Method and device for estimating carbon sequestration of pasture land by remote sensing, electronic equipment and storage medium

Abstract

The present application provides a kind of pasture carbon sequestration amount remote sensing estimation method, device, electronic equipment and storage medium, it is related to remote sensing technical field, the method comprises: obtaining the time series of remote sensing data in target period in target area;Based on remote sensing data, obtain the regional information of pasture region in target area;Based on remote sensing data and regional information, obtain the time series of enhanced vegetation index value in pasture region in target period and the cutting frequency of pasture region in target period;Based on enhanced vegetation index value and cutting frequency, obtain the estimated value of carbon sequestration amount of pasture region in target period.The present application provides a kind of pasture carbon sequestration amount remote sensing estimation method, device, electronic equipment and storage medium, can more accurately, more efficient realization of the identification of pasture region, pasture region cutting time node and the estimation of times, can improve the accuracy and efficiency of pasture region carbon sequestration estimation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing, and in particular to a method and device for estimating carbon fixation of a pasture, an electronic device, and a storage medium. BACKGROUND

[0002] Pasture generally refers to grass or other herbaceous plants, such as corn and alfalfa, for feeding livestock. A pasture is land used for growing pasture for livestock. In order to meet the growing demand for livestock and ecological protection, the area of artificial pasture is rapidly increasing. Accurate estimation of the carbon fixation of a pasture is of great significance for the production management, oxygen release and carbon fixation, nutrient maintenance, and biodiversity protection of the pasture.

[0003] In the prior art, remote sensing technology can be used to estimate the carbon fixation of a pasture. However, there are many factors that affect the accuracy of the estimation of the carbon fixation of a pasture, which makes it difficult to accurately and efficiently estimate the carbon fixation of a pasture based on the prior art.

[0004] Therefore, how to more accurately and efficiently estimate the carbon fixation of a pasture is a technical problem that needs to be solved in the field. SUMMARY

[0005] The present application provides a method and device for estimating carbon fixation of a pasture, an electronic device, and a storage medium, to solve the problem that the carbon fixation of a pasture cannot be accurately and efficiently estimated in the prior art, and to more accurately and efficiently estimate the carbon fixation of a pasture.

[0006] The present application provides a method for estimating carbon fixation of a pasture, comprising:

[0007] acquiring time-series remote sensing data of a target region in a target period;

[0008] based on the remote sensing data, acquiring regional information of a pasture region in the target region;

[0009] based on the remote sensing data and the regional information, acquiring time-series enhanced vegetation index values of the pasture region in the target period and the number of harvests of the pasture region in the target period;

[0010] based on the enhanced vegetation index values and the number of harvests, acquiring an estimated value of the carbon fixation of the pasture region in the target period.

[0011] According to the method for estimating carbon fixation of a pasture provided by the present application, when the remote sensing data includes remote sensing images of multiple time nodes of the target region in the target period, the method comprises:

[0012] acquire normalized vegetation index of each pixel in remote sensing image of each time node of the target region;

[0013] acquire regional information of the grassland region based on the normalized vegetation index of each pixel.

[0014] According to the method for estimating the carbon fixation amount of the grassland region provided by the application, the regional information of the grassland region is acquired based on the normalized vegetation index of each pixel, which comprises:

[0015] acquire normalized vegetation index average value of the target region in the target period and crop identification index value of the target region in the target period based on the normalized vegetation index of each pixel.

[0016] in the case that the target region includes the grassland region based on the normalized vegetation index average value and the crop identification index value, determine remote sensing image of a first target time node of the target region in the remote sensing data based on the normalized vegetation index of each pixel, the first target time node is located in the vigorous growth period of the grass.

[0017] acquire regional information of the grassland region based on normalized vegetation index of each pixel in remote sensing image of the first target time node of the target region.

[0018] According to the method for estimating the carbon fixation amount of the grassland region provided by the application, the regional information of the grassland region is acquired based on the normalized vegetation index of each pixel, which comprises:

[0019] acquire enhanced vegetation index value of the grassland region in the target period and the number of harvests of the grassland region in the target period based on the remote sensing data and the regional information, which comprises:

[0020] acquire enhanced vegetation index value of each time node of the grassland region based on reflectivity of a blue band, reflectivity of a red band and reflectivity of a near-infrared band of remote sensing image of each time node of the target region and the regional information.

[0021] determine the number of harvests of the grassland region in the target period based on the enhanced vegetation index value.

[0022] determine whether the number of harvests of the grassland region in the target period is twice based on the enhanced vegetation index value of the grassland region at the first time node, the enhanced vegetation index value of the grassland region at the second time node, the enhanced vegetation index value of the grassland region at the third time node, the first time node, the second time node, and the third time node;

[0023] The first time node is a time node corresponding to a maximum value in the enhanced vegetation index values of the grassland region at the time nodes.

[0024] The second time node is a time node corresponding to a minimum value in the enhanced vegetation index values of the grassland region at the time nodes later than the first time node.

[0025] The third time node is a time node corresponding to a minimum value in the enhanced vegetation index values of the grassland region at the time nodes earlier than the first time node.

[0026] According to the method for estimating the carbon fixation amount of a grassland provided by the application, after determining whether the number of harvests of the grassland region in the target period is twice, the method further comprises:

[0027] In a case where it is determined that the number of harvests of the grassland region in the target period is not twice, determine whether the number of harvests of the grassland region in the target period is thrice based on the enhanced vegetation index value of the grassland region at a fourth time node, the enhanced vegetation index value of the grassland region at a fifth time node, and the enhanced vegetation index value of the grassland region at a sixth time node.

[0028] The fourth time node is a time node corresponding to a maximum value in the enhanced vegetation index values of the grassland region at the time nodes later than the second time node.

[0029] The fifth time node is a time node corresponding to a minimum value in the enhanced vegetation index values of the grassland region at the time nodes later than the fourth time node.

[0030] The sixth time node is a time node corresponding to a minimum value in the enhanced vegetation index values of the grassland region at the time nodes earlier than the fourth time node.

[0031] According to the method for estimating the carbon fixation amount of a grassland provided by the application, after determining whether the number of harvests of the grassland region in the target period is thrice, the method further comprises:

[0032] In a case where it is determined that the number of harvests of the grassland region in the target period is not three, it is determined whether the number of harvests of the grassland region in the target period is one, based on the average value of the normalized difference vegetation index of the first target time node of the grassland region, the enhanced vegetation index value of the first time node of the grassland region, the enhanced vegetation index value of the first time node of the grassland region, and the enhanced vegetation index value of the second time node of the grassland region.

[0033] The application further provides a device for remotely estimating the carbon fixation amount of a grassland, comprising:

[0034] a data acquisition module configured to acquire time-series remote sensing data of a target region in a target period;

[0035] a region identification module configured to acquire region information of a grassland region in the target region based on the remote sensing data;

[0036] an information acquisition module configured to acquire time-series enhanced vegetation index values of the grassland region in the target period and the number of harvests of the grassland region in the target period based on the remote sensing data and the region information;

[0037] a carbon fixation amount estimation module configured to acquire an estimated value of the carbon fixation amount of the grassland region in the target period based on the enhanced vegetation index values and the number of harvests.

[0038] The application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for remotely estimating the carbon fixation amount of a grassland according to any one of the above embodiments when executing the program.

[0039] The application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program is executable on a processor to implement the method for remotely estimating the carbon fixation amount of a grassland according to any one of the above embodiments.

[0040] The application further provides a computer program product comprising a computer program, wherein the computer program is executable on a processor to implement the method for remotely estimating the carbon fixation amount of a grassland according to any one of the above embodiments.

[0041] The present application provides a method and device for estimating the carbon sequestration amount of a pasture area, and an electronic device and a storage medium. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 FIG. 1 is a flowchart of a method for estimating the carbon sequestration amount of a pasture area according to the present application;

[0044] Figure 2 FIG. 2 is a schematic diagram of a time node in the method for estimating the carbon sequestration amount of a pasture area according to the present application;

[0045] Figure 3 FIG. 3 is a structural schematic diagram of a device for estimating the carbon sequestration amount of a pasture area according to the present application;

[0046] Figure 4 FIG. 4 is a structural schematic diagram of an electronic device according to the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0048] In the description of the invention, it should be noted that unless otherwise specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0049] It should be noted that pasture land can generally be divided into two types of natural pasture land and artificial pasture land. Among them, the natural pasture land refers to the grassland mainly composed of naturally generated herbaceous plants for grazing or mowing; and the artificial pasture land refers to the grassland mainly composed of artificially planted pasture for grazing or mowing.

[0050] In order to meet the increasing demand of animal husbandry and the demand of ecological protection, in recent years, the area of artificial pasture land has increased rapidly.

[0051] Carbon fixation capacity refers to the amount of carbon fixed in the body during the process of absorbing and fixing carbon dioxide (CO2) and converting it into organic matter. In the ecological system, many plants convert carbon dioxide into organic carbon through photosynthesis and store it in the plant body. These organic carbons can be transmitted to other organisms through the food chain and eventually be decomposed or deposited in the soil or water. Carbon fixation capacity can be used to evaluate the carbon cycle and CO2 absorption capacity of the ecological system.

[0052] Accurate estimation of the carbon fixation capacity of the pasture land is of great significance for the production management of the pasture land, oxygen release and carbon fixation, nutrient maintenance, biodiversity protection, etc.

[0053] In the related art, the carbon fixation capacity of the pasture land can be estimated by manual statistical reporting, remote sensing image visual interpretation, and remote sensing image automatic recognition.

[0054] In the manual statistical reporting method, the carbon fixation capacity of the pasture land can be estimated according to the planting conditions of the pasture land reported by manual statistical reporting.

[0055] However, on the one hand, the consistency and objectivity of the data reported by manual statistical reporting cannot be guaranteed, and in the case of a large area of pasture land, the data reported by manual statistical reporting cannot effectively reflect the actual situation of the pasture land, resulting in low accuracy of the carbon fixation capacity estimation of the pasture land by the manual statistical reporting method; on the other hand, the updating frequency of the data reported by manual statistical reporting needs to be improved, resulting in low efficiency of the estimation of the carbon fixation capacity of the pasture land.

[0056] In the method for visual interpretation of remote sensing images, the target can be interpreted from the medium-high resolution remote sensing images, the grassland can be identified, and the area of the grassland, the time node and the number of times of harvesting of the grassland and other information can be obtained, and then the carbon fixation amount of the grassland can be estimated based on the above information.

[0057] However, on the one hand, visual interpretation of remote sensing images requires a lot of human and time costs, resulting in low efficiency of estimating the carbon fixation amount of the grassland; on the other hand, the accuracy of estimating the carbon fixation amount of the grassland by the method of visual interpretation of remote sensing images is uneven due to the influence of the visual interpretation skill of the technical personnel, and it is difficult to stably and accurately estimate the carbon fixation amount of the grassland.

[0058] In the method for automatic identification of remote sensing images, the remote sensing images can be automatically identified by machine learning, time series vegetation index extraction method, vegetation canopy water observation method and the like, and then the carbon fixation amount of the grassland can be estimated based on the identification result.

[0059] However, due to the time node and the number of times of harvesting of the grassland and other factors, the accuracy of estimating the carbon fixation amount of the grassland is greatly influenced, and it is difficult to obtain the time node and the number of times of harvesting of the grassland and other information by the above traditional automatic identification method of remote sensing, resulting in that it is difficult to accurately estimate the carbon fixation amount of the grassland based on the above traditional automatic identification method of remote sensing.

[0060] Combined with the artificial field investigation and analysis of the grassland, the area of the grassland and the number of times of harvesting of the grassland and other information can be obtained, and the estimation accuracy of the carbon fixation amount of the grassland can be improved,

[0061] However, the artificial field investigation and analysis of the grassland still requires a lot of time and human costs, resulting in low efficiency of estimating the carbon fixation amount of the grassland.

[0062] Therefore, the present application provides a method for estimating the carbon fixation amount of the grassland by remote sensing. The method for estimating the carbon fixation amount of the grassland by remote sensing provided by the present application comprehensively considers the difference of different vegetation indexes, the difference of vegetation indexes before and after harvesting of the grassland and the feasibility of improving the vegetation indexes, can realize automatic identification of the grassland, estimation of the time node and the number of times of harvesting of the grassland based on the time series remote sensing images, and then can more accurately and efficiently estimate the carbon fixation amount of the grassland based on the above information, and can provide data support for production management, oxygen release and carbon fixation, nutrient maintenance and biodiversity protection of the grassland.

[0063] Figure 1 is a flowchart of the method for estimating the carbon fixation amount of the grassland by remote sensing provided by the present application. The method for estimating the carbon fixation amount of the grassland by remote sensing provided by the present application will be described below. Figure 1 The method for estimating the carbon fixation amount of the grassland by remote sensing provided by the present application will be described below. Figure 1As shown, the method comprises the following steps: in step 101, remote sensing data of a target region in a target period is acquired.

[0064] It should be noted that the execution subject of the embodiment of the present application is a pasture land carbon fixation amount remote sensing estimation device.

[0065] It should be noted that the area in the target region where the pasture is planted can be referred to as the pasture area in the target region, and the area in the target region where the pasture is not planted can be referred to as the non-pasture area in the target region.

[0066] Specifically, the target region is the estimation object of the pasture land carbon fixation amount remote sensing estimation method provided by the present application. Based on the pasture land carbon fixation amount remote sensing estimation method provided by the present application, the carbon fixation amount of the pasture area in the target region in the target period can be estimated.

[0067] It should be noted that the target period can be determined according to prior knowledge and / or actual situation. For example, the target period can be one year before the current time. The target period is not limited in the embodiment of the present application.

[0068] In the embodiment of the present application, the remote sensing data of the target region in the target period can be acquired in various ways, for example, the above-mentioned remote sensing data can be acquired based on the input of the user, or the above-mentioned remote sensing data can also be received by the way of data query from other electronic devices; the specific way of acquiring the above-mentioned remote sensing data is not limited in the embodiment of the present application.

[0069] It should be noted that the above-mentioned remote sensing data can include remote sensing images of multiple time nodes of the target region in the target period; wherein the remote sensing image of any time node of the target region refers to the remote sensing image obtained by the remote sensing satellite after shooting the target region at the time node; the multiple time nodes are all in the target period; the remote sensing images of multiple time nodes of the target region in the above-mentioned remote sensing data are arranged in the order of the time nodes.

[0070] It should be noted that the above-mentioned remote sensing image is a medium-high resolution remote sensing image.

[0071] Optionally, in the embodiment of the present application, the original remote sensing data of the target region in the target period can be acquired in various ways. The above-mentioned original remote sensing data can include original remote sensing images of the target region acquired at multiple time nodes.

[0072] After the above-mentioned original remote sensing images are acquired, the above-mentioned original remote sensing data is image pre-processed, and the original remote sensing data after image pre-processing is determined as the above-mentioned remote sensing data.

[0073] In step 102, based on the remote sensing data, the area information of the pasture area in the target region is acquired.

[0074] Specifically, after obtaining the remote sensing data of the target region in the target period, the region information of the pasture region in the target region can be obtained based on the remote sensing data by numerical calculation, mathematical statistics, conditional judgment, deep learning, etc. The specific way of obtaining the region information of the pasture region in the target region based on the remote sensing data is not limited in the embodiments of the present application.

[0075] It should be noted that the region information in the embodiments of the present application can be represented by coordinates, latitude and longitude, etc.

[0076] As an optional embodiment, in the case that the remote sensing data includes remote sensing images of multiple time nodes of the target region in the target period, the region information of the pasture region in the target region based on the remote sensing data includes: obtaining the normalized vegetation index of each pixel in the remote sensing image of each time node of the target region in the remote sensing data.

[0077] It can be understood that, since the remote sensing data of the target region in the target period is arranged in the order of the time nodes, the time nodes can be identified by i in the embodiments of the present application. Wherein, 1≤i≤N, i∈Z; N represents the total number of the time nodes, i.e. the total number of the remote sensing images in the remote sensing data; Z represents a positive integer.

[0078] It can be understood that, since the remote sensing image is composed of pixels, the pixels in the remote sensing image can be identified by j in the embodiments of the present application. Wherein, 1≤j≤M, j∈Z; M represents the total number of the pixels in the remote sensing image. The position of the jth pixel in any remote sensing image is the same as the position of the jth pixel in another remote sensing image.

[0079] Specifically, based on the remote sensing image of the ith time node of the target region in the remote sensing data of the target region in the target period, the normalized vegetation index value NDVI of the jth pixel in the remote sensing image of the ith time node of the target region can be calculated by the following formula i，j :

[0080]

[0081] Wherein, represents the reflectivity of the jth pixel in the near-infrared band of the remote sensing image of the ith time node of the target region; represents the reflectivity of the jth pixel in the red band of the remote sensing image of the ith time node of the target region.

[0082] The normalized vegetation index (NDVI) value of each pixel in the remote sensing image of the target area at each time point can be represented as {NDVI} i，j |1≤i≤N,1≤j≤M,i∈Z,j∈Z), where Z represents a positive integer.

[0083] Regional information of pastureland areas is obtained based on the normalized vegetation index of each pixel.

[0084] Specifically, after obtaining the normalized vegetation index (NDI) value of each pixel in the remote sensing image of the target area at each time point, the regional information of grassland in the target area can be obtained through numerical calculation and condition judgment based on the NDI value of each pixel in the remote sensing image of the target area at each time point.

[0085] As an optional embodiment, the normalized vegetation index of each pixel is used to obtain regional information of the pasture area, including: based on the normalized vegetation index of each pixel, obtaining the average normalized vegetation index of the target area within the target time period and the crop identification index value of the target area within the target time period.

[0086] Specifically, after obtaining the normalized vegetation index (NDI) value of each pixel in the remote sensing image of the target area at each time point, the average NDI value of the remote sensing image of the target area at the i-th time point can be calculated based on the following formula.

[0087]

[0088] Obtain the average normalized vegetation index of the remote sensing image of the target area at the i-th time node. Then, the average normalized vegetation index of the target area within the target time period can be calculated based on the following formula.

[0089]

[0090] Obtain the average normalized vegetation index of the target area within the target time period. Then, the Crop Identification Index (CRI) value for the target area within the target time period can be calculated using the following formula:

[0091]

[0092] Based on the average value of the normalized vegetation index and the crop identification index, if the target area includes pastureland, the remote sensing image of the first target time node of the target area is determined in the remote sensing data based on the normalized vegetation index of each pixel. The first target time node is located within the vigorous growth period of pasture.

[0093] Specifically, the average normalized vegetation index of the target area within the target time period is obtained. After the Crop Identification Index (CRI) value, the normalized vegetation index (NDI) average of the target area within the target time period can be used. The Crop Identification Index (CRI) is used to make conditional judgments, and based on the results of these judgments, it is determined whether the target area includes pastureland.

[0094] Obtain the average normalized vegetation index of the target area within the target time period. After combining the Crop Identification Index (CRI) value, the average Normalized Difference Vegetation Index (NDVI) of the target area within the target time period can be determined. Whether it is not less than the first threshold a0, and whether the Crop Identification Index (CRI) value of the target area within the target time period is not less than the second threshold a1.

[0095] The average normalized vegetation index of the target area during the target period If the Crop Identification Index (CRI) value of the target area is not less than the first threshold a1 and the CRI value of the target area during the target time period is not less than the second threshold a2, it can be determined that the target area includes pastureland areas.

[0096] It should be noted that the first threshold a1 and the second threshold a2 can be determined based on actual circumstances and / or prior knowledge. The specific values ​​of the first threshold a1 and the second threshold a2 are not limited in this embodiment of the invention.

[0097] Optionally, the range of the first threshold a1 can be 0.35 < a1 < 0.9; the range of the second threshold a2 can be 0.2 < a2 < 0.5.

[0098] It is understandable that, since the normalized vegetation index can be used to reflect crop growth and nutritional information, after determining that the target area includes pasture areas, this embodiment of the invention can determine the remote sensing image of the first target time node of the target area in the remote sensing images of each time node of the target area within the target time period by means of numerical calculation and condition judgment based on the normalized vegetation index value of each pixel in the remote sensing image of the target area at each time node. The first target time node is located in the vigorous growth period of pasture.

[0099] The normalized vegetation index (NDI) of the remote sensing image of the target area at each time node is obtained based on formula (2). Subsequently, remote sensing images with a normalized vegetation index (NDI) average greater than the third threshold a2 can be identified as remote sensing images of the first target time node of the target area.

[0100] It should be noted that the third threshold a3 can be determined based on the actual situation and / or prior knowledge. The specific value of the third threshold a3 is not limited in this embodiment of the invention.

[0101] Optionally, the third threshold a3 can be in the range of 0.3 < a3 < 0.9.

[0102] It can be understood that the number of the first target time nodes can be one or more, and accordingly, the number of the remote sensing images of the target region first target time nodes can be one or more.

[0103] In the embodiment of the present application, the first target time nodes can be identified by x, wherein 1 ≤ x ≤ X, x ∈ Z; X represents the total number of the first target time nodes, i.e. the total number of the remote sensing images of the target region first target time nodes.

[0104] Based on the normalized vegetation index of each pixel in the remote sensing image of the target region first target time node, the regional information of the grassland region is obtained.

[0105] Specifically, after determining the remote sensing image of the target region first target time node in the remote sensing data, the regional information of the grassland region in the target region can be obtained by numerical calculation and conditional judgment based on the normalized vegetation index of each pixel in the remote sensing image of the target region first target time node.

[0106] For the jth pixel in the xth remote sensing image of the target region first target time node, the normalized vegetation index value NDVIj of the jth pixel in the xth remote sensing image of the target region first target time node is calculated. x，j If the normalized vegetation index value NDVIj of the jth pixel in the xth remote sensing image of the target region first target time node is not greater than the fourth threshold a4, it can be determined that the corresponding region of the jth pixel in the xth remote sensing image of the target region first target time node in the target region is the grassland region.

[0107] Optionally, the fourth threshold a4 can be in the range of 0.1 < a4 < 0.25.

[0108] It should be noted that the fourth threshold a4 in the embodiment of the present application can be determined according to actual conditions and / or prior knowledge. The specific value of the fourth threshold a4 in the embodiment of the present application is not limited.

[0109] After determining that each pixel in the remote sensing image of each target time of the target region corresponds to the grassland region or the non-grassland region in the target region, the regional information of the grassland region in the target region can be determined based on the position of the pixel corresponding to the grassland region in the remote sensing image and the position mapping relationship between the remote sensing image and the target region.

[0110] It should be noted that in the case where the number of the first target time nodes is multiple, if the jth pixel in the remote sensing image of any two first target time nodes of the target region corresponds to different region types in the target region, the region type corresponding to the jth pixel in the target region can be determined by means of probability calculation, random selection, etc.

[0111] The embodiment of the present application can more accurately and efficiently identify the pasture region in the target region based on the normalized vegetation index of each pixel in the remote sensing image of each time node of the target region.

[0112] Step 103, based on the remote sensing data and the region information, obtaining the time-series enhanced vegetation index value of the pasture region in the target period and the harvesting times of the pasture region in the target period.

[0113] Specifically, after obtaining the region information of the pasture region in the target region in the target period, the harvesting times of the pasture region in the target region in the target period can be obtained based on the above region information and the time-series remote sensing data of the target region in the target period by means of numerical calculation, mathematical statistics, conditional judgment and deep learning, etc. The specific way of obtaining the harvesting times of the pasture region in the target region in the target period based on the above remote sensing data and the above region information is not limited in the embodiment of the present application.

[0114] As an optional embodiment, based on the remote sensing data and the region information, obtaining the time-series enhanced vegetation index value of the pasture region in the target period and the harvesting times of the pasture region in the target period, comprises: based on the remote sensing data and the region information, obtaining the time-series enhanced vegetation index value of the pasture region in the target period.

[0115] Specifically, based on the time-series region information of the pasture region in the target region in the target period and the time-series remote sensing data of the target region in the target period, the time-series enhanced vegetation index value of the pasture region in the target region in the target period can be calculated by means of numerical calculation.

[0116] It can be understood that the enhanced vegetation index is more sensitive to crop canopy type, vegetation phase and canopy structure, and therefore, in the embodiment of the present application, the harvesting times of the pasture region in the target region in the target period are determined based on the time-series enhanced vegetation index value of the pasture region in the target region in the target period.

[0117] It should be noted that the time-series enhanced vegetation index value of the pasture region in the target region in the target period can include the enhanced vegetation index value of the target region at each time node.

[0118] As an optional embodiment, based on remote sensing data and regional information, the enhanced vegetation index value of the pasture area in the target time period is obtained, including: based on the reflectance of the blue band, the reflectance of the red band and the reflectance of the near-infrared band of the remote sensing image of the target area at each time node in the remote sensing data, and regional information, the enhanced vegetation index value of the pasture area at each time node is obtained.

[0119] Specifically, for the remote sensing image of the target area at the i-th time node in the temporal remote sensing data of the target area within the target time period, the enhanced vegetation index (EVI) value of the target area at the i-th time node can be calculated numerically in this embodiment of the invention. i The specific calculation formula is as follows:

[0120]

[0121] in, This represents the near-infrared reflectance of the remote sensing image of the target area at the i-th time node; Represents the reflectance of the red band in the remote sensing image of the target area at the i-th time node; G represents the reflectance of the blue band of the remote sensing image at the i-th time node of the target area; G, C1, and C2 are preset constants.

[0122] Optionally, the value of G can be between 2 and 3; the value of C1 can be between 5 and 7; and the value of C2 can be between 7 and 8.

[0123] Preferably, the value of G can be 2.5; the value of C1 can be 6; and the value of C2 can be 7.5.

[0124] Obtain the Enhanced Vegetation Index (EVI) value of the target region at the i-th time node. i Then, the Enhanced Vegetation Index (EVI) value at the i-th time node of the target area can be used as a basis. i In addition to the regional information of grassland areas within the target area during the target time period, the enhanced vegetation index (EVI′) value of the i-th time node of the aforementioned grassland area can be obtained through numerical calculation. i .

[0125] The enhanced vegetation index (EVI) value for each time point in the aforementioned grassland region can be represented as {EVI′} i |1≤i≤N,i∈Z}, where Z represents a positive integer.

[0126] Based on the values ​​of each enhanced vegetation index, the number of harvests in the pasture area during the target period is determined.

[0127] Specifically, after the enhanced vegetation index value of each time node of the above-mentioned pasture area is obtained, the number of harvesting of the above-mentioned pasture area in the target period can be determined based on the enhanced vegetation index value of each time node of the above-mentioned pasture area by means of numerical calculation and conditional judgment.

[0128] As an optional embodiment, based on the enhanced vegetation index value, the number of harvesting of the pasture area in the target period is determined, including: based on the enhanced vegetation index value of the first time node of the pasture area, the enhanced vegetation index value of the second time node of the pasture area, the enhanced vegetation index value of the third time node of the pasture area, the first time node, the second time node and the third time node, judging whether the number of harvesting of the pasture area in the target period is twice.

[0129] Among them, the first time node is the time node corresponding to the maximum value of the enhanced vegetation index value of each time node of the pasture area;

[0130] The second time node is the time node corresponding to the minimum value of the enhanced vegetation index value of each time node of the pasture area later than the first time node;

[0131] The third time node is the time node corresponding to the minimum value of the enhanced vegetation index value of each time node of the pasture area earlier than the first time node.

[0132] It should be noted that based on prior knowledge, the artificially planted pasture land will usually be harvested 1 to 3 times within 1 year.

[0133] Specifically, in the embodiment of the present application, t1 can be used to represent the first time node, t2 can be used to represent the second time node, t3 can be used to represent the third time node, EVI' (t1) can be used to represent the enhanced vegetation index value of the first time node of the above-mentioned pasture area, EVI' (t2) can be used to represent the enhanced vegetation index value of the second time node of the above-mentioned pasture area, and EVI' (t3) can be used to represent the enhanced vegetation index value of the third time node of the above-mentioned pasture area. t1 EVI' (t1) represents the enhanced vegetation index value of the first time node of the above-mentioned pasture area, EVI' (t2) represents the enhanced vegetation index value of the second time node of the above-mentioned pasture area, and EVI' (t3) represents the enhanced vegetation index value of the third time node of the above-mentioned pasture area. t2 EVI' (t2) represents the enhanced vegetation index value of the second time node of the above-mentioned pasture area, and EVI' (t3) represents the enhanced vegetation index value of the third time node of the above-mentioned pasture area. t3 EVI' (t3) represents the enhanced vegetation index value of the third time node of the above-mentioned pasture area.

[0134] Figure 2 The schematic diagram of the time node in the target period in the pasture land carbon sequestration remote sensing estimation method provided by the present application is shown in the figure. The relationship between the first time node t1, the second time node t2 and the third time node t3 can be shown as Figure 2 .

[0135] It can be understood that the enhanced vegetation index value EVI' (t1) of the first time node of the above-mentioned pasture area is the maximum value of the enhanced vegetation index value of each time node of the above-mentioned pasture area; t1 .

[0136] the enhanced vegetation index value EVI' of the second time node of the above-mentioned grassland region t2 , the minimum value of the enhanced vegetation index value of each time node of the above-mentioned grassland region later than the first time node;

[0137] the enhanced vegetation index value EVI' of the second time node of the above-mentioned grassland region t3 , the minimum value of the enhanced vegetation index value of each time node of the above-mentioned grassland region earlier than the first time node.

[0138] based on the enhanced vegetation index value EVI' of the first time node of the above-mentioned grassland region t1 , the enhanced vegetation index value EVI' of the second time node of the above-mentioned grassland region t2 , the second time node t2 and the first time node t1, the first determination index DV can be calculated by the following formula: 1R

[0139]

[0140] based on the enhanced vegetation index value EVI' of the first time node of the above-mentioned grassland region t1 , the enhanced vegetation index value EVI' of the third time node of the above-mentioned grassland region t3 , the third time node t3 and the first time node t1, the second determination index DV can be calculated by the following formula: 1L

[0141]

[0142] After obtaining the first determination index DV 1R and the second determination index DV 1L , it can be judged whether the enhanced vegetation index value EVI' of the first time node of the above-mentioned grassland region t1 , the enhanced vegetation index value EVI' of the second time node of the above-mentioned grassland region t2 , the enhanced vegetation index value EVI' of the third time node of the above-mentioned grassland region t3 , the first time node t1, the second time node t2, the third time node t3, the first determination index DV 1R and the second determination index DV 1L whether to meet the first preset condition.

[0143] The first preset condition includes: EVI' of the first time node of the above-mentioned grassland region t1 -EVI' of the second time node of the above-mentioned grassland region t2 ≥a5, EVI' of the third time node of the above-mentioned grassland region t1 -EVI' of the third time node of the above-mentioned grassland region t3 ​​≥a5, t1-t3≥a6, t2-t1≤a7 and DV 1R -DV 1L ≥a8.

[0144] Wherein, a5 represents the fifth threshold value; a6 represents the sixth threshold value; a7 represents the seventh threshold value; a8 represents the eighth threshold value.

[0145] It should be noted that the fifth threshold value a5, the sixth threshold value a6, the seventh threshold value a7 and the eighth threshold value a8 in the embodiment of the application can be determined according to actual conditions and / or prior knowledge. The specific values of the fifth threshold value a5, the sixth threshold value a6, the seventh threshold value a7 and the eighth threshold value a8 in the embodiment of the application are not limited.

[0146] Optionally, the value range of the fifth threshold value a5 can be 0.4

[0147] In the case that the first enhanced vegetation index value EVI' of the grassland region at the first time node, the second enhanced vegetation index value EVI' of the grassland region at the second time node, the third enhanced vegetation index value EVI' of the grassland region at the third time node, the first determination index DV, the second determination index DV and the third determination index EVI satisfy the first preset condition, it can be determined that the number of harvesting times of the grassland region in the target period is twice. t1 t2 t3 1R 1L 1L In the case that the first enhanced vegetation index value EVI' of the grassland region at the first time node, the second enhanced vegetation index value EVI' of the grassland region at the second time node, the third enhanced vegetation index value EVI' of the grassland region at the third time node, the first determination index DV, the second determination index DV and the third determination index EVI satisfy the first preset condition, it can be determined that the number of harvesting times of the grassland region in the target period is twice.

[0148] As an optional embodiment, after judging whether the number of harvesting times of the grassland region in the target period is twice, the method further comprises: in the case that it is determined that the number of harvesting times of the grassland region in the target period is not twice, judging whether the number of harvesting times of the grassland region in the target period is three times based on the enhanced vegetation index value of the grassland region at the fourth time node, the enhanced vegetation index value of the grassland region at the fifth time node and the enhanced vegetation index value of the grassland region at the sixth time node.

[0149] Wherein, the fourth time node is the time node corresponding to the maximum value in the enhanced vegetation index values of the grassland region at each time node later than the second time node;

[0150] The fifth time node is the time node corresponding to the minimum value in the enhanced vegetation index values of the grassland region at each time node later than the fourth time node;​​​​​

[0151] The sixth time node is a time node corresponding to a minimum value in the enhanced vegetation index values of the pasturage region at each time node earlier than the fourth time node.

[0152] Specifically, the fourth time node can be represented by t4, the fifth time node can be represented by t5, the sixth time node can be represented by t6, EVI' (t4) represents the enhanced vegetation index value of the pasturage region at the fourth time node, EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t4 EVI' (t4) represents the enhanced vegetation index value of the pasturage region at the fourth time node, EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t5 EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t6 EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node.

[0153] The fourth time node t4, the fifth time node t5, and the sixth time node t6 can be in the order shown in FIG. 4. Figure 2

[0154] EVI' (t4) represents the enhanced vegetation index value of the pasturage region at the fourth time node, EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t4 EVI' (t4) represents the enhanced vegetation index value of the pasturage region at the fourth time node, EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node.

[0155] EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t5 EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node.

[0156] EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t5 EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node.

[0157] EVI' (t4) represents the enhanced vegetation index value of the pasturage region at the fourth time node, EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t4 EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t5 2R

[0158]

[0159] EVI' (t4) represents the enhanced vegetation index value of the pasturage region at the fourth time node, EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t4 EVI' (t5) represents the enhanced vegetation index value of the pasturage region at the fifth time node, and EVI' (t6) represents the enhanced vegetation index value of the pasturage region at the sixth time node. t6 ​​​The third determination index DV and the fourth determination index DV can be calculated by the following formula 2L :

[0160]

[0161] The third determination index DV and the fourth determination index DV can be calculated by the following formula 2R and the fourth determination index DV 2L The enhanced vegetation index EVI of the grassland region at the fourth time node t4, the enhanced vegetation index EVI of the grassland region at the fifth time node t5, the enhanced vegetation index EVI of the grassland region at the sixth time node t6, the third determination index DV and the fourth determination index DV can be determined. t4 , the third determination index DV and the fourth determination index DV can be determined. t5 , the third determination index DV and the fourth determination index DV can be determined. t6 , the third determination index DV and the fourth determination index DV can be determined. 2R and the fourth determination index DV 2L whether the second preset condition can be met.

[0162] The second preset condition includes: EVI t4 -EVI t5 ≥ a5, EVI t4 -EVI t6 ≥ a5, t4-t6≥ a6, t5-t4≤ a7 and DV 2R -DV 2L ≥ a8.

[0163] After the third determination index DV and the fourth determination index DV are determined, the enhanced vegetation index EVI of the grassland region at the fourth time node t4, the enhanced vegetation index EVI of the grassland region at the fifth time node t5, the enhanced vegetation index EVI of the grassland region at the sixth time node t6, the third determination index DV and the fourth determination index DV can be determined. 2R , the third determination index DV and the fourth determination index DV can be determined. 2L , the third determination index DV and the fourth determination index DV can be determined. t4 , the third determination index DV and the fourth determination index DV can be determined. t5 , the third determination index DV and the fourth determination index DV can be determined. t6 , the third determination index DV and the fourth determination index DV can be determined. 2R and the fourth determination index DV 2L , it can be determined that the number of harvests of the grassland region in the target period is three.

[0164] As an optional embodiment, after determining whether the number of harvesting times of the grassland area in the target period is three times, the method further comprises: in the case that the number of harvesting times of the grassland area in the target period is determined to be not three times, determining whether the number of harvesting times of the grassland area in the target period is one time based on the average value of the normalized vegetation index of the first target time node of the grassland area, the enhanced vegetation index value of the first time node of the grassland area, the enhanced vegetation index value of the first time node of the grassland area, and the enhanced vegetation index value of the second time node of the grassland area.

[0165] Specifically, after determining the third determination index DV 2R and the fourth determination index DV 2L , it can be determined that the enhanced vegetation index value EVI′ t4 of the fourth time node of the grassland area, the enhanced vegetation index value EVI′ t5 of the fifth time node of the grassland area, the enhanced vegetation index value EVI′ t6 of the sixth time node of the grassland area, the fourth time node t4, the fifth time node t5, the sixth time node t6, the third determination index DV 2R and the fourth determination index DV 2L do not satisfy the second preset condition, and it can be determined that the number of harvesting times of the grassland area in the target period is not three times.

[0166] In the case that the number of harvesting times of the grassland area in the target period is determined to be not three times, the normalized vegetation index value of the first target time node of the grassland area can be obtained based on the normalized vegetation index value of each pixel in the remote sensing image of each time node of the target area and the regional information of the grassland area in the target area. x |1≤x≤X, x∈Z}

[0167] It should be noted that, in the case that the number of the first target time nodes X is one, the average value of the normalized vegetation index of the first target time node of the grassland area is the normalized vegetation index NDVI′ i of the first target time node of the grassland area; in the case that the number of the first target time nodes is multiple, the average value of the normalized vegetation index of the first target time node of the grassland area can be calculated based on the normalized vegetation index value of each first target time node of the grassland area. x |1≤x≤X, x∈Z}

[0168] It should be noted that, in the embodiment of the present application, EVI′1 can be used to represent the enhanced vegetation index value of the first time node of the grassland area.

[0169] the enhanced vegetation index value EVI'1 of the first time node of the forage grass region and the fifth determination index D5 satisfy the third preset condition. t1 and the enhanced vegetation index value EVI'2 of the second time node of the forage grass region. t2 The fifth determination index D5 can be calculated by the following formula:

[0170]

[0171] obtaining the average value of the normalized difference vegetation index of the first target time node of the forage grass region and the fifth determination index D5, it can be determined that the average value of the normalized difference vegetation index of the first target time node of the forage grass region whether the enhanced vegetation index value EVI'1 of the first time node of the forage grass region and the fifth determination index D5 satisfy the third preset condition.

[0172] The third preset condition includes EVI'1 t1 ≥d2 and D5≥d3.

[0173] Wherein, d1 represents the ninth threshold value; d2 represents the tenth threshold value; d3 represents the eleventh threshold value.

[0174] It should be noted that the ninth threshold value d1, the tenth threshold value d2 and the eleventh threshold value d3 in the embodiment of the application can be determined according to actual conditions and / or prior knowledge. The specific values of the ninth threshold value d1, the tenth threshold value d2 and the eleventh threshold value d3 in the embodiment of the application are not limited.

[0175] In the case that the average value of the normalized difference vegetation index of the first target time node of the forage grass region In the case that the enhanced vegetation index value EVI'1 of the first time node of the forage grass region and the fifth determination index D5 satisfy the third preset condition, it can be determined that the number of harvesting of the forage grass region in the target period is one.

[0176] The embodiment of the application can more accurately and efficiently determine the number of harvesting of the forage grass region in the target period by obtaining the enhanced vegetation index value of each time node of the forage grass region.

[0177] Step 104, based on the enhanced vegetation index value and the number of harvesting, obtaining the estimated value of the carbon fixation amount of the forage grass region in the target period.

[0178] It should be noted that based on prior knowledge, the above-ground biomass of the pasture land has a linear relationship with the enhanced vegetation index, and the above-ground biomass has a conversion relationship with the carbon fixation amount.

[0179] Specifically, based on the number of harvests of the pasture land in the target region within the target period and the enhanced vegetation index value of the pasture land region at each time node, the enhanced vegetation index value of the pasture land region at the second target time node within the pasture harvesting period can be obtained.

[0180] For example, in a case where it is determined that the number of harvests of the above-mentioned pasture land region within the target period is one, the first time node can be determined as the second target time node, and the enhanced vegetation index value EVI' of the above-mentioned pasture land region at the first time node can be determined as the enhanced vegetation index value EVI of the pasture land region at the second target time node. t1

[0181] For another example, in a case where it is determined that the number of harvests of the above-mentioned pasture land region within the target period is two, the first time node and the fourth time node can be determined as the second target time node, and the enhanced vegetation index value EVI' of the above-mentioned pasture land region at the first time node and the enhanced vegetation index value EVI' of the above-mentioned pasture land region at the second time node can be determined as the enhanced vegetation index value EVI of the pasture land region at the second target time node. t1 t2

[0182] For another example, in a case where it is determined that the number of harvests of the above-mentioned pasture land region within the target period is three, the first time node and the fourth time node can be determined as the second target time node, and the time node with the maximum enhanced vegetation index value among the time nodes belonging to the autumn season in the region where the target region is located can be determined as the second target time node.

[0183] In the embodiment of the present application, y can be used to identify the number of harvests of the above-mentioned pasture land region within the target period. Wherein, 1≤y≤3, i∈Z

[0184] The estimated value CVI of the carbon fixation amount corresponding to the yth harvest of the above-mentioned pasture land region can be calculated by the following formula: y

[0185] CVI y = F y (EVI) (10)

[0186] Wherein, F y () represents the carbon fixation amount estimation function of the yth harvest of the above-mentioned pasture land region; F y () is an empirical formula.

[0187] ​​​​The estimated value CVI of the carbon fixation amount of the pasture area in the target period can be calculated by the following formula:

[0188]

[0189] The embodiment of the present application can more accurately and efficiently realize the identification of the pasture area, the estimation of the harvesting time node and the number of the pasture area, improve the accuracy and efficiency of the estimation of the carbon fixation amount of the pasture area, and provide data support for the production management, oxygen release and carbon fixation, nutrient maintenance, and biodiversity protection of the pasture land.

[0190] Figure 3 is a structural schematic diagram of the pasture land carbon fixation amount remote sensing estimation device provided by the present application. The pasture land carbon fixation amount remote sensing estimation device provided by the present application will be described below Figure 3 The pasture land carbon fixation amount remote sensing estimation device provided by the present application will be described below, and the pasture land carbon fixation amount remote sensing estimation device described below can be correspondingly referred to the pasture land carbon fixation amount remote sensing estimation method provided by the present application described above. As shown in Figure 3 The data acquisition module 301, the area identification module 302, the information acquisition module 303, and the carbon fixation amount estimation module 304.

[0191] The data acquisition module 301 is configured to acquire the time-series remote sensing data of the target area in the target period.

[0192] The area identification module 302 is configured to acquire the area information of the pasture area in the target area based on the remote sensing data.

[0193] The information acquisition module 303 is configured to acquire the time-series enhanced vegetation index value of the pasture area in the target period and the harvesting number of the pasture area in the target period based on the remote sensing data and the area information.

[0194] The carbon fixation amount estimation module 304 is configured to acquire the estimated value of the carbon fixation amount of the pasture area in the target period based on the enhanced vegetation index value and the harvesting number.

[0195] The pasture carbon sequestration remote sensing estimation device in the embodiment of the application can more accurately and efficiently realize identification of the pasture region, estimation of the pasture region harvesting time node and number, improve the accuracy and efficiency of the pasture region carbon sequestration estimation, and provide data support for production management, oxygen release and carbon sequestration, nutrient maintenance, and biodiversity protection of the pasture.

[0196] Figure 4 An example of a schematic diagram of a physical structure of an electronic device is shown in Figure 4 The electronic device can include a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 can communicate with each other through the communications bus 440. The processor 410 can invoke a logical instruction in the memory 430 to execute a pasture carbon sequestration remote sensing estimation method, which includes: obtaining time-series remote sensing data of a target region in a target period; based on the remote sensing data, obtaining region information of a pasture region in the target region; based on the remote sensing data and the region information, obtaining time-series enhanced vegetation index values of the pasture region in the target period and the number of harvests of the pasture region in the target period; and based on the enhanced vegetation index values and the number of harvests, obtaining an estimated value of the carbon sequestration of the pasture region in the target period.

[0197] In addition, the logical instruction in the memory 430 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the application or parts of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0198] In another aspect, the present application also provides a computer program product comprising a computer program, which can be stored on a non-transitory computer readable storage medium, and the computer program, when executed by a processor, enables a computer to perform the method for estimating the carbon fixation amount of a pasture land by remote sensing, which comprises: obtaining time-series remote sensing data of a target region in a target period; obtaining regional information of a pasture land region in the target region based on the remote sensing data; obtaining time-series enhanced vegetation index values of the pasture land region in the target period and the number of harvests of the pasture land region in the target period based on the remote sensing data and the regional information; and obtaining an estimated value of the carbon fixation amount of the pasture land region in the target period based on the enhanced vegetation index values and the number of harvests.

[0199] In another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, enables a computer to perform the method for estimating the carbon fixation amount of a pasture land by remote sensing, which comprises: obtaining time-series remote sensing data of a target region in a target period; obtaining regional information of a pasture land region in the target region based on the remote sensing data; obtaining time-series enhanced vegetation index values of the pasture land region in the target period and the number of harvests of the pasture land region in the target period based on the remote sensing data and the regional information; and obtaining an estimated value of the carbon fixation amount of the pasture land region in the target period based on the enhanced vegetation index values and the number of harvests.

[0200] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0201] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary general hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in terms of the contribution to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0202] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for estimating carbon sequestration in a pasture field by remote sensing, characterized by, The method comprises the following steps: acquiring remote sensing data of a target region in a target period; based on the remote sensing data, acquiring regional information of a grassland region in the target region; based on the remote sensing data and the regional information, acquiring time-series enhanced vegetation index values of the grassland region in the target period and the number of harvests of the grassland region in the target period; based on the enhanced vegetation index values and the number of harvests, acquiring an estimated value of the amount of carbon fixation of the grassland region in the target period; the method based on the remote sensing data and the regional information, acquiring time-series enhanced vegetation index values of the grassland region in the target period and the number of harvests of the grassland region in the target period, comprises the following steps: based on the reflectivity of the blue band, the reflectivity of the red band and the reflectivity of the near-infrared band of the remote sensing image of each time node of the target region, and the regional information, acquiring the enhanced vegetation index value of each time node of the grassland region; based on each of the enhanced vegetation index values, determining the number of harvests of the grassland region in the target period; the method based on each of the enhanced vegetation index values, determining the number of harvests of the grassland region in the target period, comprises the following steps: based on the enhanced vegetation index value of the first time node of the grassland region, the enhanced vegetation index value of the second time node of the grassland region, the enhanced vegetation index value of the third time node of the grassland region, the first time node, the second time node and the third time node, determining whether the number of harvests of the grassland region in the target period is twice; wherein the first time node is the time node corresponding to the maximum value of the enhanced vegetation index value of each time node of the grassland region; the second time node is the time node corresponding to the minimum value of the enhanced vegetation index value of each time node of the grassland region later than the first time node; the third time node is the time node corresponding to the minimum value of the enhanced vegetation index value of each time node of the grassland region earlier than the first time node.

2. The method according to claim 1, wherein In the case that the remote sensing data comprises remote sensing images of multiple time nodes of the target region in the target period, the method based on the remote sensing data, acquiring the regional information of the grassland region in the target region, comprises the following steps: acquiring the normalized vegetation index of each pixel in the remote sensing image of each time node of the target region; based on the normalized vegetation index of each pixel, acquiring the regional information of the grassland region.

3. The method according to claim 2, wherein the method based on the normalized vegetation index of each pixel, acquiring the regional information of the grassland region, comprises the following steps: based on the normalized vegetation index of each pixel, acquiring the average value of the normalized vegetation index of the target region in the target period and the crop identification index value of the target region in the target period; In a case that it is determined that the target region includes the grassland region based on the average value of the normalized difference vegetation index and the crop identification index value, a remote sensing image of the target region at a first target time node is determined in the remote sensing data based on the normalized difference vegetation index of each pixel, and the first target time node is located in a vigorous growth period of the grass. The regional information of the grassland region is obtained based on the normalized difference vegetation index of each pixel in the remote sensing image of the target region at the first target time node.

4. The method according to claim 1, wherein After determining whether the number of harvesting times of the grassland region in the target period is twice, the method further includes: In a case that it is determined that the number of harvesting times of the grassland region in the target period is not twice, it is determined whether the number of harvesting times of the grassland region in the target period is three times based on the enhanced vegetation index value of the grassland region at a fourth time node, the enhanced vegetation index value of the grassland region at a fifth time node, and the enhanced vegetation index value of the grassland region at a sixth time node. The fourth time node is a time node corresponding to a maximum value in the enhanced vegetation index values of each time node of the grassland region later than the second time node. The fifth time node is a time node corresponding to a minimum value in the enhanced vegetation index values of each time node of the grassland region later than the fourth time node. The sixth time node is a time node corresponding to a minimum value in the enhanced vegetation index values of each time node of the grassland region earlier than the fourth time node.

5. The method according to claim 4, wherein After determining whether the number of harvesting times of the grassland region in the target period is three times, the method further includes: In a case that it is determined that the number of harvesting times of the grassland region in the target period is not three times, it is determined whether the number of harvesting times of the grassland region in the target period is one time based on the average value of the normalized difference vegetation index of the grassland region at the first target time node, the enhanced vegetation index value of the grassland region at the first time node, the enhanced vegetation index value of the grassland region at the first time node, and the enhanced vegetation index value of the grassland region at the second time node.

6. A pasture carbon sequestration amount remote sensing estimation device applying the pasture carbon sequestration amount remote sensing estimation method according to any one of claims 1 to 5, characterized by The method includes: a data acquisition module configured to acquire time-series remote sensing data of a target region in a target period; a region identification module configured to acquire regional information of a grassland region in the target region based on the remote sensing data; an information acquisition module configured to acquire time-series enhanced vegetation index values of the grassland region in the target period and a number of harvesting times of the grassland region in the target period based on the remote sensing data and the regional information; a carbon fixation amount estimation module configured to acquire an estimated value of a carbon fixation amount of the grassland region in the target period based on the enhanced vegetation index values and the number of harvesting times.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method for estimating a carbon fixation amount of a grassland region by remote sensing according to any one of claims 1 to 5 when executing the program.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the method for estimating a carbon fixation amount of a grassland region by remote sensing according to any one of claims 1 to 5 when executed by the processor.

Images