Predicting and preventing natural disasters is very challenging. However, since a disaster can cause tremendous damage to human life and property once it occurs, it is most important to respond preemptively in disaster management (National Disaster Management Research Institute, 2021; 2022; 2023). The types of disasters are becoming more diverse, complicated, and enlarged, and uncertainty surrounding disasters continues to increase; hence, scientific disaster management at the State level is truly essential for natural disasters.

The strength of satellite remote sensing is its capability to continuously provide land surface images. In the fields, such as disaster, requiring management through continuous monitoring, satellite remote sensing is being heavily relied on and utilized (Baek and Jung, 2019; Zhu, 2017; Lee et al., 2018a). Most of all, in cases of weather-related disasters, such as floods and landslides, it has significant strength in enabling responses to hard-to-approach areas due to deteriorating weather conditions through wide-swath observation in a timely manner. Moreover, converging spatial information in addition to satellite images, it is possible to provide visual information from information concerning the analysis of neighboring regions and identification of shelters following the occurrence of disasters. Today, by utilizing multi-temporal satellite remote sensing data observed and Geographic Information System (GIS), a wide variety of research and fieldwork supports are being conducted in relation to disaster monitoring (Rignot and van Zyl, 1993; Lee et al., 2018a). Together with rapidly advancing technologies, keywords related to ‘satellite’ and ‘Artificial Intelligence (AI)’ are taking up more and more significant roles. Throughout the globe, the aerospace industry is in transition from the government-led old space age to the new space age led by private entities. In recent years, the microsatellite constellation No.1 developed by Korea represents the case accommodating the trends of the new space age as it shows the conversion from government-led to private-led movements. Instead of developing high-expensive large-scale high-performance satellites, the measures to overcome temporal and spatial limitations through the operation of constellation formats with numerous microsatellites are garnering keen attention (Kim et al., 2023b; Park et al., 2023b; 2023c). Meanwhile, AI technology is being used for processing and analysis of big data in various fields. Above all, AI technology is also being utilized in the field of disaster by grafting with satellite images. It is used to automatically detect geographical changes or extract necessary information for disaster management. Because of these technological advancements, it is expected that real-time disaster management to be effectively executed in the future.

This study introduces the national disaster management system overseen by the Ministry of the Interior and Safety (MOIS) and aims to present the current status regarding utilization and future prospects of satellite remote sensing in the field of disaster. In more detail, the contents are comprised of 1) the national disaster management system, 2) information on the current utilization of satellite remote sensing in the field of disaster, and 3) trends in the current status of multi-type satellites and utilization of AI technology. Based on the information presented by this study, it is expected to identify the current practice of utilizing satellite remote sensing in the field of disaster and to be used to establish future prospects and responsive strategies.

According to the law of Republic of Korea, Article 3 of the Framework Act on the Management of Disasters and Safety, the term “disaster” means what actually causes or is likely to cause any harm to the lives, bodies, and property of citizens and the State. Disasters can be subdivided into natural disasters caused by climate-related natural phenomena and social disasters arising from other various factors. Throughout the world in recent years, the types of disasters have further varied due to the occurrence of disasters that are complicated and difficult to forecast, and systematic disaster management is in great need (Gang et al., 2017; Kim and Kang, 2021; Kim et al., 2016). Because of these effects, situations surrounding each disaster are being managed in accordance with the interdepartmental characteristics in Korea. Lately, many platforms are being used to utilize remote sensing technology due to advancements in the private-led aerospace industry, and it is diversely utilized in the land, marine, atmosphere, and aerospace fields (Kim et al., 2019a; Kim et al., 2018; Ryu et al., 2020; Yang et al., 2021). Especially, the satellite platform is essentially used in disaster management because of its strength for being capable of periodic and wide-field observation. In this chapter, a brief description will be offered with regard to the national disaster management system of the MOIS and what procedures are being taken in order to support disaster management by the National Disaster Management Research Institute (NDMI).

2.1. Disaster Management System of the MOIS

In Republic of Korea, MOIS manages various types of disasters, but several agencies in charge of each natural disaster are designated and dedicated according to the law, Article 3-2 (Disaster Management Supervision Agencies) of the Enforcement Decree of the Framework Act on the Management of Disasters and Safety (Table 1).

Table 1 . The lead agencies responsible for disaster management according to the type of natural disaster.

Disaster management authority	Types of natural disaster
a). Ministry of Science and ICT & Korea AeroSpace Administration.	1). A disaster caused by the breakdown or crash of natural space objects as defined in Article 2, Subparagraph 3 (b) of the 「Space Development Promotion Act?」. 2). A space weather disaster as defined in Article 51 of the 「Radio waves act」.
b). Ministry of the Interior and Safety.	1). A natural disaster as defined in Article 2, Subparagraph 2 of the 「Countermeasures against Natural Disasters Act」, caused by lightning, drought, heat waves, and cold waves. 2). A wind and flood disaster as defined in Article 2, Subparagraph 3 of the 「Countermeasures against natural disasters act」(excluding disasters caused by tides). 3). An earthquake disaster as defined in Article 2, Subparagraph 1 of the 「Act on the preparation for earthquakes and volcanic eruptions」. 4). A volcanic disaster as defined in Article 2, Subparagraph 1 of the 「Act on the preparation for earthquakes and volcanic eruptions」.
c). Ministry of Environment.	1). A disaster caused by yellow dust (Asian dust). 2). A disaster caused by the massive algal bloom in rivers or lakes.
d). Ministry of Oceans and Fisheries.	1). Damage to aquaculture and fishing facilities caused by red tides and mass occurrences of jellyfish, which are categorized as fishing disasters under Article 2, Subparagraph 3 of the 「Act on the Prevention of and countermeasures against agricultural and fishery disasters」. 2). A disaster caused by tides, classified as a wind and flood disaster under Article 2, Subparagraph 3 of the 「Countermeasures against Natural Disasters Act」.
e). Korea Forest Service.	A disaster caused by a landslide as defined in Article 2, Subparagraph 10 of the 「Forest Protection Act」.
f). Central administrative agencies as prescribed in Remark 1 and Remark 3.	Natural disasters as defined in the provisions from Subparagraph (a) to Subparagraph (e).
g). Central administrative agencies as prescribed in Remark 2 and Remark 3.	Disasters occuring in various facilities and locations caused by the types of natural disasters as defined in the provisions from Subparagraph (a) to Subparagraph (f).

Crash of space objects and disasters of cosmic radio waves are being handled by the Ministry of Science and ICT and the Korea AeroSpace Administration (KSAS), disasters from yellow dust and the massive algal bloom in rivers and lakes are overseen by the Ministry of Environment, disasters related to red tide are by the Ministry of Oceans and Fisheries, and landslides are processed by the Korea Forest Service (KFS). Once any disasters occur, a system has been built for each competent agency of disaster management to respond according to the manuals and share the information. The MOIS is in charge of managing massive-scale natural disasters, including lightning, drought, heat waves, heavy snowfall, floods (except for tidal floods), earthquakes, and yellow dust. For the purpose of massive-scale disaster management, the MOIS operates the National Disaster and Disaster and Safety Status Control Center (NDSSCC), Satety and Prevention Policy Office (SPPO), Natural Disaster Management Office (NDMO), Social Disaster Management Office (SDMO), Disaster Recovery Support Bureau (DRSB) and Emergency Preparedness Policy Bureau (EPPB), which are headed by the Disaster and Safety Management Headquarter (DSMH) (Fig. 1). Looking into major duties of each agency, the NDSSCC is responsible for the matters of comprehensive management of disaster safety and crises, receipt/identification/dissemination/estimate of disaster situations and initial reporting. The SPPO supervises the matters concerning planning/supervision/coordination of safety management policies an extension agency, is responsible for promptly identifying disaster situations in order to support the decision-making of the NDSSCC in cases of disasters by building a situation information analysis center at the Disaster Information Research Division (DIRD) and providing the information based on collection and analysis of relevant information.

Figure 1. The organizational chart for disaster safety-related tasks under the MOIS.

2.2. Procedures for Disaster Management Support

The MOIS builds a system to share the information with affiliated organizations, such as local governments, Korea Meteorological Administration (KMA), and KFS, at all times. Based on this sharing system, the situation room engages in disaster prevention and prompt responses. First, in times of peace, the situation room operates daily situation meetings and discusses the details of weather situations, disaster safety accidents, major disasters, and safety management activities as well as related media reports. Various disaster situations will be monitored through daily situation meetings, and professional training for personnel in charge of situations and disaster response training will be performed with cooperation from affiliated organizations. In the event of a disaster, the situation room responds to initial situations. Once reports on initial situations are received, it promptly notifies the competent departments of disasters by identifying the situations, identifying damage situations, and collecting related information. The NDMI utilizes advanced equipment, such as satellites and drones, in order to collect response information and expediently provide information on forecasts and analysis of unfolding situations.

Fig. 2 shows the analysis results initially delivered and procedures with regard to the forest fire that occurred in Hongseong-gun, Chungcheongnam-do on April 2, 2023. Upon the occurrence of the forest fire, the NDMI collected satellite images previously taken prior to the forest fire through websites in Korea and overseas. In cases of images following the occurrence, a request was made to the Korea Aerospace Research Institute (KARI) for new photographing of images by Korea Multi-Purpose Satellite (KOMPSAT)-3. It calculated the damaged area of forest fire from the images collected on the same day and provided the analysis results through the convergence of residential building information concerning civilians near the damaged region. This case was offered as response data on the following day of the forest fire occurred yet still in progress. Thus, the information must be provided promptly after a disaster occurs for it to be of practical value. For emergency photographing and collection of satellite images, international cooperative programs are also being used where organizations in possession of remote-sensing satellites voluntarily participate to provide satellite images in the event of global disasters. Such organizations include the International Charter and Sentinel Asia, and the NDMI acts as an Authorized User (AU), authorized to directly request an activation of the Charter in the event of a disaster. The NDMI is currently planning to establish a system to reduce the time required to acquire satellite imagery to within 48 hours by collaborating with domestic and international organizations during disaster events. By reducing collecting time, it is believed that significantly meaningful information will be expanded to support the situation room of the MOIS.

Figure 2. The process (above) and result (below) of the analysis for forest fire damaged area in Hongseong-gun in 2023 conducted by NDMI.

3.1. Forest Fire

In Korea, forest fire occurs frequently due to the dry and low-precipitation climate characteristics in spring. Statistically, an annual average of 567 cases of forest fire occurred for the past 10 years (2014 through 2023) where 4,004ha of forest were lost (Korea Forest Service, 2024), and massive damages to human lives, properties, and forest resources were inflicted. Especially in Gangwon-do with forest areas widely dispersed, the cases of massive-scale forest fire were witnessed the most (Lee and Lee, 2011), and in cases of the forest fire on the eastern coast of Korea during the year 2000, forest property damages were recorded at 23,448ha of damaged area worth over 60 billion KRW for 9 days (Lim, 2000).

For large-scale forest fire that occurs in a wide range of areas, satellite remote sensing can be effectively utilized for disaster management. Disaster management for forest fire can be divided into 3 stages: First, forest fire occurrence monitoring; second, detection of areas damaged area; and third, monitoring of vegetation recovery after the forest fire. In this section, satellite-based forest fire studies for each stage of disaster management are reviewed.

The occurrence of forest fire depends on natural conditions, such as humidity, rainfall and forest distribution; however, since ignitions are largely caused by human activities, it is difficult to forecast. Hence, it is necessary to constantly monitor a wide range of areas rather than trying to precisely forecast the areas for forest fire to occur, and satellite remote sensing can serve as a proper means for this specific purpose. Kim et al. (2013) conducted an experiment comparing the performances of daytime/nighttime forest fire detection based on the Level-1B data of Communication, Ocean and Meteorological Satellite (COMS), a geostationary weather satellite, and Multifunctional Transport Satellite (MTSAT)-2, and its results showed that even the forest fire with approximately 3ha of damaged area was detected. Lee et al. (2016a) engaged in a study to detect forest fires in the Korean Peninsula from Moderate Resolution Imaging Spectroradiometer (MODIS) products of Terra and Aqua and build the GIS database. Both of these two studies detected forest fires by using the spectral characteristics of brightness temperature derived from infrared bands (Giglio et al., 2003).

The vegetation damaged by forest fire displays a clear difference in spectral characteristics from undamaged vegetation. Using these characteristics, not only damaged areas but also the burn severity can be detected from the satellite imagery. Chae and Choi (2024) detected the damaged scope of forest fires that occurred between 2016 and 2022 by applying the U-Net (Ronneberger et al., 2015) model, deep learning-based semantic segmentation, to the Sentinel-2 satellite imagery. In this study, data augmentation was applied to the learning of the U-Net model, and the results of U-Net demonstrated the detection performance further superior to ISODATA, an unsupervised classification method. Won et al.(2019) calculated the burn severity for the damaged area of the large-scale forest fire in Gangwon-do in 2019 from KOMPSAT-2 and KOMPSAT-3 imagery. The classes of severity were classified into extreme, high, moderate, and low; and the damaged area was calculated by means of Normalized Difference Vegetation Index (NDVI) (Carlson and Ripley, 1997; Pettorelli et al., 2005) and ISODATA. As a result, it showed the detection of the area directly damaged by forest fire and the area with tree mortality due to heat damage.

The NDMI collects satellite images of the forest fire in progress in real-time and supports the information for forest fire management by detecting the damaged areas. In cases of forest fire currently in progress, the satellite images collected need to be promptly analyzed; therefore, digitizing through unsupervised classification or band composite is utilized, and GIS data is also used for analysis of damages to human lives and facilities. Fig. 3 represents the results of damaged areas detected by the NDMI with regard to the forest fire case that occurred at Yanggu-gun, Gangwon-do on April 10, 2022. Sentinel-2 images before (April 7, 2022) and after (April 12, 2022) the date of forest fire occurrence were collected, and the areas damaged by forest fire were determined based on the RGB (R: 2.190 µm, G: 0.865 µm, B: 0.665 µm) composited image and NDVI difference between the two images. Also, the GIS information of roads and buildings was indicated on the map in order to provide the facility information near the damaged area.

Figure 3. Detection of the damaged area by a forest fire that occurred in Yanggu-gun on 2022.04.10. (a) Before forest fire (2022.04.07.), (b) After forest fire (2022.04.12.). (c) NDVI difference between two images.

Forest fire causes not only direct damage of forest losses but also secondary damage of increased vulnerability for landslides in the damaged area. Thus, local governments and responsible organizations invest enormous efforts in the restoration of damaged vegetation following the occurrence of forest fires. Hwang et al. (2022) monitored the vegetation recovery by means of Landsat TM/ETM+ Sentinel-2 satellite images by targeting Samcheok-si, the area damaged by a forest fire on the eastern coast, that occurred in April of 2000. This study conducted a comparative analysis of vegetation recovery through natural and artificial restoration through the use of Normalized Burn Ratio (NBR) (Cocke et al., 2005; Roy et al., 2006) and NDVI. Kim et al. (2021) proposed a composite method for vegetation recovery monitoring following the occurrence of forest fires. Pre-processed Sentinel-2 images were used, brightness, greenness, and wetness was derived by linear regression using Tassealed Cap Transformation (Kauth and Thomas, 1976; Baig et al., 2014), and vegetation recovery was examined through RGB composed images.

3.2. Drought

Droughts basically occur due to a lack of precipitation (AghaKouchak et al., 2015). According to the IPCC (Intergovernmental Panel on Climate Change) reports on climate change, the characteristics of future precipitation in the Korean Peninsula offer prospects where the precipitation increases but the number of days with precipitation decreases (Lee et al., 2016b; Kim et al., 2024). In other words, it implies that severe droughts may occur frequently as the frequency of heavy rain and the number of non-precipitation days increase. The soils and rivers with limited capacity for water storage, probabilities for droughts to occur are bound to elevate if precipitation phenomena in the forms of heavy rain persist (Choi et al., 2013; Schlaepfer et al., 2017).

Despite the implementation of aggressive policies to prevent and respond to droughts in Korea, the nation suffers damages from frequent droughts, including severe droughts as experienced in 2014 through 2015 and 2022 through 2023 (Yoo et al., 2020; Park et al., 2023a). Unlike other natural disasters, droughts exhibit characteristics where their occurrence and termination are not clearly apparent and the borderlines of spatial distribution are also unclear. Since droughts cannot be precisely observed, the occurrence, duration, and severity of droughts may be measured through indices using the drought measurement factors, while droughts can be classified into meteorological drought using precipitation, agricultural drought utilizing soil water and vegetation index and hydrologic drought in consideration of water quantity at rivers and reservoirs depending on the standards of drought determination and responses.

Satellites capable of periodic and wide-swath remote sensing may become tools to effectively detect droughts of which spatio-temporal distribution is unclear. Tropical Rainfall Measuring Mission (TRMM) of National Aeronautical and Space Administration (NASA) (Huffman et al., 2010) or Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) (Sorooshian et al., 2000; Nguyen et al., 2018) provides the data on precipitation calculated from satellite observation, and NASA and Surface Water Ocean Topography (SWOT) of Centre National D’Etudes Spatiales (CNES) offers the data on land surface water resource monitoring (Hwang, 2020). In addition, NDVI may be computed through optics images observed from multi-spectrum sensors or can be used for the measurement of droughts through the detection of surface water or underground water and soil water from Synthetic Aperture Radar (SAR) images. In this section, drought analysis studies using drought-related factors observed through satellite remote sensing are reviewed.

Park et al. (2018) performed a study on the adjustment of satellite-observed precipitation in order to improve the accuracy of drought monitoring. In this study, the space and time resolution of TRMM TRMM Multi-satellite Precipitation Analysis (TMPA) and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) products was modified, and bias within the raw satellite data was corrected based on comparison with the precipitation from ground observation. The results of the study showed the bias where the precipitation observed by satellites was exaggerated when compared to the value from ground observation, and it also forecasted that the estimated values of corrected precipitation could be used to calculate a more accurate meteorological drought index. Won et al. (2021) conducted a study that calculated the Standardized Precipitation Index (SPI) (McKee et al., 1993), Evaporative Demand Drought Index (EDDI) (Hobbins et al., 2016) and Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano et al., 2010) from the precipitation data of Climate Hazards Infrared Precipitation with Stations (CHIRPS), and evaluated the possibility for utilization of satellite remote sensing data to monitor meteorological droughts based on the subsequent results.

Agricultural droughts can be estimated through changes in vegetation vitality caused by the lack of soil water. In cases of soil water, it can be detected through radar observation, and a number of products are being used, including Soil Moisture Active and Passive (SMAP) (Entekhabi et al., 2010), Advanced Scatterometer (ASCAT) (Bartalis et al., 2007), Soil Moisture and Ocean Salinity (SMOS) (Kerr et al., 2001). Shin et al. (2016) calculated the soil water by using data assimilation technique from MODIS and Landsat and evaluated the drought monitoring performances by means of Soil Moisture Deficit Index (SMDI) (Narasimhan and Srinivasan, 2005) For the vitality index of vegetation, NDVI using the bands observed from multispectral sensors was notably employed. Park and Kim (2009) proposed the availability of vegetation index for the assessment of drought by comparing NDVI to SPI and Palmer Drought Severity Index (PDSI).

Remotely sensed surface water can become a drought indicator by itself. A number of diverse studies have been done with respect to remote sensing of surface water through the utilization of single band-based methods or spectral index-based methods using the bands of effective Near-InfraRed (NIR) and ShortWave InfraRed (SWIR) for waterbody detection (Bijeesh and Narasimhamurthy, 2020). Lee et al. (2020) calculated the water surface area and estimated the water storage of agricultural reservoirs by using Normalized Difference Water Index (NDWI) from Sentinel-2. SAR is also effectively used for surface water detections. Choi et al. (2023) detected the waterbody of agricultural reservoirs with high accuracy by using the Swin Transformer, a deep learning model, from Sentinel-1 SAR imagery.

For monitoring of droughts, NDMI performs a study to build a system for monitoring rivers and reservoirs in the South Korean domain by means of the SAR-based waterbody detection technique previously described (National Disaster Management Research Institute, 2023). The information provider system (Fig. 4) has been built to offer surface water monitoring for each administrative district in order to assist in effective drought management of local governments, and development is underway to provide information such as specifications of each reservoir and river and water resource time series through an in-system interface.

Figure 4. The water resource monitoring system of NDMI for the purpose of providing information for drought management of local governments.

3.3. Flood

Korea has Monsoon-type climatic characteristics where over 50%of annual precipitation is concentrated in the summer season (June through August). Also, ‘Changma,’ a stationary front-type rainfall, occurs between late June and July, which causes condensed precipitation, and it may be followed by heavy rainfall due to typhoons from late summer until early autumn. Lately, with increased intensity and frequency of extreme precipitation due to climate changes (Kim et al., 2023a; In et al., 2014), the frequency and scale of disasters, such as floods, are also increasing.

As satellites are capable of remotely sensing the waterbody in a wide range of areas, they can be used for the detection of flooded areas with high effectiveness. In case of optical satellites, NDWI can be effectively used for waterbody detections: the band composition of green-SWIR is most commonly used (McFeeters, 1996). Piao et al. (2018) used Terra MODIS images for inundated area mapping of large-scale flood events arising at the Sebou River of Morocco in Northwestern Africa in 2010. This study applied the threshold to band reflectance and spectral indices and compared the results for the purpose of inundated area detection. For spectral indices, NDWI was used together with Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI). In its results, the study mentioned that the SWIR band is the most important for inundated area detections and that the red band well displaying the reflectance characteristics of flooded water with high turbidity can be also effectively employed.

Since flood is a type of disaster caused by precipitation, the use of optical satellites makes it difficult to capture images at the time of flood because of cloud cover. Thus, SAR satellites are widely used for flood detection because the satellites can be utilized under all weather conditions and are capable of easily differentiating the land and waterbody. Park and Kang (2022) detected the areas damaged by the flood that occurred between July and August 2020 in the Korean Peninsula. The satellite data used were Sentinel-1 SAR imagery pre-processed from Google Earth Engine, a web-based platform, and inundated areas were determined based on the change detection before and after the flood. Moreover, the study presented an effective monitoring method for frequently flooded areas through a time-series (2018 to 2022) analysis of the study areas.

NDMI analyzed the damages from the flood caused by the typhoon ‘Hinnamnor’ which devastated Korea on September 6, 2022. It collected Sentinel-1 SAR images before and after the flood (August 28, 2022) in the region of Pohang-si, Gyeongsangbuk-do which suffered severe damages, and detected inundated areas through thresholding (Fig. 5). Since SAR images after the occurrence of flood were obtained when 3 days elapsed from the occurrence, the increase of width of the river were visibly detected; however, it was difficult to clearly determine the inundated damages in the urban area.

Figure 5. Flood area along Hyeongsan River detected by comparing the area before (blue) and after (red) the flood Event.

With flood cases occurring in Korea, it is apparent that satellite-based studies have been conducted less than the studies in other fields of disasters, of which underlying reason is believed to be because flooded water becomes quickly drained due to the geographical characteristics of Korea where mountain areas and hilly lands are widely spread around in addition to well-constructed drainage facilities, and it is why it is difficult to capture the flood scenes from satellites. In the future, far more satellites will be in operation, and observation periods will be further shortened. Therefore, more flood observation data will be accumulated; subsequently, we have high hopes for these studies on flood to be more carried out and activated.

3.4. Landslide

In Korea, the size of the mountainous area accounts for over 65% of the national land, and the whole nation is managed at the State level to protect the forest resources (Park et al., 2008). Lately, due to the increasing reckless development of mountainous districts and heavy rainfalls, the occurrence of landslides has increased. Such landslides destroy forest resources and increase damage to human lives and properties due to soil erosion. One of the notable cases is the landslide that occurred at Umyeon Mountain in Seocho-gu, Seoul in 2011. The Umyeon Mountain landslide caused tens of human casualties, and the landslide served as a valuable lesson teaching the importance of management of danger zones. Since landslides generally occur throughout mountainous districts, substantial risks are involved to investigate based on field surveys. Though remote sensing is being conducted by means of drones in consideration of convenience for investigators, there are contingent limitations within the equipment, such as narrow sensing areas and batteries. On the other hand, satellites are capable of managing the scale of disasters occurring in Korea because of their wide sensing areas; and identification can be made with mere 1 or 2 images from the satellites with medium resolutions.

Studies on landslides conducted in Korea and overseas are being performed after subdividing the analysis step into susceptibility, possibility, and risk for the purpose of wide-field analysis. First, the susceptibility step is to analyze how vulnerable the relevant region is under the consideration of only static factors Digital Elevation Model (DEM), geological map, soil map, forest type map, etc.) of landslides. Possibility is an analysis of vulnerability in consideration of dynamic factors (rainfall, earthquake, etc.). Lastly, risks are an analysis of susceptibility or possibility in consideration of damage factors to human lives and facilities (Lee et al., 2000; Park et al., 2008). Nam et al. (2016) evaluated the vulnerability of landslides through Support Vector Machine (SVM) statistical analysis by using Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) satellite images. Clay minerals were extracted by means of ASTER satellite images, and it was confirmed that the examination of landslide vulnerability using satellite images is at the applicable level as SVM statistical analysis shows an accuracy of 76.46%. The study also mentioned that the accuracy level could be improved if additional geo-mineralogical factors were added in addition to remote sensing imagery data.

Lee et al. (2002) targeted Gumi-si as a study subject region and studied the forecast of areas prone to landslides by using satellite images and GIS. To predict landslides, the study extracted the gradient and direction of slope through DEM generated from contour layers of digital topographic maps and calculated the NDVI by using satellite images. Based on the data, the study computed the landslide occurrence index and confirmed that it is possible to extract a few regions vulnerable to landslides. It is believed that a more accurate and comprehensive analysis can be achieved if other data, including soil, underground water level, and meteorological observation, is added to this study. Park et al.(2008) prepared a land use map by using satellite images and digital maps and extracted the regions vulnerable to landslides by means of GIS. This study assessed the direct risks of farmlands, roads, and artificial structures close to human daily life. It was also found that the possibilities for the occurrence of landslides can be generated, and it was deemed that some regions are exposed to the risks of landslides according to the risk analysis. Furthermore, for the purpose of reliable risk assessments on landslides, it is believed that the latest high-resolution images and data on the occurrence of landslides must be secured.

NDMI analyzed the damages from the landslide that occurred at Yecheon-gun and Bonghwa-gun, Gyeongsangbuk-do due to localized heavy rain. It caused damage to human lives and properties when the slope collapsed by localized heavy rain while the ground was weakened due to facilities, including steel frame structures installed in some parts of the region. Through cooperation among KARI, K-water, and National Geographic Information Institute (NGII), the erosion route of soils and damaged areas was able to be precisely analyzed from the images before/after the damages. However, limitations persisted for the collection of optical satellite images allowing naked eye interpretations because of clouds. FIg. 6 is an example of the results deducted from Pleiades satellite images of CNES collected through the International Charter. For precision analysis, aerial photographs and drone images were utilized and displayed by means of GIS tools.

Figure 6. The result of a landslide damage analysis is Yecheon-gun, Gyeongsangbuk-do through cooperation with the International Charter.

3.5. Earthquake

Korea experiences earthquakes less than neighboring countries, Japan and China. Also, the ratio of earthquake occurrence is also relatively low, compared to other disasters previously discussed (Kim et al., 2014; Lee et al., 2024). In Korea, an earthquake measuring 5.8 magnitude occurred in Gyeongju, Gyeongsangbuk-do in September 2016, and the 5.4 magnitude earthquake was again recorded in Gyeongju and Pohang in November of 2017, the following year. Because of the Gyeongju/Pohang earthquake, countless human lives and properties were damaged either directly or indirectly. From the incident and on, the earthquake disaster was selected as a large-scale disaster, and its applicable management began being implemented. Looking into the cases overseas, keen attention is given to secondary disasters, such as earthquakes tsunamis, and volcanoes, caused by earthquake disasters.

Earthquake is classified into natural earthquakes caused by the energy emitted from the inner earth and artificial energy incurred by explosions, such as nuclear tests (Kim and Kim, 2003). In Korea, most earthquakes are natural earthquakes, and wide-swath observation must be conducted since the crustal movements are enormous. In order to identify fault displacements manifesting in a wide area, satellite images are essentially placed in use both in Korea and overseas. Optical/thermal infrared/SAR satellite images can be well utilized for earthquake disaster management. Kim et al. (2017) engaged in damage analysis by using the images of WorldView-2, a high-resolution optical satellite, targeting earthquakes that occurred in Katmandu, Nepal in April 2015. WorldView-2 satellite images with 0.5m of high spatial resolution confirmed that it is possible to identify collapsed buildings caused by earthquakes as well as refugee shelters, i.e. grass lawns and playing fields. In cases of building collapses, it is estimated that 108 buildings were collapsed out of 1,052 buildings in total while it was identified that there were 8 refugee shelter areas.

Moreover, analysis was conducted through the utilization of building information and digital topographic maps; and based on the above, it was confirmed that the information necessary for disaster management, including the establishment of restoration plans and assistance of daily necessities for refugees, can be deduced. Park et al. (2021a) mobilized multi-temporal images of Landsat, a medium-resolution satellite, in order to monitor soil liquefaction caused by the Pohang earthquake. To detect the soil liquefaction phenomena and subsequent changes in soil water, NDVI and Land Surface Temperature (LST) were calculated and analyzed from the multi-temporal Landsat-8 satellite images. This study is designed to identify soil liquefaction due to earthquake based on the index using satellite images and subsequently-caused ground subsidence, building damages and collapse of structures. In addition, it is believed that additional damages can be prevented and contributions could be made to prediction of and preparation for occurrence of new earthquakes. Lee et al.(2018a) suggested utilization plans to respond to earthquake and volcanic disasters based on study cases using satellite images. It presented the utilization plans in detail by subdividing detection of precursors, responses following occurence of earthquakes and restoration phase. Nonetheless, it also mentioned that automated satellite image processing systems and technical developments need to be preceded in order to be effectively utilized at the disaster phase.

Fig. 7 is an example of how NDMI responded to the 2017 Pohang earthquake using satellite imagery. The satellite used for the analysis was Pleiades, an optical satellite, and the imagery was acquired through the International Charter. The post-earthquake images, with a composition of R, G, and B bands, were utilized to identify the epicenter and aftershock locations. For rapid disaster response, only visual interpretation using optical imagery was conducted. In the future, it is expected that SAR imagery, which allows precise observation of ground changes, can be used for disaster impact assessments. NDMI studies policies on earthquake disasters, led by the earthquake disaster prevention center, and supports for disaster response policies using satellite images are expected to be enhanced in the future.

Figure 7. The result of the analysis of the epicenter and aftershocks of the 2017 earthquake in Pohang, South Korea.

The availability of decision-making technologies based on Remote Sensing and GIS periodically observing the earth is gradually expanding in the field of national disaster management, including monitoring of precursors, investigation of damage scales, responses, and emergency restorations. In the past, disaster management was limited to natural disasters; however, such technologies are now being well utilized for the management of causes of social disasters, including disaster evacuation and chemical accidents, through remote sensing and chimeric analysis of spatial information (Jeong et al., 2022; Kim et al., 2022; Oh et al., 2022). As evidenced above, demands for remote sensing information are rapidly increasing, and systems capable of wide-field and quasi-real-time responses are essential in order to be efficiently utilized in more diverse fields (Kim et al., 2012).

Recently, facing the new space age, interest in the aerospace industry is soaring throughout the world (Kim, 2022; Koo et al., 2023; Yoo and Park, 2024). The Korean government is also implementing various policies, including amendments to applicable laws and regulations and mid-to-long-term roadmaps for technical developments. As the paradigm moves from state-oriented to private-oriented paradigms, microsatellites are developed and operated in the remote sensing field both in Korea and overseas, and the time spent to collect necessary data is becoming shorter and shorter (Kim and Lee, 2024). At the same time, a number of training models have been under development through AI technologies in recent years, and it is expected that satellite images will be effectively utilized in more diverse fields. This chapter is to introduce the prospects of satellite utilization in the disaster field where satellite remote sensing technology is most actively put in use.

4.1. Convergence of Multiple Satellite Images

Disasters are being managed with a focus on preventive management prior to the occurrence, timely responses during the occurrence, and prompt follow-up restoration works (Lee and Choi, 2024; Lee and Jang, 2023). For the purpose of management of disasters which are becoming bigger and bigger, field surveys alone are bound to run into limitations in budgets and workforce. It is necessary to promptly procure initial information in order to efficiently respond to disasters by using ground/aviation/aerospace technologies from the early stage of disasters. By producing and supplying meaningful information through expedient analysis of earth observation data obtained through various channels, it needs to assist effective monitoring of disasters and timely decision-making on disasters (Kim et al, 2020). Satellite images are widely being used for their strength of capability in wide-swath observations and periodic observations. Satellites of Landsat, Sentinel series, and others, known to be earth-observation satellites, currently observe Korea in an average cycle of 10 days and provide images for free. Having a specific cycle does bring merits; however, its weakness lies in the deteriorating values of such images if the gap between the time of disaster occurrence and the time of image collection ever widens because of predetermined cycles. As satellite developments previously led by the State are now expanded to private-led developments, new trends are inevitable where large-scale satellites, such as Landsat, are replaced by small, medium-sized satellites, microsatellites, and the ones in cube units (Im et al., 2020; Kim et al., 2019b; Lee et al., 2022; Park et al., 2023).

In Korea, development of microsatellite constellation is currently in progress, and local governments are also developing satellites and building their management systems. Such miniaturization of satellites does bring some merits. First, the constellation system helps satellites supplementing each other in a group, and they are engaging in identical missions, which represent a principle similar to the ones executed by a squadron of fighter jets. The biggest strength of microsatellite constellation system is its costs. Compared to conventional medium-to-large scale satellites, it takes less time to develop them; and though their lifespan for mission is relatively short, they can be quickly re-produced and re-launched if their mission lifespan expires or they are malfunctioned. Also, another strength lies in their high temporal and spatial resolutions (Park et al., 2021b). With concurrent operation of a number of units, spatial gaps can be minimized, and practicality for use can be elevated through the utilization of high-resolution images. On the other hand, the observation range is rather limited for high-resolution images, and it demands substantial costs to purchase such images. Assuming that environmental changes in the national territory are being monitored by means of microsatellite images, it is believed to be practically and realistically impossible to purchase such images in consideration of observation swaths. Also, the issue of priority may suffer when requests are made for new photography in cases of emergency disasters since most of such satellites are operated by other countries.

Recently, KARI effectively addressed the demands of satellite images in the public sector and is developing the next generation of medium-sized satellites with an aim to expand the base of the Korean satellite industry. As of today, one unit of national satellite, the Compact Advanced Satellite (CAS)500-1 is currently in operation, and it plans to launch up to the CAS500-5. For early procurement of imagery information, it is developing an 11-unit microsatellite constellation system (Fig. 8). Lately, it succeeded in the launch of one unit of the protocol which is scheduled to execute its mission of earth observation in November. It is expected to be quite effective if small satellites or microsatellites are utilized in convergence with medium/large-size satellites. Periodic monitoring is to be performed for the purpose of disaster management, and microsatellite images are to be utilized only for concerned areas. In the future, if multiple types of sensors are converged and satellites based on resolutions are converged, scientific disaster management can be expected.

Figure 8. Earth observation satellites for disaster management purposes: (a) Compact Advanced Satellite 500-1 (CAS500-1), which was referenced from KARI website, and (b) microsatellite (NEONSAT), which was extracted from the Ministry of Science and ICT website.

4.2. AI-Based Use of Satellite Images

Lately, since unforeseeable potential and new disasters have become notable social issues, it is time for paradigm changes from the use of conventional remote sensing to new disaster management practices. With rapid advancements in data utilization technologies using artificial intelligence (AI), a variety of fields are now employing AI technologies. In the disaster field, studies based on remote sensing by means of AI technologies are also in progress (Table 2). Kim and Kim (2020) have proposed an AI-based satellite image analysis methodology pursuant to the type and phase of disasters. In conjunction with the framework for quasi-real-time comprehensive disaster monitoring, it presented ‘satellite images depending on types and phases of disasters,’ ‘algorithms and models required for analysis,’ ‘time required for collection and analysis of images,’ ‘image formats before and after analysis’ and ‘required secondary references.’ It suggested the direction of utilization for early disaster detection and calculation of damaged areas by means of AI models, including Random Forest (RF), CNN, and U-Net with respect to 10 disaster types, including forest fire and flood.

Table 2 . Research on the use of satellite image-based AI technology.

Case study	Application field	AI technology used
Kim and Kim (2020)	Early disaster detection & damage area calculation	RF, CNN, U-net
Choi et al. (2022)	Water surface area calculation for agricultural reservoirs (water resource management)	SVM, RF, ANN, AutoML
Ser and Yang (2022)	Building damage detection and assessment after a disaster	SSD-512, RetinalNet, YOLOv3
Brand and Manandhar (2021)	Environmental monitoring, disaster management (Burned area detection)	U-Net based CNN (Semantic segmentation)
Kaur et al. (2021)	Disaster management (Hurricane damage detection)	CNN, RNN, U-Net
Mohan et al. (2021)	Disaster management (Landslide detection)	CNN, RF, SVM, ANN, U-Net-based segmentation and classification
Awada et al. (2022)	Environmental monitoring (Evapotranspiration tracking)	ANN, FR, Time series models

FR: Frequency Ratio..

Choi et al. (2022) conducted a study on the calculation of the water surface area of small- and medium-sized agricultural reservoirs in Korea by using Sentinel-1 SAR images and AI techniques. Despite the growing importance of water resource management due to climate changes, limitations are inevitable for the management of approximately 18,000 reservoirs nationwide. For alternatives, it calculated the water surface areas based on AI models in order to manage the water storage by utilizing satellites capable of periodic observations. AI models used include SVM (Support Vector Machine), RF, ANN (Artificial Neural Network), and Automated Machine Learning, and the AutoML model displayed the most outstanding performance. It also mentioned that if a deep-learning algorithm, such as CNN, is put in use to make higher dimensional approaches in imagery learning in the future, it is expected to make a calculation of water surface area more effective in managing water storage.

Ser and Yang (2022) have performed a study utilizing satellite images and deep-learning models for buildings damaged by disasters. The purpose of this study was to select the most suitable model to promptly detect and assess the damages to buildings following disasters, and the study quantitatively made assessments by using 3 well-known models. The deep-learning models employed were Single Short Multibox Detector (SSD)-512, RetinalNet, and YOLOv3. For the assessment of building damages at disaster sites, high detection performance and prompt image processing speed are considered key selection factors, and the results were deduced where YOLOv3 is the most suitable for disaster sites based on the tests. The study stated that prompt detections based on deep learning are expected to be effectively utilized for disaster management. In addition to the cases in Korea introduced deep-learning models are also being used overseas for disaster management through analysis of damages from disasters, such as landslides, typhoons, and earthquakes (Brand and Manandhar, 2021; Kaur et al., 2021; Mohan et al., 2021). RS imagery is being used as a basic material for wide-field monitoring in various fields and is also utilized for input data for secondary outcomes together with numerical models (Awada et al., 2022). AI models are anticipated to be effectively used for non-linear data management with complex connections of various parameters. As more AI-learning models are currently under development, it is expected that scientific disaster management to be achievable, including advance prediction of and response to disasters based on images from satellites capable of wide-swath/periodic observations.

The frequency of disaster occurrences continues increasing due to abnormal climate, and their scale also keeps getting bigger and bigger. At the same time, because of risks for the occurrence of potential disasters previously unrecognized, it is time to change the paradigm of disaster management. With recent technical advancements, disaster response systems, which used to be limited to simple collection and offering of data, are changing into scientific disaster management systems using cutting-edge equipment and big data. The Ministry of the Interior and Safety operates the National Disaster and Safety Status Control Center and engages in comprehensive management of national disaster safety and dangerous situations. NDMI, in emergency situations, collects information necessary for decision-making at the National Disaster and Safety Status Control Center and provides analyzed information and data in quasi-real-time. Major disasters include forest fires, drought, storms and floods, landslides, and earthquakes, which mostly occur in a wide range of areas. Satellite images are essentially being utilized in order to respond to disasters occurring in a wide range of areas. Satellite images equipped with optical, SAR, and infrared sensors are mainly used as satellites. The SAR, which can observe all weather conditions, is an important sensor in disasters. However, in terms of disaster management that requires rapid response, optical images that can be read with the naked eye are highly utilized. If improvements are made to the SAR system, which requires complex processing in the future, the utilization is expected to be maximized.

Demands for satellite images are increasing lately, and the development of satellites is aggressively pursued both in Korea and overseas. In Korea, CAS 500 series satellites and microsatellite constellation systems are being developed and operated. Moreover, with the emergence of AI technology, the fields using learning models, such as machine learning and deep learning, are also increasing. Also with GIS analysis like Google Earth Engine and visualization platforms, they are aggressively used in the field of image processing in need of a massive scale of computing power. In the disaster field, it is expected to be able to effectively collect, analyze, and display the information for forecast, response, and restoration from overflowing satellite imagery big data through AI technology.

This research was funded by a study of convergence technique for disaster-risk tracking based on multi-satellite data (NDMI-PR-2024-03-01) from the National Disaster Management Research Institute (NDMI), Ministry of Interior and Safety.

No potential conflict of interest relevant to this article was reported.