• Journal of the Korean earth science society
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• v.32 no.6
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• pp.525-536
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• 2011

In any applications using various types of spatial data, it is very important to account for the scale differences among available data sets and to change the scale to the target one as well. In this paper, we propose to use a geostatistical downscaling approach based on vaiorgram deconvloution and block simulation to generate fine-scale categorical thematic maps from coarse-scale fraction data. First, an iterative variogram deconvolution method is applied to estimate a point-support variogram model from a block-support variogram model. Then, both a direct sequential simulation based on area-to-point kriging and the estimated point-support variogram are applied to produce alternative fine-scale fraction realizations. Finally, a maximum a posteriori decision rule is applied to generate the fine-scale categorical thematic maps. These analytical steps are illustrated through a case study of land-cover mapping only using the block fraction data of thematic classes without point data. Alternative fine-scale fraction maps by the downscaling method presented in this study reproduce the coarse-scale block fraction values. The final fine-scale land-cover realizations can reflect overall spatial patterns of the reference land-cover map, thus providing reasonable inputs for the impact assessment in change of support problems.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.537-544
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• 2013

Baton Peninsula, where Sejong station is located, mainly covered with snow and vegetation. Because this area is sensitive to climate change, monitoring of surface variation is important to understand climate change on the polar region. Due to the inaccessibility, the remote sensing is useful to continuously monitor the area. The objectives of this research are 1) map classification of land-cover types in the Barton Peninsular around King Sejong station and 2) grasp distribution of vegetation species in classified area. A KOMPSAT-2 multispectral satellite image was used to classify land-cover types and vegetation species. We performed classification with hierarchical procedure using KOMPSAT-2 satellite image and ground reference data, and the result is evaluated for accuracy as well. As the results, vegetation and non-vegetation were clearly classified although species shown lower accuracies within vegetation class.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Journal of Forest and Environmental Science
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• v.25 no.1
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• pp.49-55
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• 2009

The present study analyses the effect of land use on the vegetation of some desert ecosystems in Abu Dhabi, United Arab Emirates (UAE). Three sites were selected to represent different types of land use, inside Umm Al-Banadeq forest, outside the forest and along Abu Dhabi-Al Ain Trucks Road. In total, fifty-two stands were examined; including a matrix of 14 species ${\times}$ 52 stands. Based on species cover data, stands were classified using TWINSPAN and ordinated using DCA. Four vegetation groups were generated at level three of classification. Zygophyllum mandavillei was dominant in most vegetation groups; Heliotropium bacciferum dominated vegetation groups inhabited the forest. Species richness, species turnover, relative evenness and relative concentration of dominance of forest vegetation groups were 2.8, 5.7, 0.7, and 2.0, respectively. The differences were attributed to both natural variability and forestry-induced changes, including change in land use, drainage and ploughing and shading by trees. Vegetation group inhabited Abu Dhabi-Al Ain Trucks Road, that were dominated by Haloxylon salicornicum and Zygophyllum mandavillei have high total cover (8.8 m per $m^{-1}$). Most community and vegetation attributes were significantly higher inside the forest than outside. Human interventions and environmental factors affected species diversity and abundance of these communities.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.4
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• pp.411-420
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• 1999

Coastal development and reclamation work make environment of shore destroy, such as rapid change of land use and destruction of wet-land and ocean ecosystem. Therefore new technique to detect change have been needed. This study designed new change detection method and applied to study area. The change detection image and quantitative change area by each classes are calculated. Also, this study can use the basic idea-determination data for coastal development and city plan as the sense of sight by changed images that changed from any land-cover to any land-cover between two dates.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.128-133
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• 1998

To assess the flood damages and to provide necessary information for preventing future catastrophe, it is necessary to appraise the inundated area with more accurate and rapid manner. This study attempts to evaluate the potential of satellite synthetic aperture radar (SAR) data for mapping of flood inundated area in southern part of Korea. JERS L-band SAR data obtained during the summer of 1997 were used to delineate the inundated areas. In addition, Landsat TM data were also used for analyzing the land cover condition before the flooding. Once the two data sets were co-registered, each data was separately classified. The water surface areas extracted from the SAR data and the land cover map generated using the TM data were overlaid to determine the flood inundated areas. Although manual interpretation of water surfaces from the SAR image seems rather simple, the computer classification of water body requires clear understanding of radar backscattering behavior on the earth's surfaces. It was found that some surface features, such as rice fields, runaway, and tidal flat, have very similar radar backscatter to water surface. Even though satellite SAR data have a great advantage over optical remote sensor data for obtaining imagery on time and would provide valuable information to analyze flood, it should be cautious to separate the exact areas of flood inundation from the similar features.

• Journal of the Korean Geographical Society
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• v.30
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• pp.1-13
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• 1984

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1352-1352
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• 2003

• Journal of Korean Society of Environmental Engineers
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• v.36 no.3
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• pp.162-171
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• 2014

In this study, the uncertainty analysis of present land pollutant load estimation with simplified land category in TMDLs was performed and the enhanced method for land pollutant load estimation with level II land cover consisting of 23 categories was suggested, which was verified by L-THIA model. For land TP load estimation in Jinwi stream basin, the result of comparison between existing method with simplified land category (Scenario 1) and enhanced method with level II land cover (Scenario 2) showed high uncertainty in existing method. TP loads estimated by Scenario 2 for land covers included in the site land category were in the range of 3.45 to 56.69 kg/day, in which TP loads differed by sixteen times as much among them. For application of scenario 2 to TMDLs, Land TP loads were estimated by matching level II land cover to 28 land categories in serial cadastral map (Scenario 3). In order to verify accuracy of TP load estimation by scenario 3, the simulation result of L-THIA was compared with that and the difference between the two was as little as 10%. The result of this study is expected to be used as primary data for accurate estimation of land pollutant load in TMDLs.