• Korean Journal of Remote Sensing
• /
• v.21 no.6
• /
• pp.457-468
• /
• 2005

Increasing of impervious surface resulting from urban development has negative impacts on urban environment. Therefore, it is absolutely necessary to estimate and quantify the temporal and spatial aspects of impervious area for study of urban environment. In many cases, conventional image classification methods have been used for analysis of impervious surface fraction. However, the conventional classification methods have shortcoming in estimating impervious surface. The DN value of the each pixel in imagery is mixed result of spectral character of various objects which exist in surface. But conventional image classification methods force each pixel to be allocated only one class. And also after land cover classification, it is requisite to additional work of calculating impervious percentage value in each class item. This study used the spectral mixture analysis to overcome this weakness of the conventional classification methods. Four endmembers, vegetation, soil, low albedo and high albedo were selected to compose pure land cover objects. Impervious surface fraction was estimated by adding low albedo and high albedo. The study area is the Tanchon watershed which has been rapidly changed by the intensive development of housing. Landsat imagery from 1988, 1994 to 2001 was used to estimate impervious surface fraction. The results of this study show that impervious surface fraction increased from $15.6\%$ in 1988, $20.1\%$ in 1994 to $24\%$ in 2001. Results indicate that impervious surface fraction can be estimated by spectral mixture analysis with promising accuracy.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.1149-1161
• /
• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.31 no.6_2
• /
• pp.529-538
• /
• 2013

Coastline is the most dynamic part of seascape since its shape is affected by various factors. Coastal zone is an area with immense geological, geomorphological and ecological interest. Monitoring coastal change is very important for safe navigation, coastal resource management. This paper shows a result of monitoring coastal morphological changes using Remote Sensing and GIS. Study was carried out to obtain intensity of erosion, deposition and sand bar movement in the Red River Delta. Satellite images of ALOS/AVNIR-2 and Landsat were used for the monitoring of coastal morphological changes over the period of 1975 to 2009. Band rationing and threshold technique was used for the coastline extraction. Tidal levels at the time of image acquisition varied from -0.89m to 2.87m. Therefore, coastline from another image at a different tidal level in the same year was considered to get the corrected coastline by interpolation technique. A series of points were generated along the coastal line from 1975 image and were established as reference points to see the change in later periods. The changes were measured in Euclidean distances from these reference points. Positive values represented deposition to the sea and negative values are erosion. The result showed that the Red river delta area expanded to the sea 3500m in Red river mouth, and 2873m in Thai Binh river mouth from 1975 to 2009. The erosion process occurred continuously from 1975 up to now with the average magnitude 23.77m/year from 1975 to 1989 and 7.85m/year from 2001 to 2009 in Giao Thuy area. From 1975 to 2009, total 1095.2ha of settlement area was eroded by sea. On the other hand, land expanded to the sea in 4786.24ha of mangrove and 1673.98ha of aquaculture.

• Journal of Korea Planning Association
• /
• v.54 no.2
• /
• pp.93-108
• /
• 2019

This study examines the relationship between three-dimensional urban built environment and urban surface temperature using LANDSAT 8 satellite image data in Seoul city. The image was divided into 600m×600m grid units as an unit of analysis. Due to the high level of spatial dependency in surface temperature, this study uses spatial statistics to take into account spatial auto-correlation. The spatial error model shows the best goodness of fit. The analysis results show that the three-dimensional built environment and transport environment as well as natural environment have statistically significant associations with surface temperature. First, natural environment variables such as green space, streams and river, and average elevation show statistically significant negative association with surface temperature. Second, the building area shows a positive association with surface temperature. In addition, while sky view factor (SVF) has a positive association with surface temperature, surface roughness (SR) shows a negative association with it. Third, transportation related variables such as road density, railway density, and traffic volume show positive associations with surface temperature. Moreover, this study finds that SVF and SR have different effects on surface temperature in regard to the levels of total floor areas in built environment. The results indicate that interactions between floor area ratio (FAR) and three-dimensional built environmental variables such as SVF and SR should be considered to reduce urban surface temperature.

• Korean Journal of Remote Sensing
• /
• v.12 no.2
• /
• pp.139-154
• /
• 1996

In order to evaluate the sensor`s look direction bias in the Landsat TM image and to estimate trends of primary geological lineaments, we have attempted to systematically compare lineaments in TM image, relief shadowed DEM's, and actual lineaments of geologic and topographic map through the Hough transform technique. Hough transform is known to be very effective to estimate the trend of geological lineaments, and help us to obtain the true trends of lineaments. It is often necessary to compensate the preferential enhancements of terrain lineaments in a TM image occurred by to look direction bias, and that can be achieved by utilizing an auxiliary data. In this study, we have successfully adopted the relief shadowed DEM in which the illuminating azimuth angle is perpendicular to look direction of a TM image for assessing true trends of geological lineaments. The results also show that the sum of four relief shadowed DEM's directional components can possibly be used as an alternative. In Euiseong-gun area where Sindong Group and Mayans Group are mainly distributed, geological lineaments trending $N5^{\circ}$~$10^{\circ}$W are dominant, while those of $N55^{\circ}$~$65^{\circ}$ W are major trends in Cheongsong-gun area where Hayang Group, Yucheon Group and Bulguksa Granite are distributed. Using relief shadowed DEM as an auxiliary data, we found the $N55^{\circ}$~$65^{\circ}$ W lineaments which are not cleanly observed in TM image over Euiseong-gun area. Compared with the trend of Gumchon and Gaum strike-slip faults, these lineaments are considered to be an extension of the faults. Therefore these strike-slip faults possibly extend up to Sindong Group in the northwest parts in the study area.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.12 no.3
• /
• pp.13-20
• /
• 2009

Techniques for change detection using satellite images enable efficient detection of natural and artificial changes in use of land through multi-phase images. As for change detection, different results are made based on methods of calibration of satellite images, types of input data, and techniques in change analysis. Thus, an analytic technique that is appropriate to objectives of a study shall be applied as results are different based on diverse conditions even when an identical satellite and an identical image are used for change detection. In this study, Normalized Difference Vegetation Index (NDVI) and Principal Component Analysis (PCA) were conducted after geometric calibration of satellite images which went through absolute and relative radiometric calibrations and change detection analysis was conducted using Image Difference (ID) and Image Rationing (IR). As a result, ID-NDVI showed excellent accuracy in change detection related to vegetation. ID-PCA showed 90% of accuracy in all areas. IR-NDVI had 90% of accuracy while it was 70% and below as for paddies and dry fields${\rightarrow}$grassland. IR-PCA had excellent change detection over all areas.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.1
• /
• pp.80-90
• /
• 2014

The objective of this research is to estimate the stand volume of Pinus koraiensis, by using the investigated volume and the information of remote sensing(RS), in the research forest of Kangwon National University. The average volume of the research forest per hectare was $307.7m^3/ha$ and standard deviation was $168.4m^3/ha$. Before and after carrying out 3 by 3 majority filtering on TM image, eleven indices were extracted each time. Independent variables needed for linear regression equation were selected using mean pixel values by indices. The number of indices were eleven: six Bands(except for thermal Band), NDVI, Band Ratio(BR1:Band4/Band3, BR2:Band5/Band4, BR3:Band7/Band4), Tasseled Cap-Greeness. As a result, NDVI and TC G were chosen as the most suitable indices for regression before and after filtering, and R-squared was high: 0.736 before filtering, 0.753 after filtering. As a result of error verification for an exact comparison, RMSE before and after filtering was about $69.1m^3/ha$, $67.5m^3/ha$, respectively, and bias was $-12.8m^3/ha$, $9.7m^3/ha$, respectively. Therefore, the regression conducted with filtering was selected as an appropriate model because of low RMSE and bias. The estimated stand volume applying the regression was $160,758m^3$, and the average volume was $314m^3/ha$. This estimation was 1.2 times higher than the actual stand volume of Pinus koraiensis.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.3
• /
• pp.61-74
• /
• 2016

The purpose of this paper is to analyze the relationship between the location of the epicenter of a medium-sized earthquake(magnitude 4.8) that occurred on January 20, 2007 in the Odaesan area with lineament features using a shaded relief map(1/25,000 scale) and satellite images from LANDSAT-8 and KOMPSAT-2. Previous studies have analyzed lineament features in tectonic settings primarily by examining two-dimensional satellite images and shaded relief maps. These methods, however, limit the application of the visual interpretation of relief features long considered as the major component of lineament extraction. To overcome some existing limitations of two-dimensional images, this study examined three-dimensional images, produced from a Digital Elevation Model and drainage network map, for lineament extraction. This approach reduces mapping errors introduced by visual interpretation. In addition, spline interpolation was conducted to produce density maps of lineament frequency, intersection, and length required to estimate the density of lineament at the epicenter of the earthquake. An algorithm was developed to compute the Value of the Relative Density(VRD) representing the relative density of lineament from the map. The VRD is the lineament density of each map grid divided by the maximum density value from the map. As such, it is a quantified value that indicates the concentration level of the lineament density across the area impacted by the earthquake. Using this algorithm, the VRD calculated at the earthquake epicenter using the lineament's frequency, intersection, and length density maps ranged from approximately 0.60(min) to 0.90(max). However, because there were differences in mapped images such as those for solar altitude and azimuth, the mean of VRD was used rather than those categorized by the images. The results show that the average frequency of VRD was approximately 0.85, which was 21% higher than the intersection and length of VRD, demonstrating the close relationship that exists between lineament and the epicenter. Therefore, it is concluded that the density map analysis described in this study, based on lineament extraction, is valid and can be used as a primary data analysis tool for earthquake research in the future.

• Journal of the Korean association of regional geographers
• /
• v.9 no.3
• /
• pp.373-384
• /
• 2003

This study analyses the soil loss due to cropland increase in the Hoeryeung area of northeast Korea, using Landsat images of 1987 TM and 2001 ETM, together with DTED, soil and geological maps, and rainfall data of 20 years. Items of land cover and land use were categorized as cropland, settlement, forest, river zone, and sand deposit by supervised classification with spectral bands 1, 2 and 3. RUSLE model is used for estimation of soil loss, and AML language for calculation of soil loss volumes. Fourier transformation method is used for unification of the geographical grids between Landsat images and DTED. GTD was selected from 1:50,000 topographic map. Main sources of soil losses over 100 ton/year may be the river zone and settlement in the both times of 1987 and 2001, but the image of the 2001 shows that sources areas have developed up to the higher mountain slopes. In the cropland average, increases of hight and gradient are 24m and $0.8^{\circ}$ from 1987 to 2001. In the case of new developed cropland, average increases are 75m and $2.5^{\circ}$, and highest soil loss has occurred at the elevation between 300 and 500m. The soil loss 57 ton of 1987 year increased 85 ton of 2001 year. Soil loss is highest in $30{\sim}50^{\circ}$ slope zones in both years, but in 2001 year, soil loss increased under $30^{\circ}$ zones. The size of area over 200 ton/year, indicating higher risk of landslides, have increased from $28.6km^2$ of 1987 year to $48.8km^2$ of 2001 year.

• Korean Journal of Remote Sensing
• /
• v.40 no.4
• /
• pp.351-361
• /
• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.