Space-time continuation feature extraction method for wind and water erosion in northeast black soil area

Abstract

The invention discloses a space-time continuation feature extraction method for wind and water erosion in a northeast black soil area, and the method comprises the steps: dividing the year time of soil erosion in the northeast black soil area into four time nodes, the starting and ending time of a wind power dominant period I, and the starting and ending time of a hydraulic power dominant period through historical meteorological data analysis and rule summarization; and the space-time continuation characteristics of wind power and water power erosion in the black soil area are systematically revealed through meteorological data and a clustering analysis technology. According to the method, important theoretical support and a practical tool are provided for soil protection and sustainable development of the northeast black soil area, and meanwhile, the method also provides a referential thought and framework for similar soil erosion research in other areas; by integrating meteorological data and a clustering analysis technology, the spatial and temporal characteristics of soil erosion in the black soil area are disclosed comprehensively and systematically, and powerful support is provided for ecological environment protection and agricultural sustainable development.

Description

Technical Field

[0001] The present invention relates to the technical field of soil erosion, and in particular to a method for extracting spatiotemporal features of wind and water erosion in black soil areas of Northeast China. Background Art

[0002] The black soil region has become an important agricultural production base in my country due to its fertile soil characteristics. However, with the intensification of climate change, improper land use and natural disasters, soil erosion in the region has become increasingly serious, seriously affecting the sustainability of agricultural production and the ecological environment. Therefore, systematic analysis of the spatiotemporal characteristics of soil erosion has become an important topic in scientific research and management practice.

[0003] Soil erosion in the Northeast Black Soil Region is mainly affected by both wind and water. Wind erosion is particularly significant under drought and strong wind weather conditions, while water erosion often occurs after heavy rainfall events. In order to effectively deal with these erosion problems, it is necessary to deeply understand the spatiotemporal dynamic characteristics of these two erosion modes. At present, there is no spatiotemporal continuous feature extraction method for wind and water erosion in the Northeast Black Soil Region in the prior art. Therefore, the present invention proposes a systematic feature extraction method, which aims to identify and analyze soil erosion phenomena in the region through scientific data analysis methods. Summary of the invention

[0004] In view of the above technical problems in the related art, the present invention provides a method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China, which can solve the above problems.

[0005] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows: A method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China, comprising the following steps: S1. Divide a year into four time nodes, namely: Date1: start time of wind dominant period I, Date2: End time of wind dominant period I, Date3: Start time of wind dominant period II, Date4: End time of wind dominant period II; S2. Determine the scope of the black soil study area, prepare the meteorological station data within the study area, and obtain the multi-year daily precipitation data and multi-year daily maximum wind speed data of each meteorological observation station; S3, traversing the daily precipitation and maximum wind speed data of each station for many years, and finding the start and end time of the wind-dominant period I and the start and end time of the wind-dominant period II of the corresponding meteorological station according to specific conditions; S4, obtaining four groups of different meteorological stations and average date data of the meteorological stations from step S3, clustering each group of data and dividing them into five categories; S5, re-clustering the four groups of clustering results obtained in step S4 and the longitude and latitude coordinates of each meteorological station, a total of six-dimensional data, into five categories; S6. Perform spatial interpolation on the six-dimensional data clustering results obtained in step S5, and reclassify them into five categories. Recalculate the average dates of the start and end time of wind-dominant period I and the start and end time of wind-dominant period II according to the reclassified five categories.

[0006] Furthermore, the end time of the wind-dominated period I in step S1 is also the start time of the water-dominated period, and the start time of the wind-dominated period II is also the end time of the water-dominated period.

[0007] Furthermore, the multiple years in step S2 refers to twenty years and above.

[0008] Furthermore, in step S3, the daily precipitation and maximum wind speed data of each station for many years are traversed. According to the specific condition, the cycle is 6 days, and the following are searched respectively: The last date when the precipitation is greater than 0 mm from February to March, and the multi-year average date for each station is calculated in the format of: station id, xx month xx day; The last date with precipitation greater than 12 mm from June to October, and the multi-year average date for each station are calculated in the format of: station id, xx month xx day; The last day with the maximum wind speed greater than 8m / s from March to July, and the multi-year average date for each station are calculated in the format of: station id, xx month xx day; The last day with the maximum wind speed greater than 8m / s from September to October, and the multi-year average date for each site are calculated in the format of site id, xx month xx day.

[0009] Furthermore, step S4 is specifically as follows: four groups of different meteorological stations and average date data of the meteorological stations are obtained from step S3, the average date of each group of meteorological stations is converted into the xth day of the year, and then each group of date-converted data is clustered separately, using the k-means clustering method, and each group is divided into 5 categories.

[0010] Furthermore, in step S5, a k-means clustering method is used.

[0011] Furthermore, step S6 is specifically as follows: the six-dimensional data clustering result obtained in step S5 is imported into ArcGIS software in the form of an attribute table, the meteorological station attribute table is linked, spatial interpolation is performed, and the interpolation is reclassified into five categories to obtain a five-category polygon shape file. The attribute of the polygon is the category. According to each category range of the polygon where all meteorological stations are located, the category of the meteorological station is redefined. Finally, according to the category of the meteorological station, the average date of Date1, Date2, Date3, and Date4 is calculated respectively, and finally the spatiotemporal connection characteristics of wind and water erosion in the Northeast Black Soil Area are obtained.

[0012] The beneficial effects of the invention are as follows: the invention provides important theoretical support and practical tools for soil protection and sustainable development in the black soil region of Northeast China. At the same time, the method also provides ideas and frameworks for reference for similar soil erosion research in other regions. By integrating meteorological data and cluster analysis technology, the invention strives to comprehensively and systematically reveal the spatiotemporal characteristics of soil erosion in the black soil region, and provide strong support for ecological environmental protection and sustainable agricultural development. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] The present invention is further described in detail below with reference to the accompanying drawings.

[0015] Figure 1 It is a flow chart of a method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China according to an embodiment of the present invention; Figure 2 It is a range map of the Northeast Black Soil Region in an embodiment of the present invention; Figure 3 is a spatial distribution map of meteorological stations in an embodiment of the present invention; Figure 4 is a cluster diagram of day of year of four groups of meteorological stations in an embodiment of the present invention; Figure 5 It is a polygonal shape diagram obtained by clustering six-dimensional data in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0017] like Figures 1 to 5 As shown, according to the present invention, a method for extracting spatiotemporal features of wind and water erosion in the black soil area of ​​Northeast China is disclosed, comprising the following steps: S1. Divide a year into four time nodes, namely: Date1: start time of wind dominant period I, Date2: End time of wind-dominated period I (start time of water-dominated period), Date3: Start time of wind-dominated period II (end time of water-dominated period), Date4: End time of wind dominant period II; S2. Determine the scope of the black soil study area, prepare the meteorological station data within the study area, and obtain the multi-year daily precipitation data and multi-year daily maximum wind speed data of each meteorological observation station, where multi-year refers to twenty years or more; S3. Traverse the daily precipitation and maximum wind speed data of each station for many years, and according to specific conditions, find the start and end time of wind dominant period I and wind dominant period II of the corresponding meteorological station, where according to specific conditions means searching for the following in a cycle of 6 days: The last date with precipitation greater than 0 mm from February to March, and the multi-year average date (Date1) for each station is calculated in the format of: station id, xx month xx day, The last date with precipitation greater than 12 mm from June to October, and the multi-year average date (Date2) for each station is calculated in the format of: station id, xx month xx day, The last date with the maximum wind speed greater than 8m / s from March to July, and the multi-year average date (Date3) for each station are calculated in the format of station id, xx month xx day, The last date when the maximum wind speed is greater than 8m / s from September to October, and the multi-year average date (Date4) for each station is calculated in the format of station id, xx month xx day, The 6-day cycle refers to the number of days to calculate the average value of meteorological elements. For example, the last day with precipitation greater than 12 mm from June to October is the last day of the last cycle in the query month range that meets this condition, when the average precipitation for a 6-day cycle is greater than 12 mm. S4, obtaining four groups of different meteorological stations and average date data of the meteorological stations from step S3, converting the average date ("a certain day of a certain month") of each group of meteorological stations into the xth day of a year (day of year date, ranging from 1 to 366), and then clustering the day of year date of each group respectively, using the k-means clustering method, and dividing each group into 5 categories; S5, re-clustering the four groups of clustering results obtained in step S4 and the longitude and latitude coordinates of each meteorological station, a total of six-dimensional data, using the k-means clustering method to divide them into five categories; S6. Import the six-dimensional data clustering results obtained in step S5 into ArcGIS software in the form of an attribute table, link the meteorological station attribute table, perform spatial interpolation, and reclassify into 5 categories after interpolation to obtain a 5-category polygon shape file. The attribute of the polygon is the category. According to each category range of the polygon where all meteorological stations are located, redefine the category of the meteorological station (which of the 5 categories it belongs to). Finally, according to the category of the meteorological station, calculate the average date of Date1, Date2, Date3, and Date4 respectively, and finally obtain the spatiotemporal connection characteristics of wind and water erosion in the Northeast Black Soil Area.

[0018] Example: The spatial scope of the Northeast Black Soil Region provided by the Ministry of Water Resources is the research area scope of the embodiment of the present invention. Figure 2 shown.

[0019] Based on this spatial range, the precipitation and wind speed data of meteorological elements at national ground meteorological observation stations were obtained, totaling 210 meteorological stations, such as Figure 3 As shown in Figure 2, the multi-year daily precipitation data and multi-year daily maximum wind speed data of each station were obtained from 210 meteorological stations.

[0020] Traverse the multi-year daily precipitation and maximum wind speed data of each station, with a period of 6 days, find the last day date with precipitation greater than 0 mm from February to March, and calculate the multi-year average date (Date1) of each station, the format is: station id, xx month xx day (the same below); find the last day date with precipitation greater than 12 mm from June to October, and calculate the multi-year average date (Date2) of each station; find the last day date with maximum wind speed greater than 8 m / s from March to July, and calculate the multi-year average date (Date2) of each station; find the last day date with maximum wind speed greater than 8 m / s from September to October, and calculate the multi-year average date (Date4) of each station.

[0021] The above results in four groups of different meteorological stations and their dates. Each group (Date1, Date2, Date3, Date4) is clustered and divided into five categories, such as Figure 4 shown.

[0022] The four groups of clustering results and the longitude and latitude coordinates of each meteorological station, a total of six-dimensional data, were re-clustered and divided into five categories.

[0023] The obtained six-dimensional data clustering results were spatially interpolated and reclassified into five categories. The average dates of the start and end time of the wind-dominated period I and the start and end time of the hydraulic-dominated period were recalculated according to the reclassified five categories. The final results are as follows: Among them, the categories are Figure 5 The categories of polygonal shape maps obtained by clustering the six-dimensional data.

[0024] In summary: the present invention analyzes historical meteorological data, summarizes the laws, divides the one-year soil erosion in the black soil area of ​​Northeast China into four time nodes, the start and end time of the wind-dominated period I, the start and end time of the water-dominated period, and systematically reveals the spatiotemporal continuity characteristics of wind and water erosion in the black soil area through meteorological data and cluster analysis technology.

[0025] 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, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China, characterized in that: The following steps are involved: S1. Divide a year into four time nodes, namely: Date1: start time of wind dominant period I, Date2: End time of wind dominant period I, Date3: Start time of wind dominant period II, Date4: End time of wind dominant period II; S2. Determine the scope of the black soil study area, prepare the meteorological station data within the study area, and obtain the multi-year daily precipitation data and multi-year daily maximum wind speed data of each meteorological observation station; S3, traversing the daily precipitation and maximum wind speed data of each station for many years, and finding the start and end time of the wind-dominant period I and the start and end time of the wind-dominant period II of the corresponding meteorological station according to specific conditions; S4, obtaining four groups of different meteorological stations and average date data of the meteorological stations from step S3, clustering each group of data and dividing them into five categories; S5, re-clustering the four groups of clustering results obtained in step S4 and the longitude and latitude coordinates of each meteorological station, a total of six-dimensional data, into five categories; S6. Perform spatial interpolation on the six-dimensional data clustering results obtained in step S5, and reclassify them into five categories. Recalculate the average dates of the start and end time of wind-dominant period I and the start and end time of wind-dominant period II according to the reclassified five categories.

2. The method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China according to claim 1 is characterized in that: The end time of the wind-dominated period I in step S1 is also the start time of the water-dominated period, and the start time of the wind-dominated period II is also the end time of the water-dominated period.

3. The method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China according to claim 1, characterized in that: The multiple years in step S2 refers to twenty years and above.

4. The method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China according to claim 1, characterized in that: In step S3, the daily precipitation and maximum wind speed data of each station for many years are traversed. According to the specific condition, the cycle is 6 days, and the following are searched respectively: The last date when the precipitation is greater than 0 mm from February to March, and the multi-year average date for each station is calculated in the format of: station id, xx month xx day; The last date with precipitation greater than 12 mm from June to October, and the multi-year average date for each station are calculated in the format of: station id, xx month xx day; The last day with the maximum wind speed greater than 8m / s from March to July, and the multi-year average date for each station are calculated in the format of: station id, xx month xx day; The last day with the maximum wind speed greater than 8m / s from September to October, and the multi-year average date for each site are calculated in the format of site id, xx month xx day.

5. The method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China according to claim 1, characterized in that: Step S4 is specifically as follows: four groups of different meteorological stations and average date data of meteorological stations are obtained from step S3, and the average date of each group of meteorological stations is converted into the xth day of the year, and then each group of date-converted data is clustered separately, using the k-means clustering method, and each group is divided into 5 categories.

6. The method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China according to claim 1, characterized in that: In step S5, the k-means clustering method is used.

7. The method for extracting spatiotemporal features of wind and water erosion in the black soil region of Northeast China according to claim 1, characterized in that: Step S6 is specifically as follows: the six-dimensional data clustering result obtained in step S5 is imported into ArcGIS software in the form of an attribute table, the attribute table of the meteorological station is linked, spatial interpolation is performed, and the interpolation is reclassified into five categories to obtain a five-category polygon shape file. The attribute of the polygon is the category. According to each category range of the polygon where all meteorological stations are located, the category of the meteorological station is redefined. Finally, according to the category of the meteorological station, the average date of Date1, Date2, Date3, and Date4 is calculated respectively, and finally the spatiotemporal connection characteristics of wind and water erosion in the Northeast Black Soil Area are obtained.

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