Geographic information system (GIS) and remote sensing integrated land resource dynamic management method and system
By integrating GIS and remote sensing technology, dividing the management area as the basic unit, calculating the remote sensing critical value and conducting dynamic management, the problem of data lag in traditional methods is solved, and efficient and precise land resource management is achieved.
Patent Information
- Application Number
- CN202510724882.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-03
- Publication Date
- 2025-09-12
AI Technical Summary
Traditional land resource management methods rely on periodic manual surveying or paper file updates, which are time-consuming and labor-intensive, and the data lags, making it difficult to reflect land cover changes in a timely manner, resulting in management decisions being out of touch with actual conditions.
By integrating GIS and remote sensing technology, dividing management areas into basic units, continuously acquiring remote sensing images, calculating remote sensing critical values and dividing management sub-areas, and using dynamic identification modules for real-time updates and reminders, dynamic management of land resources can be achieved.
It improves the efficiency and precision of land resource management, and achieves timely land resource status monitoring and the accuracy of management decisions.
Smart Images
Figure CN120635701A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of land surveying, and in particular relates to a land resource dynamic management method and system integrating GIS and remote sensing. Background Art
[0002] With the acceleration of global urbanization and increasing ecological and environmental pressures, the rational use and dynamic regulation of land resources have become key issues for governments and research institutions worldwide. Traditional land resource management methods rely on periodic manual surveying and mapping or paper-based file updates, which are time-consuming and labor-intensive, with data lags. This makes it difficult to timely reflect land cover changes, resulting in a disconnect between management decisions and actual conditions. Summary of the Invention
[0003] The purpose of the present invention is to provide a land resource dynamic management method and system integrating GIS and remote sensing, which realizes timely and effective management of land resource changes in the management area by linking and summarizing remote sensing images within the management area through GIS model.
[0004] To solve the above technical problems, the present invention is achieved through the following technical solutions:
[0005] The present invention provides a land resource dynamic management method integrating GIS and remote sensing, comprising:
[0006] Divide the management area into basic units in a grid-like manner;
[0007] Continuously acquire remote sensing images and extract remote sensing values for each type of remote sensing item within each basic unit;
[0008] In the GIS model, the remote sensing values of each type of remote sensing project for each basic unit are marked;
[0009] Calculate the remote sensing critical value of each type of remote sensing item by combining the remote sensing values of each type of remote sensing item of the basic unit;
[0010] The management area is divided into several management sub-areas with the same internal land resource status according to the remote sensing critical value of each type of remote sensing project;
[0011] Each of the management sub-areas is marked and continuously updated in the GIS model. If the management sub-area changes, a reminder will be marked in the GIS model.
[0012] The present invention also discloses a land resource dynamic management system integrating GIS and remote sensing, comprising:
[0013] Remote sensing image interface, used to continuously receive remote sensing images;
[0014] GIS model, used to construct a digital model containing comprehensive geographical information within the management area;
[0015] Dynamic identification module, used to divide the management area into basic units in a grid shape;
[0016] Continuously acquire remote sensing images and extract remote sensing values for each type of remote sensing item within each basic unit;
[0017] In the GIS model, the remote sensing values of each type of remote sensing project for each basic unit are marked;
[0018] Calculate the remote sensing critical value of each type of remote sensing item by combining the remote sensing values of each type of remote sensing item of the basic unit;
[0019] The management area is divided into several management sub-areas with the same internal land resource status according to the remote sensing critical value of each type of remote sensing project;
[0020] Each of the management sub-areas is marked and continuously updated in the GIS model. If the management sub-area changes, a reminder will be marked in the GIS model.
[0021] The present invention marks the received remote sensing information through a dynamic identification module, and divides the management area into management sub-areas with the same internal land resource status. Once a management sub-area changes, an identification reminder is issued. Therefore, there is no need to manage the vast management area point by point, and the efficiency of dynamic land resource management is improved without reducing the management precision.
[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0024] Figure 1 A schematic diagram of the functional units and information flow of an embodiment of a land resource dynamic management system integrating GIS and remote sensing according to the present invention;
[0025] Figure 2 This is a schematic diagram of the steps of an embodiment of a land resource dynamic management method integrating GIS and remote sensing according to the present invention;
[0026] Figure 3 This is a schematic diagram of the process flow of step S4 in one embodiment of the present invention;
[0027] Figure 4 This is a schematic diagram of the process flow of step S4 in one embodiment of the present invention;
[0028] Figure 5 This is a schematic diagram of the process flow of step S421 in one embodiment of the present invention;
[0029] Figure 6 This is a schematic diagram of the process flow of step S43 in one embodiment of the present invention;
[0030] Figure 7 This is a schematic diagram of the process flow of step S5 in one embodiment of the present invention;
[0031] In the accompanying drawings, the components represented by the reference numerals are as follows:
[0032] 1-Remote sensing image interface, 2-GIS model, 3-Dynamic identification module. DETAILED DESCRIPTION
[0033] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.
[0034] It should be noted that the terms "first," "second," and the like in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with certain aspects of the present application as detailed in the appended claims.
[0035] See also Figures 1 to 2 As shown, the present invention provides a dynamic land resource management system that integrates GIS and remote sensing. Functionally, it includes a remote sensing image interface 1, a GIS model 2, and a dynamic identification module 3. The remote sensing image interface 1 is used to continuously receive remote sensing images; the GIS model 2 is used to construct a digital model containing comprehensive geographic information within the management area.
[0036] The dynamic identification module 3 in this solution is used to subsequently execute step S1 to divide the management area into basic units in a grid shape;
[0037] Next, step S2 can be executed to continuously acquire remote sensing images and extract remote sensing values for each type of remote sensing item within each basic unit. Remote sensing item types include 400–700nm visible light, 700–1300nm near-infrared light, 1300–2500nm short-wave infrared light, 8–14μm thermal infrared light, and 1mm–1m microwave light. 400–700nm visible light can be used for ground color recognition and urban expansion monitoring, 700–1300nm near-infrared light can be used for vegetation health assessment (chlorophyll content), 1300–2500nm short-wave infrared light can be used for soil moisture and mineral identification, 8–14μm thermal infrared light can be used for surface temperature inversion (urban heat island effect, drought monitoring), and 1mm–1m microwave light can be used for all-weather monitoring (radar interferometry InSAR).
[0038] Next, step S3 can be executed to annotate the remote sensing values of each type of remote sensing item for each basic unit in the GIS model. Next, step S4 can be executed to calculate the remote sensing critical value of each type of remote sensing item based on the remote sensing values of each type of remote sensing item for the basic unit. Next, step S5 can be executed to divide the management area into a number of management sub-areas with the same internal land resource status based on the remote sensing critical value of each type of remote sensing item.
[0039] See also Figure 1 and 7 As shown, during the division of management sub-regions, in order to maintain the consistency of land resource status within the management sub-regions, step S51 can first be performed to determine, for each basic unit within the management region, whether the difference in remote sensing values between the basic unit and the adjacent basic units for each type of remote sensing item is less than the corresponding remote sensing threshold. If so, step S52 can be performed to divide the basic unit and the adjacent basic unit into the same management sub-region. If not, step S53 can be performed to avoid division.
[0040] After the management sub-area division is completed, step S6 can be executed to mark each management sub-area in the GIS model and continuously update it. If the management sub-area changes, a reminder will be marked in the GIS model.
[0041] See also Figure 3As shown, in the process of calculating the remote sensing critical value of each type of remote sensing item, step S41 can first be executed to accumulate the difference in remote sensing values of each type of remote sensing item between each basic unit as the remote sensing state distinction between the basic units. Next, step S42 can be executed to classify basic units with the same remote sensing state into the same basic unit group based on the remote sensing state distinction between each pair of basic units within the management area. Finally, step S43 can be executed to extract the remote sensing critical value of each type of remote sensing item within each basic unit group based on the difference in remote sensing values of each type of remote sensing item between each pair of basic units.
[0042] See also Figure 4 and 5 As shown, during the process of dividing basic unit groups, step S421 can first be executed, where staff can set the dynamic management precision for each type of remote sensing project and determine the number of basic unit groups. Staff can directly specify the number of basic unit groups, or they can execute step S4211 to set the dynamic management precision for each type of remote sensing project as the remote sensing step value for each type of remote sensing project. Next, step S4212 can be executed to calculate and obtain the numerical range of remote sensing values for each type of remote sensing project for all basic units within the management area. Next, step S4213 can be executed to select multiple sample values evenly spaced within the numerical range of remote sensing values for all basic units for each type of remote sensing project, according to the remote sensing step value corresponding to each remote sensing project. Next, step S4214 can be executed to count the number of sample values selected for each remote sensing project. Finally, step S4215 can be executed to accumulate the number of sample values selected for each remote sensing project to determine the number of basic unit groups.
[0043] Please continue reading Figure 4 As shown, after the number of basic unit groups is determined, step S422 can be executed to randomly select multiple basic units as pre-selected basic units according to the number of basic unit groups. Step S423 can then be executed to calculate the remote sensing state distinction between each pre-selected basic unit and the remaining basic units. Step S424 can then be executed to classify each remaining basic unit other than the pre-selected basic unit into the same basic unit group with the pre-selected basic unit having the smallest remote sensing state distinction, thereby obtaining a basic unit group.
[0044] To determine whether the land resource status within the obtained basic unit groups is the same, step S425 can be executed to calculate the remote sensing mean value of all basic units contained in each basic unit group for each type of remote sensing item, and then simulate the remote sensing value of the mean basic unit of the basic unit group for each type of remote sensing item. Next, step S426 can be executed to determine whether the basic unit in the basic unit group with the smallest remote sensing status difference from the corresponding mean basic unit is the preselected basic unit.
[0045] If so, step S427 can be executed to obtain a basic unit group in which the remote sensing states of all the basic units contained therein are deemed to be identical. If not, step S428 can be executed to reclassify the basic unit groups until a basic unit group in which all the basic units contained therein are deemed to have the same remote sensing states is obtained. Specifically, within each basic unit group, the average basic unit corresponding to the basic unit group is used as a preselected basic unit, and the group is reclassified to obtain multiple basic unit groups, and the average basic unit of each basic unit group is calculated. Within each basic unit group, it is re-determined whether the basic unit group with the smallest remote sensing state difference from the corresponding average basic unit is the preselected basic unit.
[0046] To supplement the implementation of steps S421 to S428, we provide the source code for some functional modules, with cross-references and explanations provided in the comments. To prevent the leakage of data involving commercial secrets, data that does not affect the implementation of the solution is desensitized. The same applies below.
[0047]
[0048]
[0049]
[0050]
[0051]
[0052]
[0053]
[0054]
[0055]
[0056]
[0057] This code implements a complete dynamic grouping function for land resource remote sensing units. First, it is driven by step values. Based on the step values of each band set by the staff, the appropriate number of groups is automatically calculated to achieve a scientific and controllable grouping granularity. Then, intelligent iterative grouping is performed, an algorithm is used to select the initial seed, and iterative optimization is performed to ensure that the final grouping results are stable and reliable. After that, a comprehensive multi-band consideration is performed, and the weighted discrimination of multiple bands such as visible light, infrared, and microwave is calculated to achieve a comprehensive land status assessment. Finally, automatic convergence detection is performed. By matching the mean unit with the seed unit, it is automatically determined whether the grouping has converged, avoiding infinite iterations. The complete process of this function includes a complete processing flow from data preparation, range calculation, seed selection to iterative optimization. By organically combining manual experience (step value setting) with automatic calculation (iterative optimization), it provides an explainable and controllable unit grouping scheme for land resource management, which can be directly integrated into the GIS system to assist in decision-making.
[0058] See also Figure 6 As shown, in the specific process of extracting the remote sensing critical value for each type of remote sensing item, step S431 can be first executed to calculate the maximum difference value of all the basic units contained in the basic unit group for each type of remote sensing item within each basic unit group, and the ratio of the total number of basic units contained in the basic unit group as the preliminary remote sensing critical value of the basic unit group for each type of remote sensing item. Next, step S432 can be executed to select the maximum value of the preliminary remote sensing critical values corresponding to all basic unit groups for each type of remote sensing item as the remote sensing critical value. Finally, step S433 can be executed to obtain the remote sensing critical value for each type of remote sensing item.
[0059] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, systems, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and the part for the module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be performed substantially in parallel, and they can sometimes also be performed in the opposite order, depending on the function involved.
[0060] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.
[0061] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit can implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.
[0062] The embodiments of the present application have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.
Claims
1. A land resource dynamic management method integrating GIS and remote sensing, characterized in that: include, Divide the management area into basic units in a grid-like manner; Continuously acquire remote sensing images and extract remote sensing values for each type of remote sensing item within each basic unit; In the GIS model, the remote sensing values of each type of remote sensing project for each basic unit are marked; Calculate the remote sensing critical value of each type of remote sensing item by combining the remote sensing values of each type of remote sensing item of the basic unit; The management area is divided into several management sub-areas with the same internal land resource status according to the remote sensing critical value of each type of remote sensing project; Each of the management sub-areas is marked and continuously updated in the GIS model. If the management sub-area changes, a reminder will be marked in the GIS model.
2. The method according to claim 1, characterized in that Types of remote sensing projects include 400-700nm visible light, 700-1300nm near-infrared light, 1300-2500nm short-wave infrared light, 8-14μm thermal infrared light and / or 1mm-1m microwave.
3. The method according to claim 1, characterized in that The step of calculating the remote sensing critical value of each type of remote sensing item by combining the remote sensing values of each type of remote sensing item of the basic unit includes: The accumulated value of the difference between the remote sensing values of each type of remote sensing items between each basic unit is used as the remote sensing state distinction between the basic units; According to the remote sensing state distinction between each pair of basic units in the management area, the basic units with the same remote sensing state are classified into the same basic unit group; In each basic unit group, the remote sensing critical value of each type of remote sensing item is extracted according to the difference of the remote sensing values of each type of remote sensing item between each pair of basic units.
4. The method according to claim 3, characterized in that The step of classifying the basic units with the same remote sensing status into the same basic unit group according to the remote sensing status distinction between each pair of basic units in the management area includes: The staff will set the dynamic management precision of each type of remote sensing project and determine the number of basic unit groups; Randomly select multiple basic units as pre-selected basic units according to the number of basic unit groups; Calculate and obtain the remote sensing state distinction between each pre-selected basic unit and the remaining basic units; For each remaining basic unit other than the preselected basic unit, the remaining basic unit and the preselected basic unit with the smallest difference in remote sensing state are classified into the same basic unit group to obtain a basic unit group.
5. The method according to claim 4, characterized in that The steps of setting the dynamic management precision of each type of remote sensing project and deriving the number of basic unit groups by the staff include: The staff shall set the dynamic management precision of each type of remote sensing project as the remote sensing ladder value of each type of remote sensing project; Calculate and obtain the numerical range of remote sensing values of all basic units in the management area for each type of remote sensing project; Within the numerical range of remote sensing values of each type of remote sensing item in all basic units, multiple sampling values are evenly spaced according to the remote sensing step value corresponding to each remote sensing item; Count the number of sample values selected for each remote sensing project; The number of sample values selected for each remote sensing project is accumulated to obtain the number of basic unit groups.
6. The method according to claim 4 or 5, characterized in that The step of classifying the basic units with the same remote sensing status into the same basic unit group according to the remote sensing status distinction between each pair of basic units in the management area also includes: In each basic unit group, calculate and obtain the remote sensing mean value of all basic units contained in the basic unit group in each type of remote sensing project, and simulate and obtain the remote sensing value of the mean basic unit of the basic unit group in each type of remote sensing project; In each basic unit group, determining whether the basic unit in the basic unit group that has the smallest remote sensing state distinction from the corresponding mean basic unit is a preselected basic unit; If so, a basic unit group is obtained, in which the remote sensing states of all basic units contained therein are deemed to be the same; If not, the basic unit group is reclassified until a basic unit group is obtained in which the remote sensing status of all the basic units included is deemed to be the same.
7. The method according to claim 6, characterized in that The step of reclassifying the basic unit group until a basic unit group containing all basic units having the same remote sensing status is obtained, include, In each basic unit group, the average basic unit corresponding to the basic unit group is used as a reselected preselected basic unit, and the group is re-divided to obtain a plurality of basic unit groups, and the average basic unit of each basic unit group is calculated; In each basic unit group, it is re-determined whether the basic unit in the basic unit group having the smallest remote sensing state difference from the corresponding average basic unit is the pre-selected basic unit.
8. The method according to claim 3, characterized in that The step of extracting the remote sensing critical value of each type of remote sensing item according to the difference of the remote sensing value of each type of remote sensing item between each pair of basic units in each basic unit group, include, In each basic unit group, calculate the ratio of the maximum difference value of all basic units contained in the basic unit group in each type of remote sensing item to the total number of basic units contained in the basic unit group as the preliminary remote sensing critical value of the basic unit group in each type of remote sensing item; For each type of remote sensing project, the maximum value among the preliminary remote sensing critical values corresponding to all basic unit groups is selected as the remote sensing critical value; Get the remote sensing critical value of each type of remote sensing project.
9. The method according to claim 1, characterized in that The step of dividing the management area into a number of management sub-areas with the same internal land resource status according to the remote sensing critical value of each type of remote sensing project, include, For each basic unit in the management area, determine whether the difference in remote sensing value between the basic unit and the adjacent basic units in each type of remote sensing item is less than the corresponding remote sensing critical value; If so, the basic unit and the adjacent basic units are divided into the same management sub-area; If not, no division is made.
10. A dynamic land resource management system integrating GIS and remote sensing, characterized in that: include, Remote sensing image interface, used to continuously receive remote sensing images; GIS model, used to construct a digital model containing comprehensive geographical information within the management area; Dynamic identification module, used to divide the management area into basic units in a grid shape; Continuously acquire remote sensing images and extract remote sensing values for each type of remote sensing item within each basic unit; In the GIS model, the remote sensing values of each type of remote sensing project for each basic unit are marked; Calculate the remote sensing critical value of each type of remote sensing item by combining the remote sensing values of each type of remote sensing item of the basic unit; The management area is divided into several management sub-areas with the same internal land resource status according to the remote sensing critical value of each type of remote sensing project; Each of the management sub-areas is marked and continuously updated in the GIS model. If the management sub-area changes, a reminder will be marked in the GIS model.