• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.233-243
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• 2021

Photovoltaic panels are hazardous electronic waste that has heavy metal as one of the hazardous components. Each year, hazardous electronic waste is increasing worldwide and every heavy rainfall exposes the photovoltaic panel to become the source of heavy metal soil contamination. the development needs a monitoring technology for this hazardous exposure. this research use relationships between SAR temporal baseline and coherence of Sentinel-1 satellite to detected photovoltaic panel. Also, the photovoltaic plant detection tested using the difference between that photovoltaic panel and the other difference surface of coherence. The author tested the photovoltaic panel and its environment to calculate differences in coherence relationships. As a result of the experiment, the coherence of the photovoltaic panel, which is assumed to be a permanent scatterer, shows a bias that is biased toward a median value of 0.53 with a distribution of 0.50 to 0.65. Therefore, further research is needed to improve errors that may occur during processing. Additionally, the author found that the change detection using a temporal baseline is possible as the rate of reduction of coherence of photovoltaic panels differs from those of artificial objects such as buildings. This result could be an efficient way to continuously monitor regardless of weather conditions, which was a limitation of the existing optical satellite image-based photovoltaic panel detection research and to understand the spatial distribution in situations such as photovoltaic panel loss.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.560-574
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• 2011

Satellite images that have been obtained periodically and continuously are very effective data to monitor the changes of Earth's surface. Traditionally, the studies on change detection using satellite images have mainly focused on comparison between two results after analyzing two images respectively. However, the interests in researches to catch smooth trends and short duration events from continual multi-temporal images have been increased recently. In this study, we introduce and test an approach based on 3D wavelet transform to analyze the multi-temporal satellite images. 3D wavelet transform can reduce the dimensions of data conserving main trends. Also, it is possible to extract important patterns and to analyze spatial and temporal relations with neighboring pixels using 3D wavelet transform. As a result, 3D wavelet transform is useful to capture the long term trends and short-term events rapidly. In addition, we can expect to get new information through sub-bands of 3D wavelet transform which provide different information by decomposed direction.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1995.12a
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• pp.22-32
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• 1995

The purpose of this study is to estimate not only the watershed soil loss but also its temporal changes of Kyungan River basin, the study area, due to the land development. To analyze the soil loss of the river basin, USLE was employed. GIS and remote sensing were also utilized to estimate the soil loss. The data for this analysis consist of a series of thematic map and remotely sensed data. The remotely sensed images for this study are Landsat TM(Oct, 28, 1997 & Sep. 22, 1992), In Kyungan River basin, not only the detection of temporal changes of land use and GVI, but also the estimation of soil loss provided very significant factors that affect to the watershed environment quality. The management of the factors of vegetative cover, slope steepness and length were the keys to reduce soil loss and solve conservation and protection issues of Kyungan River basin. GIS application with USLE to the watershed analysis allows the planner to recognize sensitive sites and to plan strategies to minimize soil loss.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.89-113
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• 1990

The purpose of this study is to develop means to apply GIS and remote sensing technology to the analysis of Korean urban open spaces. To achieve this objective, a framework of analysis of urban open spaces was developed, and then the framework was applied for the evaluation of the potential and suitability of open spaces of Ansan City, which is a new town developed to accomodate industries relocation from Seoul, Korea, mainly due to their pollution problems. The software used in this study are IDRISI, a grid-based GIS, and KMIPS, a remote sensing analysis system. Both packages are based on IBM PC/AT computers with Microsoft DOS. Landsat MSS and TM data were used for the land use classification, land use change detection, and analysis of transformed vegetation indices. The size of the geographic data base is 110 rows and 150 columns with the spatial resolution of 100m$\times$100m. The framework of analysis includes both quanititative and qualitative analysis of open spaces. The quantitative analysis includes size and distribution of open spaces, urban develpment of open spaces, and the degree of vegree of vegetation removal of the study area. The qualitative analysis includes evaluative criteria for primary productivity of land, park use potential, major visual resources, and urban environmental control. The findings of this study can be summarized as follows. First, the size of builtup areas increased 18.73km$^2$, while the size of forest land decreased 10.86km$^2$ during last ten years. Agricultural lands maintained its size, but shifted toward outside of the city into forest. Second, the potential of open spaces for park use is limited mainly due to their lack of accessibility and connectivity among open spaces, in spite of ample acreage and good site conditions. Third, major landscape elements and historic sites should be connected to the open space system of the city by new accesses and buffers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.187-193
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• 2022

In the Yeongdong region of Gangwon-do, coastal areas are important resources in terms of cultural, social and economic aspects. However, the coast of Gangwon-do is experiencing severe erosion, and it is concerned that its adverse effects will gradually increase. In this study, coastline changes of Yangyang and Gangneung in Gangwon-do were tracked and analyzed over a long period of time. In order to build time series image data, aerial photos from the 1940s to the present were mainly used, and data from CORONA satellite, which operated from the 1960s to the early 1970s, were collected and used together. Using 51cm resolution ortho image and 2m resolution Digital Elevation Model(DEM) as reference, ground control points were selected to perform geometric correction on the aerial photos and CORONA images. Subsequently, Canny edge detector applied to these images to extract the coastlines. As a result of analyzing the extracted and vectorized coastlines by overlaying them in chronological order, erosion and deposition occurring around the artificial structures and on the nearby beaches were observed. In this study, the effect of seasonal variation, tide, and various coastal management including the beach filling were not considered. Because coastal erosion is greatly affected by geographic factors, each local government must find its own solution. Continuous research and local data accumulation are required.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.2
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• pp.49-57
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• 2016

Even if an external forcing that will drive a climate change is given uniformly over the globe, the corresponding climate change and the feedbacks by the climate system differ by region. Thus the detection of global warming signal has been made on a regional scale as well as on a global average against the internal variabilities and other noises involved in the climate change. The purpose of this study is to estimate a timing of unprecedented climate due to global warming and to analyze the regional differences in the estimated results. For this purpose, unlike previous studies that used climate simulation data, we used an observational dataset to estimate a magnitude of internal variability and a future temperature change. We calculated a linear trend in surface temperature using a historical temperature record from 1880 to 2014 and a magnitude of internal variability as the largest temperature displacement from the linear trend. A timing of unprecedented climate was defined as the first year when a predicted minimum temperature exceeds the maximum temperature record in a historical data and remains as such since then. Presumed that the linear trend and the maximum displacement will be maintained in the future, an unprecedented climate over the land would come within 200 years from now in the western area of Africa, the low latitudes including India and the southern part of Arabian Peninsula in Eurasia, the high latitudes including Greenland and the mid-western part of Canada in North America, the low latitudes including Amazon in South America, the areas surrounding the Ross Sea in Antarctica, and parts of East Asia including Korean Peninsula. On the other hand, an unprecedented climate would come later after 400 years in the high latitudes of Eurasia including the northern Europe, the middle and southern parts of North America including the U.S.A. and Mexico. For the ocean, an unprecedented climate would come within 200 years over the Indian Ocean, the middle latitudes of the North Atlantic and the South Atlantic, parts of the Southern Ocean, the Antarctic Ross Sea, and parts of the Arctic Sea. In the meantime, an unprecedented climate would come even after thousands of years over some other regions of ocean including the eastern tropical Pacific and the North Pacific middle latitudes where an internal variability is large. In summary, spatial pattern in timing of unprecedented climate are different for each continent. For the ocean, it is highly affected by large internal variability except for the high-latitude regions with a significant warming trend. As such, a timing of an unprecedented climate would not be uniform over the globe but considerably different by region. Our results suggest that it is necessary to consider an internal variability as well as a regional warming rate when planning a climate change mitigation and adaption policy.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.3
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• pp.165-173
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• 2010

A cloud detection method is introduced to improve the reliability of NOAA/AVHRR Sea Surface Temperature (SST) data processed during the daytime and nighttime in the TeraScan System. In daytime, the channels 2 and 4 are used to detect a cloud using the three tests, which are spatial uniformity tests of brightness temperature (infrared channel 4) and channel 2 albedo, and reflectivity threshold test for visible channel 2. Meanwhile, the nighttime cloud detection tests are performed by using the channels 3 and 4, because the channel 2 data are not available in nighttime. This process include the dual channel brightness temperature difference (ch3 - ch4) and infrared channel brightness temperature threshold tests. For a comparison of daytime and nighttime SST images, two data used here are obtained at 0:28 (UTC) and 21:00 (UTC) on May 13, 2009. 6 parameters was tested to understand the factors that affect a cloud masking in and around Korean Peninsula. In daytime, the thresholds for ch2_max cover a range 3 through 8, and ch4_delta and ch2_delta are fixed on 5 and 2, respectively. In nighttime, the threshold range of ch3_minus_ch4 is from -1 to 0, and ch4_delta and min_ch4_temp have the fixed thresholds with 3.5 and 0, respectively. It is acceptable that the resulted images represent a reliability of SST according to the change of cloud masking area by each level. In the future, the accuracy of SST will be verified, and an assimilation method for SST data should be tested for a reliability improvement considering an atmospheric characteristic of research area around Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.835-846
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• 2020

Recent severe climate changes and extreme weather events have caused the uncommon types of forest ecosystem disturbances such as hails and gypsy moths. This paper describes the analysis of the forest ecosystem disturbances using ISODATA (Iterative Self-organizing Data Analysis Technique Algorithm) with the RapidEye and Sentinel-2 images, regarding the cases of the hail damages in Hwasun in 2017 and the gypsy moth damages in the Chiak Mountain in 2020. In the case of hail damages, the comparison of the June image of this study and the July field survey of the previous study showed that the damage severity increased from June to July as the drought overlapped after the trees were injured by the hails. In the case of gypsy moths, significant leaf damages were found from the image of June, and the damages were mainly distributed at the low-altitude slope near Wonju City. We made sure that satellite remote sensing is a very effective method to detect various and unusual forest ecosystem disturbances caused by climate change. Also, it is expected that the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024 can be actively utilized to monitor such forest ecosystem disturbances.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1085-1095
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• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.