Farmland forest network rate monitoring method and device, electronic equipment and storage medium

Abstract

The application provides a farmland forest network rate monitoring method and device, electronic equipment and storage medium, and relates to the technical field of remote sensing. The method comprises the following steps: determining a vegetation area in a target remote sensing image based on the spectral information of the target remote sensing image of a region to be monitored; obtaining a characteristic parameter value corresponding to the vegetation area; determining a forest belt area in the vegetation area based on the characteristic parameter value corresponding to the vegetation area; obtaining the farmland forest network rate of the region to be monitored based on the forest belt area and a farmland area in the target remote sensing image; the characteristic parameters comprise a normalized vegetation index and an enhanced vegetation index, and a length-width ratio and / or information entropy; and the farmland area is determined according to the geographic information of the farmland in the region to be monitored. The farmland forest network rate monitoring method and device, electronic equipment and storage medium provided by the application can more accurately and efficiently monitor the farmland forest network rate of a large area, and can reduce the cost input of farmland forest network rate monitoring.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, and in particular to a method, device, electronic equipment, and storage medium for monitoring the rate of farmland afforestation. Background Technology

[0002] The rate of farmland shelterbelt coverage is one of the fundamental indicators for evaluating arable land quality. Obtaining the farmland shelterbelt coverage rate quickly and accurately is of great significance for the scientific management of arable land quality and the efficient supervision of farmland shelterbelt projects.

[0003] In existing technologies, the rate of farmland shelterbelt coverage is usually obtained through field surveys conducted by technicians. However, due to factors such as time, manpower, and material costs, technicians can typically only conduct field surveys of small areas, making it difficult to conduct accurate field surveys of large, comprehensive areas. Therefore, relying on field surveys by technicians is insufficient to accurately obtain the rate of farmland shelterbelt coverage over large areas. Summary of the Invention

[0004] This invention provides a method, device, electronic device, and storage medium for monitoring the rate of farmland forest coverage, in order to overcome the shortcomings of existing technologies in obtaining the rate of farmland forest coverage over large areas, and to achieve more accurate monitoring of the rate of farmland forest coverage over large areas.

[0005] This invention provides a method for monitoring the rate of farmland shelterbelt network formation, comprising:

[0006] Acquire remote sensing images of the target area to be monitored;

[0007] Based on the spectral information of the target remote sensing image, vegetation areas are determined in the target remote sensing image;

[0008] Obtain the feature parameter values ​​corresponding to the vegetation area, and determine the forest belt area in the vegetation area based on the feature parameter values ​​corresponding to the vegetation area;

[0009] Based on the forest belt area and the farmland area in the target remote sensing image, the farmland forest network rate of the area to be monitored is obtained;

[0010] The characteristic parameters include the normalized vegetation index and the enhanced vegetation index, as well as the aspect ratio and / or information entropy; the farmland area is determined based on the geographical information of the farmland in the area to be monitored.

[0011] According to the method for monitoring the rate of farmland shelterbelt networking provided by the present invention, determining the vegetation area in the target remote sensing image based on the spectral information of the target remote sensing image includes:

[0012] Based on the spectral information of the target remote sensing image in the red and near-infrared bands, the normalized vegetation index value of the target remote sensing image is obtained.

[0013] Based on the normalized vegetation index value and predefined segmentation parameter values ​​of the target remote sensing image, the target remote sensing image is segmented at multiple scales to obtain the multi-scale segmentation result of the target remote sensing image.

[0014] Based on the multi-scale segmentation results, the vegetation area is determined in the target remote sensing image.

[0015] According to a method for monitoring the rate of farmland afforestation provided by the present invention, determining the forest belt area in the vegetation area based on the characteristic parameter values ​​corresponding to the vegetation area includes:

[0016] If the characteristic parameter values ​​corresponding to the vegetation area meet the preset conditions, the vegetation area is determined as the forest belt area;

[0017] The preset conditions include a normalized vegetation index value greater than a first preset value and an enhanced vegetation index value greater than a second preset value; when the feature parameter includes an aspect ratio, the preset conditions also include an aspect ratio value greater than a third preset value; when the feature parameter includes information entropy, the preset conditions also include an information entropy value greater than a fourth preset value.

[0018] The first preset value ranges from 0.37 to 0.41; the second preset value ranges from 0.29 to 0.33; the third preset value ranges from 1.6 to 2.0; and the fourth preset value ranges from 0.53 to 0.57.

[0019] According to a method for monitoring the rate of farmland forest coverage provided by the present invention, the step of obtaining the rate of farmland forest coverage in the area to be monitored based on the forest belt area and the farmland area includes:

[0020] Based on the locational relationship between the farmland area and the forest belt area, the farmland forest belt area is determined within the forest belt area;

[0021] Based on the area of ​​the farmland forest belt region and the area of ​​the farmland region, the farmland forest network rate of the area to be monitored is obtained.

[0022] According to the method for monitoring the rate of farmland shelterbelt network provided by the present invention, the step of obtaining the characteristic parameter values ​​corresponding to the vegetation area includes:

[0023] Based on the spectral information of the target remote sensing image in the red and near-infrared bands, the normalized vegetation index (NDI) value corresponding to the vegetation area is obtained. Based on the spectral information of the target remote sensing image in the blue, red, and near-infrared bands, the enhanced vegetation index (EVI) value corresponding to the vegetation area is obtained. When the feature parameter includes aspect ratio, the aspect ratio value corresponding to the vegetation area is obtained based on the length and width values ​​of the vegetation area. When the feature parameter includes information entropy, the information entropy value corresponding to each vegetation area is obtained based on the gray-level co-occurrence matrix corresponding to the target remote sensing image.

[0024] According to a method for monitoring the rate of farmland forest coverage provided by the present invention, after determining the farmland forest belt area within the forest belt area based on the positional relationship between the farmland area and the forest belt area, the method further includes:

[0025] Obtain the geographic information of the administrative regions at a preset level in the area to be monitored;

[0026] Based on the geographic information of the preset level administrative region, determine the farmland forest belt area and farmland area corresponding to the preset level administrative region;

[0027] Based on the corresponding farmland forest belt area and farmland area of ​​the preset level administrative region, the farmland forest network rate of the preset level administrative region is obtained.

[0028] According to a method for monitoring the rate of farmland shelterbelt network provided by the present invention, after obtaining the rate of farmland shelterbelt network in the preset administrative region, the method further includes:

[0029] Based on the farmland shelterbelt network rate of the preset level administrative region, the region corresponding to the preset level administrative region is color-marked in the target remote sensing image, and annotation information is added to the target remote sensing image to obtain a thematic map of the farmland shelterbelt network rate distribution of the area to be monitored.

[0030] The annotation information includes at least one of the following: map title, legend, north arrow, coordinate grid, scale bar, and derived parameters.

[0031] The present invention also provides a monitoring device for the rate of farmland shelterbelt network, comprising:

[0032] The data acquisition module is used to acquire remote sensing images of the target area to be monitored.

[0033] The first region determination module is used to determine vegetation regions in the target remote sensing image based on the spectral information of the target remote sensing image;

[0034] The second region determination module is used to obtain the feature parameter values ​​corresponding to the vegetation region, and determine the forest belt region in the vegetation region based on the feature parameter values ​​corresponding to the vegetation region.

[0035] The farmland shelterbelt network rate calculation module is used to obtain the farmland shelterbelt network rate of the area to be monitored based on the shelterbelt area and the farmland area in the target remote sensing image;

[0036] The characteristic parameters include the normalized vegetation index and the enhanced vegetation index, as well as the aspect ratio and / or information entropy; the farmland area is determined based on the geographical information of the farmland in the area to be monitored.

[0037] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the farmland shelterbelt network monitoring method as described above.

[0038] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the farmland shelterbelt network monitoring method as described above.

[0039] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the farmland shelterbelt network monitoring method as described above.

[0040] The method, apparatus, electronic device, and storage medium for monitoring farmland forest coverage rate provided by this invention, after determining the vegetation area in the target remote sensing image based on the spectral information of the target remote sensing image, determine the forest belt area in the vegetation area based on the characteristic parameter values ​​corresponding to the vegetation area, and then obtain the farmland forest coverage rate of the area to be monitored based on the forest belt area and the farmland area in the target remote sensing image. The characteristic parameters include normalized difference vegetation index and enhanced vegetation index, as well as aspect ratio and / or information entropy. The farmland area is determined based on the geographical information of the farmland in the area to be monitored. This method can monitor the farmland forest coverage rate of a large area more accurately and efficiently, reduce the cost of monitoring farmland forest coverage rate, improve the informatization and automation level of farmland forest coverage rate monitoring, and provide data support for the scientific and efficient implementation of farmland protection, strengthening soil management, and improving farmland quality. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is one of the flowcharts of the method for monitoring the rate of farmland shelterbelt network provided by the present invention;

[0043] Figure 2 This is the second flowchart of the method for monitoring the rate of farmland shelterbelt network provided by the present invention;

[0044] Figure 3 This is a schematic diagram of the farmland shelterbelt network monitoring device provided by the present invention;

[0045] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0047] In the description of the invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0048] It should be noted that the Farmland Shelterbelt Rate (FSR) refers to the proportion of farmland covered by trees in a given area. Generally, this rate can be calculated by dividing the area of ​​forest land by the total farmland area. The Farmland Shelterbelt Rate is one of the fundamental indicators for evaluating arable land quality, reflecting the level of forestry resource utilization and ecological protection in the region. A higher Farmland Shelterbelt Rate can improve land productivity and economic efficiency, while also protecting water and soil resources and the ecological environment, and enhancing the sustainable development capacity of the ecosystem.

[0049] Typically, after conducting field surveys in a certain area and obtaining the protected area of ​​the forest belts around the farmland and the total area of ​​the farmland, technicians can calculate the farmland forest coverage rate of the area based on the protected area of ​​the forest belts around the farmland and the total area of ​​the farmland.

[0050] After obtaining the farmland shelterbelt coverage rate in the aforementioned areas, the degree of farmland shelterbelt coverage in these areas can be comprehensively determined based on this rate. The degree of farmland shelterbelt coverage can be categorized into three levels: high, medium, and low.

[0051] However, whether conducting field surveys in small or large areas, it requires a significant investment of time, manpower, and resources. Therefore, obtaining farmland shelterbelt coverage rates through field surveys conducted by technical personnel is less efficient and more costly.

[0052] The enormous time, manpower, and material costs required for large-scale field surveys make it difficult for technicians to conduct accurate surveys across the entire region. Conversely, field surveys of small areas only yield data for a limited region, failing to provide comprehensive data for the entire area. Furthermore, the accuracy of farmland shelterbelt coverage rates obtained through field surveys is susceptible to human factors such as variations in technicians' professional skills and work attitudes, making it difficult to guarantee the accuracy of the obtained rates.

[0053] Therefore, relying on field surveys conducted by technical personnel is insufficient to accurately and efficiently obtain the rate of farmland shelterbelt coverage over large areas. Furthermore, there is currently no automated monitoring method for farmland shelterbelt coverage.

[0054] Remote sensing technology has many advantages, such as wide monitoring range, fast data acquisition, high extraction accuracy and low investment cost. It has been widely used in land and resources surveys, urban planning and land resource management.

[0055] To facilitate practical application in agricultural, farmland protection, and environmental protection sectors, this invention provides a method for monitoring farmland shelterbelt coverage based on remote sensing technology. This method, based on single-period high spatial resolution remote sensing imagery and employing an object-oriented automatic identification algorithm, enables high-precision and rapid remote sensing extraction of farmland shelterbelt areas. Combined with conventional agricultural standards for calculating farmland shelterbelt coverage, it achieves high-precision and rapid remote sensing monitoring of farmland shelterbelt coverage across large areas. This improves the informatization level of farmland shelterbelt coverage acquisition, enhances the accuracy and efficiency of acquisition, and reduces the cost of acquisition. It provides information support for the scientific and efficient implementation of farmland protection, strengthened soil management, and improved farmland quality.

[0056] Figure 1 This is one of the flowcharts illustrating the method for monitoring farmland shelterbelt coverage provided by this invention. The following is a combination of... Figure 1 This invention describes a method for monitoring the rate of farmland shelterbelt network formation. For example... Figure 1As shown, the method includes: Step 101, obtaining the geographical information of farmland in the area to be monitored.

[0057] It should be noted that the implementing entity of this embodiment of the invention is a farmland shelterbelt network rate monitoring device.

[0058] Specifically, the area to be monitored is the monitoring object of the farmland shelterbelt network rate monitoring method provided by this invention. Based on the farmland shelterbelt network rate monitoring method provided by this invention, the farmland shelterbelt network rate of the area to be monitored can be monitored to obtain the farmland shelterbelt network rate of the area to be monitored.

[0059] In this embodiment of the invention, the original remote sensing image of the area to be monitored can be obtained in a variety of ways. For example, the original remote sensing image of the area to be monitored can be obtained based on user input; or, the original remote sensing image of the area to be monitored can be received from other electronic devices; or, the original remote sensing image of the area to be monitored can be obtained based on cloud computing platforms such as GEE and PIE-Engine.

[0060] After acquiring the original remote sensing image of the area to be monitored, the original remote sensing image of the area to be monitored can be directly identified as the target remote sensing image of the area to be monitored. Alternatively, the original remote sensing image can be preprocessed, and the preprocessed original remote sensing image can be identified as the target remote sensing image of the area to be monitored.

[0061] The aforementioned data preprocessing may include at least one of the following: abnormal image removal, image stitching, band combination, radiometric correction, geometric correction, image fusion, and image cropping; band combination includes single-band separation and combination of individual single bands.

[0062] Optionally, the process of preprocessing the original remote sensing images of the area to be monitored to obtain the target remote sensing images of the area can be completed in ENVI software.

[0063] Step 102: Based on the spectral information of the target remote sensing image, determine the vegetation area in the target remote sensing image.

[0064] Specifically, after acquiring the target remote sensing image of the area to be monitored, the area where vegetation is located in the target remote sensing image can be identified as a vegetated area based on the spectral information of the image. It is understood that the vegetated area may be the area where forest belts are located.

[0065] Optionally, based on the spectral information of the target remote sensing image, several vegetation areas can be determined in the target remote sensing image in various ways. For example, based on the spectral information of the target remote sensing image, several vegetation areas can be determined in the target remote sensing image through multi-scale segmentation, machine learning algorithms, or numerical calculation.

[0066] It should be noted that if, based on the spectral information of the aforementioned target remote sensing image, no vegetation area is identified in the aforementioned target remote sensing image, then the farmland shelterbelt coverage rate of the area to be monitored can be determined to be 0.

[0067] Step 103: Obtain the feature parameter values ​​corresponding to the vegetation area, and determine the forest belt area in the vegetation area based on the feature parameter values ​​corresponding to the vegetation area; wherein, the feature parameters include the normalized vegetation index and the enhanced vegetation index, as well as the aspect ratio and / or information entropy.

[0068] Specifically, after identifying several vegetation regions in the aforementioned target remote sensing image, the characteristic parameter values ​​of each vegetation region can be obtained through numerical calculations and other methods based on the spectral information of the aforementioned target remote sensing image and the location information of each vegetation region in the aforementioned target remote sensing image.

[0069] After obtaining the feature parameter values ​​of each vegetation region, several vegetation regions can be identified as forest belt regions based on these feature parameter values ​​and through conditional judgment. The forest belt region refers to the area where the forest belt is located in the aforementioned remote sensing image of the target.

[0070] It should be noted that, since the Normalized Difference Vegetation Index (NDVI) can integrate spectral information from the red and near-infrared bands, and the Enhanced Vegetation Index (EVI) can integrate spectral information from the blue, red, and near-infrared bands, both vegetation indices are highly sensitive to forest belt areas and non-forest belt areas.

[0071] Therefore, the characteristic parameters in the embodiments of the present invention include the normalized vegetation index and the enhanced vegetation index, so that the forest belt area can be more accurately determined in each vegetation area based on the normalized vegetation index value and the enhanced vegetation index value of the vegetation area.

[0072] It should be noted that forest belts typically exhibit strip-like or sheet-like shape characteristics and rough texture characteristics. Therefore, the feature parameters in the embodiments of the present invention may further include aspect ratios that reflect linear characteristics and / or information that reflects rough texture characteristics, thereby further improving the accuracy of determining forest belt areas in each vegetation region based on the aspect ratio and / or information entropy value of the vegetation region.

[0073] It should be noted that if, based on the characteristic parameter values ​​of each vegetation area, after conditional judgment, no vegetation area can be identified as a forest belt area, then the farmland forest coverage rate of the monitored area can be determined to be 0.

[0074] Step 104: Based on the forest belt area and farmland area, obtain the farmland forest network rate of the area to be monitored.

[0075] Among them, farmland areas are determined based on the geographical information of farmland in the area to be monitored.

[0076] It should be noted that, in the embodiments of the present invention, farmland refers to land used for planting crops, including crop planting areas and the associated field and field road areas.

[0077] It should be noted that the geographical information of farmland in the monitoring area may include the location information and regional information of the farmland in the monitoring area. The location information describes the location of the farmland in the monitoring area; the regional information describes the area covered by the farmland in the monitoring area, including the area and boundaries of the area.

[0078] In this embodiment of the invention, the geographical information of farmland in the area to be monitored can be obtained in a variety of ways. For example, the geographical information can be obtained based on user input; or, the geographical information can be obtained through information query.

[0079] After obtaining the geographic information of farmland in the area to be monitored, the area where the farmland is located in the remote sensing image of the target can be identified as the farmland area based on the mapping relationship between the area to be monitored and the aforementioned target remote sensing image, as well as the aforementioned geographic information.

[0080] Specifically, after identifying forest belt areas and farmland areas in the target remote sensing image of the area to be monitored, farmland forest belt areas can be identified in each forest belt area based on the farmland areas. Then, based on the area of ​​the aforementioned farmland forest belt areas and farmland areas, the farmland forest network rate of the area to be monitored can be obtained through numerical calculation.

[0081] Among them, the farmland-forest belt area refers to the area where the farmland-forest belt is located in the above-mentioned remote sensing image; the farmland-forest belt refers to the forest belt located in farmland.

[0082] This invention, through its embodiments, determines vegetation regions within the target remote sensing image based on spectral information. Then, based on the corresponding characteristic parameter values ​​of these vegetation regions, it identifies forest belt regions. Finally, based on these forest belt regions and farmland regions in the target remote sensing image, it obtains the farmland forest coverage rate of the monitored area. The characteristic parameters include the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EDI), as well as aspect ratio and / or information entropy. Since the farmland regions are determined based on the geographical information of the farmland in the monitored area, this method can more accurately and efficiently monitor the farmland forest coverage rate over large areas. It can reduce the cost of monitoring farmland forest coverage rate, improve the informatization and automation levels of farmland forest coverage rate monitoring, and provide data support for the scientific and efficient implementation of farmland protection, strengthened soil management, and improved farmland quality.

[0083] Based on the above embodiments, the vegetation area is determined in the target remote sensing image based on the spectral information of the target remote sensing image, including: obtaining the normalized vegetation index value of the target remote sensing image based on the spectral information of the red light band and near-infrared band of the target remote sensing image.

[0084] Specifically, based on the spectral information of the red and near-infrared bands of each pixel in the aforementioned target remote sensing image, the normalized vegetation index value of each pixel in the aforementioned target remote sensing image can be obtained through formula (1):

[0085]

[0086] Wherein, NDVI represents the normalized vegetation index; B4 represents the reflectance value in the near-infrared band; and B3 represents the reflectance value in the red band.

[0087] It should be noted that the normalized vegetation index (NDI) value of the aforementioned target remote sensing image includes the NDI value of each pixel in the aforementioned target remote sensing image.

[0088] Based on the normalized vegetation index value and predefined segmentation parameter values ​​of the target remote sensing image, the target remote sensing image is segmented at multiple scales to obtain the multi-scale segmentation results of the target remote sensing image.

[0089] It should be noted that surface information exhibits different characteristics at different scales (temporal or spatial span). Pixel-based information extraction methods are all performed at the same scale, namely the spatial resolution of the image. However, because pixel-based information extraction methods cannot take into account both macroscopic and microscopic features of ground objects, the information extraction effect is poor even in images with abundant information (high-resolution images), and many fragmented areas may appear.

[0090] Multi-scale segmentation is an object-oriented classification method. When performing multi-scale segmentation on images, maximizing the heterogeneity among segmented objects is crucial. This can be achieved by selecting an appropriate threshold based on image features and then merging similar spectral information generated during the segmentation process. Multi-scale segmentation makes images easier to segment and results in more precise segmentation ranges. Because ground feature images are complex and have distinct hierarchical structures, and image observation is conducted at different scales, multi-scale segmentation can more comprehensively reflect object characteristics.

[0091] Therefore, after obtaining the normalized vegetation index (NDI) value of the target remote sensing image, this embodiment of the invention can perform multi-scale segmentation of the target remote sensing image based on the NDI value of each pixel in the target remote sensing image and predefined segmentation parameters to obtain the multi-scale segmentation result of the target remote sensing image.

[0092] It should be noted that the segmentation parameters in the embodiments of the present invention include a scale factor, a shape factor, and a compactness factor. The segmentation parameter values ​​can be predefined based on prior knowledge and / or actual conditions. The embodiments of the present invention do not impose specific limitations on the above-mentioned segmentation parameter values.

[0093] Optionally, the scale factor can range from 95 to 105; the shape factor can range from 0.15 to 0.25; and the compactness factor can range from 0.35 to 0.45.

[0094] Preferably, in this embodiment of the invention, the scale factor is 100; the shape factor is 0.2; and the compactness factor is 0.4.

[0095] Based on the multi-scale segmentation results, vegetation areas are identified in the target remote sensing image.

[0096] Specifically, after obtaining the multi-scale segmentation results of the aforementioned target remote sensing image, each region included in the multi-scale segmentation results can be identified as a vegetation region.

[0097] This invention, based on the spectral information of the red and near-infrared bands of the target remote sensing image, obtains the normalized vegetation index (NDI) value of the target remote sensing image. Then, by using the NDI value and predefined segmentation parameters, the target remote sensing image is segmented at multiple scales. This allows for more accurate and efficient identification of vegetated areas within the target remote sensing image, thereby enabling more accurate identification of forest belt areas. It ensures significant heterogeneity between vegetated and non-vegetated areas in the target remote sensing image, further improving the accuracy of farmland shelterbelt monitoring.

[0098] Based on the content of the above embodiments, the forest belt area is determined in the vegetation area based on the feature parameter value corresponding to the vegetation area, including: when the feature parameter value corresponding to the vegetation area meets the preset conditions, the vegetation area is determined as the forest belt area.

[0099] The preset conditions include a normalized vegetation index value greater than a first preset value and an enhanced vegetation index value greater than a second preset value; when the feature parameter includes aspect ratio, the preset conditions also include an aspect ratio value greater than a third preset value; when the feature parameter includes information entropy, the preset conditions also include an information entropy value greater than a fourth preset value.

[0100] The first preset value ranges from 0.37 to 0.41; the second preset value ranges from 0.29 to 0.33; the third preset value ranges from 1.6 to 2.0; and the fourth preset value ranges from 0.53 to 0.57.

[0101] Specifically, after obtaining the characteristic parameter values ​​of each vegetation area, for any vegetation area, it can be determined whether the characteristic parameter values ​​of the vegetation area meet the preset conditions.

[0102] If the characteristic parameter values ​​of a vegetation area meet the preset conditions, the vegetation area can be identified as a forest belt area.

[0103] For example, if the feature parameters include normalized vegetation index, enhanced vegetation index and aspect ratio, and it is determined that the normalized vegetation index of the vegetation area is greater than the first preset value, the enhanced vegetation index of the vegetation area is greater than the second preset value, and the aspect ratio of the vegetation area is greater than the third preset value, then the vegetation area can be identified as a forest belt area.

[0104] For example, if the feature parameters include normalized vegetation index, enhanced vegetation index, aspect ratio, and information entropy, and it is determined that the normalized vegetation index of the vegetation area is greater than a first preset value, the enhanced vegetation index of the vegetation area is greater than a second preset value, the aspect ratio of the vegetation area is greater than a third preset value, and the information entropy of the vegetation area is greater than a fourth preset value, then the vegetation area can be identified as a forest belt area.

[0105] It should be noted that the first preset value, the second preset value, the third preset value and the fourth preset value in the embodiments of the present invention can be predefined based on prior knowledge and / or actual situation.

[0106] Preferably, the first preset value can be 0.39; the second preset value can be 0.31; the third preset value can be 1.8; and the fourth preset value can be 0.55.

[0107] Optionally, in this embodiment of the invention, the operation of determining whether the characteristic parameter values ​​of each vegetation area meet the preset conditions can be completed in the eCognition software.

[0108] This invention, by identifying a vegetation area as a forest belt area when the characteristic parameter values ​​corresponding to the vegetation area meet preset conditions, can more accurately and efficiently determine the forest belt area within the vegetation area.

[0109] Based on the above embodiments, the farmland forest network rate of the area to be monitored is obtained based on the forest belt area and the farmland area, including: determining the farmland forest belt area in the forest belt area based on the positional relationship between the farmland area and the forest belt area.

[0110] Specifically, after identifying forest belt areas and farmland areas in the target remote sensing image of the area to be monitored, forest belt areas whose positional relationship with the farmland areas meets the preset positional relationship can be identified as farmland-forest belt areas based on the positional relationship between the farmland areas and forest belt areas.

[0111] It should be noted that the above-mentioned preset positional relationships can be predefined based on prior knowledge and / or actual circumstances.

[0112] Optionally, the aforementioned preset location relationship can be that the forest belt area is located within a farmland area. For example, if any forest belt area is located within a certain farmland area, then that forest belt area can be identified as a farmland forest belt area.

[0113] It should be noted that if, based on the location relationship between farmland and forest belt areas, neither forest belt area can be identified as a field forest belt area, then the farmland forest coverage rate of the area to be monitored can be determined to be 0.

[0114] The farmland forest belt area and the farmland area are used to obtain the farmland forest network rate of the area to be monitored.

[0115] Specifically, after identifying the farmland-forest belt area in the aforementioned target remote sensing image, the area of ​​the farmland-forest belt area and the area of ​​the farmland area can be obtained. Then, based on the area of ​​the farmland-forest belt area and the area of ​​the farmland area, the farmland-forest network coverage rate of the area to be monitored can be calculated using formula (2):

[0116]

[0117] Wherein, FSR represents the rate of farmland afforestation in the area to be monitored; A1 represents the area of ​​farmland forest belt; A2 represents the area of ​​farmland; k is the control coefficient; in this embodiment of the invention, k is determined to be 15 based on prior knowledge and actual conditions.

[0118] Optionally, in this embodiment of the invention, the operation of determining the farmland-forest belt area within the forest belt area based on the positional relationship between the farmland area and the forest belt area, and obtaining the farmland-forest network rate of the area to be monitored based on the area of ​​the farmland-forest belt area and the area of ​​the farmland area, can be completed in eCognition software and ENVI software.

[0119] This invention, by basing its embodiments on the locational relationship between farmland and forest belt areas, can more accurately and efficiently determine farmland-forest belt areas within forest belt areas. Furthermore, based on the area of ​​farmland and the area of ​​farmland-forest belt areas, it can more accurately and efficiently obtain the farmland-forest network rate of the area to be monitored.

[0120] Based on the above embodiments, the characteristic parameter values ​​corresponding to the vegetation area are obtained, including: obtaining the normalized vegetation index value corresponding to the vegetation area based on the spectral information of the red light band and near-infrared band of the target remote sensing image; obtaining the enhanced vegetation index value corresponding to the vegetation area based on the spectral information of the blue light band, red light band and near-infrared band of the target remote sensing image; obtaining the aspect ratio value corresponding to the vegetation area based on the length and width values ​​of the vegetation area when the characteristic parameter includes aspect ratio; and obtaining the information entropy value corresponding to each vegetation area based on the gray-level co-occurrence matrix corresponding to the target remote sensing image when the characteristic parameter includes information entropy.

[0121] Specifically, after identifying several vegetation areas in the remote sensing image of the target area to be monitored, for any vegetation area, the normalized vegetation index value of each pixel in the vegetation area can be calculated by formula (1) based on the spectral information of the red light band and near-infrared band of each pixel in the vegetation area.

[0122] After obtaining the normalized vegetation index (NVI) value of each pixel in the vegetation region, the corresponding NVI value for the entire vegetation region can be obtained through numerical calculation based on the NVI value of each pixel in the vegetation region. For example, the average value of the NVI values ​​of each pixel in the vegetation region can be obtained through numerical calculation and used as the corresponding NVI value for the entire vegetation region.

[0123] For any vegetation area, the enhanced vegetation index value of each pixel in the vegetation area can be calculated using formula (3) based on the spectral information of the blue light band, red light band, and near-infrared band of each pixel in the vegetation area:

[0124]

[0125] Among them, EVI represents the enhanced vegetation index value; B1 represents the reflectance value of the blue light band; B3 represents the reflectance value of the red light band; and B4 represents the reflectance value of the near-infrared band.

[0126] After obtaining the enhanced vegetation index (EVI) value of each pixel in the vegetation region, the EVI value corresponding to the vegetation region can be obtained through numerical calculation based on the EVI value of each pixel in the vegetation region. For example, the average value of the EVI value of each pixel in the vegetation region can be obtained through numerical calculation as the EVI value corresponding to the vegetation region.

[0127] When the feature parameter includes the aspect ratio, for any vegetation area, the aspect ratio can be calculated using formula (4) based on the length and width values ​​of the vegetation area:

[0128]

[0129] Where γ represents the aspect ratio of the vegetation area; l represents the length of the vegetation area; and w represents the width of the vegetation area.

[0130] It should be noted that the length-to-width ratio of long and narrow forest belts is usually relatively large.

[0131] When the feature parameter includes information entropy, for any vegetation area, the information entropy value of the texture of the target remote sensing image object can be calculated using formula (5) based on the gray-level co-occurrence matrix corresponding to the target remote sensing image:

[0132]

[0133] Where ENT represents the information entropy value of the texture of the target remote sensing image object; M and N represent the number of rows and columns of the gray-level co-occurrence matrix, respectively; P(i,j) represents the probability value of the i-th row and j-th column in the gray-level co-occurrence matrix.

[0134] After obtaining the information entropy value ENT of the texture of the target remote sensing image object, the information entropy value ENT of the texture of the target remote sensing image object can be determined as the feature parameter value corresponding to the vegetation area.

[0135] It should be noted that the richer the information of a variable, the greater the uncertainty, and the higher the information entropy value will be. Therefore, due to the chaotic canopy spectrum and obvious granular phenomenon, the information entropy value of forest belts is usually relatively large, while the information entropy value of farmland areas with relatively uniform spectra is usually relatively small.

[0136] Optionally, the operation of obtaining the feature parameter values ​​corresponding to the vegetation area can be completed in the eCognition software.

[0137] Based on the above embodiments, after determining the farmland-forest belt area in the forest belt area based on the location relationship between the farmland area and the forest belt area, the method further includes: obtaining the geographical information of the preset level administrative region in the area to be monitored.

[0138] It should be noted that the administrative regions in this embodiment of the invention are regions divided into levels for ease of administrative management. The levels of administrative regions may include provincial, prefecture, county, and township levels.

[0139] It should be noted that the preset level can be determined based on actual circumstances and needs in the embodiments of the present invention. For example, the preset level can be the county level.

[0140] It should be noted that the geographic information of an administrative region can include its location information and its regional information. The location information describes the location of the administrative region; the regional information describes the area covered by the administrative region, including its area and boundaries.

[0141] Specifically, in this embodiment of the invention, geographic information of a preset level administrative region in the area to be monitored can be obtained in various ways. For example, geographic information of a preset level administrative region in the area to be monitored can be obtained based on user input; or, geographic information of a preset level administrative region in the area to be monitored can be obtained through information query.

[0142] It is understandable that the number of pre-defined administrative regions in the area to be monitored can be one or more.

[0143] Based on the geographic information of the pre-defined administrative regions, the farmland forest belt areas and farmland areas corresponding to the pre-defined administrative regions are determined.

[0144] Specifically, after obtaining the geographic information of the pre-defined administrative regions in the area to be monitored, the area covered by each pre-defined administrative region in the target remote sensing image can be determined based on the mapping relationship between the area to be monitored and the aforementioned target remote sensing image, as well as the pre-defined administrative regions in the area to be monitored.

[0145] After determining the area covered by each preset level of administrative region in the aforementioned target remote sensing image, for any preset level of administrative region, the farmland-forest belt area and farmland area within the area covered by the aforementioned administrative region can be identified as the farmland-forest belt area and farmland area corresponding to the aforementioned administrative region.

[0146] Based on the corresponding farmland forest belt area and farmland area of ​​the preset level administrative region, obtain the farmland forest network rate of the preset level administrative region.

[0147] Specifically, after determining the farmland forest belt area and farmland area corresponding to each preset level of administrative region, the farmland forest network rate of each preset level of administrative region can be calculated by formula (2) based on the area of ​​the farmland forest belt area and farmland area corresponding to each preset level of administrative region.

[0148] This invention, through its embodiment, determines the farmland-forest belt area and farmland area corresponding to the preset-level administrative region based on the geographical information of the region to be monitored. Then, based on the farmland-forest belt area and farmland area corresponding to the preset-level administrative region, it calculates the farmland-forest network rate of the preset-level administrative region. This enables monitoring of the forest network rate at the administrative region level, and provides data support for the scientific and efficient implementation of farmland protection, soil management, and improvement of farmland quality in each administrative region.

[0149] Based on the above embodiments, after obtaining the farmland shelterbelt network rate of the preset level administrative region, the method further includes: based on the farmland shelterbelt network rate of the preset level administrative region, color-marking the area corresponding to the preset level administrative region in the target remote sensing image, and adding annotation information to the target remote sensing image to obtain a thematic map of the farmland shelterbelt network rate distribution of the area to be monitored.

[0150] The labeling information includes at least one of the following: map title, legend, north arrow, coordinate grid, scale, and exported parameters.

[0151] Specifically, after obtaining the farmland shelterbelt coverage rate for each preset level of administrative region, the farmland shelterbelt coverage level for each preset level of administrative region can be determined based on the farmland shelterbelt coverage rate for each preset level of administrative region.

[0152] For example, if the rate of farmland shelterbelt networking in any pre-defined administrative region is greater than 80%, the farmland shelterbelt networking level of that pre-defined administrative region can be determined as high-level; if the rate of farmland shelterbelt networking in any pre-defined administrative region is less than or equal to 80% but greater than 60%, the farmland shelterbelt networking level of that pre-defined administrative region can be determined as medium-level; if the rate of farmland shelterbelt networking in any pre-defined administrative region is not greater than 60%, the farmland shelterbelt networking level of that pre-defined administrative region can be determined as low-level.

[0153] After determining the farmland shelterbelt network level for each preset administrative region, the corresponding area of ​​each preset administrative region can be color-coded in the target remote sensing image according to the correspondence between different farmland shelterbelt network levels and different colors. The annotation information can then be added to the target remote sensing image to obtain a thematic map of the farmland shelterbelt network distribution rate in the area to be monitored.

[0154] Optionally, obtaining the thematic map of the farmland shelterbelt coverage rate in the area to be monitored can be done in ArcGIS software.

[0155] To facilitate understanding of the farmland shelterbelt network monitoring method provided by this invention, an example is provided below to illustrate the farmland shelterbelt network monitoring method provided by this invention.

[0156] The remote sensing image used in this example is the Gaofen-2 (GF-2) image, which has a spatial resolution of 0.8m and uses the WGS84 coordinate system.

[0157] The remote sensing image in this example may include spectral information in four bands: B1 band (blue light band), B3 band (red light band), B4 band (near-infrared band), and G band (green light band).

[0158] Figure 2 This is the second flowchart illustrating the method for monitoring farmland shelterbelt coverage provided by this invention. For example... Figure 2 As shown, the method for monitoring the rate of farmland shelterbelt network provided by the present invention includes the following steps: acquiring the original remote sensing image of the area to be monitored, performing data preprocessing on the original remote sensing image to obtain the target remote sensing image of the area to be monitored;

[0159] Obtain the normalized vegetation index value of the target remote sensing image;

[0160] Based on the normalized vegetation index value of the target remote sensing image, the target remote sensing image is segmented at multiple scales. Based on the multi-scale segmentation results, several vegetation areas are identified in the target remote sensing image.

[0161] Obtain the normalized vegetation index, enhanced vegetation index, aspect ratio, and information entropy value for each vegetation region.

[0162] Based on the normalized vegetation index, enhanced vegetation index, aspect ratio, and information entropy value corresponding to each vegetation region, several vegetation regions are identified as forest belt regions.

[0163] Obtain geographic information of farmland in the area to be monitored;

[0164] Based on the geographic information of farmland in the area to be monitored, farmland areas are identified in the target remote sensing image;

[0165] Based on the locational relationship between farmland areas and forest belt areas, several forest belt areas were identified as farmland-forest belt areas;

[0166] Obtain geographic information of pre-defined administrative regions within the area to be monitored;

[0167] Based on the geographic information of the pre-defined administrative regions, determine the farmland areas and farmland forest belt areas corresponding to the pre-defined administrative regions;

[0168] Based on the farmland areas and farmland forest belt areas corresponding to the preset level of administrative regions, the farmland forest network rate of the preset level of administrative regions is calculated;

[0169] Based on the farmland shelterbelt coverage rate of the preset administrative regions, a thematic map of the farmland shelterbelt coverage rate distribution in the areas to be monitored is generated.

[0170] The method for monitoring farmland shelterbelt coverage provided by this invention can quickly and accurately obtain this new indicator of arable land quality, thus providing data support for the scientific management of arable land quality and efficient supervision of farmland shelterbelt projects. This method not only provides high-precision and rapid data acquisition of farmland shelterbelt coverage but also requires relatively low investment, possessing high scientific research value and practical potential, and is easily promoted and applied in agricultural departments, farmland protection departments, and other institutions.

[0171] The farmland forest coverage rate monitoring method provided by this invention is based on high spatial resolution remote sensing imagery. According to the spectral and textural characteristics of the forest belts, it comprehensively utilizes four feature parameters: NDVI, EVI, aspect ratio, and information entropy. It adopts an object-oriented automatic recognition algorithm suitable for accurate recognition of high-resolution images to identify forest belt areas across a large area. The method is then calculated according to agricultural standards for calculating farmland forest coverage rate, thereby achieving high-precision and rapid remote sensing monitoring of farmland forest coverage rate across a large area.

[0172] Figure 3 This is a schematic diagram of the farmland shelterbelt network monitoring device provided by the present invention. The following is in conjunction with... Figure 3 The farmland shelterbelt network rate monitoring device provided by this invention is described below. The farmland shelterbelt network rate monitoring device described below can be referred to in correspondence with the farmland shelterbelt network rate monitoring method provided by this invention described above. For example... Figure 3 As shown, the device includes: a data acquisition module 301, a first area determination module 302, a second area determination module 303, and a farmland shelterbelt network rate calculation module 304.

[0173] The data acquisition module 301 is used to acquire the geographic information of farmland in the area to be monitored;

[0174] The first region determination module 302 is used to determine vegetation regions in the target remote sensing image based on the spectral information of the target remote sensing image.

[0175] The second region determination module 303 is used to obtain the feature parameter values ​​corresponding to the vegetation region and determine the forest belt region in the vegetation region based on the feature parameter values ​​corresponding to the vegetation region.

[0176] Farmland shelterbelt network rate calculation module 304 is used to obtain the farmland shelterbelt network rate of the area to be monitored based on the forest belt area and the farmland area in the target remote sensing image;

[0177] The characteristic parameters include the normalized vegetation index and the enhanced vegetation index, as well as the aspect ratio and / or information entropy; the farmland area is determined based on the geographical information of the farmland in the area to be monitored.

[0178] Specifically, the data acquisition module 301, the first area determination module 302, the second area determination module 303, and the farmland shelterbelt network rate calculation module 304 are electrically connected.

[0179] The farmland forest coverage rate monitoring device in this embodiment of the invention determines the vegetation area in the target remote sensing image based on the spectral information of the target remote sensing image, and then determines the forest belt area in the vegetation area based on the characteristic parameter values ​​corresponding to the vegetation area. Based on the forest belt area and the farmland area in the target remote sensing image, the farmland forest coverage rate of the area to be monitored is obtained. The characteristic parameters include the normalized vegetation index and the enhanced vegetation index, as well as the aspect ratio and / or information entropy. The farmland area is determined based on the geographical information of the farmland in the area to be monitored. This device can more accurately and efficiently monitor the farmland forest coverage rate of large areas, reduce the cost of farmland forest coverage rate monitoring, improve the informatization and automation level of farmland forest coverage rate monitoring, and provide data support for the scientific and efficient implementation of farmland protection, strengthening soil management, and improving farmland quality.

[0180] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440. The processor 410, communication interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a method for monitoring farmland afforestation rate. This method includes: acquiring a target remote sensing image of the area to be monitored; determining vegetation areas in the target remote sensing image based on the spectral information of the target remote sensing image; acquiring feature parameter values ​​corresponding to the vegetation areas; determining forest belt areas in the vegetation areas based on the feature parameter values ​​corresponding to the vegetation areas; and acquiring the farmland afforestation rate of the area to be monitored based on the forest belt areas and farmland areas in the target remote sensing image. The feature parameters include the normalized vegetation index (NVI) and the enhanced vegetation index (VEI), as well as the aspect ratio and / or information entropy. The farmland areas are determined based on the geographical information of the farmland in the area to be monitored.

[0181] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0182] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the farmland afforestation rate monitoring method provided by the above methods. The method includes: acquiring a target remote sensing image of the area to be monitored; determining vegetation areas in the target remote sensing image based on the spectral information of the target remote sensing image; acquiring feature parameter values ​​corresponding to the vegetation areas; determining forest belt areas in the vegetation areas based on the feature parameter values ​​corresponding to the vegetation areas; and acquiring the farmland afforestation rate of the area to be monitored based on the forest belt areas and farmland areas in the target remote sensing image. The feature parameters include the normalized vegetation index and the enhanced vegetation index, as well as the aspect ratio and / or information entropy. The farmland areas are determined based on the geographical information of the farmland in the area to be monitored.

[0183] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the farmland afforestation rate monitoring method provided by the above methods. This method includes: acquiring a target remote sensing image of the area to be monitored; determining vegetation regions in the target remote sensing image based on spectral information; acquiring feature parameter values ​​corresponding to the vegetation regions; determining forest belt regions in the vegetation regions based on the feature parameter values ​​corresponding to the vegetation regions; and acquiring the farmland afforestation rate of the area to be monitored based on the forest belt regions and farmland regions in the target remote sensing image. The feature parameters include a normalized vegetation index (NVI) and an enhanced vegetation index (EVI), as well as aspect ratio and / or information entropy. The farmland regions are determined based on the geographical information of the farmland in the area to be monitored.

[0184] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the rate of farmland shelterbelt network formation, characterized in that, include: Acquire remote sensing images of the target area to be monitored; Based on the spectral information of the target remote sensing image, vegetation areas are determined in the target remote sensing image; Obtain the feature parameter values ​​corresponding to the vegetation area, and determine the forest belt area in the vegetation area based on the feature parameter values ​​corresponding to the vegetation area; Based on the forest belt area and the farmland area in the target remote sensing image, the farmland forest network rate of the area to be monitored is obtained; The characteristic parameters include the normalized vegetation index and the enhanced vegetation index, as well as the aspect ratio and / or information entropy; the farmland area is determined based on the geographical information of the farmland in the area to be monitored. The step of determining the forest belt area within the vegetation area based on the characteristic parameter values ​​corresponding to the vegetation area includes: If the characteristic parameter values ​​corresponding to the vegetation area meet the preset conditions, the vegetation area is determined as the forest belt area; The preset conditions include a normalized vegetation index value greater than a first preset value and an enhanced vegetation index value greater than a second preset value; when the feature parameter includes an aspect ratio, the preset conditions also include an aspect ratio value greater than a third preset value; when the feature parameter includes information entropy, the preset conditions also include an information entropy value greater than a fourth preset value. The first preset value ranges from 0.37 to 0.41; the second preset value ranges from 0.29 to 0.33; the third preset value ranges from 1.6 to 2.0; and the fourth preset value ranges from 0.53 to 0.

57. The process of obtaining the farmland forest network rate of the monitored area based on the forest belt area and the farmland area includes: Based on the locational relationship between the farmland area and the forest belt area, the farmland forest belt area is determined within the forest belt area; Based on the area of ​​the farmland forest belt region and the area of ​​the farmland region, the farmland forest network rate of the area to be monitored is obtained.

2. The method for monitoring farmland shelterbelt coverage rate according to claim 1, characterized in that, The step of determining vegetation areas in the target remote sensing image based on the spectral information of the target remote sensing image includes: Based on the spectral information of the target remote sensing image in the red and near-infrared bands, the normalized vegetation index value of the target remote sensing image is obtained. Based on the normalized vegetation index value and predefined segmentation parameter values ​​of the target remote sensing image, the target remote sensing image is segmented at multiple scales to obtain the multi-scale segmentation result of the target remote sensing image. Based on the multi-scale segmentation results, the vegetation area is determined in the target remote sensing image.

3. The method for monitoring farmland shelterbelt coverage rate according to claim 1 or 2, characterized in that, The step of obtaining the feature parameter values ​​corresponding to the vegetation area includes: Based on the spectral information of the target remote sensing image in the red and near-infrared bands, the normalized vegetation index (NDI) value corresponding to the vegetation area is obtained. Based on the spectral information of the target remote sensing image in the blue, red, and near-infrared bands, the enhanced vegetation index (EVI) value corresponding to the vegetation area is obtained. When the feature parameter includes aspect ratio, the aspect ratio value corresponding to the vegetation area is obtained based on the length and width values ​​of the vegetation area. When the feature parameter includes information entropy, the information entropy value corresponding to each vegetation area is obtained based on the gray-level co-occurrence matrix corresponding to the target remote sensing image.

4. The method for monitoring farmland shelterbelt coverage rate according to claim 1, characterized in that, After determining the farmland-forest belt area within the forest belt area based on the locational relationship between the farmland area and the forest belt area, the method further includes: Obtain the geographic information of the administrative regions at a preset level in the area to be monitored; Based on the geographic information of the preset level administrative region, determine the farmland forest belt area and farmland area corresponding to the preset level administrative region; Based on the corresponding farmland forest belt area and farmland area of ​​the preset level administrative region, the farmland forest network rate of the preset level administrative region is obtained.

5. The method for monitoring farmland shelterbelt coverage rate according to claim 4, characterized in that, After obtaining the farmland shelterbelt coverage rate of the preset administrative region, the method further includes: Based on the farmland shelterbelt network rate of the preset level administrative region, the region corresponding to the preset level administrative region is color-marked in the target remote sensing image, and annotation information is added to the target remote sensing image to obtain a thematic map of the farmland shelterbelt network rate distribution of the area to be monitored. The annotation information includes at least one of the following: map title, legend, north arrow, coordinate grid, scale bar, and derived parameters.

6. A monitoring device for farmland shelterbelt coverage rate, characterized in that, The data acquisition module is used to acquire remote sensing images of the target area to be monitored. The first region determination module is used to determine vegetation regions in the target remote sensing image based on the spectral information of the target remote sensing image; The second region determination module is used to obtain the feature parameter values ​​corresponding to the vegetation region, and determine the forest belt region in the vegetation region based on the feature parameter values ​​corresponding to the vegetation region. The farmland shelterbelt network rate calculation module is used to obtain the farmland shelterbelt network rate of the area to be monitored based on the shelterbelt area and the farmland area in the target remote sensing image; The characteristic parameters include the normalized vegetation index and the enhanced vegetation index, as well as the aspect ratio and / or information entropy; the farmland area is determined based on the geographical information of the farmland in the area to be monitored. The second region determination module is specifically used for: If the characteristic parameter values ​​corresponding to the vegetation area meet the preset conditions, the vegetation area is determined as the forest belt area; The preset conditions include a normalized vegetation index value greater than a first preset value and an enhanced vegetation index value greater than a second preset value; when the feature parameter includes an aspect ratio, the preset conditions also include an aspect ratio value greater than a third preset value; when the feature parameter includes information entropy, the preset conditions also include an information entropy value greater than a fourth preset value. The first preset value ranges from 0.37 to 0.41; the second preset value ranges from 0.29 to 0.33; the third preset value ranges from 1.6 to 2.0; and the fourth preset value ranges from 0.53 to 0.

57. The farmland shelterbelt network rate calculation module is specifically used for: Based on the locational relationship between the farmland area and the forest belt area, the farmland forest belt area is determined within the forest belt area; Based on the area of ​​the farmland forest belt region and the area of ​​the farmland region, the farmland forest network rate of the area to be monitored is obtained.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the farmland shelterbelt network monitoring method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the farmland shelterbelt network monitoring method as described in any one of claims 1 to 5.

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