Surface runoff estimation and flood simulation method based on urban impervious surface

Abstract

The invention relates to the technical field of urban flood disaster prevention and control, in particular to a surface runoff estimation and flood simulation method based on an urban impervious surface. The method comprises the following steps: segmenting a target town region into a plurality of sub-regions, collecting spectral information of remote sensing satellite images in each sub-region, and calculating a spectral characteristic index of each sub-region based on the spectral information; determining an impervious surface area in the target town area through a clustering algorithm based on the spectral characteristic index of each sub-area, and calculating the surface runoff of the impervious surface area based on the precipitation; acquiring soil information of a region adjacent to the impervious surface region, and calculating a permeability coefficient of the region adjacent to the impervious surface region based on the soil information; whether the flood risk exists in the impervious surface area or not is judged based on the permeability coefficient and the surface runoff, and the accuracy of surface runoff estimation and the reliability of flood simulation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban flood disaster prevention and control, and in particular to a surface runoff estimation and flood simulation method based on urban impervious surfaces. Background Art

[0002] In urban areas, different functional zones (such as commercial, residential, and industrial areas) vary significantly in terms of impervious surface proportions, topography, and drainage conditions. Lumped models cannot accurately reflect these spatial variations, resulting in surface runoff estimates that are out of sync with actual conditions. While distributed hydrological models account for spatial heterogeneity, they place extremely high demands on basic data, requiring detailed multi-source data on topography, soils, vegetation, and more. Furthermore, distributed models have numerous parameters, and the parameter determination process is complex and subject to significant uncertainty. In practical applications, efficient and accurate surface runoff estimation over large urban areas is difficult due to difficulties in data acquisition and model calibration.

[0003] Existing flood simulation methods have significant shortcomings when simulating urban flooding. Some methods fail to fully consider the unique impact of urban impervious surfaces on the rainfall-runoff relationship. The high runoff coefficient of impervious surfaces causes rainfall to be rapidly converted into surface runoff, resulting in significant accumulation of water in urban areas during heavy rainstorms. However, traditional flood simulation methods are often based on the hydrological characteristics of natural watersheds, with limited ability to simulate these rapid runoff generation and confluence processes, thereby underestimating the risk of urban flooding.

[0004] Therefore, a surface runoff estimation and flood simulation method based on urban impervious surfaces is urgently needed to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for estimating surface runoff and simulating floods based on urban impervious surfaces, so as to solve the technical problems of inaccurate surface runoff estimation and low reliability of flood simulation in the prior art.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A method for estimating surface runoff and simulating flooding based on urban impervious surfaces, comprising: The target urban area is divided into several sub-areas, the spectral information of remote sensing satellite images in each sub-area is collected, and the spectral characteristic index of each sub-area is calculated based on the spectral information; Based on the spectral characteristic index of each sub-region, the impervious surface area in the target urban area is determined through a clustering algorithm, and the surface runoff of the impervious surface area is calculated based on the precipitation; Collect soil information of the area adjacent to the impervious surface area, and calculate the permeability coefficient of the area adjacent to the impervious surface area based on the soil information; Determine whether the impervious surface area has flood risk based on the permeability coefficient and surface runoff.

[0007] Furthermore, calculating the spectral characteristic index of each sub-region based on the spectral information specifically includes the following process: Based on the spectral information, the red band surface reflectance R, the green band surface reflectance G, the near infrared band surface reflectance NIR and the mid-infrared band surface reflectance MIR in each sub-area are obtained; The red band surface reflectance R, green band surface reflectance G, near infrared band surface reflectance NIR and mid-infrared band surface reflectance MIR in each sub-region are substituted into the spectral characteristic index calculation formula to calculate the spectral characteristic index LMS. The calculation formula is as follows: .

[0008] Furthermore, the impervious surface area within the target urban area is determined by clustering algorithm based on the spectral characteristic index of each sub-area, which specifically includes the following process: After normalizing the spectral characteristic index of each sub-region, a matrix X is formed. The covariance matrix S of the sample of the matrix X is calculated. The eigenvectors e1, e2, ..., e i 、e n and eigenvalues ​​t = 1, 2, ..., n; project the data into the space of eigenvectors, using the formula: ,in, The value is the value of the dimension corresponding to the spectral characteristic index of the sub-region. Set the regional feature screening threshold for regional feature screening reference values ,Will Greater than The corresponding m sub-regions are selected and recorded as the regions to be clustered; the regions to be clustered are grouped into a set of regions to be clustered The K value is set based on the size and shape of the set of regions to be clustered, and any region to be clustered is randomly selected as the initial cluster centroid. When the number of initial centroids is less than K, is the initial center of mass; based on the objective function Calculate the distance between each area to be clustered in the set of areas to be clustered and the existing initial cluster centroid ,Will The area to be clustered corresponding to the maximum value is taken as the next initial centroid; K initial centroids are obtained in sequence, and the set of areas to be clustered is clustered based on the k initial centroids to obtain the impervious surface area in the target urban area.

[0009] Furthermore, the calculation of surface runoff in impervious areas based on precipitation specifically includes the following processes: Obtain the CN value of the impervious surface area under semi-arid and semi-humid soil conditions, and calculate the surface runoff of the impervious surface area based on precipitation and CN value: ; Where Q is the surface runoff volume of the impervious surface area, P is the rainfall, and S is the maximum possible retention volume; Calculate the impervious surface area of ​​the soil in a wet state value: ; based on Calculate the maximum possible retention S: .

[0010] Furthermore, collecting soil information of the area adjacent to the impervious surface area and calculating the permeability coefficient of the area adjacent to the impervious surface area based on the soil information specifically includes the following process: Obtain soil information of areas adjacent to impervious surfaces, including the proportion of sandy soil, the proportion of loamy sandy soil, the proportion of sandy loamy soil, and soil moisture content; Determine the weights of sandy soil, loamy sandy soil, and sandy loam: the weights of sandy soil, loamy sandy soil, and sandy loam are K1, K2, and K3 respectively; The porosity M1 is obtained according to the correlation formula, which is as follows: ; in, 、 、 are the proportion of sandy soil, loamy sandy soil, and sandy loam, respectively; V is the total volume of soil samples taken from the area adjacent to the impervious surface; and G is a constant coefficient. By calculating the proportion of soil moisture content to the mass of soil sampled in the area adjacent to the impermeable surface, the average water density μ is obtained. The permeability coefficient YT is obtained by correlating the average porosity M1 and the average water density μ. The correlation formula is: ; in, is the penetration index, 0.25≤ ≤0.6.

[0011] Furthermore, judging whether the impervious surface area has flood risk based on the permeability coefficient and surface runoff volume specifically includes the following process: Obtain the permeability coefficient of the impervious surface area and its surrounding areas; obtain surface runoff data; set a flood risk threshold for the permeability coefficient based on regional geological and climatic conditions. Areas below this threshold have a higher flood risk due to poor permeability; set a flood risk threshold for surface runoff based on the regional drainage system capacity and historical flood events. Surface runoff exceeding this threshold leads to poor drainage and a flood risk; compare the permeability coefficient of the impervious surface area with the set threshold to determine whether its permeability meets flood control requirements; compare the surface runoff of the impervious surface area with the set threshold to assess whether it exceeds the drainage system's processing capacity; if the permeability coefficient is lower than the threshold and the surface runoff exceeds the threshold, the impervious surface area has a higher flood risk; if both the permeability coefficient and the surface runoff are close to or lower than the threshold, the flood risk is relatively low, but attention still needs to be paid to potential risks under extreme weather conditions.

[0012] Furthermore, when collecting spectral information of remote sensing satellite images in each sub-area, a remote sensing satellite platform with a hyperspectral imager is selected, and the remote sensing satellite platforms include the Landsat series and the Sentinel series.

[0013] Compared with the existing solutions, the present invention achieves the following beneficial effects: Improve the accuracy of surface runoff estimates: By dividing the target urban area into several sub-regions and collecting spectral information from remote sensing satellite imagery within each sub-region, we can fully account for the spatial heterogeneity within the urban area. Different sub-regions may have different land use types, building densities, and surface cover, all of which affect the generation of surface runoff. Calculating the spectral characteristic index for each sub-region based on spectral information can more accurately reflect the surface characteristics of each sub-region, providing more accurate basic data for subsequent surface runoff estimation.

[0014] Accurate Identification of Impervious Surface Areas: A clustering algorithm is used to identify impervious surface areas within the target urban area based on the spectral signature indices of each sub-region. Compared to traditional methods, this method can more accurately identify the distribution and boundaries of impervious surfaces. Accurate identification of impervious surfaces is critical for surface runoff estimation, as the high runoff coefficient of impervious surfaces quickly converts rainfall into surface runoff. By accurately identifying impervious surface areas, runoff estimation errors caused by inaccurate impervious surface identification in traditional methods can be avoided, thereby improving the accuracy of surface runoff estimation.

[0015] Improving the reliability of flood simulations: Soil information is collected from areas adjacent to impervious surfaces and the permeability coefficient of these areas is calculated based on this soil information, fully accounting for the impact of surrounding soil conditions on flooding. In urban areas, the hydrological processes of impervious surfaces and surrounding areas are interconnected, and the permeability of surrounding areas affects the discharge and accumulation of surface runoff. By calculating the permeability coefficient of adjacent areas, the movement and evolution of floodwaters within and around impervious surfaces can be more accurately simulated, improving the reliability of flood simulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 This is a workflow diagram of a method for estimating surface runoff and simulating flooding based on urban impervious surfaces according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. can be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0020] The present invention provides a method for estimating surface runoff and simulating flooding based on urban impervious surfaces. Figure 1 This is a workflow diagram of a method for estimating surface runoff and simulating flooding based on urban impervious surfaces according to an embodiment of the present invention. Figure 1 As shown, the method includes: Step S101: Divide the target urban area into several sub-areas, collect spectral information of remote sensing satellite images in each sub-area, and calculate the spectral characteristic index of each sub-area based on the spectral information; Step S102: determining the impervious surface area within the target urban area through a clustering algorithm based on the spectral characteristic index of each sub-area, and calculating the surface runoff of the impervious surface area based on precipitation; Step S103: collecting soil information of an area adjacent to the impermeable surface area, and calculating the permeability coefficient of the area adjacent to the impermeable surface area based on the soil information; Step S104: Determine whether the impervious surface area has a flood risk based on the permeability coefficient and the surface runoff.

[0021] In summary, the present invention divides the target urban area into several sub-areas, collects spectral information of remote sensing satellite images in each sub-area, and calculates the spectral characteristic index of each sub-area based on the spectral information; determines the impervious surface area in the target urban area through a clustering algorithm based on the spectral characteristic index of each sub-area, and calculates the surface runoff of the impervious surface area based on precipitation; collects soil information of the area adjacent to the impervious surface area, and calculates the permeability coefficient of the area adjacent to the impervious surface area based on the soil information; judges whether the impervious surface area has a flood risk based on the permeability coefficient and the surface runoff, which can improve the accuracy of surface runoff estimation and enhance the reliability of flood simulation.

[0022] In some embodiments, calculating the spectral characteristic index of each sub-region based on the spectral information specifically includes the following process: Based on the spectral information, the red band surface reflectance R, the green band surface reflectance G, the near infrared band surface reflectance NIR and the mid-infrared band surface reflectance MIR in each sub-area are obtained; The red band surface reflectance R, green band surface reflectance G, near infrared band surface reflectance NIR and mid-infrared band surface reflectance MIR in each sub-region are substituted into the spectral characteristic index calculation formula to calculate the spectral characteristic index LMS. The calculation formula is as follows: .

[0023] In some embodiments, determining the impervious surface area within the target urban area using a clustering algorithm based on the spectral characteristic index of each sub-area specifically includes the following process: After normalizing the spectral characteristic index of each sub-region, a matrix X is formed. The covariance matrix S of the sample of the matrix X is calculated. The eigenvectors e1, e2, ..., e i 、e n and eigenvalues ​​t = 1, 2, ..., n; project the data into the space of eigenvectors, using the formula: ,in, The value is the value of the dimension corresponding to the spectral characteristic index of the sub-region. Set the regional feature screening threshold for regional feature screening reference values ,Will Greater than The corresponding m sub-regions are selected and recorded as the regions to be clustered; the regions to be clustered are grouped into a set of regions to be clustered , set the K value based on the size and shape of the set of regions to be clustered, and randomly select any region to be clustered as the initial cluster centroid. When the number of initial centroids is less than K, is the initial center of mass; based on the objective function , calculate the distance between each area to be clustered in the area to be clustered and the existing initial cluster centroid ,Will The area to be clustered corresponding to the maximum value is taken as the next initial centroid; K initial centroids are obtained in sequence, and the set of areas to be clustered is clustered based on the k initial centroids to obtain the impervious surface area in the target urban area.

[0024] In some embodiments, calculating the surface runoff volume of the impervious surface area based on precipitation specifically includes the following process: Obtain the CN value of the impervious surface area under semi-arid and semi-humid soil conditions, and calculate the surface runoff of the impervious surface area based on precipitation and CN value: ; Where Q is the surface runoff volume of the impervious surface area, P is the rainfall, and S is the maximum possible retention volume; Calculate the impervious surface area of ​​the soil in a wet state value: ; based on Calculate the maximum possible retention S: .

[0025] In some embodiments, collecting soil information of an area adjacent to the impermeable surface area and calculating the permeability coefficient of the area adjacent to the impermeable surface area based on the soil information specifically includes the following process: Obtain soil information of areas adjacent to impervious surfaces, including the proportion of sandy soil, the proportion of loamy sandy soil, the proportion of sandy loamy soil, and soil moisture content; Determine the weights of sandy soil, loamy sandy soil, and sandy loam: the weights of sandy soil, loamy sandy soil, and sandy loam are K1, K2, and K3 respectively; The porosity M1 is obtained according to the correlation formula, which is as follows: ; in, 、 、 are the proportion of sandy soil, loamy sandy soil, and sandy loam, respectively; V is the total volume of soil samples taken from the area adjacent to the impervious surface; and G is a constant coefficient. By calculating the proportion of soil moisture content to the mass of soil sampled in the area adjacent to the impermeable surface, the average water density μ is obtained. The permeability coefficient YT is obtained by correlating the average porosity M1 and the average water density μ. The correlation formula is: ; in, is the penetration index, 0.25≤ ≤0.6.

[0026] In some embodiments, determining whether the impervious surface area has a flood risk based on the hydraulic conductivity and the surface runoff volume specifically includes the following process: Obtain the permeability coefficient of the impervious surface area and its surrounding areas; obtain surface runoff data; set a flood risk threshold for the permeability coefficient based on regional geological and climatic conditions. Areas below this threshold have a higher flood risk due to poor permeability; set a flood risk threshold for surface runoff based on the regional drainage system capacity and historical flood events. Surface runoff exceeding this threshold leads to poor drainage and a flood risk; compare the permeability coefficient of the impervious surface area with the set threshold to determine whether its permeability meets flood control requirements; compare the surface runoff of the impervious surface area with the set threshold to assess whether it exceeds the drainage system's processing capacity; if the permeability coefficient is lower than the threshold and the surface runoff exceeds the threshold, the impervious surface area has a higher flood risk; if both the permeability coefficient and the surface runoff are close to or lower than the threshold, the flood risk is relatively low, but attention still needs to be paid to potential risks under extreme weather conditions.

[0027] It is worth noting that when collecting spectral information of remote sensing satellite images in each sub-area, a remote sensing satellite platform with a hyperspectral imager is selected, and the remote sensing satellite platforms include the Landsat series and the Sentinel series.

[0028] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0029] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0030] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0031] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only for some logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0032] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0033] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for estimating surface runoff and simulating floods based on urban impervious surfaces, characterized in that: Methods include: The target urban area is divided into several sub-areas, the spectral information of remote sensing satellite images in each sub-area is collected, and the spectral characteristic index of each sub-area is calculated based on the spectral information; Based on the spectral characteristic index of each sub-region, the impervious surface area in the target urban area is determined through a clustering algorithm, and the surface runoff of the impervious surface area is calculated based on the precipitation; Collect soil information of the area adjacent to the impervious surface area, and calculate the permeability coefficient of the area adjacent to the impervious surface area based on the soil information; Determine whether the impervious surface area has flood risk based on the permeability coefficient and surface runoff.

2. The method for estimating surface runoff and simulating floods based on urban impervious surfaces according to claim 1, characterized in that: The calculation of the spectral characteristic index of each sub-region based on spectral information specifically includes the following process: Based on the spectral information, the red band surface reflectance R, the green band surface reflectance G, the near infrared band surface reflectance NIR and the mid-infrared band surface reflectance MIR in each sub-area are obtained; The red band surface reflectance R, green band surface reflectance G, near infrared band surface reflectance NIR and mid-infrared band surface reflectance MIR in each sub-region are substituted into the spectral characteristic index calculation formula to calculate the spectral characteristic index LMS. The calculation formula is as follows: 。 3. The method for estimating surface runoff and simulating floods based on urban impervious surfaces according to claim 1, characterized in that: The process of determining the impervious surface area within the target urban area through clustering algorithm based on the spectral characteristic index of each sub-area includes the following steps: After normalizing the spectral characteristic index of each sub-region, a matrix X is formed. The covariance matrix S of the sample of the matrix X is calculated. The eigenvectors e1, e2, ..., e i 、e n and eigenvalues ​​t = 1, 2, ..., n; project the data into the space of eigenvectors, using the formula: ,in, The value is the value of the dimension corresponding to the spectral characteristic index of the sub-region. Set the regional feature screening threshold for regional feature screening reference values ,Will Greater than The corresponding m sub-regions are selected and recorded as the regions to be clustered; the regions to be clustered are grouped into a set of regions to be clustered , set the K value based on the size and shape of the set of regions to be clustered, and randomly select any region to be clustered as the initial cluster centroid. When the number of initial centroids is less than K, is the initial center of mass; based on the objective function , calculate the distance between each area to be clustered in the area to be clustered and the existing initial cluster centroid ,Will The area to be clustered corresponding to the maximum value is taken as the next initial centroid; K initial centroids are obtained in sequence, and the set of areas to be clustered is clustered based on the k initial centroids to obtain the impervious surface area in the target urban area.

4. The method for estimating surface runoff and simulating floods based on urban impervious surfaces according to claim 1, characterized in that: Calculating surface runoff from impervious areas based on precipitation involves the following steps: Obtain the CN value of the impervious surface area under semi-arid and semi-humid soil conditions, and calculate the surface runoff of the impervious surface area based on precipitation and CN value: ； Where Q is the surface runoff volume of the impervious surface area, P is the rainfall, and S is the maximum possible retention volume; Calculate the impervious surface area of ​​the soil in a wet state value: ； based on Calculate the maximum possible retention S: 。 5. The method for estimating surface runoff and simulating floods based on urban impervious surfaces according to claim 1, characterized in that: Collecting soil information of the area adjacent to the impervious surface area and calculating the permeability coefficient of the area adjacent to the impervious surface area based on the soil information specifically includes the following process: Obtain soil information of areas adjacent to impervious surfaces, including the proportion of sandy soil, the proportion of loamy sandy soil, the proportion of sandy loamy soil, and soil moisture content; Determine the weights of sandy soil, loamy sandy soil, and sandy loam: the weights of sandy soil, loamy sandy soil, and sandy loam are K1, K2, and K3 respectively; The porosity M1 is obtained according to the correlation formula, which is as follows: ； in, 、 、 are the proportion of sandy soil, loamy sandy soil, and sandy loam, respectively; V is the total volume of soil samples taken from the area adjacent to the impervious surface; and G is a constant coefficient. By calculating the proportion of soil moisture content to the mass of soil sampled in the area adjacent to the impermeable surface, the average water density μ is obtained. The permeability coefficient YT is obtained by correlating the average porosity M1 and the average water density μ. The correlation formula is: ； in, is the penetration index, 0.25≤ ≤0.

6.

6. The method for estimating surface runoff and simulating floods based on urban impervious surfaces according to claim 1, characterized in that: Determine whether the impervious surface area has flood risk based on the permeability coefficient and surface runoff. The following processes are included: Obtain the permeability coefficient of the impervious surface area and its surrounding areas; obtain surface runoff data; set a flood risk threshold for the permeability coefficient based on regional geological and climatic conditions. Areas below this threshold have a higher flood risk due to poor permeability; set a flood risk threshold for surface runoff based on the regional drainage system capacity and historical flood events. Surface runoff exceeding this threshold results in poor drainage and a flood risk; compare the permeability coefficient of the impervious surface area with the set threshold to determine whether its permeability meets flood control requirements; The surface runoff volume in the impervious surface area is compared with the set threshold to assess whether it exceeds the drainage system's handling capacity; if the permeability coefficient is lower than the threshold and the surface runoff volume exceeds the threshold, the impervious surface area is at high risk of flooding; if both the permeability coefficient and the surface runoff volume are close to or lower than the threshold, the flood risk is relatively low, but attention should still be paid to potential risks under extreme weather conditions.

7. The method for estimating surface runoff and simulating floods based on urban impervious surfaces according to claim 1, characterized in that: When collecting spectral information of remote sensing satellite images in each sub-area, a remote sensing satellite platform with a hyperspectral imager is selected. Remote sensing satellite platforms include the Landsat series and the Sentinel series.

Images