• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.786-803
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• 2004

These days, interests on land cover classification using not only multi-sensor data but also thematic GIS information, are increasing. Often, although we have useful GIS information for the classification, the traditional classification method like maximum likelihood estimation technique (MLE) does not allow us to use the information due to the fact that the MLE and the existing computer programs cannot handle GIS data properly. We proposed a new method for updating the image classification using multi-spectral and multi-temporal images. In this study, we have simultaneously extended the MLE to accommodate both multi-spectral images data and land cover data for land cover classification. In addition to the extended MLE method, we also have extended the empirical likelihood ratio estimation technique (LRE), which is one of non-parametric techniques, to handle simultaneously both multi-spectral images data and land cover data. The proposed procedures were evaluated using land cover map based on Landsat ETM+ images in the Anmyeon-do area in South Korea. As a result, the proposed methods showed considerable improvements in classification accuracy when compared with other single-spectral data. Improved classification images showed that the overall accuracy indicated an improvement in classification accuracy of $6.2\%$ when using MLE, and $9.2\%$ for the LRE, respectively. The case study also showed that the proposed methods enable the extraction of the area with land cover change. In conclusion, land cover classification produced through the combination of various GIS spatial data and multi-spectral images will be useful to involve complementary data to make more accurate decisions.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.87-92
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• 2010

Large pressure on the growing population has increased rapid change in the LULC (land use/land cover) patterns in the watershed area. Spatial distribution of LULC information and its changes are desirable for any effective planning, managing and monitoring activities. The aim of the study is to produce the 1,50,000 scaled LULC change map for the sub-watershed, Western Moyar, India using the multi-temporal satellite image dataset of IRS LISS III images for the year 1989, 1999, and 2002. About 9 classes are extracted using onscreen visual interpretation techniques for all the three years. The change detection analysis was performed using matrix method for period I (1989-1999) and period II (1999-2002). The study reveals that the changes noticed in period II (1999-2002) is comparatively more than period I (1989-1999), which is dynamic information to protect the sub-watershed area from the deterioration and paves the way to for the sustainable development.

• Aerospace Engineering and Technology
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• v.3 no.2
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• pp.141-148
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• 2004

The importance of satellite data for numerous applications is stressed by the fact that many countries have given the development of space technologies very high priority. Among these, Korea has established a medium-term space development strategy to promote space development both on a scientific as well as commercial level. As part of this strategy, the first operational earth-observation, multi-purpose satellite(KOMPSAT-1) was launched successfully in December, 1999. The Electro-Optical Camera (EOC) on board of KOMPSAT-1 supplies panchromatic images with a spatial resolution of 6.6m Until April, 2004, it collected over 150.000 images of the Korean Peninsula and the rest of the world. This paper examines the use of remote sensing data to analyze urban growth in the city of Daejeon from 1960 to 2003. By using visual interpretation, land use maps are created.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.176-192
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• 2006

The need for a systematic drought management has increased since last countrywide drought in 2001. Naturally various studies for establishing drought plan and preventing drought disaster have been conducted. MODIS image provided by Terra satellite has effective spatial and temporal resolutions to observe spatial and temporal characteristics of a region. MODIS data products are easy for preprocessing and correcting geometrically and provide various data set in regular which are applicable for drought monitoring. In this study, Ansung river and the upstream of South Han river basin was chosen for case study to identify and assess spring drought. The multi-period MODIS image and accumulated precipitation were used to detect not only the drought year but also the vegetation change of normal year and the result were compared with various spatial data. The result shows NDVI and LSWI with is more appropriate than LST for assesing spring drought in Korea and two month cumulative precipitation has moderate relationship with drought. It is necessary to use MODIS image which has same period and same space for effective drought analysis because drought is also affected by landover and altitude.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1209-1219
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• 2019

For consistent vegetation monitoring, it is necessary to generate time-series vegetation index datasets at fine temporal and spatial scales by fusing the complementary characteristics between temporal and spatial scales of multiple satellite data. In this study, we quantitatively and qualitatively analyzed the prediction accuracy of time-series change information extracted from spatio-temporal fusion models of multiple satellite data for vegetation monitoring. As for the spatio-temporal fusion models, we applied two models that have been widely employed to vegetation monitoring, including a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). To quantitatively evaluate the prediction accuracy, we first generated simulated data sets from MODIS data with fine temporal scales and then used them as inputs for the spatio-temporal fusion models. We observed from the comparative experiment that ESTARFM showed better prediction performance than STARFM, but the prediction performance for the two models became degraded as the difference between the prediction date and the simultaneous acquisition date of the input data increased. This result indicates that multiple data acquired close to the prediction date should be used to improve the prediction accuracy. When considering the limited availability of optical images, it is necessary to develop an advanced spatio-temporal model that can reflect the suggestions of this study for vegetation monitoring.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Journal of The Geomorphological Association of Korea
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• v.19 no.4
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• pp.59-71
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• 2012

This research was carried out to analyze long-term shoreline change on Busan Songjeong Beach using multi-temporal remote sensed data, GPS survey data and grain size analysis. As a result of multi-temporal satellite imagery analysis, the beach was stable status till early 2000s, but the erosion occurred over whole beach after the construction of shore protection road since 2000. In the result of DEM analysis, the elevation of beach reduced and the slope of berm increased after construction of shore protection road along the coast, this means the erosion environment was dominant on the beach. But the sedimentation was slightly stronger than the erosion in northern region of the beach, then the slope of berm was gentle. In the result of grain size analysis using in-situ samples, the coarsening-trend was found in southeastern region (Line E) of the beach, it is caused by strong wave energy from the outer sea. Consequently, major causes of the beach erosion in the study area were the interception of sand supply from a dune owing to shore protection road construction and scouring phenomenon by strong wave energy in southeastern region of the beach. If the topographic or artificial change will not occur in the future, the erosion in this area will continue. Therefore the prevention measures are required.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.21-34
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• 2013

Permanent Scatterer InSAR (PSInSAR) technique extracts permanent scatterers exhibiting high phase stability over the entire observation period and calculates precise time-series deformation at Permanent Scatterer (PS) points by using single master interferograms. This technique is not a good method to apply on nature environment such as forest area where permanent scatterers cannot be identified. Another muti-temporal Interferometric Synthetic Aperture Radar (InSAR), Small BAseline Subset (SBAS) technique using multi master interferograms with short baselines, can be effective to detect deformation in forest area. However, because of the error induced from phase unwrapping, the technique sometimes fails to estimate correct deformation from a stack of interferograms. To overcome those problems, we introduced new multi-temporal InSAR technique, called Temporarily Coherence Point InSAR (TCPInSAR), in this paper. This technique utilizes multi master interferograms with short baseline and without phase unwrapping. To compare with traditional multi-temporal InSAR techniques, we retrieved spatially changing deformation because PSs have been found enough in forest area with TCPInSAR technique and time-series deformation without phase unwrapping error. For this study, we acquired ERS-1 and ERS-2 SAR dataset on Augustine volcano, Alaska and detected deformation in study area for the period 1992-2005 with SBAS and TCPInSAR techniques.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.569-579
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• 2013

In this study, land-cover classification was implemented using features extracted from multi-temporal SAR data through 3D wavelet transform and the applicability of the 3D wavelet transform as a feature extraction approach was evaluated. The feature extraction stage based on 3D wavelet transform was first carried out before the classification and the extracted features were used as input for land-cover classification. For a comparison purpose, original image data without the feature extraction stage and Principal Component Analysis (PCA) based features were also classified. Multi-temporal Radarsat-1 data acquired at Dangjin, Korea was used for this experiment and five land-cover classes including paddy fields, dry fields, forest, water, and built up areas were considered for classification. According to the discrimination capability analysis, the characteristics of dry field and forest were similar, so it was very difficult to distinguish these two classes. When using wavelet-based features, classification accuracy was generally improved except built-up class. Especially the improvement of accuracy for dry field and forest classes was achieved. This improvement may be attributed to the wavelet transform procedure decomposing multi-temporal data not only temporally but also spatially. This experiment result shows that 3D wavelet transform would be an effective tool for feature extraction from multi-temporal data although this procedure should be tested to other sensors or other areas through extensive experiments.