; obtains a updated remote sensing image of the to—be—monitored scope, and synthesizes a updated interpretation. map, performs discriminant monitoring for a soil l5 erosion. situation. of the to—be—monitored. scope is discriminated through the discriminative model.
Description
P1113/NLpd
TIME-SPACE DYNAMIC MONITORING METHOD OF COUNTY SOIL EROSION BASED
ON REMOTE SENSING AND GIS
The present disclosure relates to a monitoring method of soil erosion.
The existing soil erosion monitoring methods first obtain professional maps such as topographic maps, geological maps, geo- morphic maps, vegetation type maps, soil maps, desertification maps, soil erosion maps, land use maps, watershed boundary maps, and China soil erosion zoning maps and TM images; at the same time, hydrological and meteorological observation data of a site should be obtained: including hydrological and sediment data of a hydrological site, soil erosion observation data of an experi- mental station, sedimentation data of a sediment storage dam; and other relevant research reports.
After checking topographic map data and TM data provided by a data group, the topographic map data is marked with national boundaries, provincial boundaries, and county boundaries, and then administrative boundaries are corrected and modified based on a topographic map in new version. After passing the quality inspec- tion, control point selection and digital entry of administrative boundary and control points are carried out according to the TM images, and an image processing analysis system is used to perform geometric correction and mosaic processing on the TM images, and finally an integration of the provincial boundaries, county bound- aries and images is realized, processing results are recorded on a tape in units of provinces, and submitted to each job group for image interpretation and analysis.
Through the analysis of soil erosion intensity reference in- dex elements, and based on a soil erosion type and intensity clas- sification, a human-computer interaction technology is used to outline to obtain a soil erosion type and intensity distribution map and soil erosion type and intensity classification data.
Although this method has a high accuracy rate, this accuracy rate is based on the accurate interpretation of images by experts with rich experience. Therefore, in order to obtain an accurate soil erosion type and intensity distribution map, a large number of professional and technical personnel must be equipped to achieve through human-computer interaction.
A time-space dynamic monitoring method of county soil erosion based on remote sensing and GIS, includes following steps: step 1, determining a to-be-monitored scope for counties di- vided by administrative regions across the country, collecting a remote sensing image and soil erosion history data within the to- be-monitored scope in past few years; step 2, extracting a soil erosion map in the soil erosion history data, determining a map spot in the soil erosion map and corresponding soil erosion type and erosion intensity and extract- ing a soil erosion situation in the soil erosion map; establishing a soil erosion vector (a, b, c¢) according to the soil erosion sit- uation, wherein a represents the erosion type, b represents an erosion type sub-category, and c represents the erosion intensity; representing by 0 if the erosion type has no erosion type sub- category, wherein the erosion type does not need to be divided in- to erosion intensity, that is, representing by 0 if there is no erosion intensity. step 3, extracting the remote sensing image within the to-be- monitored scope, and using GIS to synthesize the remote sensing image and corresponding watershed boundary maps, topographic maps and geological maps to generate an interpretation map; step 4, for the interpretation map within the to-be-monitored scope, according to a location and acreage of the map spot in the soil erosion map, marking an area with a same position and acreage as the map spot in the interpretation map as a mapping domain; ex- tracting color, shape, location, acreage, shadow, and texture in- formation of the mapping domain, and storing the color, shape, lo- cation, acreage, shadow, and texture information in a database as a discriminant feature; step 5, using an image recognition technology to divide the interpretation map to obtain a pre-divided block of the interpre- tation map, comparing the pre-divided block with the mapping do- main described in step 4, and setting a preset value q, adjusting a division plan through adjusting the determining feature, and us- ing the division plan as a final division plan of the pre-divided block of the interpretation map when a similarity between the map- ping domain and the pre-divided block is greater than the preset value qd; step 6, using the discriminant feature as an input and the soil erosion vector (a, b, c) as an output to establish a support vector machine discriminative model, and train the discriminative model, regarding the discriminative model as a qualified discrimi- native model when a discriminant accuracy rate of the discrimina- tive model is greater than w; otherwise , returning to step 4; step 7, obtaining a updated remote sensing image of the to- be-monitored scope, and synthesizing a updated interpretation map, using the final division plan of step 5 to divide the updated in- terpretation map, wherein the discriminative feature of each block after the updated interpretation map is divided is extracted and substituted into the qualified discriminative model, and determin- ing a soil erosion result corresponding to each block of the up- dated interpretation map and performing dynamic monitoring.
In order to realize dynamic and obvious monitoring, the pre- sent disclosure also includes: step 8, obtaining a soil erosion vector (a’, b’, c'’) of the updated interpretation map according to the soil erosion result of the updated interpretation map; for the to-be-monitored scope, pre-comparing a change between the soil erosion situation corresponding to an update period with the soil erosion situation at a certain time in the past, and ex- tract the soil erosion vector (a, b, c) corresponding to a certain time in the past; calculating a distance d between the soil erosion vector (a’, b’, Cc’) of the updated interpretation map and the soil erosion vector (a, b, c) corresponding to the certain time in the past;
according to the soil erosion vector (a', b', c') of the up- dated interpretation map and the distance d, performing the soil erosion situation to realize a time-space dynamic monitoring of soil erosion.
FIG. 1 is a flow schematic diagram of the present disclosure.
Specific embodiment 1: the embodiment is described with ref- erence to FIG. 1.
A time-space dynamic monitoring method of county soil erosion based on remote sensing and GIS, includes following steps: step 1, determining a to-be-monitored scope for counties di- vided by administrative regions across the country, collecting a remote sensing image and soil erosion history data within the to- be-monitored scope in past few years; step 2, extracting a soil erosion map in the soil erosion history data, determining a map spot in the soil erosion map and corresponding soil erosion type and erosion intensity and extract- ing a soil erosion situation in the soil erosion map; establishing a soil erosion vector (a, b, c) according to the soil erosion sit- uation, wherein a represents the erosion type, b represents an erosion type sub-category, and c represents the erosion intensity; representing by 0 if the erosion type has no erosion type sub- category, wherein the erosion type does not need to be divided in- to erosion intensity, that is, representing by 0 if there is no erosion intensity. step 3, extracting the remote sensing image within the to-be- monitored scope, and using GIS to synthesize the remote sensing image and corresponding watershed boundary maps, topographic maps and geological maps to generate an interpretation map; step 4, for the interpretation map within the to-be-monitored scope, according to a location and acreage of the map spot in the soil erosion map, marking an area with a same position and acreage as the map spot in the interpretation map as a mapping domain; ex- tracting color, shape, location, acreage, shadow, and texture in-
formation of the mapping domain, and storing the color, shape, lo- cation, acreage, shadow, and texture information in a database as a discriminant feature; step 5, using an image recognition technology to divide the 5 interpretation map to obtain a pre-divided block of the interpre- tation map, comparing the pre-divided block with the mapping do- main described in step 4, and setting a preset value q, adjusting a division plan through adjusting the determining feature, and us- ing the division plan as a final division plan of the pre-divided block of the interpretation map when a similarity between the map- ping domain and the pre-divided block is greater than the preset value di step 6, using the discriminant feature as an input and the soil erosion vector (a, b, ¢) as an output to establish a support vector machine discriminative model, and train the discriminative model, regarding the discriminative model as a qualified discrimi- native model when a discriminant accuracy rate of the discrimina- tive model is greater than w; otherwise, returning to step 4; step 7, obtaining a updated remote sensing image of the to- be-monitored scope, and synthesizing a updated interpretation map, using the final division plan of step 5 to divide the updated in- terpretation map, wherein the discriminative feature of each block after the updated interpretation map is divided is extracted and substituted into the qualified discriminative model, and determin- ing a soil erosion result corresponding to each block of the up- dated interpretation map and performing dynamic monitoring.
Specific embodiment 2:
The time-space dynamic monitoring method of county soil ero- sion based on remote sensing and GIS further includes: step 8, obtaining a soil erosion vector (a’, b’, cf) of the updated interpretation map according to the soil erosion result of the updated interpretation map; for the to-be-monitored scope, pre-comparing a change between the soil erosion situation corresponding to an update period with the soil erosion situation at a certain time in the past, and ex- tract the soil erosion vector (a, b, c¢) corresponding to a certain time in the past;
calculating a distance d between the soil erosion vector (a’, b’, cc’) of the updated interpretation map and the soil erosion vector (a, b, c¢) corresponding to the certain time in the past; according to the soil erosion vector (a', b', c') of the up- dated interpretation map and the distance d, performing the soil erosion situation to realize a time-space dynamic monitoring of soil erosion.
Other steps and parameters are the same as the specific em- bodiment 1.
Specific embodiment 3:
The specific process of step 5 in the embodiment is as fol- lows: step 5.1, using the image recognition technology to divide the interpretation map to obtain the pre-divided block of the in- terpretation map; step 5.2, comparing number of pre-divided block and the map- ping domain; if the number is not the same, adjusting the color, shape, location, acreage, shadow, and texture information of each mapping domain, and returning to step 5.1; otherwise, executing step 5.3; step 5.3, comparing whether the location of each pre-divided block and each mapping domain is the same, if the location is not the same, returning to step 5.1, if t the location is the same, executing step 5.4; step 5.4, comparing the shape and acreage of the pre-divided block and the mapping domain at the same position, when a shape similarity and an acreage similarity of the pre-divided block and the mapping domain are both greater than the preset value g, using the division plan as the final division plan of the pre-divided block of the interpretation map; otherwise, returning to step 5.1.
Other steps and parameters are the same as the specific em- bodiment 1 or embodiment 2.
Specific embodiment 4:
The specific process of step 5.3 in the embodiment is as fol- lows: calculating a geographic coordinate of a grid of the pre- divided block in the GIS, calculating another geographic coordi-
nate of the grid of the mapping domain in the GIS, and calculating the number of grids where the geographic coordinate of the grid of the pre-divided block is the same as the geographic coordinate of the grid of the mapping domain; if the number of grids occupies more than 97% of the number of grids in the pre-divided block and more than 97% of the number of grids in the mapping domain, con- sidering that the locations of the pre-divided block and the map- ping domain are the same, and returning step 5.4; otherwise, con- sidering different, and returning to step 5.1.
Other steps and parameters are the same as the specific em- bodiment 3.
Specific embodiment 5:
The specific process of step 5.4 in the embodiment is as fol- lows: selecting the pre-divided block and the mapping domain corre- sponding to its location, calculating an acreage size of the two, and determining an acreage similarity of the pre-divided block and the mapping domain corresponding to its location according to the acreage size of the two; calculating a boundary line similarity of the pre-divided block and the mapping domain at the same time, and using the boundary line similarity as a shape similarity; and make the acreage similarity and the shape similarity greater than the set preset value g; at this time, use image recognition technology to divide the interpretation map The division plan is the final division plan; otherwise, return to step 5.1; at this point, re- garding the division plan that uses the image recognition technol- ogy to divide the interpretation map as the final division plan; otherwise, returning to step 5.1.
Other steps and parameters are the same as the specific em- bodiment 4.
Specific embodiment 6:
The remote sensing image described in the embodiment is a TM remote sensing image after correction.
Other steps and parameters are the same as the specific em- bodiment 5.
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NL2030915A NL2030915B1 (en) | 2022-02-11 | 2022-02-11 | Time-space dynamic monitoring method of county soil erosion based on remote sensing and gis |
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