Method and program for providing water surface roughness information for searching for suitable aquaculture sites

Abstract

The objective is to obtain the water surface roughness for each finely divided area from satellite-captured water surface images, and to provide information for selecting water areas suitable for aquaculture, taking wind conditions into consideration. [Solution] A method for providing water surface roughness information for aquaculture site search, comprising: a water surface image acquisition step of acquiring multiple water surface images taken by a satellite at multiple different dates and times for a predetermined range of water surface area; a water surface roughness calculation step of calculating the water surface roughness from each of the multiple water surface images; a wind condition data acquisition step of acquiring wind condition data at multiple different dates and times for a predetermined range of water surface area; a coefficient calculation step of calculating a coefficient for water surface roughness based on the wind condition data; an integrated water surface roughness calculation step of integrating the water surface roughness of multiple water surface images based on the water surface roughness and coefficient of the multiple water surface images; and an integrated water surface roughness display step of displaying the integrated water surface roughness on a map showing a predetermined range of water surface area.

Description

Technical Field

[0001] The present invention relates to a method for providing water surface roughness information and a program for providing water surface roughness information for aquaculture site search.

Background Art

[0002] When conducting aquaculture on the sea surface or the like, it is necessary to select a sea area suitable for aquaculture. Conventionally, it has been comprehensively selected by considering natural environmental factors such as sea surface water temperature, seawater turbidity, and average tidal current, as well as social environmental factors such as distance from the port, fishing rights, and marine protected areas. (For example, see Patent Document 1)

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Data on natural environmental factors such as sea surface water temperature, seawater turbidity, and average tidal current can only be obtained for a wide sea area, and it was only possible to evaluate whether a sea area was suitable for aquaculture within a divided range that roughly divided the sea area. In addition, in aquaculture, the state of the sea surface, whether it is calm or rough, greatly affects the stability and safety of the floating net cage, the safety and ease of movement and work of workers on the sea. Furthermore, the state of the sea surface strongly depends on the wind. There has been no means to appropriately and simply evaluate this state of the sea surface in consideration of the wind conditions.

[0005] Therefore, an object of the present invention is to appropriately and simply provide information for selecting a water area suitable for aquaculture by using a water surface image taken by a satellite to finely divide the water area, obtaining the water surface roughness, which is one of the natural environmental factors, in each divided range, and considering the wind conditions.

Means for Solving the Problems

[0006] To solve the aforementioned problems, the present invention has the following configuration. A water surface image acquisition step involves acquiring multiple water surface images taken by a satellite at different times for a predetermined range of water surface area, A water surface roughness calculation step, which calculates the water surface roughness from each of the multiple water surface images, A wind condition data acquisition step involves acquiring wind condition data for multiple different dates and times for the water surface area within the predetermined range, A coefficient calculation step for calculating the coefficient of water surface roughness based on the wind condition data, A combined water surface roughness calculation step, which calculates a combined water surface roughness by integrating the water surface roughness of multiple water surface images based on the water surface roughness of multiple water surface images and the coefficient, A combined water surface roughness display step, which displays the combined water surface roughness on a map showing the water surface area within a predetermined range, A method for providing water surface roughness information for searching for suitable aquaculture sites, characterized by having the following features.

[0007] Furthermore, the present invention comprises the following configurations. A water surface roughness information providing program for causing an information processing device to perform an information output process that outputs the integrated water surface roughness in a predetermined range of water surface area to a display means on a map showing a predetermined range of water surface area, The aforementioned information output process is: From multiple water surface images taken by the satellite at different times and obtained for the predetermined range of water surface area, the water surface roughness is calculated for each image. Based on wind condition data from multiple different dates and times obtained for the predetermined water surface area, the coefficient of the water surface roughness is calculated. Based on the surface roughness of the multiple water surface images and the coefficient, an integrated water surface roughness is calculated by integrating the surface roughness of the multiple water surface images. Map information is created by plotting the integrated water surface roughness on a map showing the water surface area within the predetermined range. A water surface roughness information providing program characterized by the process of outputting the aforementioned map information to the aforementioned display means. [Effects of the Invention]

[0008] The water surface roughness information provision method and water surface roughness information provision program of the present invention can provide precise information for selecting water areas suitable for aquaculture. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows a floating / submersible fish tank 1 according to an embodiment of the present invention. [Figure 2] This is an illustrative diagram showing the rising and sinking of a floating and sinking fish tank. [Figure 3] This figure shows the selection of a region for searching for a suitable location for a floating / submersible fish farm according to an embodiment of the present invention. [Figure 4] This figure shows a mesh obtained by subdividing the area used for searching for suitable locations for a floating fish farm according to an embodiment of the present invention. [Figure 5] This figure shows a satellite image including an area for searching for suitable locations for a floating fish farm according to an embodiment of the present invention, and an example of zone aggregate values ​​for each hexagonal area obtained from the satellite image. [Figure 6] This figure shows an example of the scores for each hexagonal area in the region used for searching for suitable locations for a floating / submersible fish farm according to an embodiment of the present invention. [Figure 7] This figure shows satellite images including an area for searching for suitable locations for a floating fish farm according to an embodiment of the present invention, and other examples of zone aggregate values ​​for each hexagonal area obtained from the satellite images. [Figure 8] This figure shows another example of the scores for each hexagonal area in the region used for searching for suitable locations for the floating and submersible fish farm according to an embodiment of the present invention. [Figure 9] This figure shows a mesh from which wind condition data can be obtained for a region used to search for suitable locations for a floating fish farm according to an embodiment of the present invention. [Figure 10] This figure shows wind condition data in a mesh area that includes a region for searching for a suitable location for a floating fish farm according to an embodiment of the present invention. [Figure 11]It is a diagram showing the coefficient of the score determined from the wind condition data in the mesh including the area for the site search of the floating and sinking fish fence according to the embodiment of the present invention. [Figure 12] It is a diagram showing the wind condition data in the mesh including the area for the site search of the floating and sinking fish fence according to the embodiment of the present invention. [Figure 13] It is a diagram showing the coefficient of the score determined from the wind condition data in the mesh including the area for the site search of the floating and sinking fish fence according to the embodiment of the present invention. [Figure 14] It is a diagram showing a list of acquired SAR satellite image data in the mesh including the area for the site search of the floating and sinking fish fence according to the embodiment of the present invention. [Figure 15] It is a diagram showing the calculation of the final sea surface roughness in the mesh including the area for the site search of the floating and sinking fish fence according to the embodiment of the present invention.

Mode for Carrying Out the Invention

[0010] [Embodiment] In the present embodiment, as an example of the aquaculture of the present invention, a method for providing sea surface roughness information for searching for a suitable site for installing an aquaculture fish fence using SAR (Synthetic Aperture Radar) satellite data and its program for the aquaculture of fish using a floating and sinking fish fence will be described. Note that the present invention is not limited to the aquaculture of fish using a floating and sinking fish fence as aquaculture, and can be used for searching for suitable sites for other arbitrary aquaculture such as aquaculture of fish using a non-floating and sinking fish fence, aquaculture of shellfish such as scallops without using a net, etc. Further, as the sea surface image data, it is not limited to the image of the SAR satellite, and images of other satellites can also be used. Further, although the present embodiment is an example of calculating the sea surface roughness, it can be applied not only to the sea surface but also to any other water surface such as a lake surface.

[0011] [Floating and sinking fish fence] FIG. 1 is a diagram showing the floating and sinking fish fence 1 according to the embodiment of the present invention, and FIG. 2 is an image diagram showing the state of floating and sinking of the floating and sinking fish fence. The floating fish tank 1 includes a fish tank frame 11, a support frame 13 fixed to the fish tank frame 11 by brackets 12, and a net 14 supported by the support frame 13. The fish tank frame 11 is a hollow tube that, when viewed from above, has a circular, rectangular, or other shape and is divided into multiple compartments. Buoyancy is adjusted by injecting and discharging air and water into each compartment. Primarily, when the sea is rough and the waves are high, the buoyancy of the fish farms is reduced and they are submerged to protect the fish farms. Under normal circumstances, their buoyancy is increased and they float to the surface. The floating and submersible fish tank is a well-known concept, so a detailed explanation will be omitted.

[0012] [Rough Screening] When starting an aquaculture business, it is necessary to select several potential aquaculture sites. This selection involves narrowing down a certain area from a broad sea area by considering marine environmental data such as seawater surface temperature, suspension density, water depth, and ocean currents, as well as socio-environmental factors (constraints) such as distance from ports, fishing rights, and marine protected areas. This is called rough screening (water surface area selection step). Figure 3 shows the selection of an area for searching for a suitable location for a floating / submersible fish farm according to an embodiment of the present invention, where (a) is a wide-area map and (b) is a selected sea area. In this embodiment, as one of the candidate sites for aquaculture, we selected the area around an island located a short distance from the peninsula (hereinafter referred to as the candidate island), as indicated by the arrow in Figure 3(a).

[0013] [Detailed Screening] Within the sea area surrounding the candidate islands selected above, further selection is made to determine which areas are suitable for aquaculture. This is called detailed screening. The following describes the methods and programs for providing specific sea surface roughness information in detailed screening. The proposed island measures 1.8 km from north to south and 1.2 km from east to west, and the plan is to consider farming yellowtail fish in the surrounding area.

[0014] The candidate island is inhabited and has fishing port facilities. For aquaculture, the candidate island will be the base for daily operations such as raising the fish, feeding, administering medication, and unloading the catch. Therefore, the location for setting up the fish pens will be selected from within a 2km radius circle from the center of the candidate island, as indicated by the circle in Figure 3(b), because moving them too far from the island would reduce efficiency.

[0015] Figure 4 shows a mesh obtained by subdividing the area used for searching for suitable locations for a floating fish farm according to an embodiment of the present invention. The fish cages used in this embodiment of aquaculture are circular cages with a diameter of 20m to 30m each, and two or more cages are placed side by side in the aquaculture farm. Including the fish cages and weights used to fix them in the sea, the aquaculture farm is approximately 250m in circumference. Therefore, the space used to evaluate the suitability of the installation was divided into hexagonal meshes in units of 250m. Dividing the circle shown in Figure 3 with this hexagonal mesh results in 277 hexagonal areas. We then evaluate which of these 277 hexagonal areas is more suitable.

[0016] [Water surface image acquisition step] Figure 5 shows an example of a satellite image of an embodiment of the present invention, (a) including an area for searching for suitable locations for a floating fish farm, and (b) an example of zone aggregate values ​​for each hexagonal area obtained from the satellite image. SAR satellites emit radar waves toward the Earth's surface (ground, sea surface) and generate a planar image based on the intensity of the returned waves. Figure 5(a) shows a grayscale SAR satellite image of a wide area including the candidate island at the time of the first imaging. Due to the unevenness of the Earth's surface, radar waves are scattered, and the intensity of the radar waves returning to the SAR satellite differs. Since radar waves are generally emitted at an oblique angle to the Earth's surface, if the Earth's surface is smooth, the radar waves returning to the SAR satellite are weak, and conversely, if the Earth's surface is uneven, the radar waves returning to the SAR satellite are strong. When the intensity of the radar waves returning to the SAR satellite is displayed in grayscale from white to black, white areas indicate that the Earth's surface has many (large) irregularities, and black areas indicate that the Earth's surface has few (small) irregularities. Figure 5(a) is an image of an area approximately 12 km east-west and 24 km north-south, taken when the SAR satellite itself passed from north to south.

[0017] As shown in Figure 5(a), generally, land with a lot of radio wave scattering (many irregularities) appears predominantly white, while sea surfaces with little scattering (smooth irregularities) appear predominantly black. A calm sea surface like a mirror will appear black, but a lot of speckle noise can be interpreted as high sea surface roughness. This suggests that the sea surface may not be calm at the site. Observing the area around the candidate island in Figure 5(a), black is prominent on the east side of the candidate island, suggesting low sea surface roughness (calm sea surface). To express this more clearly, the pixel values ​​of the image are aggregated by zone for each hexagonal area set in Figure 4.

[0018] "Water surface roughness calculation step" Figure 5(b) shows the 8-bit, 256-level (numerical value: 0-255) image data shown in Figure 5(a), with the zone summation performed for each hexagonal area and the corresponding numerical values ​​entered within the hexagons. Zone summation is the calculation of the average of multiple pixel values ​​contained within a hexagonal area, and this average value of multiple pixel values ​​contained within a hexagonal area is called the zone summation value. Before zone summation, the land area was cut out from the image shown in Figure 5(a) to remove noise near land.

[0019] Figure 6 shows an example of the scores for each hexagonal area in the region used for searching for suitable locations for a floating fish farm according to an embodiment of the present invention. In this embodiment, the sea surface roughness (water surface roughness) for each hexagonal area is not the zone aggregate value itself, but rather a score obtained by dividing the zone aggregate value into multiple groups. To visually emphasize the differences, the zone aggregate values ​​in the 277 hexagonal areas of Figure 5(b) are arranged in descending order of magnitude, divided into three equal parts, and the group with the lowest aggregate values ​​is labeled with a score of 2, the middle group with a score of 1, and the group with the highest aggregate values ​​with a score of 0. Figure 6 is a classification map in which scores of 2 are displayed in white, scores of 1 in gray, and scores of 0 in black. Note that it is also possible to display the data using color instead of grayscale. The group with a score of 2 has a small zone aggregate value and little surface irregularity, i.e., low surface roughness. The group with a score of 0 has a large zone aggregate value and a lot of surface irregularity, i.e., high surface roughness. The group with a score of 1 falls somewhere in between. In this embodiment, the numbers from 0 to 255 are divided into three scores, but the number of scores to be divided into is not limited to three; it can be set to any number.

[0020] Figure 6 clearly shows the relative calmness at the first time of imaging within a 2km radius of the candidate island. From Figure 6, it can be seen that a large area with a score of 2 is concentrated to the east of the candidate island, particularly to the northeast, indicating a large area of ​​calm sea. In Figure 6, the calm sea area, which could not be clearly identified from the SAR satellite image itself shown in Figure 5(a), can be clearly identified.

[0021] Figure 7 shows an embodiment of the present invention, illustrating (a) another example of a satellite image including an area for searching for suitable locations for a floating fish farm, and (b) another example of zone aggregate values ​​for each hexagonal area obtained from the satellite image. Figure 8 shows another example of the scores for each hexagonal area in the region used for searching for suitable locations for a floating fish farm according to an embodiment of the present invention. In this example, a large concentration of scores 2 is found to the west of the candidate island, particularly to the northwest, indicating a calm sea area.

[0022] As shown in Figures 6 and 8, it is possible to identify calm areas during imaging. However, since the sea surface is constantly changing, Figures 6 and 8 alone cannot be used to evaluate sea areas suitable for fish farm installation throughout the year. Furthermore, while it would be ideal to continuously acquire sea surface roughness data for the selected sea area, SAR satellites orbit the Earth, and only a limited number of images of the selected area can be acquired. Therefore, a simple method for estimating and evaluating the sea surface roughness of the sea area around candidate islands throughout the year from a limited number of SAR satellite images will be described below.

[0023] [Wind Condition Data Acquisition Steps] Figure 9 is a diagram showing a mesh from which wind condition data can be obtained for the area where a suitable location search for a floating-type fish farm according to an embodiment of the present invention is performed. This diagram shows the mesh superimposed on a part of the wide-area map shown in Figure 3(a). The Japan Meteorological Agency provides a Grid Point Value (GPV) coastal wave numerical weather prediction model in file format. This model subdivides the grid spacing to 0.05 degrees (approximately 5 km) and the directional resolution to 36 directions. This file contains wave height [m], period [seconds], wave direction [degrees], east-west component of sea surface wind [m / s], and north-south component of sea surface wind [m / s]. The forecast data distributed within this file uses the analyzed values ​​at 0:00, 06:00, 12:00, and 18:00 UTC as initial values. By extracting only the analyzed values ​​from previously distributed GPV coastal wave numerical weather prediction model datasets, it is possible to understand past wind conditions.

[0024] In Figure 9, one grid represents a mesh unit of approximately 5 km, and meteorological data is associated with this grid. From the GPV coastal wave numerical weather prediction model dataset, only the meshes containing candidate islands are extracted, and the values ​​for the east-west component [m / s] and north-south component [m / s] of the sea surface wind at a height of 10 m above sea level are obtained. Since one year's worth of analysis data is obtained, 365 days × 4 times = 1460 wind condition data points are acquired.

[0025] [Coefficient calculation step] Figure 10 shows wind condition data for one year in a mesh area that includes the region for searching for a suitable location for a floating fish farm according to an embodiment of the present invention. Figure 10(a) shows a table summarizing the frequency of wind speed [m / s] and wind direction (8 directions) calculated based on the east-west component [m / s] and north-south component [m / s] of the sea surface wind at a height of 10m above sea level for the mesh containing the candidate islands. The rows of the table represent the 8 wind directions, and the columns represent average wind speed less than 3m / s, 3m / s or more, total, and frequency. An average wind speed of 3m / s is used as a benchmark because it is used as one of the indicators for sea surface movement and sea surface work on small vessels used in aquaculture. The total in the column is the sum of the frequencies of less than 3m / s and 3m / s or more, frequency (%) is the ratio of the sum of the frequencies of less than 3m / s and 3m / s or more for each direction to the total of all frequencies (=1460), and frequency (%)U:3m / s or more is the ratio of the frequency of 3m / s or more for each direction to the total frequency of 3m / s or more (=1265). While 3 m / s was used as the threshold for differentiating wind speeds, other values ​​can be used depending on the working conditions, and the threshold can be set as appropriate. Figure 10(b), which is a graph of the table in Figure 10(a), shows that in the mesh containing the candidate islands, the frequency of SW, W, and NW winds is high throughout the year. As described below, the coefficient for the score mentioned above is set using frequency (%) U: 3 m / s or higher. This is because, when the wind speed is less than 3 m / s, the sea surface is generally calm and does not hinder work or movement, and it is sufficient to analyze only the wind condition data that has a significant impact on the sea surface when the wind speed is 3 m / s or higher.

[0026] Figure 11 is a table showing the coefficients of the score determined from wind condition data in a mesh area that includes the region for searching for a suitable location for a floating fish farm according to an embodiment of the present invention. In other words, Figure 11 shows the coefficients obtained from the frequency of wind directions with an average wind speed of 3 m / s or more in Figure 10. The coefficients for all eight directions have been adjusted so that they add up to 1 from the "Frequency (%) of 3 m / s or more" in Figure 10(a). That is, these coefficients are numerical values ​​that correspond to the proportion of wind frequency for each wind direction around the candidate island over the entire year.

[0027] Figure 12 shows wind condition data for a mesh area that includes a region for searching for a suitable location for a floating fish farm according to an embodiment of the present invention. Unlike Figure 10, which shows the frequency for the entire year, Figure 12 summarizes the frequency quarterly, or by season. While it is possible to set up fish pens and raise fish for a year, for some fish species, such as fast-growing salmon, tilapia, and milkfish, it is possible to raise them for six months. For example, a six-month period from April to September is sufficient for aquaculture. In Figure 12, the average wind speed categories 0-3 indicate the frequency when the average wind speed is less than 3 m / s, and 3+ indicates the frequency when the average wind speed is 3 m / s or more.

[0028] Figure 13 shows the coefficient of the score determined from wind condition data in a mesh area that includes the region for searching for a suitable location for a floating fish farm according to an embodiment of the present invention. In other words, Figure 13 shows the coefficients derived from the frequency of wind directions with an average wind speed of 3 m / s or more from April to September, as shown in Figure 12. The coefficients for all eight directions were adjusted so that the sum of the coefficients from the frequency (%) of winds with an average wind speed of 3 m / s or more from April to September in Figure 12 equals 1. That is, these coefficients are numerical values ​​that correspond to the proportion of wind frequency for each wind direction around the candidate island over the six-month period from April to September.

[0029] [Integrated Water Surface Roughness Calculation Step] Figure 14 is a table showing an example of a list of one year's worth of SAR satellite image data for a mesh area that includes the region where a suitable location for a floating fish farm according to an embodiment of the present invention is searched. To make it easier to understand, we have listed one satellite image data for each of the eight different wind directions. The same table shows wind condition data for the mesh containing candidate islands for the date and time closest to the acquisition date and time of each photographic data. In addition, the coefficients derived from the frequency of wind directions with an average wind speed of 3 m / s or higher over a one-year period, as shown in Figure 11, are also presented.

[0030] The left half of Figure 15 shows the calculation of integrated sea surface roughness in a mesh area that includes a region for searching for suitable locations for a floating / submersible fish farm according to an embodiment of the present invention. Integrated sea surface roughness (integrated water surface roughness) is a sea surface roughness obtained by integrating sea surface roughness calculated from multiple different SAR satellite images, and is an index that shows the sea surface roughness for each hexagonal area over a one-year period. In this embodiment, the integrated sea surface roughness is a score obtained by multiplying the score calculated from each SAR satellite image for each hexagonal area by a coefficient corresponding to the wind direction at the time each SAR satellite image was captured, and then summing the results. The integrated sea surface roughness will be a value between 0 and 2.0.

[0031] [Integrated water surface roughness display step] The right half of Figure 15 is a diagram showing the integrated sea surface roughness displayed on a mesh that includes an area for searching for suitable locations for a floating and submersible fish farm according to an embodiment of the present invention. The diagram (see Figure 4) shows a hexagonal area defined around the candidate island, and the integrated sea surface roughness is displayed for each hexagonal area. In Figure 15, scores from 0 to 2.0 are divided into four categories: 0 to 0.5, greater than 0.5 and less than or equal to 1.0, greater than 1.0 and less than or equal to 1.5, and greater than 1.5 and less than or equal to 2.0, and are displayed in grayscale (black, dark gray, light gray, and white). Figure 15 shows that the eastern or northern side of the candidate island has a high score, meaning that the sea surface roughness is low and the area is highly suitable for the installation of floating fish cages.

[0032] The present invention is not limited to the embodiments described above, but can also be implemented in the following embodiments. In the water surface image acquisition step, SAR satellite imagery is used as the image of the sea surface, but other types of satellite imagery can also be used as long as they show the topography of the Earth's surface (ground and sea surface). In the surface roughness calculation step, the surface roughness of each hexagonal area is calculated using scores obtained by dividing the zone aggregate values ​​into multiple groups, rather than the zone aggregate values ​​themselves. However, the zone aggregate values ​​themselves can also be used directly as the surface roughness. In this case, the calculation of the integrated surface roughness in the integrated surface roughness calculation step is performed by multiplying the zone aggregate values ​​calculated from each SAR satellite image for each hexagonal area by the corresponding coefficient and summing them up. When displaying the integrated surface roughness, it is helpful to display the zone aggregate values ​​in addition to (or differently from) the zone aggregate values, dividing the zone aggregate values ​​into multiple groups and displaying each hexagon in grayscale or color.

[0033] In the wind condition data acquisition step, the GPV file of the coastal wave numerical weather prediction model provided by the Japan Meteorological Agency is acquired. However, it is not limited to this, and any wind condition data (wind direction, wind speed data, etc.) that has been observed without gaps throughout the year (at least throughout the aquaculture period) is acceptable. Furthermore, the wind condition data may be observation data from points included in the water body where integrated water surface roughness is acquired, but it is preferable to use observation data where statistics such as frequency do not become meaningless, such as data continuously observed at a single point, and observation data that more accurately represents the water body where integrated water surface roughness is acquired, such as observation data from points near the center of the water body where integrated water surface roughness is acquired. In the coefficient calculation step, the coefficient was calculated only from data with an average wind speed of 3 m / s or higher. However, the coefficient may also be calculated from all data, including data with an average wind speed of less than 3 m / s. In that case, it is preferable to adjust the contribution of data with an average wind speed of 3 m / s or higher and data with an average wind speed of less than 3 m / s in the coefficient calculation. For example, it is preferable to make the contribution of data with an average wind speed of less than 3 m / s smaller than the contribution of data with an average wind speed of 3 m / s or higher. In the integrated surface roughness calculation step, the integrated surface roughness is calculated by integrating the sea surface roughness for one year. However, as mentioned above, when aquaculture is carried out for a period of less than one year, such as six months, the integrated surface roughness may be calculated by integrating the sea surface roughness only for the period of aquaculture, for example, six months, four months, or three months, instead of using the sea surface roughness for one year. In this case, the coefficient of sea surface roughness may be calculated from wind condition data for the period of aquaculture.

[0034] Furthermore, in the integrated water surface roughness calculation step, for the sake of clarity, we use one satellite image data for each of the eight different wind directions. However, in reality, it is not always possible to obtain satellite image data equally for all eight wind directions. It is common for satellite image data to not be available for some wind directions, or for the number of satellite image data points to be different for all eight wind directions, resulting in an uneven distribution of satellite image data. In that case, you can deal with it in the following way. When multiple satellite image data exist for a single wind direction, the average of the scores (or zone aggregate values) calculated from the multiple satellite image data is used as the unified sea surface roughness for that wind direction. If satellite image data is unavailable for a particular wind direction, the average of the scores (or zone aggregate values) calculated from satellite image data for the adjacent wind directions (for example, N and E in the case of NE) is used as the integrated sea surface roughness for that wind direction. If there are many wind directions for which satellite image data is unavailable among the eight wind directions, the directions are rounded down to four instead of eight, and the integrated sea surface roughness is calculated. In this case, the satellite image data for the eight wind directions that does not correspond to satellite image data for the four wind directions are divided in half and distributed to the two adjacent directions. For example, if the score for NE is 1.5, 0.75 is distributed to N and E respectively, and these scores are added together. In the coefficient calculation step and the integrated sea surface roughness calculation step, eight wind directions were used, but four, sixteen, or thirty-two wind directions can also be used.

[0035] [Water surface roughness information provision program] The above calculation of integrated sea surface roughness can be performed by an application stored on an information processing device such as a laptop or tablet. The water surface roughness information provision program consists of the following program group:

[0036] "Water surface roughness calculation program" This program overlays acquired SAR satellite images of the area around the candidate island (see Figures 5(a) and 7(a)) with map data that divides the water area around the candidate island into multiple hexagonal zones. It then calculates the zone aggregate value for each hexagonal zone from the SAR satellite image data and determines a score from the zone aggregate value according to the set grouping.

[0037] [Wind Condition Data Acquisition Program] The wind condition data acquisition program extracts and obtains analytical values ​​from the acquired coastal wave numerical weather prediction model GPV dataset for a set period (e.g., one year, six months), which are numerical values ​​of the east-west component [m / s] and north-south component [m / s] of the sea surface wind at a height of 10m above sea level for a mesh including the waters around the candidate island.

[0038] [Coefficient Calculation Program] The coefficient calculation program determines the coefficient for each wind direction from the wind condition data acquired by the wind condition data acquisition program. The coefficient calculation program first calculates wind speed and wind direction (for example, 8 directions) from the acquired wind condition data, namely the east-west component and the north-south component of the sea surface wind, to obtain wind speed and wind direction data for the set period. Next, the frequency of each wind direction is counted based on a threshold set to divide the wind speed, a set period, or a division period set to further subdivide the set period. Finally, based on the frequency of each wind direction that has been counted, a coefficient corresponding to each wind direction is determined.

[0039] [Integrated Water Surface Roughness Calculation Program] The integrated water surface roughness calculation program calculates the integrated water surface roughness for each hexagonal area based on the score data for each hexagonal area determined by the water surface roughness calculation program and the coefficients for each wind direction determined by the coefficient calculation program. The integrated water surface roughness calculation program calculates a score for each hexagonal area from each SAR satellite image, multiplies it by a coefficient corresponding to the wind direction at the time each SAR satellite image was captured, and then sums the results.

[0040] [Integrated Water Surface Roughness Display Program] The integrated water surface roughness display program displays the integrated water surface roughness of each hexagonal area, obtained by the integrated water surface roughness calculation program, in each hexagonal area set in the waters surrounding the candidate island. The integrated water surface roughness display program creates display data to show the integrated water surface roughness for each hexagonal area in a diagram (see Figure 4) that shows hexagonal areas set around the candidate island.

[0041] [Other Programs] In rough screening, a program may be used to select one or more sea areas from an arbitrary wide area of ​​sea based on marine environmental data such as seawater surface temperature, density of suspension, water depth, and ocean currents, as well as socio-environmental factors (constraints) such as distance from ports, fishing rights, and marine protected areas. In the water surface image acquisition step, a program may be used to automatically acquire SAR satellite images from the web based on the set water area and period. In the wind condition data acquisition step, a program may be used to automatically acquire coastal wave numerical weather forecasting model GPV files from the web based on the set water area and period.

[0042] The above describes in detail, with reference to the drawings, a method for providing sea surface roughness information for searching for suitable locations for installing fish farming cages using SAR satellite data, and a program for providing the same, for fish farming using a floating and submersible cage according to an embodiment of the present invention. However, the specific configuration is not limited to these embodiments, and any design changes, etc., that do not depart from the gist of the present invention are also included. [Explanation of symbols]

[0043] 1. Bamboo mat 11 Screen frame 12 brackets 13 Support Slots 14 Net

Claims

1. A water surface image acquisition step involves acquiring multiple water surface images taken by a satellite at different times for a predetermined range of water surface area, A water surface roughness calculation step, which calculates the water surface roughness from each of the multiple water surface images, A wind condition data acquisition step involves acquiring wind condition data for multiple different dates and times for the water surface area within the predetermined range, A coefficient calculation step for calculating the coefficient of water surface roughness based on the wind condition data, A combined water surface roughness calculation step, which calculates a combined water surface roughness by integrating the water surface roughness of multiple water surface images based on the water surface roughness of multiple water surface images and the coefficient, A combined water surface roughness display step, which displays the combined water surface roughness on a map showing the water surface area within a predetermined range, A method for providing water surface roughness information for searching for suitable aquaculture sites, characterized by having the following features.

2. The method for providing water surface roughness information according to claim 1, characterized in that the wind condition data includes at least wind direction data.

3. The water surface roughness information provision method according to claim 2, characterized in that the wind condition data includes wind speed data, and the coefficient is adjusted using the wind speed data.

4. A method for providing water surface roughness information according to any one of claims 1 to 3, characterized by having a water surface area selection step of selecting a water surface area within a predetermined range based on marine environmental data and / or constraints.

5. The aforementioned marine environmental data includes at least one of the following: seawater surface temperature, seawater suspension density, water depth, and ocean currents. The method for providing water surface roughness information according to claim 4, characterized in that the aforementioned constraints are at least one of distance from a port, fishing rights, and marine protected areas.

6. A water surface roughness information providing program for causing an information processing device to perform an information output process that outputs the integrated water surface roughness in a predetermined range of water surface area to a display means on a map showing a predetermined range of water surface area, The aforementioned information output process is: From multiple water surface images taken by the satellite at different times and obtained for the predetermined range of water surface area, the water surface roughness is calculated for each image. Based on wind condition data from multiple different dates and times obtained for the predetermined water surface area, the coefficient of the water surface roughness is calculated. Based on the surface roughness of the multiple water surface images and the coefficient, an integrated water surface roughness is calculated by integrating the surface roughness of the multiple water surface images. Map information is created by plotting the integrated water surface roughness on a map showing the water surface area within the predetermined range. A water surface roughness information providing program characterized by the process of outputting the aforementioned map information to the aforementioned display means.

7. The water surface roughness information provision program according to claim 6, characterized in that it acquires multiple water surface images taken by satellites at multiple different dates and times for the water surface area within the predetermined range.

8. The water surface roughness information provision program according to claim 6, characterized in that it acquires wind condition data for a predetermined period of time for the water surface area within the predetermined range.

9. The water surface roughness information provision program according to claim 6, characterized in that the wind condition data includes at least wind direction data.

10. The water surface roughness information provision program according to claim 9, characterized in that the wind condition data includes wind speed data, and the coefficient is adjusted using the wind speed data.

11. The aforementioned information output process is: A water surface roughness information provision program according to any one of claims 6 to 10, characterized in that it selects a predetermined range of water surface areas based on marine environmental data and / or constraints.

12. The aforementioned marine environmental data includes at least one of the following: seawater surface temperature, seawater suspension density, water depth, and ocean currents. The water surface roughness information provision program according to claim 11, characterized in that the constraints are at least one of distance from a port, fishing rights, and marine protected areas.

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