KR102034193B1 - Automatic Information System for hazard map of facilities collapse using Modis satellite image and AWS materials - Google Patents

Abstract

The present invention relates to an automatic generation system of facility collapse risk map of spatial information integrated snowfall analysis using Moderate Resolution Imaging Spectroradiometer (MODIS) phase image and AWS data of the Korea Meteorological Administration.
To this end, the present invention, the image receiving antenna and satellite image database to receive and collect the modis phase image to find the area covered with snow, the characteristics of the snow fell by using the temperature, humidity, snow quantity information of AWS data (wet / construction) AWS database that collects and stores AWS data, and collects and stores AWS data, smart farm map vinyl house and building information of GIS building integration information, and applies facility collapse criteria to collect and store facility vector data to find facilities that are at risk of collapse due to heavy snowfall. Networking a facility vector database; Modified satellite image and AWS data-based snowfall analysis, snow area / dangerous area calculation and GIS mapping, using satellite image database of database server, AWS database and facility vector database, graphical user interface of system drive server, system drive and display, And it is characterized by performing snow storage analysis result storage.

Description

Automatic Information System for hazard map of facilities collapse using Modis satellite image and AWS materials}

The present invention relates to an automatic generation system of facility collapse risk map of spatial information integrated snowfall analysis using Moderate Resolution Imaging Spectroradiometer (MODIS) phase image and AWS data of the Korea Meteorological Administration.

The safety consciousness of modern people is managed by various methods in complex natural and social environments, and it can be seen that the types of disasters and safety accidents are very extensive. In order to effectively manage disasters and accidents, the Safety Administration is enacting and notifying the Disaster Management Standards and Safety Management Standards that central and local governments and public institutions must comply with.

The Disaster Management Standards include criteria for disaster reduction planning, disaster management organization, disaster management continuity management, disaster continuity management, disaster management monitoring, disaster forecast / alarm, situation propagation and command system, systematic situation management and resource management. It includes, among other things, a uniform definition of disaster-related terminology that is used differently or that has no clear definition. Safety management standards present safety management standards for establishing safety management plans to prevent safety accidents, implementing and operating them, evaluating operational conditions, and improving safety management measures.

According to the categorization and definition of disasters and safety accidents, natural disasters include typhoons, floods, heavy rain, strong winds, storms, storm surges, tsunamis, tides, heavy snow, lightning strikes, droughts, earthquakes, yellow dust, red tide, hail, heat waves, cold waves, and landslides. It is classified into 20 types such as steep slope collapse, volcanic eruption and space disaster.

Social disasters include infectious diseases, livestock epidemics, explosions, gas accidents, NBC accidents, traffic accidents, building collapse, energy infrastructure destruction, information and communication facilities destruction, transportation transportation infrastructure destruction, health care facilities destruction, waste disposal facility destruction It is classified into 37 types such as water infrastructure destruction, fire, forest fire, environmental pollution, and cyber terrorism.

On the other hand, snowfall in winter is a weather phenomenon in which a large amount of snow falls in a short time, and more than 3 Cm per hour or 20 Cm or more within 24 hours. The average annual damage caused by heavy snowfall was 170 billion won, which was the second largest damage following the flood. Therefore, the damage caused by heavy snow is mainly caused by property and life damage caused by the collapse of buildings.

As the main cause of heavy snow damage is the collapse of facilities, it is very important to promptly proactively extract the risk information of facilities collapse in case of heavy snowfall. The National Institute of Disaster and Safety Research has developed a snowfall analysis program using Modis satellite imagery and AWS data.

However, the snowfall analysis program is a snow area analysis using satellite imagery and snow quality (construction / wetland) analysis in conjunction with AWS data.However, the area expected to be damaged by snowfall in terms of decision makers for disaster management, quantitative figures, etc. Secondary information was needed.

Therefore, the present inventors have developed to extract information on the risk of decay of facilities in rural areas that are most affected by heavy snowfall, so that the establishment of facility decay criteria in consideration of the connection with the already constructed GIS data, the type of facility and the characteristics of snow, We are working together to support rational decision-making through the effective presentation of facility collapse risk information.

The present invention has been invented in view of the above-described technology of the automated snowfall analysis system, and can provide quasi-real-time snowfall disaster response, situation detection, and damage minimization by using photo-expected snowfall area information and facility collapsed danger area information. The purpose is to provide an automatic generation system for facility collapse maps that can implement spatial information integrated snowfall analysis automation.

The present invention for achieving the above object, the image receiving antenna and satellite image database for receiving and storing the modis phase image to find the area covered with snow, the characteristics of the snow fell using the temperature, humidity, snow quantity information of the AWS data ( AWS database, which stores AWS data to identify wet and snow, and building information of GIS building integrated information with vinyl farms on smart farm maps, applying facility collapse criteria to identify facility hazards due to heavy snow. Networking a facility vector database for storing; Modified satellite imagery and AWS data-based snowfall analysis, snow area / risk area calculation and GIS mapping, using satellite image database of database server, AWS database and facility vector database, graphical user interface of system drive server, system drive and display, And it is characterized by performing snow storage analysis result storage.

Another specific feature of the present invention is an image receiving antenna and satellite image database for receiving and storing modis phase image finding snow covered area by calculating snow area, and using snow, temperature, humidity and snow quantity information of AWS data. AWS database that stores AWS data to grasp snow characteristics (wetland / construction), and by extracting risk information, by linking the building information of smart farm map's plastic house and GIS building integrated information, the collapse of snowfall by applying facility collapse criteria Network a facility vector database that stores facility vector data to locate hazardous facilities; Modified satellite image and AWS data-based snowfall analysis, snow area / dangerous area calculation and GIS mapping, using satellite image database of database server, AWS database and facility vector database, graphical user interface of system drive server, system drive and display, And perform snow storage analysis result storage management; In the Modis-based snowfall analysis, the snow area is calculated by preprocessing and indexing the Modis satellite image received in real time from the satellite receiving antenna; Snow area / dangerous area calculation and GIS mapping link GIS data with snow area extraction results and extract hazardous facility information in snow area by applying facility collapse criteria; The result storage management of snowfall analysis is that the analysis results can be produced and generated as GeoTiff files, PNG, JPG, and KML data for the operator's data modification or production for each analysis image.

As described above, according to the present invention, a mode that corresponds to a region in which heavy snow comes from using Modis satellite image acquired with a satellite image receiving antenna constructed at the National Disaster Safety Research Institute and AWS data input through FTP (File Transfer Protocol) of the Korea Meteorological Administration It has the effect of automatically making judgments about whether there is a risk of collapse of animal and plant facilities of GIS building integration information or plastic houses of smart farm map.

1 is a system configuration diagram for explaining the automatic generation system of facility collapse risk map according to an embodiment of the present invention,
2 is a view for explaining the AWS data related to that shown in FIG.
3 is a view for explaining the matter related to the facility vector material shown in FIG.
4 is a display screen for explaining a graphical user interface (GUI) shown in FIG.
5 is a screen showing graphical user interface (GUI) preferences,
6 and 7 is a characteristic flow chart for explaining in detail the automatic generation system of facility collapse risk map of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a system configuration diagram for explaining a facility collapse risk map automatic generation system according to an embodiment of the present invention, the present invention is a facility destruction risk map automatic generation system using a Modis (MODIS) satellite image and AWS data of the Meteorological Administration will be.

In other words, the facility corresponding to the area with heavy snow using the Modis satellite image acquired by the satellite image receiving antenna (1) established at the National Disaster Safety Research Institute and the AWS data (2) coming from FTP through a file transfer service from the Korea Meteorological Administration. This is an automatic generation system of facility decay risk map that automatically determines whether there is a danger of collapse, and the facility vector data (3) from Ministry of Agriculture, Food and Rural Affairs and Ministry of Land, Infrastructure and Transport is used to extract the facilities of decay risk using snowfall. A facility vector database (DB) 6 is entered.

Therefore, in the snow information calculation, snow characteristics analysis, and risk information extraction, the snow cover area can be found by using Modis phase image, and the temperature, humidity, and snow quantity information of AWS data (2) of the Korea Meteorological Agency can be found. It can identify the characteristics of snow (wetland / construction) by using it, and it is possible to find facilities that collapse risks due to snowfall by applying the facility collapse standard by linking the building information of the vinyl farm of smart farm map with the GIS building integrated information. It is.

The database (DB) server consists of the Modis satellite image database (4), AWS # 1 (temperature and humidity) DB, AWS # 2 (snow) DB AWS database (5), facility vector database (6) The drive server consists of a graphical user interface (GUI) 7, a system drive 8, and a display 9.

The description of the AWS database 5 of the Korea Meteorological Agency will be described in detail in FIG. 2, and the description of the facility vector database 6 in FIG. 3, respectively. Description will be made in FIG. 4, and description of the system drive 6 and display 7 will be described in detail in FIGS. 6 and 7, respectively.

As described above, according to the present invention, a facility that can implement a semi-real-time heavy snow disaster response and situational integration and automation of integrated spatial information analysis snowfall can be minimized by using real-time snowfall information and information on the danger of collapse of the facility. It is possible to provide an automatic generation system for collapse maps.

2 is a view for explaining the AWS data related items shown in FIG.

In the input data collection phase, AWS data is being transmitted to the satellite image receiving server of the Korea Institute of Disaster and Safety (hereinafter referred to as disaster retardation) through FTP file transfer service from the Korea Meteorological Administration. And AWS # 2 are being acquired, AWS # 1 is the minute AWS data coming into the fire retardant, and the temperature and humidity are obtained from the data, and AWS # 2 is snow-related data.

Since the AWS data is transmitted in the form of TXT on the right screen, the AWS data is stored in the AWS # 1 and AWS # 2 databases (DB), temperature, humidity, and snow related data, respectively.

The AWS TXT data is then converted to point vector data via latitude and longitude plots within the TXT data (see the right screen in Figure 2), where each point in the point vector data is the temperature for that point. , Humidity, snow cover, etc.

*

The data converted into points are used by the Kriging method to convert them into images. Then, as shown in the figure on the right, the image files of regional temperature change, humidity change, and snow quantity change can be obtained. . It is stored in an AWS database on a database server.

3 is a view for explaining the matter related to the facility vector data shown in FIG.

GIS building integrated information and smart farm map information are transmitted as the facility vector data at the input data collection stage. Among them, GIS building integrated information can be downloaded from the Internet, and smart farm map can be provided from the Rural Development Administration system. have.

In the present invention, only the flora and fauna related facilities are extracted from the GIS building integration information, and other information is deleted. In the case of Smart Farm Map, the original data is used as it is.

The raster file is converted into a point file using ArcGIS, a well-known spatial information software. Since the raster file has a large capacity, the raster file is converted to a point in order to reduce processing speed on the system.

Therefore, it is stored in the facility vector database of a database server.

FIG. 4 is a display screen for explaining a graphical user interface (GUI) illustrated in FIG. 1. It is displayed on the screen by the setting unit, the operation unit, and the display unit.

In the graphical user interface of the server running the system, the operation is divided into automatic and manual settings, which automatically acquires modis satellite images. It is automatically analyzed, and manual is to analyze the desired image when the user wants.

The right setting section displays the snowfall analysis mode of the algorithm configuration, input / output routing, AWS data collection setting, construction / wetland threshold setting, and GIS facility collapse risk threshold setting.

The MOD03 and MOD021KM folders of the MODIS satellites are input to the input / output path setting of the setting unit. AWS is a point vector file, and the database information stored in AWS can be entered in the configuration.

The AWS data collection setting of the setting part is necessary when manually inputting, and in the automatic setting, the module searches and processes the AWS data at the time of image acquisition by itself. The wet and dry thresholds and the GIS facility collapse risk thresholds can be set by the user.

5 is a screen illustrating a graphical user interface (GUI) configuration.

The environment setting is indicated as automatic mode setting, fixed data path setting, AWS data collection DB in the DB setting, and snowfall analysis result storage DB.

It is divided into automatic mode and manual mode, and automatic mode can acquire satellite image. It is being analyzed automatically. The manual mode analyzes the desired image at the time desired by the user.

The MOD03 and MOD021KM images of the MODIS satellites are entered in the setting section, and AWS accesses and loads each database (DB) in which AWS # 1 and AWS # 2 are stored as point vector files. The wet and dry thresholds and the GIS facility collapse risk thresholds can be set by the user.

6 and 7 is a characteristic flow chart for explaining in detail the automatic generation system of facility collapse risk map of the present invention.

In the facility generation risk map automatic generation system of the present invention, it is divided into input data collection step, Modis satellite image and AWS data based snow analysis step, snow area / risk area calculation and GIS mapping step, and snow storage analysis result storage step.

AWS data of temperature, humidity, and snow received directly from the Meteorological Agency through the network at the input data collection stage is as shown in FIG. 2, and the image files produced using AWS # 1 and # 2 are generated with temperature, humidity, and snow quantity images. have. The map image of the temperature and humidity is sent after the snowfall analysis result image generation process, while the map image of the snow amount is sent after the image generation process divided into wet and construction.

In the Modis-based snowfall analysis stage, the snow area is calculated through preprocessing and exponential calculation of Modis satellite images received in real time from the satellite receiving antenna.

In other words, the sinusoidal changes to a sinusoidal waveform by the coordinate transformation during the preprocessing correction process of the satellite image, and the UTM coordinates are moved from the same point to the north as the north and south lines indicating the distance from an arbitrary point to the east. It is a method of determining the location on the map by the east-west line indicating the distance. Therefore, after converting from synonymous coordinates to UTM coordinates, atmospheric correction is performed through satellite image cropping and ocean region removal.

Atmospheric correction is a method of compensating satellite images, and the MODIS satellite senses energy emitted from the earth's surface to generate an image. At this time, energy entering the sensor by the atmosphere is attenuated or scattered. Correcting these attenuated or scattered values is atmospheric correction, and the known QUAC algorithm provided by ENVI is a method of atmospheric correction.

Following atmospheric correction, terrain correction is a method of correcting an error in reflectance value due to shadow in the mountainous region in the satellite image, and finds and corrects an error error due to shadow. The known COSINE algorithm provided by ENVI is a method of terrain correction.

The snow index NDSI (Normalized Difference Snow Index) after the preprocessing correction of satellite images is used to calculate the visible and short wavelength areas, and the visible light uses the green wavelength band and the short wavelength area (SWIR) uses (1 to 3 um). .

In addition, in order to remove various effects included in the satellite image, that is, the eye index threshold value is NDSI> 0.4, the water effect removal is Red band (0.86um)> 0.11, the dark pixel removal is Green band (0.55um) > 0.1 and hot spot removal is a thermal infrared band <283K.

As described above, the threshold value is applied in the snowfall analysis process, and the result of the analysis is to obtain the TIFF file which is snowfall analyzed in the image generation process.

As a result of analysis, the process of image generation and temperature and humidity image generation process are performed by image generation process that separates wet and construction.

Wetland and construction are calculated with the following thresholds: areas where temperatures are between -7 and +1 degrees in AWS temperature data, and areas where humidity is above 70% in AWS humidity data are classified as wetland, and the rest is constructed. It is supposed to specify. Each area on the map shows a comparison of potentially collapsing buildings.

The process of extracting facilities for risk of collapse using snowfall is performed through the image generation process and the snowfall image generation process, which are divided between wet and construction.

It is the standard of risk of collapse of facilities due to snow quality. In construction, it is 54cm in plastic houses, 140cm in barn buildings, and 20cm in plastic houses: 50cm in barns.

Therefore, it is possible to extract collapsed facilities by comparing the images of wet and construction with snow images. In other words, as shown on the map, it is possible to know the current state of decay risk flora and fauna and the state of decay risk plastic house.

Therefore, the amount of snow can be used to represent a comparison of buildings that could collapse in each region in the process of producing a decay-hazardous facility, followed by the production of a report of collapse.

As shown in FIG. 7, the present invention is divided into input data collection step, Modis satellite image and AWS data-based snowfall analysis step, snow area / dangerous area calculation and GIS mapping step, and snowfall analysis result storage management step.

The present invention enables the operator to manage the drive system automatically and manually, and through the configuration information shown in Figure 5 it is possible to set the thresholds required for heavy snow analysis, namely the wet / construction classification criteria and facility collapse risk criteria.

In the input data collection phase, Modis phase image is received in real time from the satellite receiving antenna, AWS data is received directly from the Meteorological Agency through the network, and facility vector data is provided by vinyl house information provided by Smart Farm Map and GIS building integrated information. Plantation and flora and fauna data are available.

In the snowfall analysis step based on the Modis satellite image and the AWS data, the snow area is calculated through preprocessing and exponential calculation of the Modis satellite image, and the AWS data is used as the average of the data within 24 hours based on the temperature and humidity information based on the time of satellite shooting. Red settings can utilize the new settings at the time of satellite shooting.

AWS data provided in the form of points can be interpolated in a grid using kriging interpolation to overlap with satellite images. By overlapping the above information, the snow cover can be divided into wet and construction areas.

The snow area / risk area calculation and GIS mapping stages link the GIS data with snow area extraction results, extract the hazardous facility information in the snowfall area by applying the facility collapse criteria, and the extracted facility information is analyzed in units of administrative districts. Proceed to calculate statistical indicators.

As a result of snowfall analysis result storage management, the analysis result can be produced and generated as GeoTiff file, PNG, JPG, and KML data for the operator's data modification or production for each analysis image. Reports and data generated from heavy snow analysis can be configured to work with the catalog system for ease of data acquisition and sharing.

As described above, according to the automatic generation system of facility collapse risk map according to the present invention, the Modis satellite image acquired by the satellite image reception antenna constructed at the National Institute of Disaster and Safety and the AWS data received through the FTP (File Transfer Protocol) of the Korea Meteorological Administration are used. Therefore, it has an effect of automatically generating a judgment as to whether there is a danger of collapse of the flora and fauna facilities of the GIS building integrated information or the smart farm map corresponding to the area where heavy snow comes.

Facility automatic destruction risk map automatic generation system of the present invention is not limited to the described embodiments, it can be variously modified and modified without departing from the spirit and scope of the present invention to those skilled in the art Self-explanatory

Therefore, such modifications or variations will have to belong to the claims of the present invention.

1: satellite image receiving antenna
2: AWS Resources
3: facility vector material
4: Satellite image database
5: AWS database
6: Facility vector database
7: Graphical User Interface (GUI)
8: System operation
9: display

Claims (5)

AWS data to understand the characteristics of snow (wet / construction) by using image receiving antenna and satellite image database, satellite image database, and AWS data to receive and collect Modis phase image to find snow covered area Networking facility vector database that collects and stores the facility vector data to find the facilities that are collapsed by heavy snow by applying the facility collapse standard by linking the AWS database that collects and stores the information, and the vinyl farm of Smart Farm Map and the building information of GIS building integration information. and;
Modified satellite image and AWS data-based snowfall analysis, snow area / dangerous area calculation and GIS mapping, using satellite image database of database server, AWS database and facility vector database, graphical user interface of system drive server, system drive and display, And in the automatic information system of facility decay risk map using Modis satellite image and AWS data that performs storage and management of snowfall analysis results,
In addition to AWS # 2 for acquiring snow-related data, the AWS database includes AWS # 1 for acquiring snow characteristics (humidity or construction) by temperature and humidity,
Unlike AWS # 2, where the new information on the time of satellite shooting is used, AWS # 1 uses the data provided as an average of data within 24 hours from the time of satellite shooting.
The user interface is provided as a setting unit, an operation unit, and a display unit on the screen, and construction and wet snow are automatically divided from the AWS # 1 data by a construction / wetland reference value set according to a temperature and humidity range in the setting unit. Lose,
The facility vector material of the facility vector database corresponds to the construction and the wet language differently so that the disintegration risk according to the construction and the wet snow is displayed differently.
Disintegration of dangerous facilities is carried out by using snowfall through the process of image generation between wet and construction and snowfall image generation process, and the collapse risk facilities are extracted by comparing images of wet and construction and snowfall images. ,
AWS data provided in the form of points are interpolated in a grid form using kriging interpolation for superimposition with satellite images. Disintegration risk map automatic information system for facilities using Modis satellite image and AWS data, characterized in that configured to implement automation.
The method of claim 1,
The facility vector data is extracted only from plants and animals related facilities in the GIS building integration information, and in the case of a smart farm map, the original data is used as it is;
Automated facility collapse risk map information system using Modis satellite imagery and AWS data, characterized by converting raster files into point files using known spatial information software ArcGIS.
The method of claim 1,
In the graphical user interface of the system drive server, the automatic setting unit acquires modis satellite image. It is automatically analyzed, and the manual information about the facility collapse risk map automatic information system using Modis satellite imagery and AWS data, characterized in that the user analyzes the desired image at the desired time.
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