An ecological carrying capacity estimation system and method based on multi-source remote sensing data

Abstract

This invention relates to the field of ecological carrying capacity estimation technology, and discloses an ecological carrying capacity estimation system and method based on multi-source remote sensing data. The system integrates multi-source remote sensing data with actual ground-collected data through a data acquisition module, and divides the region into grids. It can construct basic ecological carrying capacity grid by grid, significantly improving the spatial resolution and data richness of the assessment. This overcomes the shortcomings of traditional statistical methods, such as single data and coarse spatial representation. Then, through an ecological carrying capacity assessment module, the basic ecological carrying capacity is integrated with three impact assessment directions: landscape restoration impact, nighttime light intrusion, and supply-demand differences. The resulting actual ecological carrying capacity construction retains the objectivity of remote sensing data-driven approaches while incorporating multi-dimensional anthropogenic and natural disturbance factors. It comprehensively reflects the true carrying capacity of the regional ecosystem, providing a scientific basis for resource management, ecological compensation, and sustainable development policy formulation.

Description

Technical Field

[0001] This invention relates to the field of ecological carrying capacity estimation technology, specifically to an ecological carrying capacity estimation system and method based on multi-source remote sensing data. Background Technology

[0002] Ecosystem carrying capacity is a key indicator for measuring a region's sustainable development capacity. It is defined as the total amount of resources or the population size that can be sustainably provided without compromising the structure and function of the ecosystem. Traditional methods for estimating ecological carrying capacity often rely on ground-based statistical data and simplified ecological footprint models, which are insufficient to reflect spatial heterogeneity and dynamic changes. In recent years, with the rapid development of remote sensing technology, multi-source satellite data (such as MODIS, Landsat, Sentinel, and nighttime light data) have provided rich spatiotemporal information for regional-scale ecological carrying capacity assessment, making rasterized estimation based on conventional factors such as net primary productivity (NPP), land use type, and vegetation indices possible. Ecological carrying capacity research is a prerequisite for ecological environment planning and the realization of coordinated ecological and environmental development. Constrained by numerous factors and spatiotemporal conditions, the estimation of ecological carrying capacity has significant uncertainties. How to quantitatively evaluate and simulate the prediction of regional ecological carrying capacity remains a challenge. Currently, the main estimation methods include the net primary productivity estimation method of natural vegetation, the ecological footprint method, the Gaussian method, and the supply-demand balance method. These methods have been applied to varying degrees in the estimation of ecological carrying capacity, but they also have many problems. Currently, there are no recommended technical methods for assessing ecological carrying capacity. This is due to issues such as significant regional differences, difficulty in obtaining some basic data, and the fact that existing methods are limited in scope and cannot provide a comprehensive assessment of ecological carrying capacity. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an ecological carrying capacity estimation system and method based on multi-source remote sensing data, which has the advantage of providing relatively accurate estimation of ecological carrying capacity and solves the aforementioned technical problems.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an ecological carrying capacity estimation system based on multi-source remote sensing data, comprising: The data acquisition module acquires remote sensing data and divides the current area into grids. Based on the remote sensing data of each grid, it constructs the basic ecological carrying capacity of each grid. The landscape restoration impact assessment module selects several similar landscapes based on the restoration data of several landscapes in historical data, and constructs landscape restoration impact assessment coefficients based on the similar landscapes. The nighttime light intrusion impact assessment module constructs the nighttime light intrusion impact assessment coefficient for each grid based on the remote sensing data acquired in the data acquisition module. At the same time, it assesses the nighttime light intrusion impact assessment coefficient. If the nighttime light intrusion impact assessment coefficient exceeds the preset nighttime intrusion threshold, it issues a first warning command and executes a first warning strategy. The supply and demand difference impact module constructs the supply and demand difference impact coefficient for the current region based on the remote sensing data acquired by the data acquisition module. If the supply and demand difference impact coefficient exceeds the preset supply and demand difference threshold, a second early warning command is issued and a second early warning strategy is executed. The ecological carrying capacity assessment module comprehensively constructs the actual ecological carrying capacity based on the basic ecological carrying capacity, the landscape restoration impact assessment coefficient, the nighttime light intrusion impact assessment coefficient, and the supply and demand difference impact coefficient.

[0005] As a preferred technical solution of the present invention, the specific steps for constructing the basic ecological carrying capacity of each grid are as follows: Based on the Remote sensing data of raster cells are used to obtain different types of terrain and their corresponding areas, and the first raster cell is constructed. The basic ecological carrying capacity of each grid The basic ecological carrying capacity is obtained by normalizing the basic ecological carrying capacity of all grids using the min-max method. ; The first The basic ecological carrying capacity of each grid The specific expression is as follows: in, Indicates the total number of terrain features. To express summation, Indicates the first The influence weight of different terrain types Indicates the first The resource conversion coefficient of a certain terrain type in a sampling period. Indicates the first Resource consumption per person per sampling period within each grid cell Indicates the first Primary productivity of a terrain type in a sampling period.

[0006] As a preferred technical solution of the present invention, the first Influence weight of terrain The specific expression is: in, Represents the grid area. Indicates the first Within the nth grid The area of ​​this type of terrain.

[0007] As a preferred technical solution of the present invention, the landscape restoration impact assessment module includes a landscape screening unit and a landscape restoration assessment unit; The landscape selection unit, based on the restored data of several landscapes in historical data, selects the top k similar landscapes. The specific steps are as follows: Step Aa.1: Obtain the first... The landscape type of each grid cell is specified, including natural landscapes and architectural landscapes. If the landscape type is only architectural, the filtering process terminates, and the first grid cell is output. Landscape restoration impact assessment coefficient for each grid If it is a natural landscape, proceed to step Aa.2; Step Aa.2: Construct the first The similarity evaluation coefficient between the landscape of each grid and the k-th landscape. Specifically, the first The area deviation between the k-th grid and the k-th grid and the Environmental deviation rate between the landscape of each grid cell and the k-th landscape The mean; Step Aa.3: [The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require The landscapes in each grid were sorted in descending order of their similarity evaluation coefficients with all other landscapes. Landscapes that had not been disturbed were then eliminated, and the top-performing landscapes were selected. The ranking is based on similar landscapes.

[0008] As a preferred technical solution of the present invention, the first The landscape of the first grid and the first Environmental deviation rate of each landscape The expression is as follows: in, This indicates the total number of environmental parameters. Indicates the first The landscape of the first grid Environmental parameters, Indicates the first The landscape of the first landscape Environmental parameters, Represents absolute value. To express summation; No. The landscape of the first grid and the first Area deviation of individual landscapes Specifically, the first The absolute value of the difference between the landscape area of ​​the kth grid and the area of ​​the kth landscape is obtained by comparing this absolute value with the grid area.

[0009] As a preferred embodiment of the present invention, the landscape restoration assessment unit is invoked after the landscape screening unit is executed, and a landscape restoration impact assessment coefficient is constructed based on similar landscapes. The specific steps are as follows: Step Ab.1: Calculate the time it takes for similar landscapes to recover from disturbance to the mean deviation rate of all environmental parameters being less than a set value, and use this time as the recovery time of similar landscapes; Step Ab.2: Construct the first Landscape restoration index per grid The specific expression is as follows: in, Indicates the first The landscape of the first grid and the first The weight coefficient of the similar landscape is specifically the weight coefficient of the first similar landscape. The first grid Similarity rating coefficient of similar landscapes The reciprocal, Indicates the first The coefficient of restoration of similar landscapes The specific expression is as follows: in, Indicates the first The recovery time for a similar landscape; Step Ab.3: [The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the Landscape restoration index per grid Normalization yields the first Landscape restoration impact assessment coefficient for each grid The specific expression is as follows: in, This indicates the maximum value of the landscape restoration index. This represents the minimum value of the landscape restoration index.

[0010] As a preferred embodiment of the present invention, the specific steps of the nighttime light intrusion impact assessment module are as follows: Step B1: Extract brightness from remote sensing data and obtain the brightness value of each pixel. Pixels with brightness values ​​lower than the set brightness value are regarded as pixels without light, and pixels with brightness values ​​not lower than the set brightness value are regarded as pixels affected by light. Step B2: Read all pixels corresponding to the natural landscape. If it is a pixel affected by light, set its element attribute to 1; otherwise, set it to 0. Collect all pixels with an element attribute of 1 as a set of light-affected pixels. ; Step B3: Construct the first Assessment coefficient of nighttime light intrusion impact for each grid cell The specific expression is as follows: in, This indicates summing over the set of light effects. Indicates the first The element attributes of the pixels corresponding to the natural landscape of each grid. Indicates the first Within the nth grid The shortest pixel distance between the light source center point in the current grid and the center point of the surrounding up to 8 grids. To express summation, Represents the natural constant. This represents the distance attenuation scale parameter. Indicates the first Within the nth grid The normalized values ​​of the brightness min-max of each pixel; Step B4: When the first Assessment coefficient of nighttime light intrusion impact for each grid cell If the preset nighttime intrusion threshold is exceeded, a first warning command will be issued and a first warning strategy will be executed.

[0011] As a preferred embodiment of the present invention, the specific steps of the supply and demand difference impact module are as follows: Step C1: Mark the supply area and the corresponding demand area of ​​the study area in the rasterized remote sensing data, and determine the centroid coordinates of the supply area and the corresponding demand area. Step C2: Construct the supply-demand difference impact coefficient based on the centroid coordinates of all supply areas and their corresponding demand areas. The specific expression is as follows: in, This represents the length of the diagonal of the smallest bounding rectangle of the study area. Represents the maximum value function. Indicates the coordinates of the centroid of the supply area. Indicates the coordinates of the centroid of the demand area; Step C3: Obtain the supply and demand difference impact coefficient Then, an assessment is conducted to determine the impact coefficient of the supply-demand difference. When the supply-demand difference exceeds the preset critical value, a second early warning instruction is issued and a second early warning strategy is executed.

[0012] As a preferred technical solution of the present invention, the ecological carrying capacity assessment module comprehensively constructs the actual ecological carrying capacity based on the basic ecological carrying capacity, the landscape restoration impact assessment coefficient, the nighttime light intrusion impact assessment coefficient, and the supply-demand difference impact coefficient. The specific expression is as follows: in, This represents the coefficient representing the impact of supply and demand differences. Indicates the total number of grid cells. Indicates the first Assessment coefficient of nighttime light intrusion impact for each grid cell. Indicates the first Landscape restoration impact assessment coefficient for each grid cell. To express summation, It represents the basic ecological carrying capacity.

[0013] This invention also provides a method for estimating ecological carrying capacity based on multi-source remote sensing data. Based on the above-mentioned ecological carrying capacity estimation system based on multi-source remote sensing data, the method is characterized by the following steps: S1: Acquire remote sensing data and divide the current area into grids, and construct the basic ecological carrying capacity of each grid based on the remote sensing data of each grid; S2: Based on the restoration data of several landscapes in the historical data, select several similar landscapes, and construct landscape restoration impact assessment coefficients based on the similar landscapes; S3: Based on remote sensing data, construct the nighttime light intrusion impact assessment coefficient for each grid, and evaluate the nighttime light intrusion impact assessment coefficient. If the nighttime light intrusion impact assessment coefficient exceeds the preset nighttime intrusion threshold, issue the first warning command and execute the first warning strategy. S4: Based on remote sensing data, construct the supply and demand difference impact coefficient for the current region. If the supply and demand difference impact coefficient exceeds the preset supply and demand difference threshold, issue a second early warning instruction and execute the second early warning strategy. S5: The actual ecological carrying capacity is constructed by comprehensively considering the basic ecological carrying capacity, the landscape restoration impact assessment coefficient, the nighttime light intrusion impact assessment coefficient, and the supply-demand difference impact coefficient.

[0014] Compared with existing technologies, this invention provides an ecological carrying capacity estimation system and method based on multi-source remote sensing data, which has the following beneficial effects: This invention integrates multi-source remote sensing data with actual ground-based data through a data acquisition module and divides the region into grids. It can construct basic ecological carrying capacity grid by grid, significantly improving the spatial resolution and data richness of the assessment. This overcomes the shortcomings of traditional statistical methods, such as single data and coarse spatial representation. Then, through the ecological carrying capacity assessment module, the basic ecological carrying capacity is integrated with three impact assessment directions: landscape restoration impact, nighttime light intrusion, and supply and demand differences. The constructed actual ecological carrying capacity retains the objectivity of remote sensing data-driven assessment while incorporating multi-dimensional anthropogenic and natural disturbance factors. It can comprehensively reflect the true carrying capacity of the regional ecosystem and provide a scientific basis for resource management, ecological compensation, and sustainable development policy formulation. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the system framework of the present invention; Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0016] 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.

[0017] Please see Figure 1 An ecological carrying capacity estimation system based on multi-source remote sensing data includes: The data acquisition module acquires remote sensing data and divides the current area into grids. Based on the remote sensing data of the corresponding area of ​​each grid and the actual acquired data, it constructs the basic ecological carrying capacity of each grid. The landscape restoration impact assessment module is used to measure the ability of each grid's landscape to recover to its original state after being disturbed. Specifically, it selects several similar landscapes based on the restoration data of several landscapes in historical data, and constructs a landscape restoration impact assessment coefficient based on the similar landscapes. The nighttime light intrusion impact assessment module constructs the nighttime light intrusion impact assessment coefficient for each grid based on the remote sensing data acquired in the data acquisition module. At the same time, it assesses the nighttime light intrusion impact assessment coefficient. If the nighttime light intrusion impact assessment coefficient exceeds the preset nighttime intrusion threshold, it issues a first warning command and executes a first warning strategy. The supply and demand difference impact module constructs the supply and demand difference impact coefficient for the current region based on the remote sensing data acquired by the data acquisition module. If the supply and demand difference impact coefficient exceeds the preset supply and demand difference threshold, a second early warning command is issued and a second early warning strategy is executed. The ecological carrying capacity assessment module comprehensively constructs the actual ecological carrying capacity based on the basic ecological carrying capacity, the landscape restoration impact assessment coefficient, the nighttime light intrusion impact assessment coefficient, and the supply and demand difference impact coefficient. The data acquisition module constructs the basic ecological carrying capacity of each grid based on remote sensing data of the corresponding area and actual collected data. The specific steps are as follows: Based on the Remote sensing data of raster cells are used to obtain different types of terrain and their corresponding areas, and the first raster cell is constructed. The basic ecological carrying capacity of each grid The basic ecological carrying capacity is obtained by normalizing the basic ecological carrying capacity of all grids using the min-max method. The specific expression is as follows: in, Indicates the total number of terrain features. Indicates the first The primary productivity of a terrain type in a sampling period can be obtained from the historical database. The average carbon content of the products of each terrain type during the sampling period. Indicates the first The resource conversion coefficient of a certain terrain type in a sampling period. Indicates the first Resource consumption per person per sampling period within each grid cell To express summation, Indicates the first The influence weight of each terrain type is expressed as follows: in, Represents the grid area. Indicates the first Within the nth grid The area of ​​each type of terrain is obtained through remote sensing data. The terrain can be extracted using existing image recognition algorithms, supervised clustering algorithms (such as maximum likelihood), or unsupervised clustering algorithms (K-means) for identification and classification. These will not be elaborated on here. In this embodiment, the terrain identified includes: forest, grassland, farmland, and buildings. No. Resource conversion coefficient of a type of terrain in a sampling period Specifically, it is the average value of historical carbon resource conversion, that is, the average value of the carbon content of the outputs converted into resource equivalents in different terrains within the sampling period; No. Resource consumption per person per sampling period within each grid cell The method of obtaining the data is to convert the regional statistical value into carbon equivalent to obtain the specific value as the per capita resource consumption during the sampling period. The landscape restoration impact assessment module includes a landscape screening unit and a landscape restoration assessment unit; The landscape selection unit, based on the restored data of several landscapes in historical data, selects the top k similar landscapes. The specific steps are as follows: Step Aa.1: Obtain the first... The landscape type of each grid cell is specified, including natural landscapes and architectural landscapes. If the landscape type is only architectural, the filtering process terminates, and the first grid cell is output. Landscape restoration impact assessment coefficient for each grid If it is a natural landscape, proceed to step Aa.2; In this embodiment, the distinction between natural landscapes and architectural landscapes is made by manual annotation on remote sensing data. In practice, those skilled in the art may also use other methods to distinguish between natural landscapes and architectural landscapes. Step Aa.2: Construct the first The similarity evaluation coefficient between the landscape of each grid and the k-th landscape. The specific expression is as follows: in, Indicates the first The area deviation between the k-th grid and the k-th grid. Indicates the first The environmental deviation rate between the landscape of the kth grid and the landscape of the kth grid is obtained by the following steps: based on the kth grid... The average value of the environmental parameters of the k-th landscape and the landscape of the k-th grid is calculated, and the specific expression is as follows: in, This indicates the total number of environmental parameters. Indicates the first The landscape of the first grid Environmental parameters, Represents the landscape of the k-th landscape. Various environmental parameters are required; please refer to Table 1 below for specific selection criteria. Table 1 Environmental Parameters The k-th landscape is not limited to landscapes in the current region; it can be other landscapes in the existing database. No. The area deviation between the k-th grid and the k-th grid The measurement is taken by comparing the area deviation rate between the two landscapes, as shown in the following expression: in, Indicates the first The landscape area of ​​each grid This represents the area of ​​the k-th landscape. It's important to note that the area difference represents the pixel area of ​​the remote sensing data at the same scale. Represents the grid area; Step Aa.3: [The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require The landscapes in each grid were sorted in descending order of their similarity evaluation coefficients with all other landscapes. Landscapes that had not been disturbed were then eliminated, and the top-performing landscapes were selected. The ranking is based on similar landscapes; The landscape restoration assessment unit is invoked after the landscape screening unit is executed. It constructs landscape restoration impact assessment coefficients based on similar landscapes. The specific steps are as follows: Step Ab.1: Calculate the time required for similar landscapes to recover from disturbances until the mean deviation rate of all environmental parameters is less than a set value, and use this time as the recovery time for similar landscapes. ; Step Ab.2: Construct the first Landscape restoration index per grid The specific expression is as follows: in, Indicates the first The landscape of the first grid and the first The weighting coefficients for the similar landscapes are specifically for the . The similarity evaluation coefficients of all similar landscapes in the r grid are normalized after taking their reciprocals. For the r grid... The first grid The similarity evaluation coefficient for each similar landscape is: , Indicates the first The coefficient of recovery for similar landscapes Overall, the first The landscape restoration index for each grid cell is a weighted normalized form consisting of the reciprocal of the restoration time. Overall, it is positively correlated with the landscape's ecological carrying capacity; that is, the shorter the restoration time, the higher the ecological carrying capacity. The higher, that is, the first Landscape restoration index per grid The higher; Step Ab.3: [The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the Landscape restoration index per grid Normalization yields the first Landscape restoration impact assessment coefficient for each grid The specific expression is as follows: in, This indicates the maximum value of the landscape restoration index. This represents the minimum value of the landscape restoration index; The nighttime light intrusion impact assessment module constructs the nighttime light intrusion impact assessment coefficient for each grid based on remote sensing data acquired in the data acquisition module. At the same time, it assesses the nighttime light intrusion impact assessment coefficient. If the nighttime light intrusion impact assessment coefficient exceeds the preset nighttime intrusion threshold, it issues a first warning command and executes a first warning strategy. This is used to quantify the intensity of human nighttime light activity intrusion into natural ecological space and reflect the negative impact of human stress on the carrying capacity of the ecosystem. The specific steps are as follows: Step B1: Extract brightness from remote sensing data and obtain the brightness value of each pixel. Pixels with brightness values ​​lower than the set brightness value are regarded as pixels without light, and pixels with brightness values ​​not lower than the set brightness value are regarded as pixels affected by light. Step B2: Read all pixels corresponding to the natural landscape. If it is a pixel affected by light, set its element attribute to 1; otherwise, set it to 0. Collect all pixels with an element attribute of 1 as a set of light-affected pixels. ; Step B3: Construct the first Assessment coefficient of nighttime light intrusion impact for each grid cell The specific expression is as follows: in, This indicates summing over the set of light effects. Indicates the first The element attributes of the pixels corresponding to the natural landscape of each grid. Indicates the first Within the nth grid The distance of each pixel to the center point of the light source among the maximum of eight surrounding grids is the shortest pixel distance within the grid itself. For grids located at the boundary of the study area, the number of surrounding grids may be 3 or 5. Represents the natural constant. This represents the distance attenuation scale parameter; in this embodiment, a pixel distance of 1-3 km is selected. Indicates the first Within the nth grid The normalized values ​​of the brightness min-max of each pixel; When the Assessment coefficient of nighttime light intrusion impact for each grid cell When the preset nighttime intrusion threshold is exceeded, a first warning command is issued and a first warning strategy is executed. The first warning strategy is as follows: dispatch drones or ground personnel to confirm the specific type of intrusion source, suspend the construction of new outdoor LED screens and high-mast lights within 500m of the grid, and resume construction after the staff confirms that there is no problem. The supply-demand difference impact module constructs a supply-demand difference impact coefficient for the current region based on remote sensing data acquired by the data acquisition module. If the supply-demand difference impact coefficient exceeds a preset supply-demand difference threshold, a second early warning command is issued, and a second early warning strategy is executed. The specific steps are as follows: Step C1: Mark the supply area and the corresponding demand area of ​​the study area in the rasterized remote sensing data, and determine the centroid coordinates of the supply area and the corresponding demand area. The determination of the centroid coordinates of the supply area and the demand area can be carried out by the following steps: obtain all supply areas and calculate the corresponding centroid coordinates of the supply area using the Monte Carlo method; at the same time, select the demand area corresponding to the supply area and solve for the centroid coordinates of the demand area in the same way. Step C2: Construct the supply-demand difference impact coefficient based on the centroid coordinates of all supply areas and their corresponding demand areas. The specific expression is as follows: in, This represents the diagonal length of the smallest bounding rectangle of the study area, which is calculated on the same scale as the centroid coordinates of the supply area and the demand area. Represents the maximum value function. Indicates the coordinates of the centroid of the supply area. This represents the centroid coordinates of the demand zones. It's important to note that there must be a one-to-one correspondence between supply and demand zones. For example, there are two supply zones, A and B, and three demand zones (Aa, Ab, and Bb). Supply zone A has a supply-demand relationship with demand zones Aa and Ab, and supply zone B has a supply-demand relationship with demand zones Bb and Ab. Therefore, during the calculation, only the distance between supply zone A and demand zones Aa and Ab is considered, ignoring the distance between supply zone A and demand zone Bb. After traversing all supply and demand zones, the final output is... The value; Step C3: Obtain the supply and demand difference impact coefficient Then, an assessment is conducted to determine the impact coefficient of the supply-demand difference. When the supply-demand difference exceeds the preset critical value, a second early warning instruction will be issued and a second early warning strategy will be implemented. The second early warning strategy is as follows: a reminder will be issued to the staff, that is, a comprehensive supply-demand survey will be carried out in the whole region, and the monitoring interval will be shortened by 20% to 50%. The ecological carrying capacity assessment module comprehensively constructs the actual ecological carrying capacity based on the basic ecological carrying capacity, the landscape restoration impact assessment coefficient, the nighttime light intrusion impact assessment coefficient, and the supply-demand difference impact coefficient. The specific expression is as follows: in, This represents the total number of grid cells, in the process of obtaining... Then, by calculating the deviation rate between the current detection results and the actual ecological carrying capacity obtained from the previous sampling, it can ensure that the current ecological carrying capacity is reflected. This not only preserves the objectivity of remote sensing data-driven approaches but also integrates multi-dimensional human and natural disturbance factors, which can comprehensively reflect the true carrying capacity of the regional ecosystem and provide a scientific basis for resource management, ecological compensation, and sustainable development policy formulation. Please see Figure 2 An ecological carrying capacity estimation method based on multi-source remote sensing data includes the following steps: S1: Acquire remote sensing data and divide the current area into grids, and construct the basic ecological carrying capacity of each grid based on the remote sensing data of each grid; S2: Based on the restoration data of several landscapes in the historical data, select several similar landscapes, and construct landscape restoration impact assessment coefficients based on the similar landscapes; S3: Based on remote sensing data, construct the nighttime light intrusion impact assessment coefficient for each grid, and evaluate the nighttime light intrusion impact assessment coefficient. If the nighttime light intrusion impact assessment coefficient exceeds the preset nighttime intrusion threshold, issue the first warning command and execute the first warning strategy. S4: Based on remote sensing data, construct the supply and demand difference impact coefficient for the current region. If the supply and demand difference impact coefficient exceeds the preset supply and demand difference threshold, issue a second early warning instruction and execute the second early warning strategy. S5: The actual ecological carrying capacity is constructed by comprehensively considering the basic ecological carrying capacity, the landscape restoration impact assessment coefficient, the nighttime light intrusion impact assessment coefficient, and the supply-demand difference impact coefficient.

[0018] This embodiment only provides one feasible solution and does not represent all the protection content. The size of the threshold value is set for ease of comparison. The size of the threshold value depends on the amount of sample data and the number of base values ​​set by those skilled in the art for each group of sample data. As long as it does not affect the proportional relationship between the parameter and the quantized value, it is acceptable. The weight can be determined by those skilled in the art based on each sample data and multiple rounds of experiments. Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An ecological carrying capacity estimation system based on multi-source remote sensing data, characterized in that: include: The data acquisition module acquires remote sensing data and divides the current area into grids. Based on the remote sensing data of each grid, it constructs the basic ecological carrying capacity of each grid. The landscape restoration impact assessment module selects several similar landscapes based on the restoration data of several landscapes in historical data, and constructs landscape restoration impact assessment coefficients based on the similar landscapes. The nighttime light intrusion impact assessment module constructs the nighttime light intrusion impact assessment coefficient for each grid based on the remote sensing data acquired in the data acquisition module. At the same time, it assesses the nighttime light intrusion impact assessment coefficient. If the nighttime light intrusion impact assessment coefficient exceeds the preset nighttime intrusion threshold, it issues a first warning command and executes a first warning strategy. The supply and demand difference impact module constructs the supply and demand difference impact coefficient for the current region based on the remote sensing data acquired by the data acquisition module. If the supply and demand difference impact coefficient exceeds the preset supply and demand difference threshold, a second early warning command is issued and a second early warning strategy is executed. The ecological carrying capacity assessment module comprehensively constructs the actual ecological carrying capacity based on the basic ecological carrying capacity, the landscape restoration impact assessment coefficient, the nighttime light intrusion impact assessment coefficient, and the supply and demand difference impact coefficient.

2. The ecological carrying capacity estimation system based on multi-source remote sensing data according to claim 1, characterized in that: The specific steps for constructing the basic ecological carrying capacity of each grid are as follows: Based on the Remote sensing data of raster cells are used to obtain different types of terrain and their corresponding areas, and the first raster cell is constructed. The basic ecological carrying capacity of each grid The basic ecological carrying capacity is obtained by normalizing the basic ecological carrying capacity of all grids using the min-max method. ; The first The basic ecological carrying capacity of each grid The specific expression is as follows: in, Indicates the total number of terrain features. To express summation, Indicates the first The influence weight of different terrain types Indicates the first The resource conversion coefficient for a given terrain type over a sampling period is specifically the average value of the carbon content of the outputs converted into resource equivalents for different terrain types within the sampling period. Indicates the first Resource consumption per person per sampling period within each grid cell Indicates the first Primary productivity of a terrain type in a sampling period.

3. The ecological carrying capacity estimation system based on multi-source remote sensing data according to claim 2, characterized in that: The first Influence weight of terrain The specific expression is: in, Represents the grid area. Indicates the first Within the nth grid The area of ​​this type of terrain.

4. The ecological carrying capacity estimation system based on multi-source remote sensing data according to claim 2, characterized in that: The landscape restoration impact assessment module includes a landscape screening unit and a landscape restoration assessment unit; The landscape selection unit, based on the restored data of several landscapes in historical data, selects the top k similar landscapes. The specific steps are as follows: Step Aa.1: Obtain the first... The landscape type of each grid cell is specified, including both natural and architectural landscapes. If only architectural landscapes are selected, the filtering process terminates, and the first grid cell is output. Landscape restoration impact assessment coefficient for each grid If it is a natural landscape, proceed to step Aa.2; Step Aa.2: Construct the first The similarity evaluation coefficient between the landscape of each grid and the k-th landscape. Specifically, the first The area deviation between the k-th grid and the k-th grid and the Environmental deviation rate between the landscape of each grid cell and the k-th landscape The mean; Step Aa.3: [The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the full context.] The landscapes in each grid were sorted in descending order of their similarity evaluation coefficients with all other landscapes. Landscapes that had not been disturbed were then eliminated, and the top-performing landscapes were selected. The ranking is based on similar landscapes.

5. The ecological carrying capacity estimation system based on multi-source remote sensing data according to claim 4, characterized in that: The first The landscape of the first grid and the first Environmental deviation rate of each landscape The expression is as follows: in, This indicates the total number of environmental parameters. Indicates the first The landscape of the first grid Environmental parameters, Indicates the first The landscape of the first landscape Environmental parameters, Represents absolute value. To express summation; No. The landscape of the first grid and the first Area deviation of individual landscapes Specifically, the first The absolute value of the difference between the landscape area of ​​the kth grid and the area of ​​the kth landscape is obtained by comparing this absolute value with the grid area.

6. The ecological carrying capacity estimation system based on multi-source remote sensing data according to claim 4, characterized in that: The landscape restoration assessment unit is invoked after the landscape screening unit is executed. It constructs landscape restoration impact assessment coefficients based on similar landscapes. The specific steps are as follows: Step Ab.1: Calculate the time it takes for similar landscapes to recover from disturbance to the mean deviation rate of all environmental parameters being less than a set value, and use this time as the recovery time of similar landscapes; Step Ab.2: Construct the first Landscape restoration index per grid The specific expression is as follows: in, Indicates the first The landscape of the first grid and the first The weight coefficient of the similar landscape is specifically the weight coefficient of the first similar landscape. The first grid Similarity rating coefficient of similar landscapes The reciprocal, Indicates the first The coefficient of restoration of similar landscapes The specific expression is as follows: in, Indicates the first The recovery time for a similar landscape; Step Ab.3: [The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the full context.] Landscape restoration index per grid Normalization yields the first Landscape restoration impact assessment coefficient for each grid The specific expression is as follows: in, This indicates the maximum value of the landscape restoration index. This represents the minimum value of the landscape restoration index.

7. The ecological carrying capacity estimation system based on multi-source remote sensing data according to claim 6, characterized in that: The specific steps of the nighttime light intrusion impact assessment module are as follows: Step B1: Extract brightness from remote sensing data and obtain the brightness value of each pixel. Pixels with brightness values ​​lower than the set brightness value are regarded as pixels without light, and pixels with brightness values ​​not lower than the set brightness value are regarded as pixels affected by light. Step B2: Read all pixels corresponding to the natural landscape. If it is a pixel affected by light, set its element attribute to 1; otherwise, set it to 0. Collect all pixels with an element attribute of 1 as a set of light-affected pixels. ; Step B3: Construct the first Assessment coefficient of nighttime light intrusion impact for each grid cell The specific expression is as follows: in, This indicates summing over the set of effects of light. Indicates the first The element attributes of the pixels corresponding to the natural landscape of each grid. Indicates the first Within the nth grid The shortest pixel distance between the light source center point in the current grid and the center point of the surrounding up to 8 grids. To express summation, Represents the natural constant. This represents the distance attenuation scale parameter. Indicates the first Within the nth grid The normalized values ​​of the brightness min-max of each pixel; Step B4: When the first Assessment coefficient of nighttime light intrusion impact for each grid cell If the preset nighttime intrusion threshold is exceeded, a first warning command will be issued and a first warning strategy will be executed.

8. The ecological carrying capacity estimation system based on multi-source remote sensing data according to claim 7, characterized in that: The specific steps of the supply and demand difference impact module are as follows: Step C1: Mark the supply area and the corresponding demand area of ​​the study area in the rasterized remote sensing data, and determine the centroid coordinates of the supply area and the corresponding demand area. Step C2: Construct the supply-demand difference impact coefficient based on the centroid coordinates of all supply areas and their corresponding demand areas. The specific expression is as follows: in, This represents the length of the diagonal of the smallest bounding rectangle of the study area. Represents the maximum value function. Indicates the coordinates of the centroid of the supply area. Indicates the coordinates of the centroid of the demand area; Step C3: Obtain the supply and demand difference impact coefficient Then, an assessment is conducted to determine the impact coefficient of the supply-demand difference. When the supply-demand difference exceeds the preset critical value, a second early warning instruction is issued and a second early warning strategy is executed.

9. The ecological carrying capacity estimation system based on multi-source remote sensing data according to claim 8, characterized in that: The ecological carrying capacity assessment module comprehensively constructs the actual ecological carrying capacity based on the basic ecological carrying capacity, landscape restoration impact assessment coefficient, nighttime light intrusion impact assessment coefficient, and supply-demand difference impact coefficient. The specific expression is as follows: in, This represents the coefficient representing the impact of supply and demand differences. Indicates the total number of grid cells. Indicates the first Assessment coefficient of nighttime light intrusion impact for each grid cell. Indicates the first Landscape restoration impact assessment coefficient for each grid cell. To express summation, It represents the basic ecological carrying capacity.

10. A method for estimating ecological carrying capacity based on multi-source remote sensing data, based on the ecological carrying capacity estimation system based on multi-source remote sensing data as described in any one of claims 1-9, characterized in that: Includes the following steps: S1: Acquire remote sensing data and divide the current area into grids, and construct the basic ecological carrying capacity of each grid based on the remote sensing data of each grid; S2: Based on the restoration data of several landscapes in the historical data, select several similar landscapes, and construct landscape restoration impact assessment coefficients based on the similar landscapes; S3: Based on remote sensing data, construct the nighttime light intrusion impact assessment coefficient for each grid, and evaluate the nighttime light intrusion impact assessment coefficient. If the nighttime light intrusion impact assessment coefficient exceeds the preset nighttime intrusion threshold, issue the first warning command and execute the first warning strategy. S4: Based on remote sensing data, construct the supply and demand difference impact coefficient for the current region. If the supply and demand difference impact coefficient exceeds the preset supply and demand difference threshold, issue a second early warning instruction and execute the second early warning strategy. S5: The actual ecological carrying capacity is constructed by comprehensively considering the basic ecological carrying capacity, the landscape restoration impact assessment coefficient, the nighttime light intrusion impact assessment coefficient, and the supply-demand difference impact coefficient.

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