Detection device and detection method

Abstract

Provided is a detection device 1 that detects an upwelling region, wherein the detection device 1 comprises: a data integration unit 14 that integrates sea surface temperature data in a predetermined region, chlorophyll-a data, sea surface altitude data, and sea bottom altitude data; and a classification unit 15 that identifies an upwelling region from the integrated data by using a classification algorithm.

Description

Detection device and detection method

[0001] The present disclosure relates to a detection device and a detection method.

[0002] There is a technology to detect upwelling regions. Conventionally, upwelling regions have been detected using sea surface temperature (SST) data and chlorophyll-a (Chl-a) data (see Non-Patent Document 1).

[0003] Anass El Aouni and others, “Robust Detection of the North-West African Upwelling from SST Images”, IEEE Geo-science and Remote Sensing Letters, Vol.18, No.4, April 2021, p.573-p.576

[0004] However, since upwelling regions were detected using only SST and Chl-a data, false detection of upwelling regions occurred in some offshore areas.

[0005] The present disclosure has been made in consideration of the above circumstances, and an object of the present disclosure is to provide a technology capable of accurately detecting upwelling regions.

[0006] A detection device according to one embodiment of the present disclosure is a detection device for detecting upwelling regions, and includes a data integration unit that integrates sea surface temperature data, chlorophyll-a data, sea surface altitude data, and seafloor altitude data for a specified region, and a classification unit that uses a classification algorithm to identify upwelling regions from the integrated data.

[0007] One embodiment of the detection method of the present disclosure is a detection method for detecting upwelling regions, in which a detection device integrates sea surface temperature data, chlorophyll-a data, sea surface altitude data, and seafloor altitude data for a specified region, and uses a classification algorithm to identify upwelling regions from the integrated data.

[0008] According to the present disclosure, a technology capable of accurately detecting upwelling regions can be provided.

[0009] Fig. 1 is a diagram showing a functional block configuration of a detection device. Fig. 2 is a diagram showing a processing flow of the detection device. Fig. 3 is a diagram showing a heat map of the Atlantic coastal region. Fig. 4 is a diagram showing a hardware configuration of the detection device.

[0010] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the description of the drawings, the same parts are designated by the same reference numerals and the description thereof will be omitted.

[0011] [Summary] Conventional methods have been prone to false detection of upwelling areas, especially in areas with complex seafloor topography.

[0012] Therefore, this study also uses sea surface height (SSH) data and seafloor height data (bathymetry data) to detect upwelling regions. That is, SSH data and seafloor height data are integrated with conventional SST and Chl-a data, and the integrated data are processed by a classification algorithm.

[0013] Specifically, we first collect and integrate SST, Chl-a, SSH, and seafloor height data for a given area, and then use a classification algorithm to identify upwelling regions from these integrated data.

[0014] In this way, by combining SSH data and seafloor height data with conventional SST and Chl-a data, it is possible to take into account the complexity of the seafloor topography and detect upwelling regions more accurately than conventional methods that use only SST and Chl-a data.In addition, the use of a classification algorithm allows for accurate and stable identification of upwelling regions.

[0015] [Functions of the Detection Apparatus] FIG. 1 is a diagram showing the functional block configuration of the detection apparatus 1. As shown in FIG.

[0016] The detection device 1 includes a collection unit 11 , a storage unit 12 , a preprocessing unit 13 , a data integration unit 14 , a classification unit 15 , a calculation unit 16 , an index integration unit 17 , and an output unit 18 .

[0017] The collection unit 11 has a function of collecting SST data, Chl-a data, SSH data, and seafloor elevation data for a predetermined area and storing the data in the storage unit 12.

[0018] The memory unit 12 has a function to store SST data, Chl-a data, SSH data, and seafloor altitude data for a specific area. The measured values ​​of these data (sea surface temperature, chlorophyll-a, sea surface altitude, and seafloor altitude) are associated with latitude and longitude location information.

[0019] The pre-processing unit 13 has a function of gridding the SST data, Chl-a data, SSH data, and seafloor height data of a predetermined area on a map, and performing pre-processing on the gridded grid data.

[0020] The data integration unit 14 has a function of integrating the four pre-processed grid data.

[0021] The classification unit 15 has a function of identifying upwelling regions from the integrated grid data after integration using a classification algorithm.

[0022] The calculation unit 16 has a function of calculating an upwelling index for SST data, Chl-a data, SSH data, and seafloor height data for a predetermined area.

[0023] The index integration unit 17 has a function of integrating the calculated upwelling indices and generating heat map data on a map based on the integrated upwelling indices and the identification results of the upwelling regions.

[0024] The output unit 18 has a function of outputting heat map data including the integrated upwelling index.

[0025] [Operation of the Detection Apparatus] FIG. 2 is a diagram showing a processing flow of the detection apparatus 1.

[0026] Step S1: The collection unit 11 collects SST data and Chl-a data for a predetermined area from a server on the Internet and stores them in the storage unit 12. The collection unit 11 also collects SSH data and seafloor elevation data for the predetermined area from the server on the Internet and stores them in the storage unit 12. The collection unit 11 may also accept and store various data input by a user.

[0027] Step S2: The preprocessing unit 13 reads the SST data and Chl-a data from the storage unit 12, generates grid data in which areas on the map are color-coded according to the data values ​​for each data, and performs operations such as unifying the spatial resolution and normalizing the data values ​​for the two generated grid data.

[0028] The pre-processing unit 13 also reads the SSH data and seabed altitude data from the memory unit 12, generates grid data in which areas on the map are color-coded according to the data values ​​for each data, and performs unification of the spatial resolution, normalization of the data values, and specific processing on the two generated grid data to conform to the above-mentioned normalization, etc.

[0029] Step S3: The data integration unit 14 integrates the four preprocessed grid data. For example, the data integration unit 14 integrates l x*y matrices into an x*y*l matrix using a Python library, where l is the number of grid data, and x and y are latitude and longitude.

[0030] Step S4: The classification unit 15 uses the Fuzzy c-means algorithm to probabilistically distinguish between upwelling regions and non-upwelling regions from the integrated grid data after integration. That is, the classification unit 15 classifies (clusters) each point included in the above-mentioned predetermined region into an upwelling region or a non-upwelling region.

[0031] Step S5: The calculation unit 16 calculates a common upwelling index for the SST data and Chl-a data using the integrated grid data.

[0032] On the other hand, for the SSH data and seabed elevation data, the calculation unit 16 calculates the upwelling index for each of them individually. That is, the calculation unit 16 calculates the upwelling index for the SSH data directly using the SSH data stored in the memory unit 12. Similarly, the calculation unit 16 calculates the upwelling index for the seabed elevation data directly using the seabed elevation data stored in the memory unit 12.

[0033] Step S6: The index integrating unit 17 integrates the calculated upwelling indices and generates heat map data on a map based on the integrated upwelling indices and the discrimination results of upwelling regions (or non-upwelling regions).

[0034] Step S7: The output unit 18 outputs the heat map data including the integrated upwelling index to the display.

[0035] Users can identify upwelling and non-upwelling regions from the heat map data output on the display, the upwelling index, and the colors of each area color-coded by upwelling index.

[0036] [Example] We focus on the Atlantic coastal area of ​​Morocco. This area is known as the Canary Current upwelling system and has rich fishery resources. We used SST data, Chl-a data, SSH data, and seafloor height data from the coastal area.

[0037] The data were subjected to a preprocessing unit 13 to standardize the spatial resolution and normalize the data values, and then integrated in a data integration unit 14. Next, the data were classified into upwelling regions and non-upwelling regions in a classification unit 15. The Fuzzy c-means algorithm was used for this classification.

[0038] Then, a common upwelling index was calculated using the SST data and Chl-a data in the calculation unit 16, and individual upwelling indices were calculated using the SSH data and seafloor height data. After that, the index integration unit 17 integrated these upwelling indices and generated heat map data in which the map was color-coded according to the level of the upwelling index.

[0039] Figure 3 shows heat maps for the coastal region. Figure 3(a) is a heat map using only SST and Chl-a data. Figure 3(b) is a heat map using SST, Chl-a, SSH, and seafloor height data. Figure 3(b) confirms that the false positives of upwelling regions in the area indicated by the ellipse are reduced.

[0040] In this way, by combining SSH data and seafloor height data with conventional SST and Chl-a data and using a classification algorithm to identify upwelling regions, upwelling regions can be accurately detected even in areas offshore and around isolated islands that would previously have been misclassified.

[0041] The integrated upwelling index is used by users to assess the validity of upwelling potential. This disclosure can also be applied to areas such as the waters around Japan, and is expected to contribute to fisheries resource management and environmental monitoring.

[0042] [Effects] According to this embodiment, SST data, Chl-a data, SSH data, and seafloor height data for a specified area are integrated, and a classification algorithm is used to identify upwelling regions from the integrated data, thereby enabling accurate and stable detection of upwelling regions.

[0043] [Others] The present disclosure is not limited to the above-described embodiments, and various modifications are possible within the scope of the gist of the present disclosure.

[0044] The detection device 1 of the present embodiment described above can be realized, for example, by using a general-purpose computer system including a CPU 901, a memory 902, a storage 903, a communication device 904, an input device 905, and an output device 906, as shown in Fig. 4. The memory 902 and the storage 903 are storage devices. In the computer system, the CPU 901 executes a predetermined program loaded on the memory 902, thereby realizing each function of the detection device 1.

[0045] The detection device 1 may be implemented by one computer, or by multiple computers, or may be a virtual machine implemented on a computer.

[0046] The program for the detection device 1 can be stored in a computer-readable recording medium such as a HDD, SSD, USB memory, CD, or DVD. The computer-readable recording medium is, for example, a non-transitory recording medium. The program for the detection device 1 can also be distributed via a communication network.

[0047] REFERENCE SIGNS LIST 1 detection device 11 collection unit 12 storage unit 13 preprocessing unit 14 data integration unit 15 classification unit 16 calculation unit 17 index integration unit 18 output unit 901 CPU 902 memory 903 storage 904 communication device 905 input device 906 output device

Claims

1. A detection device for detecting upwelling regions, comprising: a data integration unit that integrates sea surface temperature data, chlorophyll-a data, sea surface altitude data, and seafloor altitude data for a specified region; and a classification unit that uses a classification algorithm to identify upwelling regions from the integrated data.

2. A detection method for detecting upwelling regions, comprising: a detection device integrating sea surface temperature data, chlorophyll-a data, sea surface height data, and seafloor height data for a predetermined area; and using a classification algorithm to identify upwelling regions from the integrated data.

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