• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1303-1322
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• 2020

Indonesia is more prone to natural disasters due to its geological condition under the three main plates, making Indonesia experience frequent seismic activity, causing earthquakes, volcanic eruption, and tsunami. Those disasters could lead to other disasters such as landslides, floods, land subsidence, and coastal inundation. Monitoring those disasters could be essential to predict and prevent damage to the environment. We reviewed the application of remote sensing and Geographic Information System (GIS) for detecting natural disasters in the case of Indonesia, based on 43 articles. The remote sensing and GIS method will be focused on InSAR techniques, image classification, and susceptibility mapping. InSAR method has been used to monitor natural disasters affecting the deformation of the earth's surface in Indonesia, such as earthquakes, volcanic activity, and land subsidence. Monitoring landslides in Indonesia using InSAR techniques has not been found in many studies; hence it is crucial to monitor the unstable slope that leads to a landslide. Image classification techniques have been used to monitor pre-and post-natural disasters in Indonesia, such as earthquakes, tsunami, forest fires, and volcano eruptions. It has a lack of studies about the classification of flood damage in Indonesia. However, flood mapping was found in susceptibility maps, as many studies about the landslide susceptibility map in Indonesia have been conducted. However, a land subsidence susceptibility map was the one subject to be studied more to decrease land subsidence damage, considering many reported cases found about land subsidence frequently occur in several cities in Indonesia.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.107-109
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• 2003

Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

• The Korean Journal of Air & Space Law and Policy
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• v.20 no.1
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• pp.87-107
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• 2005

Land Remote Sensing' is defined as the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. Narrowly speaking, this is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information. Remote sensing technology was initially developed with certain purposes in mind ie. military and environmental observation. However, after 1970s, as these high-technologies were taught to private industries, remote sensing began to be more commercialized. Recently, we are witnessing a 0.61-meter high-resolution satellite image on a free market. While privatization of land remote sensing has enabled one to use this information for disaster prevention, map creation, resource exploration and more, it can also create serious threat to a sensed nation's national security, if such high resolution images fall into a hostile group ie. terrorists. The United States, a leading nation for land remote sensing technology, has been preparing and developing legislative control measures against the remote sensing industry, and has successfully created various policies to do so. Through the National Oceanic and Atmospheric Administration's authority under the Land Remote Sensing Policy Act, the US can restrict sensing and recording of resolution of 0.5 meter or better, and prohibit distributing/circulating any images for the first 24 hours. In 1994, Presidential Decision Directive 23 ordered a 'Shutter Control' policy that details heightened level of restriction from sensing to commercializing such sensitive data. The Directive 23 was even more strengthened in 2003 when the Congress passed US Commercial Remote Sensing Policy. These policies allow Secretary of Defense and Secretary of State to set up guidelines in authorizing land remote sensing, and to limit sensing and distributing satellite images in the name of the national security - US government can use the civilian remote sensing systems when needed for the national security purpose. The fact that the world's leading aerospace technology country acknowledged the magnitude of land remote sensing in the context of national security, and it has made and is making much effort to create necessary legislative measures to control the powerful technology gives much suggestions to our divided Korean peninsula. We, too, must continue working on the Korea National Space Development Act and laws to develop the necessary policies to ensure not only the development of space industry, but also to ensure the national security.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.500-505
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• 2002

Based on the real-time monitoring by remote sensing and dynamic management by GIS on agricultural land and water resources in arid area, we solved the practicability and popularization of small-scale spatial information service system. Through demonstration, the standards of spatial information service database of agricultural land and water resources is set up, and the agricultural land and water resources management system in Shehezi City of Xinjiang Autonomy is established, which provides periodically the spatial information services needed by agricultural production to support for sustainable development in arid area.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.99-110
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• 2023

Land surface temperature (LST) is a critical environmental indicator affected by land cover (LC) changes. Currently, the most convenient and fastest way to retrieve LST is to use remote sensing images due to their continuous monitoring of the Earth's surface. The work intended to investigate land cover change and temperature response inAn-Najaf province. Landsat multispectral imageries acquired inAugust 1989, 2004, and 2021 were employed to estimate land cover change and LST responses. The findings exhibited an increase in water bodies, built-up areas, plantations, and croplands by 7.78%, 7.27%, 6.98%, 3.24%, and 7.78%, respectively, while bare soil decreased by 25.27% for the period (1989-2021). This indicates a transition from barren lands to different land cover types. The contribution index (CI) was employed to depict how changes in land cover categories altered mean region surface temperatures. The highest LSTs recorded were in bare lands (42.2℃, 44.25℃, and 46.9℃), followed by built-up zones (41.6℃, 43.96℃, and 44.89℃), cropland (30.9℃, 32.96℃, and 34.76℃), plantations (35.4℃, 36.97℃, and 38.92℃), and water bodies (27.3℃, 29.35℃, and 29.68℃) respectively, in 1989, 2004, and 2021. Consequently, these changes resulted in significant variances in LST between different LC types.

• Journal of Environmental Impact Assessment
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• v.4 no.1
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• pp.59-64
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• 1995

Environmental Impact Assesment(EIA) is composed of various procedures, such as screening, scoping, inventory survey, prediction, assessment, mitigation measure, alternative assessment, and post management. Remote sensing introduced lately begins to be applied ecosystem and land use in inventory survey and assessment of EIA. This study explains on land use classification, buffering analysis of residential area, and overlaying analysis of odor predictive data with residential area for application to EIA with remote sensing data. Residential area extracted from land use classification of remote sensing provides effectively buffering analysis of residential area in selection of landfill site with GIS. It could assess also residential effect to an offensive odor by overlaying analysis. Application methods in EIA should be enlarged to assess effectively.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1299-1312
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• 2019

As land remote sensing applications are expanding to the extraction of quantitative information, the importance of atmospheric correction is increasing. Considering the difficulty of atmospheric correction for land images, it should be applied when it is necessary. The quantitative information extraction and time-series analysis on biophysical variables in land surfaces are two major applications that need atmospheric correction. Atmospheric aerosol content and column water vapor, which are very dynamic in spatial and temporal domain, are the most influential elements and obstacles in retrieving accurate surface reflectance. It is difficult to obtain aerosol and water vapor data that have suitable spatio-temporal scale for high- and medium-resolution multispectral imagery. Selection of atmospheric correction method should be based on the availability of appropriate aerosol and water vapor data. Most atmospheric correction of land imagery assumes the Lambertian surface, which is not the case for most natural surfaces. Further BRDF correction should be considered to remove or reduce the anisotropic effects caused by different sun and viewing angles. The atmospheric correction methods of optical imagery over land will be enhanced to meet the need of quantitative remote sensing. Further, imaging sensor system may include pertinent spectral bands that can help to extract atmospheric data simultaneously.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1275-1277
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• 2003

This paper presents a geological remote sensing information extraction system, the aim of which is to provide practical models and powerful tools to extract geological information from remote sensing images for geological exploration applications. After reviewing and analyzing the existing methods for geological information extraction, we developed more than ten models to enhance and extract geological information, such as alteration information, linear features and special lithological characters. The system is developed based on Erdas Imagine using its programming language. It has been successfully used in the 'reat Investigation of Land and Natural Resources of China' program.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1442-1444
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• 2003

This paper presents a geological remote sensing information extraction system, the aim of which is to provide practical models and powerful tools to extract geological information from remote sensing images for geological exploration applications. After reviewing and analyzing the existing methods for geological information extraction, we developed more than ten models to enhance and extract geological information, such as alteration information, linear features and special lithological characters. The system is developed based on Erdas Imagine using its programming language. It has been successfully used in the ‘Great Investigation of Land and Natural Resources of China’ program.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.337-340
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• 2007

Many researches conducted to investigate the relationship between surface emissivity and surface temperature in the past two decades and pointed out that the emissivity play a key role in applying remote sensing data to retrieve surface temperature. The task of surface temperature estimation is so important in many research fields, such as earth energy budgets, evapotranspiration, drought, global change and heat island effect. Therefore, it is indispensable to develop an effective and accurate technique to estimate the emissivity for accurate surface temperature estimations. This study developed an improved emissivity estimation technique for the use of surface temperature retrievals with MODIS data. The result of applying this improved technique using Band 31 of MODIS shows that the accuracy of estimated surface temperatures will be improved. This study also uses MODIS data observed in 2005 to establish the relationship between the surface emissivity correction factor and NDVI. Through the use of these correction factors, the land surface temperature can be retrieved more accurate with MODIS data.