Multi-temporal image landscape pattern index batch calculation method based on suburban space segmentation

Abstract

The invention provides a multi-temporal image landscape pattern index batch calculation method based on suburban space segmentation. According to the method, an equal-area buffer area is constructed outwards based on administrative regions or custom boundary data meeting input threshold requirements, and the equal-area buffer area represents cities and suburbs after segmentation. According to basic calculation units after suburban segmentation, multi-temporal remote sensing images are used for preprocessing time phase data, the steps of coordinate unification, image cutting, mask processing and the like are included, the landscape pattern index of each time phase image is calculated through the automatic batch processing technology, and a time sequence index sequence of each unit is formed. And integrating data of each time phase to realize quantitative analysis of the evolution process of the suburban landscape pattern in the research area. According to the method, automatic batch calculation of the large-scale multi-temporal image landscape indexes can be efficiently and accurately completed only by relying on the remote sensing images and the spatial segmentation data, and the method has good applicability and popularization prospects.

Description

Technical Field

[0001] The present invention relates to the application technology of spatial information technology in the field of landscape ecology, and particularly to a method for batch calculating landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation. The method can be applied to multiple aspects such as urban expansion monitoring, ecological planning evaluation, and land use dynamic analysis. Background Art

[0002] With the acceleration of the urbanization process, the core area of the city continues to expand, and the land use pattern in the surrounding suburbs also changes significantly. Traditional landscape pattern analysis methods usually only focus on the whole city or a single boundary range, and it is difficult to finely distinguish the heterogeneity between the urban core and the suburbs. Systematic batch calculation of landscape pattern indices for multi-temporal remote sensing images can more intuitively reveal the dynamic evolution process of urban-rural landscapes at the spatio-temporal scale, providing a scientific basis for urban planning, ecological environment protection, and land use management.

[0003] Most current urban-rural division methods rely on a single scale or administrative boundaries, and it is difficult to take into account the dynamic change characteristics of the urban core area at different urbanization stages. On the one hand, simple administrative divisions cannot reflect the real urban land coverage and construction intensity; on the other hand, if only relying on a fixed buffer distance for zoning, it is easy to cause the problem that the research scope does not match the actual urban form. At the same time, the data scale of multi-temporal remote sensing images is huge, the cost of manual processing is high, and it is easy to generate data processing bottlenecks, which is difficult to meet the requirements of high timeliness and high precision results for urban planning and ecological environment monitoring.

[0004] Based on this, the present invention proposes a method for batch calculating landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation. By accurately delimiting the urban core and suburban areas and combining automated scripts or process management tools, the multi-temporal remote sensing images are uniformly processed and the indices are batch calculated, which can significantly improve the efficiency and accuracy. Summary of the Invention

[0005] The object of the present invention is to solve the problems in the prior art, and proposes a method for batch calculating landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation.

[0006] The present invention is realized by the following technical solutions. The present invention proposes a method for batch calculating landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation, and the method includes the following steps:

[0007] (a) Acquisition of urban-rural boundary data: Acquire administrative region or custom boundary data that meets the input threshold requirements as the boundary of the urban core area;

[0008] (b) Equal-area buffer zone construction: Based on the area of the urban core area, equal-area buffer zones are constructed outward to obtain the urban core and suburban areas;

[0009] (c) Preprocessing of multi-temporal remote sensing images: coordinate unification, image cropping and masking of remote sensing images acquired at different times to ensure that images of different temporal phases can be compared and analyzed in the same spatial reference system;

[0010] (d) Batch calculation after urban-suburban segmentation: Taking urban core and suburban areas as basic calculation units, automatic batch processing technology is used for each phase image to calculate the time series data of the following landscape pattern indicators: Shannon diversity index, uniformity index, number of patches, patch richness, fragmentation index, landscape shape index, maximum patch index, interlacing index, segmentation index, and aggregation degree;

[0011] (e) Result integration and analysis: The obtained landscape pattern indicators were integrated and compared in time series to quantify the evolution of landscape patterns in the urban core and suburbs within the study area.

[0012] Furthermore, in step (a), the administrative boundary data is input or the artificial surface coverage ratio in the input area is higher than 40%.

[0013] Furthermore, in step (b), the buffer radius is inversely calculated based on the geometric area of the urban core area, and then an equal-area buffer zone is constructed outwardly through a buffer algorithm so that the area of the external buffer zone is equivalent to that of the urban core area.

[0014] Furthermore, in step (b), after the buffer zone is generated, a geometric difference operation is performed between it and the urban core area. The part overlapping with the core area is eliminated through the difference, and finally two areas with non-overlapping and comparable areas are obtained, namely the urban core and the suburbs.

[0015] Furthermore, in step (c), the remote sensing images acquired at different times are first unified into the same coordinate reference system; then the images are cropped using the urban core and suburban areas as masks to retain the valid areas; finally, the image files are combined with the quality inspection, and clouds, shadows, invalid pixels or pixels not considered in the study are removed according to the preset threshold to obtain valid data for subsequent landscape index calculations.

[0016] Furthermore, the step (d) is specifically:

[0017] (1) Classify the cropped image or directly identify different types of objects based on pixel values;

[0018] (2) Using patch connectivity analysis and morphological operations to obtain patch area, perimeter, and adjacency attributes;

[0019] (3) According to the formulas of Shannon diversity index, evenness index, patch number, patch richness, fragmentation index, landscape shape index, largest patch index, interspersion index, division index and aggregation degree, batch calculate the temporal indicators of each patch and the overall landscape;

[0020] (4) Taking the urban core and suburbs as units, output the calculation results of each time phase as corresponding temporal data files.

[0021] Furthermore, the definitions and formulas for calculating each index are as follows:

[0022] The Shannon diversity index H measures the diversity of the landscape, taking into account the number and proportion of different types of patches in the landscape. m is the total number of patch types, and p i is the proportion of the area of the i-th type of patch to the total area; the evenness index E measures the evenness of different patch types in the landscape, representing the proportion of the Shannon diversity index relative to the maximum possible value. H is the Shannon diversity index; m is the total number of patch types; the patch number NP is the total number of all patches in the landscape. n i is the number of the i-th type of patch; the patch richness PR is the number of patch types in the landscape, that is, the total number of different patch types, PR = m; the fragmentation index SI is an index measuring the degree of landscape fragmentation, indicating the degree to which the landscape is divided into multiple small patches. A is the total area of the landscape, and a i is the area of the i-th type of patch; the landscape shape index LSI describes the complexity of the landscape boundary. E is the total boundary length of all patches in the landscape, and minE is the minimum possible boundary length; the largest patch index LPI represents the proportion of the largest patch in the landscape to the total area. max(a i ) is the area of the largest patch, and A is the total area of the landscape; the interspersion index IJI measures the degree of interspersion of different types of patches in space. p ij is the proportion of the boundary length between category i and category j to the total boundary length; the division index DIV represents the degree of patch division in the landscape, and the higher the value, the higher the degree of fragmentation. A is the total area of the landscape, and a i is the area of the i-th type of patch; the aggregation degree AI measures the degree of aggregation of patches of the same category in space, and the higher the value, the more concentrated the patches. g i is the number of pixel adjacencies inside the i-th type of patch; G i is the maximum possible number of pixel adjacencies of the i-th type of patch.

[0023] Further, in step (e), when performing temporal integration and analysis on each landscape pattern index, visualization techniques or statistical analysis methods are used to conduct trend analysis, change rate calculation, or difference evaluation on each index, and the results of the urban core and the suburbs are compared to obtain the dynamic evolution characteristics of the urban-rural landscape pattern.

[0024] The present invention also proposes an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method for batch calculating landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation are implemented.

[0025] The present invention also proposes a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the method for batch calculating landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation are implemented.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] The present invention provides a method for batch calculating landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation. Through the organic combination of the foregoing steps, the present invention successfully solves the problems in the prior art of inaccurate division of urban-rural areas, low efficiency of preprocessing multi-temporal remote sensing images and calculating landscape indices. It can complete the automatic recognition of urban-rural areas, unified cropping and quality control of multi-temporal images, as well as batch calculation and comparative analysis of various landscape pattern indices in the same process, ensuring the accuracy of the results and significantly improving the processing efficiency. The present invention can operate only relying on remote sensing images and spatial segmentation data, without the need for complex external auxiliary information, and has good applicability and promotion prospects. It can be widely applied to multiple fields such as urban expansion research, ecological planning evaluation, and land use dynamic monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0029] Figure 1 It is a flowchart of the method for batch calculating landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation according to the present invention.

[0030] Figure 2 It is a schematic diagram of the spatial ranges of the urban core area and the suburbs.

[0031] Figure 3 Schematic diagram of comparative analysis of landscape index between urban core and suburbs. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings 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 without creative work are within the scope of protection of the present invention.

[0033] Combination Figures 1 - 3 The present invention proposes a method for batch calculation of landscape pattern index of multi-temporal images based on urban-suburban spatial segmentation, the method comprising the following steps:

[0034] (a) Obtaining urban-rural boundary data: Obtain administrative area or custom boundary data that meets the input threshold requirements as the boundary of the urban core area; to ensure the effectiveness and pertinence of the statistical analysis results, it is recommended to input administrative boundary data or input an artificial surface coverage ratio higher than 40% in the area, or set a suitable threshold to screen the urban core boundary based on research needs. After obtaining the boundary, it is necessary to fully consider factors such as urban land use layout, construction intensity and administrative divisions to ensure that the selected core area has a certain degree of representativeness and accuracy, and to lay the foundation for the subsequent construction of equal-area buffer zones.

[0035] (b) Construction of equal-area buffers: Based on the area of the urban core area, equal-area buffers are constructed outward to obtain the urban core and suburbs. In step (b), the buffer radius is inferred from the geometric area of the urban core area, and then an equal-area buffer is constructed outward through the buffer algorithm, so that the area of the external buffer is equivalent to that of the urban core area, so as to reasonably divide the urban core and suburbs in space. When performing buffering, it is necessary to fully evaluate the shape of the urban core area and the surrounding geographical environment characteristics to avoid buffer gaps or redundancies due to complex or irregular boundaries. After the buffer is generated, a geometric difference operation is performed between it and the urban core area. The part overlapping with the core area is eliminated through the difference, and finally two non-overlapping and comparable areas are obtained, namely the urban core and suburbs. This step can not only avoid the deviation caused by relying solely on administrative boundaries or fixed buffer distances, but also more objectively reflect the actual land use scale and external impact of the city.

[0036] (c) Preprocessing of multi-temporal remote sensing images: coordinate unification, image cropping and mask processing are performed on remote sensing images acquired at different times to ensure that images of each temporal phase can be compared and analyzed in the same spatial reference system; in step (c), remote sensing images acquired at different times are first unified to the same coordinate reference system to ensure the consistency of spatial reference of multi-temporal images; then the images are cropped with the urban core and suburban areas as masks to retain the valid areas; finally, combined with the quality inspection image file, clouds, shadows, invalid pixels or pixels not considered in the study are removed according to the preset threshold to obtain valid data for subsequent landscape index calculation. This step also needs to pay attention to the spatial resolution, spectral characteristics and image time differences between multi-source remote sensing data. If necessary, atmospheric correction, orthorectification and other methods can be used to minimize the impact of temporal or sensor differences and lay a high-quality data foundation for subsequent analysis.

[0037] (d) Batch calculation after urban-suburban segmentation: Taking urban core and suburban areas as basic calculation units, automatic batch processing technology is used for each phase image to calculate the time series data of the following landscape pattern indicators: Shannon diversity index, uniformity index, number of patches, patch richness, fragmentation index, landscape shape index, maximum patch index, interlacing index, segmentation index, and aggregation degree;

[0038] The step (d) is specifically:

[0039] (1) According to the research needs, select an appropriate classification method (such as supervised classification, unsupervised classification, or threshold segmentation directly based on pixel values) to classify the cropped image or directly identify different types of objects based on pixel values;

[0040] (2) Using patch connectivity analysis and morphological operations to obtain patch area, perimeter, and adjacency attributes;

[0041] (3) Based on the formulas of Shannon diversity index, uniformity index, number of patches, patch richness, fragmentation index, landscape shape index, maximum patch index, interlacing index, segmentation index and aggregation, batch calculate the temporal indicators of each patch and the landscape as a whole;

[0042] (4) Taking the urban core and suburbs as units, the calculation results of each time phase are output as corresponding time series data files. In order to improve processing efficiency and reduce human intervention, the classification, patch identification and index calculation are integrated into a unified workflow through scripts or process management tools, so as to realize the automatic calculation of large batches of multi-phase images, and the results are output as time series data files after each calculation is completed, so as to facilitate subsequent visualization and statistical analysis.

[0043] The definitions and formulas for calculating each indicator are as follows:

[0044] The Shannon Diversity Index (H) measures the diversity of a landscape, taking into account the number of different types of patches in the landscape and their proportions. m is the total number of patch types, and p i is the proportion of the area of the i-th type of patch to the total area; the Evenness Index (E) measures the degree of evenness of different patch types in the landscape and represents the proportion of the Shannon Diversity Index relative to the maximum possible value. H is the Shannon Diversity Index; m is the total number of patch types; the Number of Patches (NP) is the total number of all patches in the landscape. n i is the number of the i-th type of patch; the Patch Richness (PR) is the number of patch types in the landscape, that is, the total number of different patch types, and PR = m; the Splitting Index (SI) is an index that measures the degree of fragmentation of the landscape and represents the degree to which the landscape is divided into multiple small patches. A is the total area of the landscape, and a i is the area of the i-th type of patch; the Landscape Shape Index (LSI) describes the complexity of the landscape boundary. E is the total boundary length of all patches in the landscape, and minE is the minimum possible boundary length; the Largest Patch Index (LPI) represents the proportion of the largest patch in the landscape to the total area. max(a i ) is the area of the largest patch, and A is the total area of the landscape; the Interspersion and Juxtaposition Index (IJI) measures the degree of interspersion of different types of patches in space. p ij is the proportion of the boundary length between category i and category j to the total boundary length; the Division Index (DIV) represents the degree of fragmentation of the patches in the landscape, and a higher value indicates a higher degree of fragmentation. A is the total area of the landscape, and a i is the area of the i-th type of patch; the Aggregation Index (AI) measures the degree of aggregation of patches of the same category in space, and a higher value indicates that the patches are more concentrated. g i is the number of pixel adjacencies within the i-th type of patch; G iis the maximum possible number of pixel adjacencies for the i-th type of patch.

[0045] (e) Result integration and analysis: Integrate and compare the obtained landscape pattern metrics over time to quantitatively study the evolution process of the urban core and suburban landscape patterns within the study area.

[0046] In step (e), when integrating and analyzing the landscape pattern metrics over time, use visualization techniques or statistical analysis methods to perform trend analysis, calculate the change rate, or evaluate the differences of each metric, and compare the results of the urban core and suburbs to obtain the dynamic evolution characteristics of the urban-rural landscape pattern.

[0047] Integrate the landscape pattern metrics obtained in step (d) in chronological order to form a time-series metric sequence of the urban core and suburbs under multi-temporal images; then, according to research needs, use visualization means (such as line charts, bar charts, or heat maps, etc.) to display the change trends of different metrics at each time phase, or use statistical analysis methods to quantitatively evaluate the change rate, trend direction, and differences of the metrics. By comparing the index change characteristics of the urban core and suburbs, key ecological and landscape phenomena during urbanization processes such as core area expansion, suburban fragmentation, or change in aggregation degree can be further revealed, and based on this, provide a scientific basis for urban planning, ecological environmental protection, and land use management. If it is necessary to analyze data in a larger area or more time phases, the above steps (a) to (e) can be repeatedly executed in the same computing environment, and batch processing can be achieved by means of automated scripts, so as to balance high efficiency and high precision. The method can efficiently and accurately complete the batch calculation of landscape indices of the urban core and suburbs in multi-temporal images only relying on remote sensing images and spatial segmentation data, and has good applicability and promotion prospects.

[0048] Embodiment

[0049] The present invention provides a method for batch calculation of landscape pattern indices of multi-temporal images based on urban-rural spatial segmentation, and the method includes the following steps:

[0050] Step (a), obtaining urban-rural boundary data. Obtain administrative region or custom boundary data that meets the input threshold requirements as the boundary of the urban core area;

[0051] Specifically, step (a) is: input a vector file in the shapefile format from a labeled map service system, an administrative boundary database (such as the GADM administrative division database), or a custom area that meets the recommended threshold requirements.

[0052] Step (b), constructing an equal-area buffer. According to the area of the urban core area, construct an equal-area buffer outward to obtain two areas: the urban core and the suburbs;

[0053] Step (b) is specifically as follows: inversely deduce the buffer radius based on the geometric area, and calculate the radius using the math library of Python. where A is the area of the core area; then use the buffer method of the geopandas and shapely libraries to generate an external buffer. Use the geopandas.overlay method to perform a geometric difference operation on the buffer and the urban core area to obtain the spatial ranges of the urban core area and the suburbs as Figure 2 shown.

[0054] Step (c), preprocessing of multi-temporal remote sensing images. Perform coordinate unification, image cropping, and masking on the remote sensing images obtained at different times to ensure that the images in each time phase can be compared and analyzed under the same spatial reference system;

[0055] Step (c) is specifically as follows: use the reproject function of the rasterio library to unify each time-phase image into the same coordinate reference system (such as WGS84 or CGCS2000); use the urban core and suburban ranges as masks, and crop the images using rasterio.mask to ensure that the valid areas are retained; combined with the quality inspection process, use the Google Earth Engine or opencv library to identify clouds, shadows, and invalid pixels in the images, and remove the interference data based on the set NDVI or NDSI thresholds to obtain high-quality data for subsequent landscape index calculations.

[0056] Step (d), batch calculation after urban-rural segmentation. Using the urban core and suburbs as basic calculation units, for each time-phase image, adopt an automated batch processing technology to calculate the time-series data of the following landscape pattern indicators respectively: Shannon diversity index, evenness index, number of patches, patch richness, fractal index, landscape shape index, largest patch index, interspersion index, division index, aggregation degree;

[0057] Step (d) is specifically as follows: classify the cropped images, and a pixel-based random forest classification method (sklearn.ensemble.RandomForestClassifier) or a classifier of Google Earth Engine can be used, or directly segment according to the spectral threshold (such as NDVI>0.3 as vegetation); use the connected region analysis (label method) of scipy.ndimage and the opencv morphological operation method to extract attributes such as patch area, perimeter, and adjacency relationship;

[0058] And calculate the landscape index according to the following method, where:

[0059] Shannon diversity index, calculate using the log function of numpy;

[0060] The uniformity index, Calculate using the log function of numpy;

[0061] The number of patches, Directly calculate the total number;

[0062] Patch richness, PR = m, count the total number of categories;

[0063] The fragmentation index, Calculate based on the adjacency matrix;

[0064] The landscape shape index, Calculate using numpy.sqrt;

[0065] The largest patch index, Implement equivalent calculation based on Python;

[0066] The interspersion index, Implement equivalent calculation based on Python;

[0067] The division index, Implement equivalent calculation based on Python;

[0068] The aggregation degree, Implement equivalent calculation based on Python;

[0069] To improve processing efficiency and reduce human intervention, integrate the classification, patch recognition, and index calculation steps into a unified workflow through scripts or process management tools, thereby realizing automated calculation of large batches of multi-temporal images, and outputting the results as time-series data files after each calculation to facilitate subsequent visualization and statistical analysis.

[0070] Step (e), result integration and analysis. Conduct time-series integration and comparative analysis on the obtained landscape pattern indicators to quantitatively study the evolution process of the urban core and suburban landscape patterns in the study area;

[0071] The specific content of step (e) is as follows: Conduct time-series integration on each landscape pattern indicator, use matplotlib and seaborn libraries for visualization, use groupby of pandas to calculate the change trend, and calculate the change rate based on numpy.gradient or regression analysis (scipy.stats.linregress); For difference evaluation, the K-S test (scipy.stats.ks_2samp) or Mann-Kendall trend test (pyMannKendall library) can be used; Finally, conduct comparative analysis on the landscape indices of the urban core and suburbs to reveal the dynamic evolution characteristics of the urban-rural landscape pattern, such as Figure 3as shown

[0072] Through the above technical solutions, the present invention provides a method for batch calculating landscape pattern indices of multi-temporal images based on suburban spatial segmentation, which successfully solves the problems of inaccurate suburban scope division, low efficiency of preprocessing multi-temporal remote sensing images and calculating landscape indices in the prior art. It can complete the automatic recognition of suburban areas, the unified cropping and quality control of multi-temporal images, as well as the batch calculation and comparative analysis of various landscape pattern indices in the same process, ensuring both the accuracy of the results and significantly improving the processing efficiency.

[0073] In this embodiment, the proposed method for batch calculating landscape pattern indices of multi-temporal images based on suburban spatial segmentation can run only relying on remote sensing images and spatial segmentation data, without the need for complex external auxiliary information. It has good applicability and promotion prospects and can be widely applied to many fields such as urban expansion research, ecological planning assessment, land use dynamic monitoring, etc.

[0074] The present invention also proposes an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method for batch calculating landscape pattern indices of multi-temporal images based on suburban spatial segmentation are realized.

[0075] The present invention also proposes a computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the steps of the method for batch calculating landscape pattern indices of multi-temporal images based on suburban spatial segmentation are realized.

[0076] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DRRAM). It should be noted that the memory of the method described in the present invention is intended to include but not limited to these and any other suitable types of memories.

[0077] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another, for example, the computer instructions may be transmitted from a website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a high-density digital video disc (DVD)), or a semiconductor medium (such as a solid state disc (SSD)), etc.

[0078] In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware processor, or executed and completed by a combination of the hardware and software modules in the processor. The software module may be located in a mature storage medium in the art such as a random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0079] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiments can be completed by the integrated logic circuit in the hardware of the processor or instructions in software form. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or can be executed and completed by the combination of the hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0080] The above has introduced in detail a method for batch calculation of multi-temporal image landscape pattern indices based on suburban space segmentation proposed by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A batch calculation method for landscape pattern indices of multi-temporal images based on suburban spatial segmentation, characterized in that, The method includes the following steps: (a) Obtaining suburban boundary data: Obtaining administrative region boundary data or custom boundary data that meets the input threshold requirements as the boundary of the urban core area; (b) Constructing equal-area buffer zones: Constructing equal-area buffer zones outward according to the area of the urban core area to obtain two parts: the urban core and the suburbs; (c) Preprocessing multi-temporal remote sensing images: Conducting coordinate unification, image cropping, and masking processing on remote sensing images obtained at different times to ensure that the images of each time phase can be compared and analyzed under the same spatial reference system; (d) Batch calculation after urban-rural segmentation: Using the urban core and suburbs as basic calculation units, adopting an automated batch processing technology for each time-phase image to calculate the time-series data of the following landscape pattern indices respectively: Shannon diversity index, evenness index, patch number, patch richness, fractal dimension index, landscape shape index, largest patch index, interspersion index, splitting index, aggregation index; (e) Result integration and analysis: Conducting time-series integration and comparative analysis on the obtained landscape pattern indices to quantitatively study the evolution process of the landscape pattern between the urban core and the suburbs in the research area.

2. The method according to claim 1, wherein In step (a), input administrative boundary data or the proportion of artificial surface coverage in the input area is higher than 40%.

3. The method according to claim 1, wherein In step (b), the buffer radius is inversely deduced according to the geometric area of the urban core area, and then an equal-area buffer zone is constructed outward through a buffer algorithm so that the area of the external buffer zone is equivalent to the area of the urban core area.

4. The method according to claim 3, wherein In step (b), after the buffer zone is generated, a geometric difference operation is performed between it and the urban core area, and the overlapping part with the core area is removed through the difference operation, and finally two non-overlapping areas with comparable areas are obtained, namely the urban core and the suburbs.

5. The method according to claim 1, wherein In step (c), first, remote sensing images obtained at different times are unified into the same coordinate reference system; then, using the urban core and suburban ranges as masks, the images are cropped to retain the effective area; finally, combined with the quality inspection image files, clouds, shadows, invalid pixels, or pixels not within the scope of the research consideration are removed according to the preset threshold to obtain the effective data for subsequent landscape index calculation.

6. The method according to claim 1, wherein The specific content of step (d) is as follows: (1) Classifying the cropped images or directly identifying different land cover types based on pixel values; (2) Using patch connectivity analysis and morphological operation methods to obtain the area, perimeter, and adjacency relationship attributes of patches; (3) Batch calculating the time-series indices of each patch and the overall landscape according to the formulas of Shannon diversity index, evenness index, patch number, patch richness, fractal dimension index, landscape shape index, largest patch index, interspersion index, splitting index, and aggregation index; (4) Taking the urban core and suburbs as units, outputting the calculation results of each time phase as corresponding time-series data files.

7. The method according to claim 6, wherein The definitions and formulas for calculating each index are as follows: The Shannon diversity index H measures the diversity of the landscape, taking into account the number of different types of patches in the landscape and their proportions. m is the total number of patch types, p i is the proportion of the area of the i-th type of patch to the total area; the evenness index E measures the degree of evenness of different patch types in the landscape and represents the proportion of the Shannon diversity index relative to the maximum possible value. H is the Shannon diversity index; m is the total number of patch types; the number of patches NP is the total number of all patches in the landscape. n i is the number of the i-th type of patch; the patch richness PR is the number of patch types in the landscape, that is, the total number of different patch types, PR = m. The fragmentation index SI, an indicator for measuring the fragmentation degree of a landscape, represents the degree to which the landscape is divided into multiple small patches. A is the total area of the landscape, and a i is the area of the i-th type of patch; The landscape shape index LSI describes the complexity of the landscape boundary. E is the total boundary length of all patches in the landscape, and minE is the minimum possible boundary length; The largest patch index LPI represents the proportion of the largest patch in the landscape to the total area. max(a i ) is the area of the largest patch, and A is the total area of the landscape. The Interspersion and Juxtaposition Index (IJI) measures the degree of interspersion of different types of patches in space. p ij is the proportion of the boundary length between class i and class j to the total boundary length. The splitting index DIV represents the degree of fragmentation of patches in the landscape. The higher the value, the higher the degree of fragmentation. A is the total landscape area, and a i is the area of the i-th type of patch; the aggregation index AI measures the degree of spatial aggregation of patches of the same category. The higher the value, the more concentrated the patches. g i is the number of pixel adjacencies within the i-th type of patch; G i is the maximum possible number of pixel adjacencies for the i-th type of patch.

8. The method according to claim 1, characterized in that, In step (e), when conducting time-series integration and analysis on each landscape pattern index, visualization technology or statistical analysis methods are used to conduct trend analysis, change rate calculation, or difference evaluation on each index, and the results of the urban core and suburbs are compared to obtain the dynamic evolution characteristics of the urban-rural landscape pattern.

9. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1-8 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that, When the computer instructions are executed by the processor, the steps of the method according to any one of claims 1-8 are implemented.

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