Ship monitoring using satellite synthetic aperture radar (SAR) images consists of ship detection, ship discrimination, and ship classification. A large number of methods have been proposed to improve the detection and discrimination capabilities, while only a few studies exist for ship classification. Thus, many studies for the ship classification are needed to construct ship monitoring system having high performance. Note that constructing database (DB), which contains both SAR images and labels of various ships, is important for research on the ship classification. In the airborne SAR classification, many methods have been developed using moving and stationary target acquisition and recognition (MSTAR) DB. However, there has been no publicly available DB for research on the ship classification using satellite SAR images. Recently, Shanghai Key Laboratory has constructed OpenSARShip DB using both SAR images of various ships generated from Sentinel-1 satellite of European Space Agency (ESA) and automatic identification system (AIS) information. Thus, the applicability of OpenSARShip DB for ship classification should be investigated by using the concepts of airborne SAR classification which have shown high performances. In this study, ship classification using satellite SAR images are conducted by applying the concepts of airborne SAR classification to OpenSARShip DB, and then the applicability of OpenSARShip DB is investigated by analyzing the classification performances.

The current U.S. remote sensing act was made in 1992 and has been criticized for being outdated and inappropriate in view of the modern technological development. In order to enhance the American competitiveness and leadership in the world, President Trump announced Space Policy Directive (SPD) – 2 on May 24, which is designed to modernize the regulations related to commercial space activities including private remote sensing system operations. It should be noted that the regulatory reform efforts are made within broader terms of the National Security Strategy on Dec. 17, 2017, pursuing the enhancement of national security and economic prosperity as well. A legislative support in Congress has also been added to the Administration’s efforts. The proposed regulatory reform on the licensing of commercial remote sensing system operations outlines the features of lessening administrative burden on applicants by simplifying the overall application process and of limiting the operations only when there is an impact upon the national security with clear and convincing evidence. But, due to a different regulatory system of each country, such a movement to expand an individual’s freedom to explore and utilize outer space may result in an international dispute or a violation of international obligations, so there should be a merit in paying attention to the U.S. commercial remote sensing regulatory reform, and it is desirable to establish international norms as flexible and appropriate to the level of space technology and space industry.

This study demonstrated the ability of a Landsat-8 OLI multispectral data to identify and delineate hydrothermal alteration zones around auriferous prospects within the crystalline basement, North-western Nigeria. Remote sensing techniques have been widely used in lithological, structural discrimination and alteration rock delineation, and in general geological studies. Several artisanal mining activities for gold deposit occur in the surrounding areas within the basement complex and the search for new possible mineralized zones have heightened in recent times. Systematic Landsat-8 OLI data processing methods such as colour composite, band ratio and minimum noise fraction were used in this study. Colour composite of band 4, 3 and 2 was displayed in Red-Green￾Blue colour image to distinguish lithologies. Band ratio image displayed in red was used to highlight ferric-ion bearing minerals(hematite, goethite, jarosite) associated with hydrothermal alteration, band ratio image displayed in green was used to highlight ferrous-ion bearing minerals such as olivine, amphibole and pyroxenes, while ratio image displayed in blue was used to highlight clay minerals, micas, talc-carbonates, etc. Band rationing helped to reduce the topographic illumination effect within images. The result of this study showed the distribution of the lithological units and the hydrothermal alteration zone which can be further prospected for mineral reserves.

In this study, the annual surface soil erosion amount of before (2007 year) and after (2015 year) the river improvement projects were calculated using RUSLE (Revised Universal Soil Loss Equation) in the Geumgang basin (Daecheong-Dam to Geumgang Estuary-Bank). After the results were classified into five classes, the results were compared and analyzed with the results of the change in the land cover. In order to generate each factor of RUSLE, various spatial information data, such as land cover maps for 2007 and 2015 years, national basic spatial information, soil map, and average annual precipitation data were utilized. The results of the analysis are as follows: 1) annual surface soil erosion in the study area increased the area of class 1 in 2015 years compared to 2007, 2) the area of class 2, 3 and 5 decreased, 3) the area of class 4 increased. It is believed that the average annual amount of surface soil erosion decreased in most areas due to the reduction of annual average precipitation, the formation of ecological parks, the expansion of artificial facilities, and the reduction of illegal farmland.

The ground sample distance of KOMPSAT-3 is 0.7 m for panchromatic band, 2.8 m for multi-spectral band, and the swath width of KOMPSAT-3 is 16 km. Therefore, an image of an area wider than the swath width (16 km) cannot be acquired with a single scanning. Thus, after scanning multiple areas in units of swath width, the acquired images should be made into one image. At this time, the necessary algorithm is called image mosaicking or image stitching, and is used for cartography. Mosaic algorithm generally consists of the following 4 steps: (1) Feature extraction and matching, (2) Radiometric balancing, (3) Seamline estimation, and (4) Image blending. In this paper, we have studied an effective seamline estimation method for satellite images. As a result, we can estimate the seamline more accurately than the existing method, and the heterogeneity of the mosaiced images was minimized.

Natural and environmental disasters are recently increasing in frequency and complexity worldwide due to the rapid expansion of overpopulation, industrialization, and urbanization. Thus, analyzing past critical events/disasters in deep and preparing for future disasters in terms of risk identification, assessment and management are imperative requirements. In this special issue, we introduce several interesting studies covering disaster risk management and observation technologies for the heat waves, particulate matters, floods, drought, and earthquake using remote sensing and GIS performed by i-SEED (School of Integrated Science for Sustainable Earth & Environmental Disaster at Pukyong National University). We expect that the results of this special issue provide comprehensive information on the risk management and damage prevention of natural and environmental disasters and offer guidance on the application to future disasters to reduce their risks and impacts.

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about –0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

As remote sensing and GIS have been considered to be essential technologies for disasters information production, researches on developing methods for analyzing spatial data, and developing new technologies for such purposes, have been actively conducted. Especially, it is assumed that the use of remote sensing and GIS for disaster management will continue to develop thanks to the launch of recent satellite constellations, the use of various remote sensing platforms, the improvement of acquired data processing and storage capacity, and the advancement of artificial intelligence technology. This spatial issue presents 10 research papers regarding ship detection, building information extraction, ocean environment monitoring, flood monitoring, forest fire detection, and decision making using remote sensing and GIS technologies, which can be applied at the disaster prediction, monitoring and response stages. It is anticipated that the papers published in this special issue could be a valuable reference for developing technologies for disaster management and academic advancement of related fields.

This study evaluates the accuracy in identifying the burned area in South Korea using multi- temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.

Recently, the number of natural and environmental disasters is rapidly increasing due to extreme weather caused by climate change, and the scale of economic losses and damage to human life is increasing accordingly. In addition, with urbanization and industrialization, the characteristics and scale of extreme weather appearance are becoming more complex and large in different ways from the past, and need for remote sensing and artificial intelligence technology for responding and managing global environmental disasters. This special issue investigates environmental disaster observation and management research using remote sensing and artificial intelligence technology, and introduces the results of disaster-related studies such as drought, flood, air pollution, and marine pollution, etc. in South Korea performed by the i-SEED (School of Integrated Science for Sustainable Earth and Environmental Disaster at Pukyong National University). In this special issue, we expect that the results can contribute to the development of monitoring and management technologies that may prevent environmental disasters and reduce damage in advance.