A method and system for monitoring the use of cultivated land based on multi-source remote sensing data

Abstract

The application discloses a kind of based on multi-source remote sensing data's cultivated land planting purpose monitoring method and system, belong to planting purpose monitoring technical field;Monitoring the vegetation index and soil humidity of the planting area of cultivated land planting land, calculate the cultivated land vegetation-soil index of planting area, construct the index time chain and index time chain matrix of planting area, calculate the inter-chain coupling between adjacent index time chain;Pre-set inter-chain coupling threshold, obtain all similar planting areas, construct similar area set;Calculate the index change trend of index time chain;Pre-set index change trend threshold, analyze and monitor the purpose of all planting areas in similar area set.The present application realizes the accurate monitoring and intelligent early warning of cultivated land planting purpose by remote sensing monitoring, index modeling, time series analysis and regional similarity evaluation, which helps relevant personnel to timely grasp the utilization of cultivated land, and improves the sustainability and scientific decision-making ability of agricultural production.

Description

Technical Field

[0001] This invention relates to the field of monitoring technology for planting use, specifically to a method and system for monitoring the planting use of arable land based on multi-source remote sensing data. Background Technology

[0002] With the acceleration of global agricultural modernization, remote sensing technology is increasingly widely used in farmland management and crop monitoring. Traditional crop monitoring methods mainly rely on field surveys and statistical analysis, but are limited by high labor costs, limited coverage, and poor real-time performance, making it difficult to meet the needs of large-scale dynamic farmland monitoring. In recent years, using technologies such as satellite remote sensing, hyperspectral imaging, and UAV remote sensing to assess farmland planting conditions has become a research hotspot, with vegetation indices (such as NDVI) and soil moisture monitoring becoming important parameters. Farmland use monitoring methods based on remote sensing data have made significant progress in agricultural ecological assessment, crop growth monitoring, and land use classification, but still have certain limitations, such as decreased monitoring accuracy due to data heterogeneity, inconsistent farmland status classification standards, and a lack of multi-time-series data analysis methods.

[0003] Existing methods for monitoring the planting use of arable land fail to fully consider the dynamic changes in multi-source remote sensing data and the interactions between different monitoring indicators. For example, some studies rely solely on vegetation indices to determine arable land use, neglecting the influence of soil moisture, resulting in low accuracy in distinguishing between dryland and fallow land. Furthermore, traditional classification methods are often based on fixed thresholds or simple statistical regression models, lacking time-series evolution analysis and making it difficult to accurately identify trends in planting use changes. Therefore, in practical applications, existing methods cannot effectively adapt to complex agricultural planting patterns, especially under conditions of different crop rotations, changes in irrigation management, or fluctuations in environmental conditions, resulting in low reliability of monitoring results. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for monitoring the planting use of cultivated land based on multi-source remote sensing data, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A method for monitoring the planting use of cultivated land based on multi-source remote sensing data includes the following steps: Step S1: Dividing the cultivated land use area into several planting zones; monitoring planting data in the planting zones, including vegetation index and soil moisture; Step S2: Calculating the cultivated land vegetation-soil index of the planting zone based on the vegetation index and soil moisture; constructing an index time chain for the planting zone based on the cultivated land vegetation-soil index; Step S3: Obtaining the index time chains of all planting zones and constructing an index time chain matrix; calculating the inter-chain coupling degree between adjacent index time chains based on the index time chain matrix; setting an inter-chain coupling degree threshold to obtain all similar planting zones and constructing a similar zone set; Step S4: Calculating the index change trend of the index time chain based on the cultivated land vegetation-soil index; setting an index change trend threshold to analyze and monitor the use of all planting zones in the similar zone set.

[0007] As a preferred embodiment of the method for monitoring the planting use of cultivated land based on multi-source remote sensing data described in this invention, a high-precision GPS device is used to survey the usage range of cultivated land. A grid-based partitioning technique is used to divide the usage range of the cultivated land into several planting areas. These planting areas are then uniformly numbered, and based on the numbering information, they are classified to generate a set of planting areas, denoted as AR = {PA}. a |a∈[1,A]}, where PA a Let A represent the a-th planting area, and let A represent the total number of planting areas.

[0008] Equipped with remote sensing survey equipment, drones are used to monitor the PA planting area. a The planting data, including vegetation index and soil moisture, is used to classify the planting area PA. a The vegetation index and soil moisture within the area are denoted as VI (PA). a ) and SH(PA) a ).

[0009] It should be noted that vegetation indices are typically used to assess vegetation cover, growth status, and crop type. For example, a high vegetation index value usually corresponds to healthy leafy crops, while a low vegetation index value may indicate bare soil, harvested fields, or fallow conditions. Soil moisture can determine whether the land is suitable for rice cultivation or whether it is in a fallow state.

[0010] As a preferred embodiment of the method for monitoring cultivated land planting use based on multi-source remote sensing data described in this invention, based on the vegetation index VI (PA) a ) and soil moisture SH (PA) a ), calculate the PA of the planting area a The arable land vegetation-soil index is calculated using the following formula:

[0011]

[0012] Among them, CVSI(PA) a ) indicates the planting area PA a arable land vegetation-soil index, VI ref This represents the preset vegetation index reference value, SH ref This represents the preset soil moisture reference value, and α and β represent the preset vegetation index VI (PA). a ) and soil moisture SH (PA) a Influencing factors.

[0013] Based on planting area PA a Cultivated land vegetation-soil index (CVSI) a ), construct planting area PA a The exponential time chain is as follows:

[0014] Using months as the unit, a data collection cycle is constructed, and the PA of the planting area is obtained for each data collection cycle. a The cultivated land vegetation-soil index will be used to determine the PA of the planting area in the i-th data collection period. a The cultivated land vegetation-soil index is denoted as the Cultivated Land Vegetation-Soil Periodic Index (CVSI). i (PA a ).

[0015] The arable land vegetation-soil periodic index (CVSI) i (PA a Arranged sequentially according to the time chronological order of the data collection period, all the arranged cultivated land vegetation-soil cycle indices are denoted as planting area PA. a The exponential time chain, denoted as {CVSI} i (PA a )|i∈[1,I]}, where I represents the total number of data acquisition cycles.

[0016] It should be noted that the vegetation index VI (PA) is used to measure the vegetation density. a ) and soil moisture SH (PA) a The calculated arable land vegetation-soil index (CVSI) a ), can plant area PA aDifferent types of arable land are classified, such as: vigorous growth period (high vegetation index + suitable soil moisture): normally growing crops, such as rice, corn, and fruit trees; drought stress (high vegetation index + low soil moisture): possibly dryland crops with insufficient irrigation or farmland affected by drought; waterlogging (low vegetation index + high soil moisture): possibly paddy fields or abnormal conditions caused by waterlogging due to rainfall; fallow / abandoned land (low vegetation index + low soil moisture): possibly fallow land, uncultivated farmland, or wasteland.

[0017] Furthermore, if the arable land vegetation-soil index (CVSI) (PA) a If the value remains high for a long period, it can be identified as a stable planting area (food crops / cash crops). If the arable land vegetation-soil index (CVSI) remains high, it can be identified as a stable planting area. a If there are significant seasonal variations, it can be identified as a crop rotation area (wheat-corn, rapeseed-rice, etc.). If the arable land vegetation-soil index (CVSI) is significantly different, it can be identified as a crop rotation area. a If the value remains low for an extended period, it can be identified as abandoned land, fallow land, or pasture. If the vegetation-soil index (CVSI) of cultivated land is low, it can be classified as such. a If the fluctuations are abnormal at certain times, it can be identified as facility agriculture (such as greenhouses).

[0018] As a preferred embodiment of the method for monitoring cultivated land planting use based on multi-source remote sensing data described in this invention, based on the planting area PA a Exponential time chain {CVSI i (PA a For each i ∈ [1, I], the index time chain of all planting areas is obtained, and an index time chain matrix is ​​constructed. The columns of the index time chain matrix are the cultivated land vegetation-soil cycle index of all planting areas under all data collection periods, as follows:

[0019]

[0020] Among them, CVSI I (PA A ) represents the A-th planting area PA in the I-th data collection period. A The vegetation-soil cycle index of cultivated land.

[0021] Based on the exponential time chain matrix, the inter-chain coupling degree between adjacent exponential time chains is calculated using the following formula:

[0022]

[0023] Wherein, D(PA) a PA b) represents the exponential time chain {CVSI} i (PA a )|i∈[1,I]} and exponential time chain {CVSI i (PA b Inter-chain coupling degree between chains i∈[1,I]}, CVSI i (PA b ) represents the planting area PA in the i-th data collection period. b The vegetation-soil cycle index of cultivated land.

[0024] Preset inter-chain coupling threshold, if exponential time chain {CVSI i (PA a )|i∈[1,I]} and exponential time chain {CVSI i (PA b The inter-chain coupling degree D(PA) between |i∈[1,I]} a PA b If the degree of interchain coupling is greater than or equal to the threshold value, then the planting area PA is determined to be... a With planting area PA b There are similarities; obtain all data related to the planting area PA. a Similar planting areas, construct planting area PA a A set of similar regions.

[0025] As a preferred embodiment of the method for monitoring cultivated land planting use based on multi-source remote sensing data described in this invention, based on the planting area PA a The set of similar regions and the planting area PA in the i-th data collection period a Cultivated land vegetation-soil periodic index (CVSI) i (PA a ), calculate the exponential time chain {CVSI} i (PA a The exponential trend of |i∈[1,I]} is calculated using the following formula:

[0026]

[0027] Among them, G(PA) a ) represents the exponential time chain {CVSI} i (PA a The exponential trend of |i∈[1,I]}, CVSI i-1 (PA a ) represents the planting area PA in the (i-1)th data collection period. a The vegetation-soil cycle index of cultivated land.

[0028] Preset threshold for index trend, if the index time chain {CVSI}i (PA a If the exponential change trend of |i∈[1,I]} is less than the exponential change trend threshold, then the planting area PA is considered to be... a If there are abnormal land use conditions (declining agricultural productivity (reduced yields, soil degradation, water shortages), the land is being fallowed or abandoned, land use may be changing (urbanization, aquaculture, ecological restoration, etc.), or the land may be subject to extreme weather, pests, or human damage), then the planting area is determined to be PA. a If all planting areas in a set of similar regions show abnormal usage, an alert will be issued to the staff.

[0029] A monitoring system for cultivated land planting use based on multi-source remote sensing data. The system includes: a regional division and data acquisition module, an index calculation and time chain construction module, a matrix construction and coupling degree calculation module, and a trend calculation, analysis and early warning module.

[0030] The regional division and data acquisition module divides the arable land into several planting areas; monitors the planting data of the planting areas, including vegetation index and soil moisture.

[0031] The index calculation and time chain construction module calculates the cultivated land vegetation-soil index of the planting area based on the vegetation index and soil moisture; and constructs the index time chain of the planting area based on the cultivated land vegetation-soil index of the planting area.

[0032] The matrix construction and coupling degree calculation module: obtains the exponential time chains of all planting areas and constructs an exponential time chain matrix; based on the exponential time chain matrix, calculates the inter-chain coupling degree between adjacent exponential time chains; presets an inter-chain coupling degree threshold, obtains all similar planting areas, and constructs a set of similar areas.

[0033] The trend calculation and analysis early warning module calculates the index change trend of the index time chain based on the cultivated land vegetation-soil index; it presets the index change trend threshold and analyzes and monitors the use of all planting areas in the similar area set.

[0034] Furthermore, the region division and data acquisition module includes a region division unit and a data acquisition unit.

[0035] The regional division unit: using high-precision GPS equipment, surveying the usage range of cultivated land, using grid division technology to divide the usage range of cultivated land into several planting areas, uniformly numbering the planting areas, classifying the planting areas based on the numbering information, and generating a set of planting areas.

[0036] The data acquisition unit: equipped with remote sensing survey equipment on the drone, it monitors the planting data of the planting area, including vegetation index and soil moisture.

[0037] Furthermore, the exponent calculation and time chain construction module includes an exponent calculation unit and a time chain construction unit.

[0038] The index calculation unit calculates the arable land vegetation-soil index of the planting area based on the vegetation index and soil moisture.

[0039] The time chain construction unit: Based on the cultivated land vegetation-soil index of the planting area, an index time chain of the planting area is constructed, specifically as follows: a data collection cycle is constructed on a monthly basis, and the cultivated land vegetation-soil index of the planting area is obtained under each data collection cycle. The cultivated land vegetation-soil index of the planting area under a single data collection cycle is recorded as the cultivated land vegetation-soil cycle index; the cultivated land vegetation-soil cycle indices are arranged in chronological order according to the time sequence of the data collection cycle, and all the arranged cultivated land vegetation-soil cycle indices are recorded as the index time chain of the planting area.

[0040] Furthermore, the matrix construction and coupling degree calculation module includes a matrix construction unit and a coupling degree calculation unit.

[0041] The matrix construction unit: Based on the index time chain of the planting area, obtain the index time chain of all planting areas and construct an index time chain matrix. The columns of the index time chain matrix are the arable land vegetation-soil cycle index of a single planting area under all data collection periods.

[0042] The coupling degree calculation unit: calculates the inter-chain coupling degree between adjacent exponential time chains based on the exponential time chain matrix; presets an inter-chain coupling degree threshold; if the inter-chain coupling degree between adjacent exponential time chains is greater than or equal to the inter-chain coupling degree threshold, it determines that there is similarity between adjacent planting areas; obtains all planting areas similar to the current planting area, and constructs a set of similar areas for the current planting area.

[0043] Furthermore, the trend calculation and analysis early warning module includes a trend calculation unit and an analysis early warning unit.

[0044] The trend calculation unit calculates the index change trend of the index time chain based on the set of similar regions of the current planting area and the cultivated land vegetation-soil cycle index of the current planting area under a single data collection period.

[0045] The analysis and early warning unit: presets an index change trend threshold. If the index change trend of the index time chain is less than the index change trend threshold, it indicates that there is an abnormal use in the current planting area. Then it determines that all planting areas in the set of similar areas of the current planting area have abnormal use, and issues an early warning to the staff.

[0046] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides a method and system for monitoring the planting use of cultivated land based on multi-source remote sensing data. It utilizes high-precision GPS equipment to survey the cultivated land area and employs gridding technology to divide the cultivated land into multiple planting areas. Simultaneously, it uses UAV remote sensing equipment to monitor vegetation indices and soil moisture, thereby establishing a basic data set for planting areas and providing accurate data support for subsequent analysis. Next, based on vegetation indices and soil moisture, the cultivated land vegetation-soil index is calculated, and a time-series index of changes over time is constructed. This allows for the identification of the cultivated land status in the planting area, including conditions such as vigorous growth, drought stress, excessive waterlogging, or fallow / abandonment, laying the foundation for subsequent use analysis. By constructing an index time-series matrix and calculating the inter-chain coupling degree between adjacent time chains, areas with similar planting characteristics are identified, and a set of similar areas is constructed. This step not only improves the accuracy of planting area identification but also effectively filters out cultivated land with the same changing trends, improving the stability and reliability of monitoring. Finally, based on the exponential time chain, the trend of exponential changes is calculated, and thresholds are set to detect anomalies in planting uses, such as declining agricultural productivity, land fallowing, or land use conversion. Early warnings are issued to managers when anomalies occur, ensuring the rational use of agricultural resources and the efficiency of land management. Overall, this method, through remote sensing monitoring, exponential modeling, time series analysis, and regional similarity assessment, achieves precise monitoring and intelligent early warning of arable land planting uses. This helps agricultural management departments and relevant personnel to promptly grasp the status of arable land use, improving the sustainability of agricultural production and scientific decision-making capabilities. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0048] Figure 1 This is a schematic diagram illustrating the steps of a method for monitoring the planting use of cultivated land based on multi-source remote sensing data according to the present invention.

[0049] Figure 2 This is a schematic diagram of the structure of a farmland planting use monitoring system based on multi-source remote sensing data according to the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 In this first embodiment: a method for monitoring the planting use of cultivated land based on multi-source remote sensing data is provided, the method including the following steps:

[0052] Step S1: Divide the arable land into several planting areas; monitor the planting data of the planting areas, including vegetation index and soil moisture.

[0053] Specifically, using high-precision GPS equipment, the usage area of ​​arable land is surveyed. Grid-based segmentation technology is used to divide the arable land usage area into several planting zones. These planting zones are then uniformly numbered, and based on the numbering information, they are classified to generate a set of planting zones, denoted as AR = {PA}. a |a∈[1,A]}, where PA a Let A represent the a-th planting area, and let A represent the total number of planting areas.

[0054] Furthermore, remote sensing survey equipment can be mounted on drones to monitor the PA planting area. a The planting data, including vegetation index and soil moisture, is used to classify the planting area PA. a The vegetation index and soil moisture within the area are denoted as VI (PA). a ) and SH(PA) a ).

[0055] It should be noted that vegetation indices are typically used to assess vegetation cover, growth status, and crop type. For example, a high vegetation index value usually corresponds to healthy leafy crops, while a low vegetation index value may indicate bare soil, harvested fields, or fallow conditions. Soil moisture can determine whether the land is suitable for rice cultivation or whether it is in a fallow state.

[0056] Step S2: Calculate the cultivated land vegetation-soil index of the planting area based on vegetation index and soil moisture; construct the index time chain of the planting area based on the cultivated land vegetation-soil index of the planting area.

[0057] Specifically, based on the vegetation index VI (PA) a ) and soil moisture SH (PA) a ), calculate the PA of the planting area aThe arable land vegetation-soil index is calculated using the following formula:

[0058]

[0059] Among them, CVSI(PA) a ) indicates the planting area PA a arable land vegetation-soil index, VI ref This represents the preset vegetation index reference value, SH ref This represents the preset soil moisture reference value, and α and β represent the preset vegetation index VI (PA). a ) and soil moisture SH (PA) a Influencing factors.

[0060] Furthermore, based on the planting area PA a Cultivated land vegetation-soil index (CVSI) a ), construct planting area PA a The exponential time chain is as follows:

[0061] Using months as the unit, a data collection cycle is constructed, and the PA of the planting area is obtained for each data collection cycle. a The cultivated land vegetation-soil index will be used to determine the PA of the planting area in the i-th data collection period. a The cultivated land vegetation-soil index is denoted as the Cultivated Land Vegetation-Soil Periodic Index (CVSI). i (PA a ).

[0062] The arable land vegetation-soil periodic index (CVSI) i (PA a Arranged sequentially according to the time chronological order of the data collection period, all the arranged cultivated land vegetation-soil cycle indices are denoted as planting area PA. a The exponential time chain, denoted as {CVSI} i (PA a )|i∈[1,I]}, where I represents the total number of data acquisition cycles.

[0063] It should be noted that the vegetation index VI (PA) is used to measure the vegetation density. a ) and soil moisture SH (PA) a The calculated arable land vegetation-soil index (CVSI) a ), can plant area PA aDifferent types of arable land are classified, such as: vigorous growth period (high vegetation index + suitable soil moisture): normally growing crops, such as rice, corn, and fruit trees; drought stress (high vegetation index + low soil moisture): possibly dryland crops with insufficient irrigation or farmland affected by drought; waterlogging (low vegetation index + high soil moisture): possibly paddy fields or abnormal conditions caused by waterlogging due to rainfall; fallow / abandoned land (low vegetation index + low soil moisture): possibly fallow land, uncultivated farmland, or wasteland.

[0064] Furthermore, if the arable land vegetation-soil index (CVSI) (PA) a If the value remains high for a long period, it can be identified as a stable planting area (food crops / cash crops). If the arable land vegetation-soil index (CVSI) remains high, it can be identified as a stable planting area. a If there are significant seasonal variations, it can be identified as a crop rotation area (wheat-corn, rapeseed-rice, etc.). If the arable land vegetation-soil index (CVSI) is significantly different, it can be identified as a crop rotation area. a If the value remains low for an extended period, it can be identified as abandoned land, fallow land, or pasture. If the vegetation-soil index (CVSI) of cultivated land is low, it can be classified as such. a If the fluctuations are abnormal at certain times, it can be identified as facility agriculture (such as greenhouses).

[0065] Furthermore, traditional remote sensing monitoring typically relies solely on vegetation indices, making it susceptible to factors such as precipitation and soil type. This invention, by incorporating soil moisture, improves the stability and accuracy of land use monitoring. Moreover, many farmland analyses focus only on the state at a single moment, neglecting long-term trends. This invention integrates data from multiple time points through an index time chain, making land use change trends more interpretable.

[0066] Step S3: Obtain the exponential time chains of all planting areas and construct an exponential time chain matrix; based on the exponential time chain matrix, calculate the inter-chain coupling degree between adjacent exponential time chains; preset the inter-chain coupling degree threshold, obtain all similar planting areas, and construct a similar area set.

[0067] Specifically, based on the planting area PA a Exponential time chain {CVSI i (PA a For each i ∈ [1, I], the index time chain of all planting areas is obtained, and an index time chain matrix is ​​constructed. The columns of the index time chain matrix are the cultivated land vegetation-soil cycle index of all planting areas under all data collection periods, as follows:

[0068]

[0069] Among them, CVSII (PA A ) represents the A-th planting area PA in the I-th data collection period. A The vegetation-soil cycle index of cultivated land.

[0070] Furthermore, based on the exponential time chain matrix, the inter-chain coupling degree between adjacent exponential time chains is calculated using the following formula:

[0071]

[0072] Wherein, D(PA) a PA b ) represents the exponential time chain {CVSI} i (PA a )|i∈[1,I]} and exponential time chain {CVSI i (PA b Inter-chain coupling degree between chains i∈[1,I]}, CVSI i (PA b ) represents the planting area PA in the i-th data collection period. b The vegetation-soil cycle index of cultivated land.

[0073] Preset inter-chain coupling threshold, if exponential time chain {CVSI i (PA a )|i∈[1,I]} and exponential time chain {CVSI i (PA b The inter-chain coupling degree D(PA) between |i∈[1,I]} a PA b If the degree of interchain coupling is greater than or equal to the threshold value, then the planting area PA is determined to be... a With planting area PA b There are similarities; obtain all data related to the planting area PA. a Similar planting areas, construct planting area PA a A set of similar regions.

[0074] It should be noted that traditional methods typically classify farmland based on geographical location, neglecting the similarity of planting patterns. This invention introduces coupling degree calculation, enabling even spatially discontinuous farmland to be categorized into the same similar region set as long as the planting patterns are similar. Furthermore, the exponential time chain matrix provides a structured storage method, making data analysis more hierarchical while avoiding redundant calculations and improving data processing efficiency.

[0075] Step S4: Based on the cultivated land vegetation-soil index, calculate the index change trend of the index time chain; preset the index change trend threshold, analyze and monitor the use of all planting areas in the similar area set.

[0076] Specifically, based on the planting area PA a The set of similar regions and the planting area PA in the i-th data collection period a Cultivated land vegetation-soil periodic index (CVSI) i (PA a ), calculate the exponential time chain {CVSI} i (PA a The exponential trend of |i∈[1,I]} is calculated using the following formula:

[0077]

[0078] Among them, G(PA) a ) represents the exponential time chain {CVSI} i (PA a The exponential trend of |i∈[1,I]}, CVSI i-1 (PA a ) represents the planting area PA in the (i-1)th data collection period. a The vegetation-soil cycle index of cultivated land.

[0079] Furthermore, a preset threshold for the exponential change trend is defined, if the exponential time chain {CVSI} i (PA a If the exponential change trend of |i∈[1,I]} is less than the exponential change trend threshold, then the planting area PA is considered to be... a If there are abnormal land use conditions (declining agricultural productivity (reduced yields, soil degradation, water shortages), the land is being fallowed or abandoned, land use may be changing (urbanization, aquaculture, ecological restoration, etc.), or the land may be subject to extreme weather, pests, or human damage), then the planting area is determined to be PA. a If all planting areas in a set of similar regions show abnormal usage, an alert will be issued to the staff.

[0080] It should be noted that traditional monitoring of changes in arable land use relies on annual statistical data, which has a significant lag. This invention achieves real-time monitoring through index trend analysis, improving the dynamic management capabilities of land use. Furthermore, by combining index trends with sets of similar regions, it can more accurately identify anomalies in farmland use, such as reduced yields due to drought or fallow land due to policy adjustments, thereby providing targeted agricultural management strategies.

[0081] Please see Figure 2In this second embodiment, a farmland planting use monitoring system based on multi-source remote sensing data is provided. The system includes: a regional division and data acquisition module, an index calculation and time chain construction module, a matrix construction and coupling degree calculation module, and a trend calculation, analysis and early warning module.

[0082] The regional division and data acquisition module divides the arable land into several planting areas; monitors the planting data of the planting areas, including vegetation index and soil moisture.

[0083] The index calculation and time chain construction module calculates the cultivated land vegetation-soil index of the planting area based on the vegetation index and soil moisture; and constructs the index time chain of the planting area based on the cultivated land vegetation-soil index of the planting area.

[0084] The matrix construction and coupling degree calculation module: obtains the exponential time chains of all planting areas and constructs an exponential time chain matrix; based on the exponential time chain matrix, calculates the inter-chain coupling degree between adjacent exponential time chains; presets an inter-chain coupling degree threshold, obtains all similar planting areas, and constructs a set of similar areas.

[0085] The trend calculation and analysis early warning module calculates the index change trend of the index time chain based on the cultivated land vegetation-soil index; it presets the index change trend threshold and analyzes and monitors the use of all planting areas in the similar area set.

[0086] Furthermore, the region division and data acquisition module includes a region division unit and a data acquisition unit.

[0087] The regional division unit: using high-precision GPS equipment, surveying the usage range of cultivated land, using grid division technology to divide the usage range of cultivated land into several planting areas, uniformly numbering the planting areas, classifying the planting areas based on the numbering information, and generating a set of planting areas.

[0088] The data acquisition unit: equipped with remote sensing survey equipment on the drone, it monitors the planting data of the planting area, including vegetation index and soil moisture.

[0089] Furthermore, the exponent calculation and time chain construction module includes an exponent calculation unit and a time chain construction unit.

[0090] The index calculation unit calculates the arable land vegetation-soil index of the planting area based on the vegetation index and soil moisture.

[0091] The time chain construction unit: Based on the cultivated land vegetation-soil index of the planting area, an index time chain of the planting area is constructed, specifically as follows: a data collection cycle is constructed on a monthly basis, and the cultivated land vegetation-soil index of the planting area is obtained under each data collection cycle. The cultivated land vegetation-soil index of the planting area under a single data collection cycle is recorded as the cultivated land vegetation-soil cycle index; the cultivated land vegetation-soil cycle indices are arranged in chronological order according to the time sequence of the data collection cycle, and all the arranged cultivated land vegetation-soil cycle indices are recorded as the index time chain of the planting area.

[0092] Furthermore, the matrix construction and coupling degree calculation module includes a matrix construction unit and a coupling degree calculation unit.

[0093] The matrix construction unit: Based on the index time chain of the planting area, obtain the index time chain of all planting areas and construct an index time chain matrix. The columns of the index time chain matrix are the arable land vegetation-soil cycle index of a single planting area under all data collection periods.

[0094] The coupling degree calculation unit: calculates the inter-chain coupling degree between adjacent exponential time chains based on the exponential time chain matrix; presets an inter-chain coupling degree threshold; if the inter-chain coupling degree between adjacent exponential time chains is greater than or equal to the inter-chain coupling degree threshold, it determines that there is similarity between adjacent planting areas; obtains all planting areas similar to the current planting area, and constructs a set of similar areas for the current planting area.

[0095] Furthermore, the trend calculation and analysis early warning module includes a trend calculation unit and an analysis early warning unit.

[0096] The trend calculation unit calculates the index change trend of the index time chain based on the set of similar regions of the current planting area and the cultivated land vegetation-soil cycle index of the current planting area under a single data collection period.

[0097] The analysis and early warning unit: presets an index change trend threshold. If the index change trend of the index time chain is less than the index change trend threshold, it indicates that there is an abnormal use in the current planting area. Then it determines that all planting areas in the set of similar areas of the current planting area have abnormal use, and issues an early warning to the staff.

[0098] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0099] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the planting use of cultivated land based on multi-source remote sensing data, characterized in that, The method includes the following steps: Step S1: Divide the arable land into several planting areas; monitor the planting data of the planting areas, including vegetation index and soil moisture; Step S2: Calculate the cultivated land vegetation-soil index of the planting area based on vegetation index and soil moisture; construct the index time chain of the planting area based on the cultivated land vegetation-soil index of the planting area. Step S3: Obtain the exponential time chains of all planting areas and construct an exponential time chain matrix; based on the exponential time chain matrix, calculate the inter-chain coupling degree between adjacent exponential time chains; preset the inter-chain coupling degree threshold, obtain all similar planting areas, and construct a similar area set; Step S4: Based on the cultivated land vegetation-soil index, calculate the index change trend of the index time chain; preset the index change trend threshold, analyze and monitor the use of all planting areas in the similar area set; The specific implementation process of step S1 includes: Using high-precision GPS equipment, the usable area of ​​arable land is surveyed. Grid-based segmentation technology is used to divide the usable area into several planting zones. These planting zones are then uniformly numbered, and based on these numbers, they are categorized to generate a set of planting zones, denoted as […]. ,in, Let A represent the a-th planting area, and A represent the total number of planting areas. Equipping drones with remote sensing survey equipment to monitor planting areas. The planting data, including vegetation index and soil moisture, is used to classify the planting areas. The vegetation index and soil moisture within are recorded as follows: and ; The specific implementation process of step S2 includes: Based on vegetation index and soil moisture Calculate the planting area The arable land vegetation-soil index is calculated using the following formula: ； in, Indicates planting area Farmland vegetation-soil index This represents the preset vegetation index reference value. This indicates the preset soil moisture reference value. and These represent the preset vegetation indices. and soil moisture Influence factors; Based on planting area Farmland vegetation-soil index Construct planting areas The exponential time chain is as follows: The data collection cycle is constructed on a monthly basis, and the planting area is obtained for each data collection cycle. The cultivated land vegetation-soil index will be used to determine the planting area in the i-th data collection period. The cultivated land vegetation-soil index is denoted as the cultivated land vegetation-soil periodic index. ; The vegetation-soil cycle index of cultivated land Arranged sequentially according to the time sequence of the data collection period, all the arranged cultivated land vegetation-soil cycle indices are recorded as planting areas. The exponential time chain, denoted as Where I represents the total number of data collection cycles.

2. The method for monitoring cultivated land planting use based on multi-source remote sensing data according to claim 1, characterized in that, The specific implementation process of step S3 includes: Based on planting area exponential time chain The index time chains of all planting areas are obtained, and an index time chain matrix is ​​constructed. The columns of the index time chain matrix are the arable land vegetation-soil cycle indices of all planting areas under all data collection periods, as detailed below: ； in, This represents the A-th planting area during the I-th data collection period. Cultivated land vegetation-soil cycle index; Based on the exponential time chain matrix, the inter-chain coupling degree between adjacent exponential time chains is calculated using the following formula: ； in, Represents the exponential time chain With exponential time chain Inter-chain coupling This represents the planting area during the i-th data collection period. Cultivated land vegetation-soil cycle index; Preset inter-chain coupling threshold, if exponential time chain With exponential time chain Inter-chain coupling If the inter-chain coupling degree is greater than or equal to the threshold value, then the planting area is determined. With planting area There are similarities; retrieve all planting areas Similar planting areas, construct planting areas A set of similar regions.

3. The method for monitoring cultivated land planting use based on multi-source remote sensing data according to claim 2, characterized in that, The specific implementation process of step S4 includes: Based on the planting area The set of similar regions and the planting area in the i-th data collection period Farmland vegetation-soil cycle index Calculate the exponential time chain The trend of the index change is calculated using the following formula: ； in, Represents the exponential time chain The trend of index changes, This represents the planting area during the (i-1)th data collection period. Cultivated land vegetation-soil cycle index; Preset threshold for index trend, if the index time chain If the trend of the index change is less than the threshold value, it indicates that the planting area... If there is an abnormal use, then the planting area is determined. If all planting areas in a set of similar regions show abnormal usage, an alert will be issued to the staff.

4. A system for monitoring cultivated land planting use based on multi-source remote sensing data, comprising executing the method for monitoring cultivated land planting use based on multi-source remote sensing data as described in any one of claims 1-3, characterized in that, The system includes: a region division and data acquisition module, an index calculation and time chain construction module, a matrix construction and coupling degree calculation module, and a trend calculation, analysis and early warning module; The regional division and data acquisition module divides the arable land use area into several planting areas; monitors the planting data of the planting areas, including vegetation index and soil moisture. The index calculation and time chain construction module calculates the cultivated land vegetation-soil index of the planting area based on vegetation index and soil moisture; and constructs the index time chain of the planting area based on the cultivated land vegetation-soil index of the planting area. The matrix construction and coupling degree calculation module: obtains the exponential time chains of all planting areas and constructs an exponential time chain matrix; calculates the inter-chain coupling degree between adjacent exponential time chains based on the exponential time chain matrix; presets an inter-chain coupling degree threshold, obtains all similar planting areas, and constructs a set of similar areas; The trend calculation and analysis early warning module calculates the index change trend of the index time chain based on the cultivated land vegetation-soil index; it presets the index change trend threshold and analyzes and monitors the use of all planting areas in the similar area set.

5. A farmland planting use monitoring system based on multi-source remote sensing data according to claim 4, characterized in that: The region division and data acquisition module includes a region division unit and a data acquisition unit; The area division unit: using high-precision GPS equipment, surveying the usage range of cultivated land, using grid division technology to divide the usage range of cultivated land into several planting areas, assigning a unified number to each planting area, classifying the planting areas based on the numbering information, and generating a set of planting areas; The data acquisition unit: equipped with remote sensing survey equipment on the drone, it monitors the planting data of the planting area, including vegetation index and soil moisture.

6. A farmland planting use monitoring system based on multi-source remote sensing data according to claim 5, characterized in that: The exponent calculation and time chain construction module includes an exponent calculation unit and a time chain construction unit; The index calculation unit calculates the arable land vegetation-soil index of the planting area based on the vegetation index and soil moisture. The time chain construction unit: Based on the cultivated land vegetation-soil index of the planting area, an index time chain of the planting area is constructed, specifically as follows: a data collection cycle is constructed on a monthly basis, and the cultivated land vegetation-soil index of the planting area is obtained under each data collection cycle. The cultivated land vegetation-soil index of the planting area under a single data collection cycle is recorded as the cultivated land vegetation-soil cycle index; the cultivated land vegetation-soil cycle indices are arranged in chronological order according to the time sequence of the data collection cycle, and all the arranged cultivated land vegetation-soil cycle indices are recorded as the index time chain of the planting area.

7. A farmland planting use monitoring system based on multi-source remote sensing data according to claim 6, characterized in that: The matrix construction and coupling degree calculation module includes a matrix construction unit and a coupling degree calculation unit; The matrix construction unit: Based on the index time chain of the planting area, obtain the index time chain of all planting areas and construct the index time chain matrix. The behavior of the index time chain matrix is ​​the cultivated land vegetation-soil cycle index of a single planting area under all data collection periods. The columns of the index time chain matrix are the cultivated land vegetation-soil cycle indices of all planting areas under a single data collection period. The coupling degree calculation unit calculates the inter-chain coupling degree between adjacent exponential time chains based on the exponential time chain matrix; it presets an inter-chain coupling degree threshold, and if the inter-chain coupling degree between adjacent exponential time chains is greater than or equal to the inter-chain coupling degree threshold, it determines that there is similarity between adjacent planting areas. Get all planting areas similar to the current planting area and construct a set of similar areas for the current planting area.

8. A farmland planting use monitoring system based on multi-source remote sensing data according to claim 7, characterized in that: The trend calculation and analysis early warning module includes a trend calculation unit and an analysis early warning unit; The trend calculation unit calculates the index change trend of the index time chain based on the set of similar areas of the current planting area and the cultivated land vegetation-soil cycle index of the current planting area under a single data collection cycle. The analysis and early warning unit: presets an index change trend threshold. If the index change trend of the index time chain is less than the index change trend threshold, it indicates that there is an abnormal use in the current planting area. Then it determines that all planting areas in the set of similar areas of the current planting area have abnormal use, and issues an early warning to the staff.

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