Small-range topographic change monitoring method based on geographic information system

Abstract

The invention discloses a small-range topographic change monitoring method based on a geographic information system. The method comprises the following steps: S1, obtaining and preprocessing topographic data of a target area; s2, carrying out format conversion on the data, grouping the data according to a time sequence, and carrying out time alignment on small-region grids; s3, performing difference analysis on the small area grids by using a least square method, and screening a topographic change area; s4, positioning the boundary of the change area by using spatial interpolation, constructing a three-dimensional space model, and outputting a space coordinate and a change amplitude; s5, reasoning time sequence characteristics of topographic changes through trend analysis, and evaluating the influence of the changes on the target area; s6, in combination with real-time monitoring data, correcting a topographic change trend, and updating model parameters; and S7, generating a monitoring graph and an early warning prompt, and pushing to monitoring personnel in real time. Through the steps, high-precision and small-range topographic change monitoring is realized, and real-time and scientific data support is provided for disaster prevention and control and urban planning.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and particularly to a method for monitoring small-scale terrain changes based on a geographic information system. Background Art

[0002] With the continuous progress of technology, geographic information system (GIS) technology has been widely applied in various fields, especially in terrain monitoring and environmental management. The geographic information system can efficiently collect, process, and analyze geographic data, providing strong technical support for various terrain change monitoring. By combining with remote sensing technology, GIS can obtain surface data in real time and provide powerful data support for environmental monitoring, disaster warning, urban planning, etc. Nevertheless, existing terrain change monitoring methods still face some problems, especially there are certain technical bottlenecks in small-scale terrain change monitoring and analysis.

[0003] Currently, the monitoring of terrain changes mostly relies on traditional remote sensing images or ground survey techniques. Remote sensing technology obtains large-scale geographic data through means such as satellites and drones. Although it has good application effects in large-scale areas, there are often certain limitations when monitoring terrain changes in small-scale areas. First, due to the limited resolution of remote sensing images, especially in cases where terrain details are complex or there are minor changes, the accuracy of remote sensing images may not be sufficient to clearly reveal the subtle changes in the terrain. Second, the processing and analysis of remote sensing data often require a long time period and it is difficult to achieve real-time monitoring. For some application scenarios that urgently require immediate response, such as disaster warning and engineering construction, the existing monitoring technologies appear to be relatively lagging and inefficient.

[0004] In addition, traditional terrain change monitoring methods mostly rely on a single data source or static model and lack comprehensive consideration of spatio-temporal changes. In practical applications, terrain changes are often affected by multiple factors, such as natural factors and human interventions, and these changes have significant spatio-temporal characteristics. Most of the existing technologies have not effectively combined these spatio-temporal characteristics, resulting in the inability to accurately capture the temporal sequence characteristics and spatial distribution laws of changes when monitoring small-scale terrain changes. Therefore, how to accurately and efficiently monitor terrain changes on the basis of a geographic information system by combining real-time data of small-scale areas has become a difficult problem in the current technical field.

[0005] To address the above issues, some researchers have attempted to improve the accuracy and real-time nature of monitoring by optimizing traditional monitoring methods and introducing more precise data processing methods. For example, recent studies have proposed a terrain change monitoring method based on multi-temporal remote sensing data, which uses image data from multiple time periods for comparison in order to identify terrain changes. However, this method still has certain limitations, which are mainly reflected in two aspects. First, the comparison method of multi-temporal remote sensing data often relies on the quality of the image and is greatly affected by factors such as image noise and cloud fog, resulting in low monitoring accuracy in complex environments. Second, due to the lack of effective spatial relationship analysis and temporal feature reasoning, existing technologies still find it difficult to effectively capture the dynamic process of terrain change, especially changes in small areas, which are difficult to accurately track.

[0006] To address the shortcomings of existing technologies, some researchers have proposed combining geographic information systems with spatial interpolation techniques. This method uses interpolation algorithms to spatially compensate for terrain data, filling in gaps in the data and thus improving monitoring accuracy. However, in practical applications, this method suffers from high computational complexity and slow processing speed. Especially when processing large-scale geographic data, data processing and analysis often takes a long time. Therefore, the real-time and efficiency of this method still cannot meet the requirements for high-precision monitoring of small-scale terrain changes.

[0007] In summary, existing technologies for monitoring small-scale terrain changes suffer from low accuracy, poor real-time performance, and inefficient data processing. These issues primarily stem from the existing technologies' lack of comprehensive analysis of spatiotemporal relationships, particularly the inadequate modeling of the temporal characteristics and spatial distribution patterns of terrain changes within small areas. Therefore, how to provide a small-scale terrain change monitoring method that accurately captures terrain changes, efficiently processes data, and provides real-time feedback within the framework of a geographic information system (GIS) by combining high-precision data acquisition, spatial interpolation, and spatiotemporal correlation analysis has become a key issue in the current technology landscape.

[0008] Therefore, how to provide a small-scale terrain change monitoring method based on a geographic information system is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0009] An object of the present invention is to propose a method for monitoring small - scale terrain changes based on a geographic information system. The present invention makes full use of geographic information systems, remote sensing technology, and spatio - temporal correlation analysis methods, and details a way to achieve real - time monitoring and analysis of small - scale terrain changes through a combination of high - precision terrain data collection, spatial interpolation, and spatio - temporal change trend reasoning. This method can accurately capture terrain changes in a small - scale area through technical means such as difference analysis, change area screening, spatial positioning, and spatio - temporal correlation models, and provide real - time monitoring, early warning, and dynamic correction functions.

[0010] A method for monitoring small - scale terrain changes based on a geographic information system according to an embodiment of the present invention includes the following steps:

[0011] S1. Obtain the terrain data of the target area and perform pre - processing;

[0012] S2. Convert the pre - processed terrain data into a data format available for a geographic information system, group it according to time sequence, divide it into multiple small - area grids, and at the same time perform time alignment processing on the small - area grids;

[0013] S3. Use the least - squares method to perform difference analysis on each small - area grid, calculate the terrain change value of the grid area at different time periods, and screen out the terrain change area through the threshold judgment method;

[0014] S4. Based on the difference analysis results, use the spatial interpolation method to locate the boundary of the change area, construct a three - dimensional space model, and output the specific spatial coordinates and change amplitude of the change area;

[0015] S5. According to the spatial relationship between the change area and the surrounding unchanged areas, use the trend analysis method to infer the temporal characteristics of the terrain change and evaluate the impact degree of the terrain change on the target area;

[0016] S6. Combine real - time monitoring data, adopt an adaptive optimization strategy to dynamically correct the terrain change trend, and update the three - dimensional space model parameters to ensure the real - time nature and accuracy of the monitoring results;

[0017] S7. Generate a dynamic monitoring map and early warning prompt for the change area according to the corrected terrain change trend, and push them to the monitoring personnel in real - time through a mobile device.

[0018] Optionally, the terrain data includes elevation data, slope data, aspect data, contour line data, three - dimensional terrain data, image data, geological data, watershed data, and terrain classification data.

[0019] Optionally, the pre - processing includes noise removal, missing value filling, and data standardization.

[0020] Optionally, S2 specifically includes:

[0021] S21. Group the preprocessed terrain data by time sequence to obtain data sets for multiple time periods. The terrain data within each time period represents the terrain information at a specific time point.

[0022] S22. Divide the data set for each time period into multiple small regional grids according to the geographical location of the target area. Each small regional grid represents a small area within the target area, and the grid division method is determined according to the terrain characteristics of the target area and the required monitoring accuracy.

[0023] S23. Perform time alignment processing on the data within each small regional grid to ensure that the corresponding grid positions are consistent in different time periods.

[0024] S24. Calculate the spatial relationship between adjacent grids based on the data of the small regional grids within each time period to ensure that the data between grids can be reasonably stitched together to form complete terrain monitoring data.

[0025] Optionally, S3 specifically includes:

[0026] S31. Set the terrain change value of each small regional grid within the target area as D i,t , where t = 1, 2, …, T, i = 1, 2, …, N, and D i,t represents the terrain change value of the i-th small regional grid at the t-th time point, T is the total number of collected time periods, and N is the total number of small regional grids.

[0027] S32. Conduct a difference analysis on the terrain data of each small regional grid between time points t1 and t2, calculate the change value of the small regional grid within each time period and perform an optimization solution through the least squares method to obtain the optimal fitting value of the terrain data change. Set the objective function as:

[0028]

[0029] where a represents the change rate coefficient, b represents the initial terrain value, (a·t2 + b) represents the optimal fitting value of the i-th small regional grid at time point t2. The goal is to minimize the fitting error of all small regional grids, represents the terrain change value of the i-th small regional grid at the t2-th time point, represents the terrain change value of the i-th small regional grid at the t1-th time point, and t2 > t1 > 0;

[0030] S33. Use the threshold judgment method to screen all small regional grids. If ΔD iIf it is ≥θ, it is determined that the i-th small area grid is a terrain change area, where θ is the change threshold, representing the critical value of the change.

[0031] Optionally, the S4 specifically includes:

[0032] S41. Based on the difference analysis result, set the boundary of the i-th change area as B i , and use the inverse distance weighted method to locate the boundary of the change area to obtain the spatial range of the change area, and set the spatial point in the change area as P i,k , where P i,k represents the coordinates of the k-th spatial point in the i-th change area;

[0033] S42. According to the spatial point P i,k in the change area and the corresponding terrain change value ΔD i,k , use the Kriging method for spatial interpolation to solve the terrain change value at the spatial point outside the boundary of the change area:

[0034]

[0035] Among them, P i,j represents the coordinates of the j-th spatial point in the i-th change area, ΔD i,j represents the terrain change value of the j-th spatial point in the i-th change area, ΔD i,k represents the terrain change value of the k-th spatial point in the i-th change area, d(P i,j , P i,k ) represents the distance between the j-th spatial point and the k-th spatial point, α is the weight coefficient of interpolation, controlling the influence range between spatial points, and n is the total number of spatial points in the boundary area;

[0036] S43. According to the interpolation result, construct a three-dimensional spatial model of the change area, and the coordinates of each spatial point P i,j in the three-dimensional spatial model are represented as (x i,j , y i,j , z i,j ), where x i,j , y i,j , z i,j are the x, y, and z coordinates of the spatial point P i,j in the three-dimensional space respectively, and output the specific spatial coordinates and change amplitude of the change area.

[0037] Optionally, the spatial range of the change region is obtained by the inverse distance weighting method, including the region boundaries of all terrain changes. The inverse distance weighting method calculates the distances between each spatial point and other points, and assigns different weight values according to the distances. The closer the points are, the greater the weight. The region boundary is composed of a set of interconnected spatial points, which define the external boundary positions of the change region. The boundary of this region is dynamically adjusted according to the magnitude of the change, spatial distribution, and the relationship with adjacent grids to ensure that it can accurately describe the geographical range of terrain changes.

[0038] Optionally, the three-dimensional spatial model of the change region is constructed based on the interpolation results. According to the coordinates (x i,j , y i,j , z i,j ) of each spatial point P i,j and the corresponding terrain change value ΔD i,j , the relative change magnitude of the spatial point is calculated, and each spatial point in the change region is connected to its adjacent points to form a three-dimensional grid structure. The z coordinate of each spatial point represents the terrain change magnitude in the vertical direction, while the x and y coordinates represent the spatial positions in the horizontal plane. Through the distribution and change magnitude of these spatial points, the specific shape and range of terrain changes are further visually displayed.

[0039] Optionally, S5 specifically includes:

[0040] S51. Construct a spatio-temporal correlation model between the change region and the surrounding unchanged regions according to the terrain change values and spatial relationships of the change region. Set the time series of the change region as T i = {t1, t2, …, t k}, where t1, t2, …, t k are the timestamps of different time points in the change region, and k is the length of the time series data;

[0041] S52. Set the spatial distance d(P i,j , P o,l ) between the change region and the unchanged regions, and calculate the spatial relationship between the change region and the unchanged regions, where P i,j represents the jth spatial point in the ith change region, and P o,l represents the lth spatial point in the oth unchanged region;

[0042] S53. Combine the time series T i of the change region and the spatial relationship d(P i,j , P n,l ), and use the trend analysis method to infer the time series characteristics of the change region and predict the regional time series change trend:

[0043]

[0044] Among them, ΔT i represents the temporal change trend of the i-th change region, and β t is the time coefficient, representing the influence degree of time on the change trend. ΔD i,t is the terrain change value of the i-th change region at time point t, and γ is the spatial attenuation coefficient, controlling the influence of spatial distance on the temporal trend;

[0045] S54. According to the deduced temporal change trend ΔT i , evaluate the influence degree of the change region on the target region, and set the influence evaluation value as the comprehensive evaluation result of the change trend:

[0046]

[0047] Among them, I i represents the influence evaluation value of the i-th change region on the target region, and α t is the time weight coefficient, representing the influence weight of time point t on the change trend, and λ is the temporal attenuation coefficient, representing the attenuation degree of time distance on the influence evaluation. |t - T0| is the time difference between time point t and the reference time T0;

[0048] S55. According to the temporal change trend ΔT i of the change region and the influence evaluation value I i , generate an influence report of the change region, and output the temporal characteristics, influence evaluation value and influence range on the target region of the change region through a visualization method.

[0049] Optionally, the spatio-temporal association model establishes a dynamic connection between the change region and the surrounding unchanged regions by combining the time series T i of the change region and the spatial distance d(P i,j , P o,l ), forming a multi-dimensional analysis framework. The terrain change value ΔD i,t of the change region at different time points is used to construct the change trend, and then the temporal change trend ΔT i is deduced. At the same time, considering the influence of the spatial distance d(P i,j , P o,l ) on the temporal change trend, the adjustment effect of the spatial position on the time series change is reflected by the spatial attenuation coefficient γ.

[0050] The beneficial effects of the present invention are:

[0051] First, the present invention can efficiently and accurately capture and analyze terrain changes in a small area. By adopting advanced difference analysis and spatial interpolation methods, it can solve the problem of insufficient monitoring accuracy of terrain changes in a small area in traditional monitoring technologies, thereby improving the accuracy of detecting changed areas. Compared with the traditional remote sensing image method, the present invention achieves a higher spatial resolution in a small area, can effectively identify minor terrain changes, and avoids the problem of inaccurate monitoring caused by insufficient resolution of remote sensing images.

[0052] Secondly, the present invention has made important innovations in spatio-temporal correlation analysis, and constructed a spatio-temporal correlation model between the changed area and the unchanged areas around it. This model can simultaneously consider the influencing factors of time and space, accurately infer the temporal characteristics of terrain changes, and dynamically correct the terrain change trend in combination with factors such as spatial distance attenuation. This enables terrain change monitoring not only to be limited to static comparison, but also to update the change trend in real time and provide early warnings, thereby greatly enhancing the real-time performance and response ability. This innovation solves the problem of insufficient spatio-temporal correlation analysis in the prior art, and can provide change data in a timely manner when rapid changes occur to assist in making quick decisions.

[0053] Finally, through three-dimensional spatial modeling technology, the present invention can comprehensively present the spatial characteristics of terrain changes and accurately evaluate the influence range of the changed area. This comprehensive analysis ability not only enhances the comprehensive understanding of terrain changes, but also provides more scientific and reliable data support for fields such as geological disaster early warning and urban planning. Combining real-time monitoring data and adaptive optimization strategies can effectively improve the accuracy of monitoring and the efficiency of data processing, and provide real-time, comprehensive, and accurate reference information for decision-makers in related fields. Description of the Drawings

[0054] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0055] Figure 1 is a flowchart of a method for monitoring small-area terrain changes based on a geographic information system proposed by the present invention;

[0056] Figure 2 is a flowchart of difference analysis and least squares method optimization for a method for monitoring small-area terrain changes based on a geographic information system proposed by the present invention;

[0057] Figure 3 is a flowchart of three-dimensional space model construction and output for a method for monitoring small-area terrain changes based on a geographic information system proposed by the present invention. Detailed Embodiments

[0058] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only schematically showing the basic structure of the present invention, so they only show the components related to the present invention.

[0059] Reference Figures 1-3 , a small - scale terrain change monitoring method based on a geographic information system, comprising the following steps:

[0060] S1. Obtain the terrain data of the target area and perform pre - processing;

[0061] S2. Convert the pre - processed terrain data into a data format available for the geographic information system, group it according to time sequence, divide it into multiple small - area grids, and at the same time perform time alignment processing on the small - area grids;

[0062] S3. Use the least - squares method to perform difference analysis on each small - area grid, calculate the terrain change values of the grid area at different time periods, and screen out the terrain change areas through the threshold judgment method;

[0063] S4. Based on the difference analysis results, use the spatial interpolation method to locate the boundaries of the change areas, construct a three - dimensional space model, and output the specific spatial coordinates and change amplitudes of the change areas;

[0064] S5. According to the spatial relationship between the change areas and the surrounding unchanged areas, use the trend analysis method to infer the time - series characteristics of the terrain changes and evaluate the impact degree of the terrain changes on the target area;

[0065] S6. Combine real - time monitoring data, adopt an adaptive optimization strategy to dynamically correct the terrain change trend, and update the three - dimensional space model parameters to ensure the real - time performance and accuracy of the monitoring results;

[0066] S7. According to the corrected terrain change trend, generate a dynamic monitoring map and a warning prompt for the change areas, and push them to the monitoring personnel in real - time through mobile devices.

[0067] The present invention provides a small - scale terrain change monitoring method based on a geographic information system, which can efficiently obtain the terrain data of the target area and perform pre - processing. Through time alignment and grid division methods, it ensures the accuracy and consistency of the data, thus providing high - quality basic data for subsequent difference analysis and change detection. This method effectively improves the accuracy of terrain change monitoring and provides reliable technical support for practical applications.

[0068] In this embodiment, the terrain data includes elevation data, slope data, aspect data, contour data, three - dimensional terrain data, image data, geological data, watershed data, and terrain classification data.

[0069] The present invention provides a more comprehensive and accurate terrain change monitoring method by combining various terrain data (such as elevation data, slope data, etc.), enabling adaptive analysis under different geological conditions to ensure multi-dimensional coverage of data and high reliability of monitoring results. This method makes up for the deficiencies of single data and lack of comprehensive analysis in traditional monitoring methods.

[0070] In this embodiment, the preprocessing includes noise removal, missing value filling, and data standardization.

[0071] During the preprocessing of terrain data in the present invention, steps such as noise removal, missing value filling, and data standardization are added to ensure the quality of the collected data and the stability of the processing process. These preprocessing steps effectively avoid the interference of outliers in the data on the subsequent analysis results and improve the monitoring accuracy.

[0072] In this embodiment, the specific steps of S2 include:

[0073] S21: Group the preprocessed terrain data according to time series to obtain data sets for multiple time periods. The terrain data within each time period represents the terrain information at a specific time point.

[0074] S22: Divide the data set of each time period into multiple small regional grids according to the geographical location of the target area. Each small regional grid represents a small area within the target area, and the grid division method is determined according to the terrain characteristics of the target area and the required monitoring accuracy.

[0075] S23: Perform time alignment processing on the data within each small regional grid to ensure that the corresponding grid positions are consistent in different time periods.

[0076] S24: Calculate the spatial relationship between adjacent grids based on the data of the small regional grids within each time period to ensure that the data between grids can be reasonably spliced to form complete terrain monitoring data.

[0077] The present invention groups the preprocessed data according to time series and divides it into small regional grids to ensure that the terrain data within each grid is consistent in different time periods and the data of adjacent grids can be reasonably spliced. This step greatly improves the spatial accuracy of terrain change monitoring and can better capture small-scale terrain changes.

[0078] In this embodiment, the specific steps of S3 include:

[0079] S31: Set the terrain change value of each small regional grid within the target area as D i,t , where t = 1, 2, …, T, i = 1, 2, …, N, and D i,t represents the terrain change value of the i-th small regional grid at the t-th time point. T is the total number of collected time periods, and N is the total number of small regional grids.

[0080] S32. Perform a difference analysis on the terrain data of each small - area grid between time points t1 and t2, and calculate the change value of the small - area grid in each time period. And perform an optimized solution through the least - squares method to obtain the optimal fitting value of the terrain data change. Set the objective function as:

[0081]

[0082] Where a represents the change rate coefficient, b represents the initial terrain value, (a·t2 + b) represents the optimal fitting value of the i - th small - area grid at time point t2. The goal is to minimize the fitting error of all small - area grids. represents the terrain change value of the i - th small - area grid at the t2 - th time point. represents the terrain change value of the i - th small - area grid at the t1 - th time point, and t2>t1>0.

[0083] S33. Use the threshold determination method to screen all small - area grids. If ΔD i ≥θ, then determine that the i - th small - area grid is a terrain change area, where θ is the change threshold, representing the critical value of the change.

[0084] The present invention performs a difference analysis on the terrain data by using the least - squares method and combines the threshold determination method to screen out the terrain change areas. This technology not only improves the detection accuracy of terrain changes but also ensures the efficiency of the change detection process through an optimized algorithm, avoiding misjudgment and missed - judgment problems, thereby more accurately identifying terrain changes.

[0085] In this embodiment, the S4 specifically includes:

[0086] S41. Based on the difference analysis results, set the boundary of the i - th change area as B i , and use the inverse - distance weighted method to locate the boundary of the change area to obtain the spatial range of the change area. Set the spatial points within the change area as P i,k , where P i,k represents the coordinates of the k - th spatial point within the i - th change area.

[0087] S42. According to the spatial points P i,k within the change area and the corresponding terrain change value ΔD i,k , use the Kriging method for spatial interpolation to solve the terrain change value at the spatial points outside the boundary of the change area:

[0088]

[0089] Where P i,jDenote the coordinates of the j-th spatial point in the i-th change region, ΔD i,j Denote the terrain change value of the j-th spatial point in the i-th change region, ΔD i,k Denote the terrain change value of the k-th spatial point in the i-th change region, d(P i,j ,P i,k ) represents the distance between the j-th spatial point and the k-th spatial point. α is the weight coefficient for interpolation, controlling the influence range between spatial points, and n is the total number of spatial points in the boundary region;

[0090] S43. According to the interpolation result, construct a three-dimensional spatial model of the change region. Each spatial point P i,j in the three-dimensional spatial model is represented by coordinates (x i,j ,y i,j ,z i,j ). Among them, x i,j ,y i,j ,z i,j are respectively the x, y, and z coordinates of the spatial point P i,j in the three-dimensional space, and output the specific spatial coordinates and change amplitude of the change region.

[0091] The present invention adopts a spatial interpolation method to locate the boundary of the change region and construct a three-dimensional spatial model through the inverse distance weighting method and the Kriging method. This method can effectively handle the discontinuity of spatial data, provide accurate boundaries of the terrain change region, and provide reliable spatial data support for subsequent spatial analysis and impact assessment.

[0092] In this embodiment, the spatial range of the change region is obtained through the inverse distance weighting method, including the boundaries of all regions with terrain changes. The inverse distance weighting method calculates the distance between each spatial point and other points, and assigns different weight values according to the distance. The closer the points are, the greater the weight. The region boundary is composed of a set of interconnected spatial points, which define the external boundary position of the change region. The boundary of this region is dynamically adjusted according to the magnitude of the change, spatial distribution, and the relationship with adjacent grids to ensure that it can accurately describe the geographical range of the terrain change.

[0093] The present invention introduces a spatio-temporal correlation model to infer the change trend by combining time series and spatial relationships. This model can dynamically analyze the spatio-temporal relationship between the change region and the surrounding unchanged regions, and provide real-time change trend prediction. This method improves the timeliness and accuracy of terrain change monitoring, can respond to terrain changes in real time, and supports disaster warning and emergency response.

[0094] In this embodiment, the three-dimensional spatial model of the change region is constructed according to the interpolation result. According to each spatial point P i,jThe coordinates (x i,j , y i,j , z i,j ) and the corresponding terrain change value ΔD i,j , calculate the relative change amplitude of the spatial points, and connect each spatial point in the change area with its adjacent points to form a three-dimensional grid structure. The z coordinate of each spatial point represents the terrain change amplitude in the vertical direction, while the x and y coordinates represent the spatial positions in the horizontal plane. Through the distribution and change amplitude of these spatial points, the specific form and range of the terrain change are further visually displayed.

[0095] Through the construction of a three-dimensional space model and volume calculation, the present invention can visually display the spatial form of the terrain change and provide the volume information of the change area. This model not only enhances the comprehensive understanding of the terrain change, but also provides a more accurate decision-making basis for fields such as engineering design and environmental management, ensuring a comprehensive assessment of the impact of the change area.

[0096] In this embodiment, the S5 specifically includes:

[0097] S51. According to the terrain change value and spatial relationship of the change area, construct a spatio-temporal correlation model between the change area and the surrounding unchanged area, and set the time series of the change area as T i ={t1, t2,..., t k}, where t1, t2,..., t k are the timestamps of different time points in the change area, and k is the length of the time series data;

[0098] S52. Set the spatial distance d(P i,j , P o,l ) between the change area and the unchanged area, and calculate the spatial relationship between the change area and the unchanged area, where P i,j represents the jth spatial point in the ith change area, and P o,l represents the lth spatial point in the oth unchanged area;

[0099] S53. Combine the time series T i of the change area and the spatial relationship d(P i,j , P n,l ), and use the trend analysis method to reason about the time series characteristics of the change area and predict the time series change trend of the area:

[0100]

[0101] Among them, ΔT i represents the time series change trend of the ith change area, β t is the time coefficient, indicating the influence degree of time on the change trend, and ΔD i,tis the terrain change value of the i-th change region at time point t, and γ is the spatial attenuation coefficient, which controls the influence of spatial distance on the temporal trend;

[0102] S54. According to the inferred temporal change trend ΔT i , evaluate the influence degree of the change region on the target region, and set the influence evaluation value as the comprehensive evaluation result of the change trend:

[0103]

[0104] where I i represents the influence evaluation value of the i-th change region on the target region, α t is the time weight coefficient, which represents the influence weight of time point t on the change trend, λ is the temporal attenuation coefficient, which represents the attenuation degree of the time distance on the influence evaluation, and |t - T0| is the time difference between time point t and the reference time T0;

[0105] S55. According to the temporal change trend ΔT i of the change region and the influence evaluation value I i , generate an influence report of the change region, and output the temporal characteristics, influence evaluation value and influence range on the target region of the change region through a visualization method.

[0106] The present invention evaluates the influence degree of terrain change on the target region, combines the time series and the spatial attenuation coefficient, and calculates and predicts the temporal change trend of the change region. The evaluation result not only helps to grasp the dynamic of terrain change in real time, but also provides an accurate influence evaluation for decision-makers, providing a scientific basis for subsequent environmental protection, urban planning, etc.

[0107] In this embodiment, the spatio-temporal correlation model establishes a dynamic connection between the change region and the surrounding unchanged regions by combining the time series T i of the change region and the spatial distance d(P i,j , P o,l ), forms a multi-dimensional analysis framework, and the terrain change value ΔD i,t of the change region at different time points is used to construct the change trend, and then the temporal change trend ΔT i is derived. At the same time, considering the influence of the spatial distance d(P ` i,j , P o,l ) on the temporal change trend, the regulatory effect of the spatial position on the time series change is reflected by the spatial attenuation coefficient γ.

[0108] By introducing a spatio-temporal correlation model and combining time series and spatial distance, the present invention comprehensively analyzes the spatio-temporal relationship between the changing area and the surrounding unchanged areas. This model can consider the regulatory effect of spatial position on temporal changes, thereby more accurately deriving the temporal trend of terrain changes, and effectively improving the accuracy of terrain change prediction through this model.

[0109] Example 1:

[0110] To verify the feasibility of the present invention in implementation, the present invention is applied to the monitoring of small-scale terrain changes in a seismically active area. This area has been under the influence of frequent small-scale earthquakes for a long time, with complex terrain changes and a relatively fast change speed. Traditional terrain monitoring methods have failed to provide sufficiently accurate and real-time monitoring data, resulting in untimely disaster warnings and responses, posing great challenges to local disaster prevention and control work. Therefore, the terrain change monitoring method based on geographic information system can effectively improve the change detection and real-time monitoring capabilities for small-scale areas.

[0111] This area is located in a mountainous region with a total area of about 150 square kilometers, large terrain undulations, and various landform features such as hills, canyons, and landslide areas. Due to frequent small earthquake activities, obvious changes have occurred in the terrain within the region. If these changes cannot be discovered in time, more serious geological disasters may be triggered, especially in urban expansion and infrastructure construction, with huge potential risks. In the past, relevant departments mainly relied on traditional remote sensing data and on-site surveys for terrain monitoring. However, due to the insufficient resolution of remote sensing images and the long time cycle of on-site surveys, it was difficult to effectively monitor and warn of these small-scale and rapidly occurring terrain changes.

[0112] The method of the present invention can monitor and predict terrain changes within the region at different time periods by collecting terrain data in real time and conducting precise analysis. In specific implementation, first, a plurality of ground sensors and unmanned aerial vehicles are deployed within the region to regularly collect high-precision terrain data, including information such as elevation, slope, and aspect. At the same time, contour data is extracted from remote sensing images, and combined with historical geological data and watershed data, a comprehensive assessment of the terrain characteristics of the target area is carried out. After preprocessing all the collected data, it is converted into a format suitable for the geographic information system, and time series grouping and grid division are performed to ensure the precise matching of data at different time points.

[0113] In the core steps of terrain change detection, the method of the present invention uses spatial interpolation technology to accurately locate the boundaries of the changed areas, and fits the data of different time periods by the least squares method to obtain the specific coordinates and change amplitudes of the changed areas. Through this method, the details of terrain changes can be clearly captured, avoiding the problems of low resolution and data delay in traditional monitoring means. At the same time, with the help of the spatio-temporal correlation model, the change trend is inferred and evaluated, further improving the timeliness and accuracy of the monitoring results.

[0114] After 3 months of actual monitoring, the method of the present invention successfully detected multiple small-scale terrain change areas in this region, generated a detailed change report within 24 hours after the changes occurred, and issued early warnings to relevant departments in a timely manner. This achievement has greatly improved the response speed of earthquake disaster early warnings and effectively reduced the risks caused by terrain changes in this region. The specific data is as follows:

[0115] Table 1 Monitoring results of terrain changes in the monitored area

[0116]

[0117] In Table 1, the data shows the terrain change situations in different monitored areas. Among them, "elevation change" and "slope change" respectively represent the vertical change of the terrain and the change of the slope, "change amplitude" represents the overall amplitude of the terrain change, and the impact evaluation value is the change impact score calculated by the spatio-temporal correlation model. Table 1 shows that in different monitored areas, the terrain change amplitudes vary greatly. Among them, the change amplitudes in the landslide area and the hill area are relatively significant. Especially on January 10th and January 25th, the change amplitude reached more than 2 meters, and the impact evaluation value is also relatively high, indicating the potential risks of terrain changes in this region.

[0118] The above data verifies the effectiveness of the method of the present invention. Especially in an environment where terrain changes occur rapidly, it can obtain high-precision data in a relatively short time, and conduct real-time monitoring and evaluation of terrain changes. Through this efficient and accurate monitoring method, potential geological disasters can be discovered and warned in a timely manner, greatly improving the response speed and effect of disaster prevention and control work, and providing strong technical support for practical applications.

[0119] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A method for monitoring small-scale terrain changes based on a geographic information system, characterized in that, It includes the following steps: S1. Obtain the terrain data of the target area and perform preprocessing; S2. Convert the preprocessed terrain data into a data format that can be used in a geographic information system, group it according to time series, divide it into multiple small area grids, and at the same time perform time alignment processing on the small area grids; S3. Use the least squares method to perform difference analysis on each small area grid, calculate the terrain change values of the grid area at different time periods, and screen out the terrain change areas through the threshold judgment method; S4. Based on the difference analysis results, use the spatial interpolation method to locate the boundaries of the changed areas, construct a three-dimensional space model, and output the specific spatial coordinates and change amplitudes of the changed areas; S5. According to the spatial relationship between the changed areas and the unchanged areas around them, use the trend analysis method to infer the time series characteristics of the terrain changes and evaluate the impact degree of the terrain changes on the target area; S6. Combine the real-time monitoring data, adopt an adaptive optimization strategy to dynamically correct the terrain change trend, and update the three-dimensional space model parameters to ensure the real-time and accuracy of the monitoring results; S7. Generate a dynamic monitoring map and early warning prompt for the changed areas according to the corrected terrain change trend, and push them to the monitoring personnel in real time through mobile devices.

2. The method for monitoring small - scale terrain changes based on a geographic information system according to claim 1, wherein, The terrain data includes elevation data, slope data, aspect data, contour data, three-dimensional terrain data, image data, geological data, watershed data, and terrain classification data.

3. A method for monitoring small - scale terrain changes based on a geographic information system according to claim 1, characterized in that, The preprocessing includes noise removal, missing value filling, and data standardization.

4. A small-scale terrain change monitoring method based on a geographic information system according to claim 1, characterized in that, The specific content of S2 includes: S21. Group the preprocessed terrain data according to time series to obtain data sets for multiple time periods. The terrain data within each time period represents the terrain information at a time point; S22. Divide the data set of each time period into multiple small area grids according to the geographical location of the target area. Each small area grid represents a small range within the target area, and the grid division method is determined according to the terrain characteristics of the target area and the required monitoring accuracy; S23. Perform time alignment processing on the data within each small area grid to ensure that the corresponding grid positions are consistent in different time periods; S24. Calculate the spatial relationship between adjacent grids according to the data of the small area grids within each time period to ensure that the data between grids can be reasonably spliced to form complete terrain monitoring data.

5. A method for monitoring small-scale terrain changes based on a geographic information system according to claim 1, characterized in that, The specific content of S3 includes: S31. Set the terrain change value of each small area grid in the target area to D i,t , where t = 1, 2, …, T, i = 1, 2, …, N, and D i,t represents the terrain change value of the i-th small area grid at the t-th time point, T is the total number of collected time periods, and N is the total number of small area grids; S32. Perform differential analysis on the terrain data of each small-area grid between time points t1 and t2, and calculate the change value of the small-area grid in each time period. And perform optimization and solution through the least squares method to obtain the optimal fitting value of the terrain data change. Set the objective function as: Among them, a represents the change rate coefficient, b represents the initial terrain value, and (a·t2 + b) represents the optimal fitting value of the i-th small regional grid at time point t2. The goal is to minimize the fitting error of all small regional grids. represents the terrain change value of the i-th small regional grid at the t2-th time point. represents the terrain change value of the i-th small regional grid at the t1-th time point, and t2 > t1 > 0; S33. Use the threshold determination method to screen all small regional grids. If ΔD i ≥θ, it is determined that the i-th small regional grid is a terrain change area, where θ is the change threshold, representing the critical value of the change.

6. The small-range terrain change monitoring method based on a geographic information system according to claim 1, wherein The specific content of S4 includes: S41. Based on the difference analysis result, set the boundary of the i-th change region as B i , and use the inverse distance weighted method to locate the boundary of the change region to obtain the spatial range of the change region. Set the spatial points within the change region as P i,k , where P i,k represents the coordinates of the k-th spatial point within the i-th change region; S42. According to the spatial point P within the change region i,k and the corresponding terrain change value ΔD i,k , perform spatial interpolation using the Kriging method to solve for the terrain change value at the spatial points outside the boundary of the change region: Among them, P i,j represents the coordinates of the j-th spatial point in the i-th change region, and ΔD i,j represents the terrain change value of the j-th spatial point in the i-th change region, and ΔD i,k represents the terrain change value of the k-th spatial point in the i-th change region, and d(P i,j , P i,k ) represents the distance between the j-th spatial point and the k-th spatial point. α is the weight coefficient of interpolation, which controls the influence range between spatial points, and n is the total number of spatial points in the boundary region; S43. Construct a three-dimensional space model of the change region according to the interpolation result. The coordinates of each spatial point P in the three-dimensional space model are represented as (x, y, z), where x, y, z are the x, y, z coordinates of the spatial point P in the three-dimensional space respectively, and output the specific spatial coordinates and change amplitude of the change region. i,j of which are (x i,j , y i,j , z i,j ), where x i,j , y i,j , z i,j are the x, y, z coordinates of the spatial point P i,j in the three-dimensional space respectively, and output the specific spatial coordinates and change amplitude of the change region.

7. A method for monitoring small - scale terrain changes based on a geographic information system according to claim 6, characterized in that, The spatial range of the changed area is obtained by the inverse distance weighted method, which includes the boundaries of all terrain change areas. The inverse distance weighted method calculates the distance between each spatial point and other points, and assigns different weight values according to the distance. The closer the distance, the greater the weight of the point. The area boundary is composed of a group of connected spatial points, which define the external boundary position of the changed area. The boundary of this area is dynamically adjusted according to the magnitude of the change amplitude, spatial distribution, and the relationship between adjacent grids to ensure that it can accurately describe the geographical scope of the terrain changes.

8. A method for monitoring small-scale terrain changes based on a geographic information system according to claim 6, characterized in that The three-dimensional spatial model of the change area is constructed based on the interpolation results. For each spatial point P i,j with coordinates (x i,j , y i,j , z i,j ) and the corresponding terrain change value ΔD i,j , the relative change amplitude of the spatial point is calculated, and each spatial point in the change area is connected to its adjacent points to form a three-dimensional grid structure. The z coordinate of each spatial point represents the terrain change amplitude in the vertical direction, while the x and y coordinates represent the spatial position in the horizontal plane. Through the distribution and change amplitude of these spatial points, the specific shape and scope of the terrain change are further visually displayed.

9. A method for monitoring small - scale terrain changes based on a geographic information system according to claim 1, characterized in that, The specific content of S5 includes: S51. Construct a spatio-temporal correlation model between the changed area and the surrounding unchanged areas according to the terrain change value and spatial relationship of the changed area, and set the time series of the changed area as T i ={t1, t2, …, t k}, where t1, t2, …, t k are the timestamps of different time points of the changed area, and k is the length of the time series data; S52. Set the spatial distance d(P i,j , P o,l ) between the changed area and the unchanged area, and calculate the spatial relationship between the changed area and the unchanged area, where P i,j represents the j-th spatial point in the i-th changed area, and P o,l represents the l-th spatial point in the o-th unchanged area; S53. Combine the time series T of the changing area i and the spatial relationship d(P i,j , P n,l ), and use the trend analysis method to infer the temporal characteristics of the changing area and predict the temporal change trend of the area: Among them, ΔT i represents the temporal change trend of the i-th change region, and β t is the time coefficient, indicating the influence degree of time on the change trend. ΔD i,t is the terrain change value of the i-th change region at the time point t, and γ is the spatial attenuation coefficient, which controls the influence of spatial distance on the temporal trend; S54. According to the inferred temporal change trend ΔT i , evaluate the influence degree of the changed area on the target area, and set the influence evaluation value as the comprehensive evaluation result of the change trend: Among them, I i represents the impact evaluation value of the i-th change region on the target region, and α t is the time weight coefficient, representing the influence weight of time point t on the change trend, λ is the time series attenuation coefficient, representing the attenuation degree of the time distance on the impact evaluation, and |t - T0| is the time difference between time point t and the reference time T0; S55. According to the temporal change trend ΔT of the changed area i and the impact evaluation value I i , generate an impact report for the changed area, and output the temporal characteristics of the changed area, the impact evaluation value, and the impact range on the target area through a visualization method.

10. A method for monitoring small-scale terrain changes based on a geographic information system according to claim 9, characterized in that, The spatio-temporal correlation model establishes a dynamic connection between the changing region and the surrounding unchanged regions by combining the time series T of the changing region i and the spatial distance d(P i,j , P o,l ), forming a multi-dimensional analysis framework. The terrain change value ΔD i,t of the changing region at different time points is used to construct the change trend, and then the time series change trend ΔT i is derived. At the same time, considering the influence of the spatial distance d(P i,j , P o,l ) on the time series change trend, the adjustment effect of the spatial position on the time series change is reflected by the spatial decay coefficient γ.

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