Land desertification monitoring method, device and equipment for soil improvement

Abstract

The invention relates to a land desertification monitoring method, a land desertification monitoring device and land desertification monitoring equipment for soil improvement, and relates to the technical field of computers. The method comprises the following steps: determining land desertification reference information of a first region to indicate a land desertification condition of the first region under a cooperative relationship between vegetation coverage and a surface type, and then determining a second region of which the land desertification condition meets a condition from the first region according to the land desertification reference information of the first region, the vegetation index of the second area is obtained according to the visible light image of the second area so as to indicate the vegetation coverage obtained after vegetation enhancement, and then the land desertification type of the second area and the corresponding soil improvement strategy are determined. The desertification process of the land can be accurately evaluated, and then the accuracy of the soil improvement strategy is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and particularly relates to a land desertification monitoring method, device and equipment for soil improvement. BACKGROUND

[0002] Land desertification is an ecological degradation process that land productivity declines, vegetation gradually thins out or disappears, and finally evolves into a desert state under the combined action of natural factors (such as drought, wind erosion) and human factors (such as overgrazing, excessive reclamation and deforestation). At present, soil improvement can curb the degradation process and restore soil fertility and land function.

[0003] In the related art, researchers evaluate the land desertification process based on the vegetation index of the land, and then determine the applicable soil improvement means. However, the single index of the vegetation index is difficult to comprehensively evaluate the land desertification process, resulting in inaccurate evaluation results, and then affecting the accuracy of the soil improvement means. SUMMARY

[0004] The present application provides a land desertification monitoring method, device and equipment for soil improvement, and the technical solution of the present application is as follows:

[0005] In a first aspect, the present application provides a land desertification monitoring method for soil improvement, and the method comprises the following steps:

[0006] According to the satellite remote sensing image of the first region, a first vegetation index and a surface albedo of the first region are obtained, the first vegetation index indicates the vegetation coverage of the first region, and the surface albedo indicates the surface type of the first region;

[0007] The first vegetation index and the surface albedo are fitted to obtain land desertification reference information of the first region, the land desertification reference information indicates the land desertification condition of the first region under the synergistic relationship between the vegetation coverage and the surface type;

[0008] According to the land desertification reference information, at least one second region with a land desertification condition meeting a condition is determined from the first region;

[0009] According to the first visible light image of each second region, a second vegetation index of each second region is obtained, the second vegetation index indicates the vegetation coverage obtained after the vegetation of the second region is enhanced;

[0010] According to the land desertification reference information and the second vegetation index of each second region, a land desertification type of each second region is determined;

[0011] According to the land desertification type of each second region, a soil improvement strategy of each second region is determined.

[0012] In some implementations, the fitting the first vegetation index and the surface albedo to obtain the land desertification reference information of the first region comprises:

[0013] The fitting the first vegetation index and the surface albedo according to formula (1) and formula (2) to obtain the land desertification reference information;

[0014] Surface albedo A = a x vegetation index N + b; a x p = -1 (1)

[0015] Land desertification reference information = p x vegetation index N - surface albedo A (2)

[0016] Wherein, a is a fitting coefficient, b is a preset parameter, p is a weight balance coefficient, surface albedo A is the surface albedo of the first region, and vegetation index N is the first vegetation index.

[0017] In some implementations, the second vegetation index of each second region is obtained according to the first visible light image of each second region, comprising:

[0018] Based on the conversion relationship between the red-green-blue (RGB) color model and the hue-saturation-lightness (HSL) color model, the first visible light image of the second region is processed to obtain a second visible light image of the second region, wherein the first visible light image belongs to the RGB color model, and the second visible light image belongs to the HSL color model.

[0019] The vegetation in the second visible light image is enhanced to obtain the second vegetation index of the second region.

[0020] In some implementations, the second vegetation index of the second region is obtained by enhancing the vegetation in the second visible light image, comprising:

[0021] The hue of the second visible light image is adjusted based on formula (3) and formula (4) to enhance the vegetation in the second visible light image to obtain the second vegetation index of the second region.

[0022]

[0023] Wherein, H HSL (x, y) is the original hue of the second visible light image, H 调整(x, y) is the adjusted color tone of the second visible light image, AH 暗部 and AH 亮部 are the adjustment coefficients of dark and bright parts, respectively, m 暗部 and m 亮部 are the adjustment intensities of dark and bright parts, respectively; n 暗部 and n 亮部 are the brightness threshold values for determining the starting points of dark and bright part adjustments, respectively, a 暗部 and a 亮部 are the amplitudes of the adjustment functions of dark and bright parts, respectively, b 暗部 and b 亮部 indicate the decay rates of the adjustment functions of dark and bright parts, respectively; g 暗部 and g 亮部 indicate the adjustment offsets of dark and bright parts, respectively, L 阈值 is the segmentation brightness value for distinguishing the dark part and the bright part adjustment regions, the dark part refers to the green region in the second visible light image, and the bright part refers to the yellow region in the second visible light image.

[0024] In some implementations, the determining, according to the land desertification reference information and the second vegetation index of each second region, of a land desertification type of each second region comprises:

[0025] If the land desertification reference information and the second vegetation index of the second region both indicate that the land desertification type of the second region is a first type, the land desertification type of the second region is determined to be the first type.

[0026] If the land desertification reference information indicates that the land desertification type of the second region is the first type and the second vegetation index of the second region indicates that the land desertification type of the second region is a second type, the land desertification type of the second region is determined to be the second type.

[0027] In some implementations, the land desertification type comprises one or more of the following: potential desertification, light desertification, moderate desertification, severe desertification, extreme desertification, sandy desertification, vegetation degradation type desertification, and salinization desertification; and the soil improvement strategy comprises one or more of the following: physical improvement, chemical improvement, biological improvement, and agronomic control.

[0028] In a second aspect, the present application provides a land desertification monitoring device for soil improvement, the device comprising:

[0029] The first obtaining module is configured to obtain a first vegetation index and a surface albedo of the first region according to a satellite remote sensing image of the first region, the first vegetation index indicating a vegetation coverage of the first region, and the surface albedo indicating a surface type of the first region.

[0030] The fitting module is configured to fit the first vegetation index and the surface albedo to obtain land desertification reference information of the first region, the land desertification reference information indicating a land desertification condition of the first region under a synergistic relationship between the vegetation coverage and the surface type.

[0031] The first determining module is configured to determine at least one second region with a land desertification condition meeting a condition from the first region according to the land desertification reference information.

[0032] The second obtaining module is configured to obtain a second vegetation index of each second region according to a first visible light image of each second region, the second vegetation index indicating a vegetation coverage obtained after vegetation of the second region is enhanced.

[0033] The second determining module is configured to determine a land desertification type of each second region according to the land desertification reference information and the second vegetation index of each second region.

[0034] The third determining module is configured to determine a soil improvement strategy of each second region according to the land desertification type of each second region.

[0035] In some implementations, the fitting module is configured to:

[0036] fit the first vegetation index and the surface albedo according to formula (1) and formula (2) to obtain the land desertification reference information.

[0037] Surface albedo A = a x vegetation index N + b; a x p = -1 (1)

[0038] Land desertification reference information = p x vegetation index N - surface albedo A (2)

[0039] wherein a is a fitting coefficient, b is a preset parameter, p is a weight balance coefficient, the surface albedo A is the surface albedo of the first region, and the vegetation index N is the first vegetation index.

[0040] In some implementations, the second obtaining module is configured to:

[0041] perform image processing on the first visible light image of the second region based on a conversion relationship between a red-green-blue (RGB) color model and a hue-saturation-lightness (HSL) color model, to obtain a second visible light image of the second region, wherein the first visible light image belongs to the RGB color model, and the second visible light image belongs to the HSL color model;

[0042] enhance vegetation in the second visible light image to obtain a second vegetation index of the second region.

[0043] In some implementations, the second obtaining module is configured to:

[0044] adjust a hue of the second visible light image based on the formula (3) and the formula (4) to enhance the vegetation in the second visible light image, to obtain a second vegetation index of the second region;

[0045]

[0046] wherein H HSL (x, y) is the original hue of the second visible light image, H 调整 (x, y) is the adjusted hue of the second visible light image, ΔH 暗部 and ΔH 亮部 are adjustment coefficients of dark parts and bright parts, m 暗部 and m 亮部 are adjustment intensities of the dark parts and the bright parts; n 暗部 and n 亮部 are luminance threshold values for determining starting points of the dark parts and the bright parts, α 暗部 and α 亮部 are amplitudes of adjustment functions of the dark parts and the bright parts, β 暗部 and β 亮部 indicate decay speeds of the adjustment functions of the dark parts and the bright parts; γ 暗部 and γ 亮部 indicate adjustment offsets corresponding to the dark parts and the bright parts, L 阈值 is a segmentation luminance value for distinguishing the dark parts and the bright parts, the dark parts refer to green regions in the second visible light image, and the bright parts refer to yellow regions in the second visible light image.

[0047] In some implementations, the second determining module is configured to:

[0048] if the land desertification reference information and the second vegetation index of the second region both indicate that a land desertification type of the second region is a first type, determine that the land desertification type of the second region is the first type.

[0049] If the land desertification reference information indicates that the land desertification type of the second region is the first type and the second vegetation index of the second region indicates that the land desertification type of the second region is a second type, it is determined that the land desertification type of the second region is the second type.

[0050] In some implementations, the land desertification type includes one or more of the following: potential desertification, light desertification, moderate desertification, severe desertification, extreme desertification, sandy desertification, vegetation degradation type desertification, salinization desertification; and the soil improvement strategy includes one or more of the following: physical improvement, chemical improvement, biological improvement, and agronomic regulation.

[0051] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, the memory storing program code, and the processor being configured to execute the program code to implement the above-mentioned land desertification monitoring method for soil improvement.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium, comprising: when program code in the computer-readable storage medium is executed by a processor of an electronic device, the electronic device is enabled to execute the above-mentioned land desertification monitoring method for soil improvement.

[0053] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is a schematic diagram of an implementation environment of a land desertification monitoring method for soil improvement;

[0055] Figure 2 is a flowchart of a land desertification monitoring method for soil improvement;

[0056] Figure 3 is a schematic diagram of a land desertification monitoring method for soil improvement;

[0057] Figure 4 is a structural schematic diagram of a land desertification monitoring device for soil improvement;

[0058] Figure 5 is a structural schematic diagram of an electronic device. DETAILED DESCRIPTION

[0059] In order to make ordinary people in the art better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings.

[0060] The data involved in the present application can be data authorized by the user or data sufficiently authorized by all parties.

[0061] Figure 1 is a schematic diagram of an implementation environment of a land desertification monitoring method for soil improvement, see Figure 1 The implementation environment includes: the implementation environment includes: a terminal 101 and a server 102.

[0062] The terminal 101 can be at least one of a smart phone, a smart watch, a desktop computer, a laptop computer, a virtual reality terminal, an augmented reality terminal, a wireless terminal, and a laptop computer. The terminal 101 has a communication function and can access a wired network or a wireless network. The terminal 101 can generally refer to one of a plurality of terminals, and the present embodiment is only exemplified by the terminal 101. Those skilled in the art can know that the number of the above-mentioned terminals can be more or less. Illustratively, the terminal 101 can install and run an application program for providing original data for the land desertification monitoring method for soil improvement provided by the present application, such as satellite remote sensing images, visible light images, etc., which are not limited.

[0063] The server 102 can be a standalone physical server, a server cluster or a distributed file system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms. In some embodiments, the server 102 is directly or indirectly connected to the terminal 101 through wired or wireless communication, and the present embodiment is not limited in this regard. Optionally, the number of the above-mentioned server 102 can be more or less, and the present embodiment is not limited in this regard. Of course, the server 102 can also include other functional servers to provide more comprehensive and diversified services. In the present application, the server 102 undertakes the main computing work, and the terminal 101 undertakes the secondary computing work.

[0064] Figure 2 is a flowchart of a land desertification monitoring method for soil improvement. As Figure 2 shown, the method is executed by the server and includes the following steps 201 to 206.

[0065] 201, the server obtains a first vegetation index and a surface albedo of a first region according to a satellite remote sensing image of the first region, the first vegetation index indicating the vegetation coverage of the first region, and the surface albedo indicating the surface type of the first region.

[0066] The first region can be a suburb, a rural area, a desert, or a designated area set by humans, and the application does not limit the geographical location and area of the first region. Illustratively, the server obtains a satellite remote sensing image of the first region, and after preprocessing (such as radiation calibration, atmospheric correction, splicing and cutting, etc.) of the satellite remote sensing image, obtains a first vegetation index and a surface albedo of the first region.

[0067] The first vegetation index is, for example, a normalized difference vegetation index (NDVI), which is obtained by using a general calculation formula of NDVI. Generally, the lower the NDVI, the sparser the vegetation, and the higher the risk of desertification. Therefore, the first vegetation index can be used to indicate the vegetation coverage of the first region.

[0068] The surface albedo is the ratio of the total reflected radiation flux to the incident radiation flux for the ground. Generally, the higher the surface albedo, the brighter the ground (such as bare sand and desert), and the more severe the desertification. Therefore, the surface albedo can be used to indicate the surface type of the first region.

[0069] 202. The server fits the first vegetation index and the surface albedo to obtain land desertification reference information of the first region, which indicates the land desertification of the first region under the synergistic relationship between the vegetation coverage and the surface type.

[0070] The server fits the first vegetation index and the surface albedo of the first region according to the following formula (1) and formula (2) to obtain the land desertification reference information:

[0071] Surface albedo A = a x vegetation index N + b; a x p = -1 (1)

[0072] Land desertification reference information = p x vegetation index N - surface albedo A (2)

[0073] wherein a is a fitting coefficient, b is a preset parameter, p is a weight balance coefficient, the surface albedo A is the surface albedo of the first region, and the vegetation index N is the first vegetation index of the first region.

[0074] The first vegetation index and the surface albedo of the first region are linearly regressed and fitted by the above formula (1), which reflects the synergistic relationship between the vegetation coverage and the surface type of the first region, provides a slope parameter for the calculation of the land desertification reference information, and then the land desertification reference information is calculated by formula (2). Exemplarily, the greater the value of the land desertification reference information, the lower the vegetation coverage, the more bare soil, and the more serious the land desertification.

[0075] In the present application, the first region can be divided into a plurality of sub-regions, for example, the first region is divided into a plurality of sub-regions according to the vegetation coverage of the first region, for another example, the first region is evenly divided into a plurality of sub-regions according to the area of the first region, and the like, which are not limited in the present application. Exemplarily, the land desertification reference information of the first region includes the land desertification reference information corresponding to each sub-region in the first region.

[0076] 203. The server determines at least one second region with a land desertification condition meeting a condition from the first region according to the land desertification reference information.

[0077] Wherein, the first region can be divided into a plurality of sub-regions, the server determines at least one sub-region with a land desertification condition meeting a condition from the first region according to the land desertification information of the first region, the determined sub-region is named as the second region in the present embodiment, and the number of the second region is not limited in the present application. The condition can be set according to business requirements, for example, according to the value of the land desertification reference information, the land desertification condition is divided into five levels of potential, mild, moderate, severe and extreme desertification, and the condition is set as the land desertification condition belonging to severe and extreme desertification.

[0078] Through the above steps 201 to 203, two core indexes of vegetation index and surface albedo are introduced for the satellite remote sensing image of the first region, breaking through the limitation that a single vegetation index cannot distinguish between vegetation degradation and desertification driven by surface type, and through fitting the synergistic relationship between the two, the causes and degree of land desertification can be more accurately described. Based on this, the second region with more serious desertification is further determined from the first region, which provides technical support for subsequent development of soil improvement strategies for the second region.

[0079] 204. The server obtains a second vegetation index of each second region according to a first visible light image of each second region, the second vegetation index indicating the vegetation coverage after the vegetation of the second region is enhanced.

[0080] The server obtains a first visible light image of the second region, where the first visible light image is an image obtained by photographing the second region by using a camera, for example, an image obtained by photographing the second region by using a camera on an unmanned aerial vehicle flying at a height of 300 meters.

[0081] In this embodiment, the server performs image processing on the first visible light image of the second region based on a conversion relationship between a Red-Green-Blue (RGB) color model and a Hue-Saturation-Lightness (HSL) color model, to obtain a second visible light image of the second region, and enhances vegetation in the second visible light image to obtain a second vegetation index of the second region.

[0082] The first visible light image belongs to the RGB color model, and the second visible light image belongs to the HSL color model. That is, the server converts the first visible light image from the RGB color model to the HSL color model to obtain the second visible light image. In this way, the green feature of the vegetation can be more accurately extracted through the second visible light image, because the HSL color model can provide more intuitive color description than the RGB color model. In addition, the HSL color model can better meet the needs of computer vision applications and provide more intuitive color representation.

[0083] For example, the server enhances the vegetation in the second visible light image to obtain the second vegetation index of the second region, including: adjusting the hue of the second visible light image based on the following formulas (3) and (4) to enhance the vegetation in the second visible light image to obtain the second vegetation index of the second region.

[0084]

[0085] H HSL (x, y) is the original hue of the second visible light image, H 调整 (x, y) is the hue of the adjusted second visible light image, that is, the second vegetation index, ΔH 暗部 and ΔH 亮部 are the adjustment coefficients of the dark part and the bright part, m 暗部 and m 亮部 are the adjustment intensities of the dark part and the bright part; n 暗部 and n 亮部 are the brightness thresholds for determining the starting points of the dark part and the bright part, α 暗部 and α 亮部 are the amplitudes of the adjustment functions of the dark part and the bright part, β 暗部 and β 亮部 indicate the decay rates of the adjustment functions of the dark part and the bright part; γ暗部 and γ 亮部 respectively indicate the adjustment offset corresponding to the dark part and the bright part, L 阈值 is a split brightness value for distinguishing the dark part and the bright part adjustment region, the dark part refers to a green region in the second visible light image, and the bright part refers to a yellow region in the second visible light image.

[0086] In some implementations, the second vegetation index of the second region can be obtained according to the preprocessed second visible light image, wherein the preprocessing refers to adjusting the saturation and brightness components to k times of their original values, k is a value greater than 1 and less than 2, for example, k is 1.4. In this way, the sensitivity of green vegetation response can be improved, and the contrast between vegetation and bare soil can be optimized.

[0087] In the above manner, the green tone of the dark part in the second visible light image can be enhanced, and the yellow tone of the bright part can be reduced, and the contrast between vegetation and bare soil is enhanced. Therefore, the second vegetation index can accurately capture the vegetation features and clearly define the edges of the vegetation canopy, effectively avoiding the problems of high light detail loss and color distortion under extreme lighting conditions, achieving accurate extraction of shallow grassland vegetation on bare land, and significantly improving the accuracy and robustness of vegetation extraction.

[0088] 205、The server determines the land desertification type of each second region according to the land desertification reference information and the second vegetation index of each second region.

[0089] For any second region, the server determines the land desertification type of the second region according to the land desertification reference information and the second vegetation index corresponding to the second region. Exemplarily, the land desertification type includes one or more of the following: potential desertification, mild desertification, moderate desertification, severe desertification, extreme desertification, sandy desertification, vegetation degradation type desertification, salinization desertification (or saline-alkali desertification), etc., without limitation, and the land desertification type can be divided according to the monitoring needs of land desertification and the needs of soil improvement.

[0090] In some implementations, if the land desertification reference information corresponding to the second region and the second vegetation index of the second region both indicate that the land desertification type of the second region is the first type, the land desertification type of the second region is determined to be the first type; if the land desertification reference information indicates that the land desertification type of the second region is the first type and the second vegetation index of the second region indicates that the land desertification type of the second region is the second type, the land desertification type of the second region is determined to be the second type. That is, when the land desertification types indicated by the land desertification reference information corresponding to the second region and the second vegetation index are the same, the land desertification type is determined to be the type indicated by the two types of information; when the land desertification types indicated by the land desertification reference information corresponding to the second region and the second vegetation index are different, the land desertification type is determined to be the type indicated by the second vegetation index. It should be understood that the land desertification reference information is obtained according to a satellite remote sensing image and belongs to a land desertification type evaluated from a macroscopic level, while the second vegetation index is obtained according to a visible light image and belongs to a land desertification type further evaluated from a microscopic level, which can assist in evaluating the land desertification type and improve the accuracy of the land desertification type.

[0091] 206. The server determines a soil improvement strategy for each second region according to the land desertification type of each second region.

[0092] For any one second region, the server determines a soil improvement strategy for it according to the land desertification type of the second region. Exemplarily, the soil improvement strategy includes one or more of the following: physical improvement, chemical improvement, biological improvement, and agronomic control. That is, one or more ways can be flexibly combined to formulate the soil improvement strategy according to the land desertification type.

[0093] For example, if the land desertification type of a certain second region is sandy desertification, the soil improvement strategy includes: physical improvement: laying grass square sand barrier to fix sand, and locally mixing clay soil to improve soil particle structure; biological improvement: transplanting deep-rooted drought-tolerant shrubs such as Haloxylon ammodendron and Caragana in the sand barrier, and inoculating arbuscular mycorrhizal fungi to promote root sand fixation and nutrient absorption; chemical improvement: applying soil improvement agent; and agronomic control: adopting strip planting combined with stubble retention and no-tillage to reduce wind erosion and fertilize the soil.

[0094] For example, if the desertification type of a certain second region is salinization desertification, the soil improvement strategy includes: physical improvement: build a subsurface pipe salt drainage system to lower the groundwater level, and cover the surface with thick straw to inhibit evaporation and salt return; biological improvement: plant salt-tolerant plants such as shrubs and herbs to improve soil organic matter and structure; chemical improvement: apply soil conditioners to adjust soil alkalinity, such as solid waste-based desulfurization gypsum-biochar composite modifier (the specific ratio can be set according to actual needs, which is not limited in the present application); agronomic control: use drip irrigation technology under the film to accurately supply water, and plant salt-tolerant crops to gradually reduce the soil salt content.

[0095] Reference is made below to Figure 3 The soil desertification monitoring method for soil improvement shown in steps 201 to 206 is summarized. Figure 3 is a schematic diagram of a soil desertification monitoring method for soil improvement. As shown in Figure 3 , the server first acquires a satellite remote sensing image of a first region, and acquires a first vegetation index and a surface albedo of the first region according to the satellite remote sensing image, and fits the first vegetation index and the surface albedo to obtain reference information of land desertification of the first region; then, the server determines a plurality of second regions meeting the conditions from the first region according to the reference information of land desertification of each sub-region in the first region (for example, each sub-region is divided by averaging the first region); thereafter, a first visible light image of each second region is acquired, the first visible light image is converted from an RGB color model to an HSL color model to obtain a second visible light image, the vegetation in the second visible light image is enhanced to obtain a second vegetation index of the second region; finally, the type of land desertification of the second region is comprehensively evaluated according to the second vegetation index of the second region and the reference information of land desertification, and a corresponding soil improvement strategy is formulated for the second region. As can be seen, through this macro and micro coordination, the combination of surface albedo and different types of vegetation index, the desertification process of the land can be accurately evaluated, and the accuracy of the soil improvement strategy is improved.

[0096] Figure 4 is a structural schematic diagram of a land desertification monitoring device for soil improvement. Referring to Figure 4 , the device includes a first acquisition module 401, a fitting module 402, a first determination module 403, a second acquisition module 404, a second determination module 405, and a third determination module 406.

[0097] The first acquisition module 401 is configured to acquire a first vegetation index and a surface albedo of a first region according to a satellite remote sensing image of the first region, the first vegetation index indicating the vegetation coverage of the first region, and the surface albedo indicating the surface type of the first region.

[0098] The fitting module 402 is configured to fit the first vegetation index and the surface albedo to obtain land desertification reference information of the first region, the land desertification reference information indicating a land desertification condition of the first region under a synergistic relationship between vegetation coverage and a surface type.

[0099] The first determining module 403 is configured to determine at least one second region with a land desertification condition meeting a condition from the first region according to the land desertification reference information.

[0100] The second acquiring module 404 is configured to acquire a second vegetation index of each second region according to the first visible light image of each second region, the second vegetation index indicating a vegetation coverage obtained after vegetation in the second region is enhanced.

[0101] The second determining module 405 is configured to determine a land desertification type of each second region according to the land desertification reference information and the second vegetation index of each second region.

[0102] The third determining module 406 is configured to determine a soil improvement strategy of each second region according to the land desertification type of each second region.

[0103] In some implementations, the fitting module 402 is configured to:

[0104] fit the first vegetation index and the surface albedo according to the formula (1) and the formula (2) to obtain the land desertification reference information.

[0105] The surface albedo A = a x vegetation index N + b; a x p = -1 (1)

[0106] The land desertification reference information = p x vegetation index N - surface albedo A (2)

[0107] wherein a is a fitting coefficient, b is a preset parameter, p is a weight balance coefficient, the surface albedo A is the surface albedo of the first region, and the vegetation index N is the first vegetation index.

[0108] In some implementations, the second acquiring module 404 is configured to:

[0109] perform image processing on the first visible light image of the second region based on a conversion relationship between a red-green-blue (RGB) color model and a hue-saturation-lightness (HSL) color model to obtain a second visible light image of the second region, wherein the first visible light image belongs to the RGB color model, and the second visible light image belongs to the HSL color model.

[0110] The vegetation in the second visible light image is enhanced to obtain a second vegetation index of the second region.

[0111] In some implementations, the second acquisition module 404 is configured to:

[0112] The hue of the second visible light image is adjusted based on the formula (3) and the formula (4) to enhance the vegetation in the second visible light image, and a second vegetation index of the second region is obtained.

[0113]

[0114] wherein H HSL (x, y) is the original hue of the second visible light image, H 调整 (x, y) is the adjusted hue of the second visible light image, AH 暗部 and AH 亮部 are the adjustment coefficients of the dark part and the bright part, m 暗部 and m 亮部 are the adjustment intensities of the dark part and the bright part; n 暗部 and n 亮部 are the luminance thresholds for determining the starting points of the dark part and the bright part adjustment, a 暗部 and a 亮部 are the amplitudes of the adjustment functions of the dark part and the bright part, b 暗部 and b 亮部 indicate the decay rates of the adjustment functions of the dark part and the bright part; g 暗部 and g 亮部 indicate the corresponding adjustment offsets of the dark part and the bright part, L 阈值 is a segmentation luminance value for distinguishing the dark part and the bright part adjustment region, the dark part refers to a green region in the second visible light image, and the bright part refers to a yellow region in the second visible light image.

[0115] In some implementations, the second determination module 405 is configured to:

[0116] If the land desertification reference information and the second vegetation index of the second region both indicate that the land desertification type of the second region is the first type, the land desertification type of the second region is determined to be the first type.

[0117] If the land desertification reference information indicates that the land desertification type of the second region is the first type and the second vegetation index of the second region indicates that the land desertification type of the second region is the second type, the land desertification type of the second region is determined to be the second type.

[0118] In some implementations, the land desertification types include one or more of the following: potential desertification, light desertification, moderate desertification, severe desertification, extreme desertification, sandy desertification, vegetation degradation type desertification, salinization desertification; and the soil improvement strategies include one or more of the following: physical improvement, chemical improvement, biological improvement, agronomic regulation.

[0119] Through the above device, the land desertification reference information of the first region is determined according to the satellite remote sensing image of the first region to indicate the land desertification condition of the first region under the synergistic relationship between the vegetation coverage and the ground type, and then the second region with a land desertification condition meeting the condition is determined from the first region according to the land desertification reference information of the first region, the vegetation index of the second region is obtained according to the visible light image of the second region to indicate the vegetation coverage obtained after the vegetation is enhanced, and then the land desertification type of the second region and the corresponding soil improvement strategy are determined. Through the macro and micro synergistic combination of the ground albedo and different types of vegetation index, the land desertification process can be accurately evaluated, and the accuracy of the soil improvement strategy is improved.

[0120] Figure 5 is a structural schematic diagram of an electronic device. The electronic device can be configured as the server described above, wherein the electronic device 500 can have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) 501 and one or more memories 502, wherein the one or more memories 502 store at least one program code, the at least one program code is loaded and executed by the one or more processors 501 to implement the processes performed by the server in the land desertification monitoring method for soil improvement provided in the above various method embodiments. Of course, the electronic device 500 can also have a wired or wireless network interface, a keyboard, and an input and output interface, and other components for realizing the functions of the device, which are not described here.

[0121] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0122] It is to be understood that the application is not limited to the precise construction already described above and shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application should only be limited by the claims appended hereto.

Claims

1. A method for monitoring land desertification for soil improvement, characterized in that, The method includes: Based on satellite remote sensing images of the first region, a first vegetation index and a surface albedo are obtained for the first region. The first vegetation index indicates the vegetation coverage of the first region, and the surface albedo indicates the surface type of the first region. By fitting the first vegetation index and the surface albedo, land desertification reference information for the first region is obtained. The land desertification reference information indicates the land desertification situation in the first region under the synergistic relationship between vegetation cover and surface type. Based on the land desertification reference information, at least one second region that meets the conditions for land desertification is determined from the first region; Based on the first visible light image of each second region, a second vegetation index is obtained for each second region, the second vegetation index indicating the vegetation coverage obtained after enhancing the vegetation of the second region; Based on the aforementioned land desertification reference information and the second vegetation index of each second region, the land desertification type of each second region is determined; Based on the land desertification type of each second region, soil improvement strategies for each second region are determined.

2. The method according to claim 1, characterized in that, The process of fitting the first vegetation index and the surface albedo to obtain land desertification reference information for the first region includes: The land desertification reference information is obtained by fitting the first vegetation index and the surface albedo according to formulas (1) and (2); Surface albedo A =a × vegetation index N +b;a×p=-1 (1) Land desertification reference information = p × vegetation index N - Surface albedo A (2) Where a is the fitting coefficient, b is the preset parameter, p is the weight balance coefficient, surface albedo A is the surface albedo of the first region, and vegetation index N is the first vegetation index.

3. The method according to claim 1, characterized in that, The step of obtaining the second vegetation index for each second region based on the first visible light image of each second region includes: Based on the conversion relationship between the RGB color model and the HSL color model, the first visible light image of the second region is processed to obtain the second visible light image of the second region. The first visible light image belongs to the RGB color model, and the second visible light image belongs to the HSL color model. The vegetation in the second visible light image is enhanced to obtain the second vegetation index of the second region.

4. The method according to claim 3, characterized in that, The enhancement of vegetation in the second visible light image to obtain a second vegetation index for the second region includes: The hue of the second visible light image is adjusted based on formulas (3) and (4) to enhance the vegetation in the second visible light image, thereby obtaining the second vegetation index of the second region. H 调整 (x,y)=H HSL (x,y)+ΔH 暗部 ×T 暗部 (L HSL (x,y))-ΔH 亮部 ×T 亮部 (L HSL (x,y)) Among them, H HSL (x, y) represents the original hue of the second visible light image, H 调整 (x, y) represents the hue of the adjusted second visible light image, ΔH 暗部 and ΔH 亮部 These are the adjustment coefficients for shadows and highlights, respectively, m 暗部 and m 亮部 Adjust the intensity of the shadows and highlights respectively; n 暗部 and n 亮部 The brightness thresholds α are used to adjust the starting point for determining the dark and bright areas, respectively. 暗部 and α 亮部 β represents the magnitude of the adjustment functions for the dark and bright areas, respectively. 暗部 and β 亮部 These indicate the decay rate of the adjustment function for dark and bright areas, respectively; γ 暗部 and γ 亮部 L indicates the adjustment offset for the dark and bright areas respectively. 阈值 It is a segmented brightness value that distinguishes between dark and bright areas. The dark area refers to the green area in the second visible light image, and the bright area refers to the yellow area in the second visible light image.

5. The method according to claim 1, characterized in that, The step of determining the land desertification type for each second region based on the land desertification reference information and the second vegetation index for each second region includes: If both the land desertification reference information and the second vegetation index of the second region indicate that the land desertification type of the second region is the first type, then the land desertification type of the second region is determined to be the first type. If the land desertification reference information indicates that the land desertification type of the second region is the first type and the second vegetation index of the second region indicates that the land desertification type of the second region is the second type, then the land desertification type of the second region is determined to be the second type.

6. The method according to claim 1, characterized in that, The types of land desertification include one or more of the following: potential desertification, mild desertification, moderate desertification, severe desertification, extreme desertification, sandy desertification, vegetation degradation-type desertification, and salinization desertification; the soil improvement strategies include one or more of the following: physical improvement, chemical improvement, biological improvement, and agronomic regulation.

7. A land desertification monitoring device for soil improvement, characterized in that, The device includes: The first acquisition module is used to acquire a first vegetation index and a surface albedo of the first region based on satellite remote sensing images of the first region. The first vegetation index indicates the vegetation coverage of the first region, and the surface albedo indicates the surface type of the first region. The fitting module is used to fit the first vegetation index and the surface albedo to obtain land desertification reference information for the first region. The land desertification reference information indicates the land desertification situation in the first region under the synergistic relationship between vegetation cover and surface type. The first determining module is used to determine, based on the land desertification reference information, at least one second region from the first region that meets the conditions for land desertification. The second acquisition module is used to acquire a second vegetation index for each second region based on a first visible light image of each second region. The second vegetation index indicates the vegetation coverage obtained after enhancing the vegetation in the second region. The second determining module is used to determine the land desertification type of each second region based on the land desertification reference information and the second vegetation index of each second region; The third determination module is used to determine the soil improvement strategy for each second region based on the land desertification type of each second region.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing program code, and the processor executing the program code to implement the land desertification monitoring method for soil improvement as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, When the program code in the computer-readable storage medium is executed by the processor of the electronic device, the electronic device is able to perform the land desertification monitoring method for soil improvement as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor of an electronic device, enables the electronic device to perform the land desertification monitoring method for soil improvement as described in any one of claims 1 to 6.

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